A multi-criteria adaption power quality evaluation method, system, medium and product

Abstract

A multi-standard adaptation power quality evaluation method, system, medium and product, relating to the field of power quality evaluation. The method comprises: acquiring power quality detection data and detection configuration parameters, and performing validity verification on the detection configuration parameters to obtain a verification result; when the verification fails, determining abnormal parameters in the detection configuration parameters and correcting them to obtain corrected detection configuration parameters; based on the corrected detection configuration parameters or the verified detection configuration parameters, determining dynamic adaptation standards corresponding to each power quality index; based on the power quality detection data and each dynamic adaptation standard, generating a qualifiedness determination result of each power quality index, and generating a power quality evaluation report. The application can improve the limit value adaptation accuracy in a dynamic scene.

Description

Technical Field

[0001] This application relates to the field of power quality assessment, and in particular to a multi-standard adapted power quality assessment method, system, medium and product. Background Technology

[0002] In the grid connection testing of new energy power plants, testing agencies need to perform limit adaptation and compliance evaluation of power quality indicators under different voltage levels and capacity parameters according to multiple national standards, and issue Word reports and Excel statistical tables. The traditional method of manually consulting standard tables is inefficient and prone to errors. Testing personnel need to repeatedly check the tables and manually compare them. When parameters such as short-circuit capacity and agreed capacity change, the limits need to be recalculated, which can easily lead to evaluation deviations due to negligence.

[0003] To improve efficiency, some testing software uses standardized limit configuration files for management: fixed limits such as harmonic voltage and voltage deviation for each voltage level are pre-entered into the configuration file. The software reads the corresponding limit based on the voltage level input by the user, automatically generates the pass / fail judgment result, and outputs a report. This method automates the limit matching of routine indicators.

[0004] However, the limits for harmonic currents are not fixed values ​​but need to be dynamically calculated based on parameters such as minimum short-circuit capacity, reference short-circuit capacity, protocol capacity, and single-user status. Configurable solutions can only store static values ​​or simple expressions and cannot express the dynamic calculation logic of such multi-parameter coupling. When the short-circuit capacity or protocol capacity of the monitored item changes, the software still reads the same static limit, causing the adaptation results to deviate from the standard requirements. Users can only manually modify the configuration file or maintain multiple sets of files. This process is prone to introducing errors due to parameter omissions or incorrect file selection, reducing the accuracy of limit adaptation in dynamic scenarios. Summary of the Invention

[0005] This application provides a multi-standard adaptive power quality evaluation method, system, medium, and product to solve the technical problem of how to improve the accuracy of limit adaptation in dynamic scenarios.

[0006] In a first aspect, embodiments of this application provide a multi-standard adapted power quality evaluation method, including: Acquire power quality detection data and detection configuration parameters, verify the validity of the detection configuration parameters, and obtain the verification results; When the verification result indicates that the verification fails, the abnormal parameters in the detection configuration parameters are identified, and the abnormal parameters are corrected based on the preset parameter correction rules to obtain the corrected detection configuration parameters. Based on the corrected detection configuration parameters, or the detection configuration parameters that have passed the verification as characterized by the test results, determine the dynamic adaptation standards corresponding to each power quality index. Based on power quality testing data and various dynamic adaptation standards, the pass / fail determination results of each power quality indicator are generated, and based on the pass / fail determination results, a power quality evaluation report is generated.

[0007] Optionally, power quality indicators include harmonic current, harmonic voltage, three-phase unbalance, voltage deviation, frequency deviation, overvoltage, and voltage flicker. Based on the corrected detection configuration parameters, or the detection configuration parameters that have passed verification as indicated by the test results, the dynamic adaptation standards corresponding to each power quality indicator are determined, including: extracting voltage level, minimum short-circuit capacity, reference short-circuit capacity, protocol capacity, single-user status identifier, grid-connected capacity, detection point type, and system grounding method from the corrected detection configuration parameters, or the detection configuration parameters that have passed verification as indicated by the test results; based on the minimum short-circuit capacity, reference short-circuit capacity, protocol capacity, and single-user... Status indicators are used to determine the dynamic adaptation standard for harmonic currents; based on voltage levels and a preset standard configuration library, dynamic adaptation standards corresponding to harmonic voltages and three-phase unbalance are determined; based on voltage levels, deviation evaluation modes are determined and used as the dynamic adaptation standard for voltage deviations; based on grid-connected capacity, dynamic adaptation standards corresponding to frequency deviations are determined; based on detection point types, short-time flicker limits and long-time flicker limits corresponding to detection points are determined, resulting in dynamic adaptation standards for voltage flicker; based on voltage levels and system grounding methods, transient overvoltage multiple limits and power frequency overvoltage limits are determined, resulting in dynamic adaptation standards for overvoltages.

[0008] Optionally, the feature is that, based on the minimum short-circuit capacity, the reference short-circuit capacity, the protocol capacity, and the single-user status identifier, a dynamic adaptation standard for harmonic current is determined, including: calculating the ratio of the minimum short-circuit capacity to the reference short-circuit capacity, and calculating the product of the ratio and a preset harmonic baseline limit to obtain the point of common coupling limit for harmonic current; when the single-user status identifier indicates a single user, the power supply capacity is obtained, and based on the power supply capacity and the protocol capacity, the user injection limit is calculated using the following formula as the dynamic adaptation standard for harmonic current: I hi =I hp ×(S i / S t ) (1 / α) , among which, I hi Inject limits to users, I hp For the common connection point limit, S i For protocol capacity, S t The power supply capacity is represented by α, which is a preset phase superposition coefficient. When the single-user status identifier indicates a multi-user, the common connection point limit is used as the dynamic adaptation standard for harmonic current.

[0009] Optionally, the deviation evaluation mode includes a first evaluation mode, a second evaluation mode, and a third evaluation mode. The deviation evaluation mode is determined based on the voltage level and serves as a dynamic adaptation standard for voltage deviation. Specifically: when the voltage level is greater than or equal to a first voltage threshold, and the power supply system at the detection point is a three-phase system (A, B, and C), the first evaluation mode is used as the dynamic adaptation standard for voltage deviation. The sum of the absolute values ​​of the positive and negative voltage deviations for phases A, B, and C is calculated respectively. The first evaluation mode is used to characterize that all deviation sums are lower than preset deviation sum limits. When the voltage level is less than... When the voltage deviation is equal to or equal to the second voltage threshold, and the power supply system of the detection point is three-phase (A, B, C), the second evaluation mode is used as the dynamic adaptation standard for voltage deviation. The second evaluation mode is used to characterize the voltage deviation of the three phases (A, B, C) by setting the maximum positive deviation percentage and the maximum negative deviation percentage, and the absolute values ​​of the positive and negative percentages are the same. The second voltage threshold is less than the first voltage threshold. When the power supply system of the detection point is single-phase, the third evaluation mode is used as the dynamic adaptation standard for voltage deviation. The third evaluation mode is used to characterize the voltage deviation of the single phase by setting positive and negative asymmetrical deviation limits.

[0010] Optionally, for harmonic currents, the compliance judgment result includes the judgment result of the harmonic current under each phase. Based on the compliance judgment result, a power quality evaluation report is generated, including: based on the judgment result of the harmonic current under each phase, filtering out the number of the excessive harmonics and the corresponding phases; classifying each excessive harmonic into three-phase all-excess group, two-phase excessive group, and single-phase excessive group according to the excessive phase; generating excessive harmonic conclusion text based on the total number and phases contained in each excessive group, the excessive harmonic conclusion text includes phase combination, harmonic number, detection value, and limit information; generating corresponding spectrum diagrams according to the grouping type, where the three-phase all-excess group corresponds to a three-phase composite diagram, the two-phase excessive group corresponds to a two-phase superimposed diagram, and the single-phase excessive group corresponds to a single-phase diagram; and automatically filling all spectrum diagrams and excessive harmonic conclusion texts into the power quality evaluation report.

[0011] Optionally, based on power quality monitoring data and various dynamic adaptation standards, the generation of compliance judgment results for each power quality indicator also includes: real-time monitoring of the state characteristics of the power grid operation condition; when a condition transition node triggering the switching of dynamic adaptation standards is identified, a transient data segment with a preset time window is extracted centered on the condition transition node; a smooth transition curve is constructed within the transient data segment window, and the power quality monitoring data is compared with the smooth transition curve to generate a compliance judgment result for the transient data segment; for steady-state data segments other than transient data segments, abnormal segments exceeding various dynamic adaptation standards are extracted from the steady-state data segments, and the duration and energy accumulation value of the abnormal segments are recorded; when the duration exceeds a preset duration and the energy accumulation value exceeds a preset energy threshold, the abnormal segment is judged as a real violation event, and a compliance judgment result for the steady-state data segment is obtained.

[0012] Optionally, the method further includes: when the detection object is a new energy power station with both grid-connected and islanded operation modes, monitoring the status of the power station's grid-connected switch in real time; when a switch from grid-connected mode to islanded mode or vice versa is detected, recording the start time and completion time of the switch; extracting a first dynamic standard set at the start time of the switch and a second dynamic standard set at the completion time of the switch; within the transition time window between the start time and the completion time of the switch, constructing multiple standard interpolation curves that change continuously with time, where the value of each standard interpolation curve at the start time of the switch is equal to the corresponding limit of the first dynamic standard set, and the value at the completion time of the switch is equal to the corresponding limit of the second dynamic standard set; within the transition time window, comparing the power quality detection data with each standard interpolation curve to generate a transition period judgment result with a time stamp; and automatically filling the start time, completion time, and transition period judgment result into the power quality evaluation report.

[0013] In a second aspect, embodiments of this application provide a multi-standard adaptable power quality evaluation system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors invoke the computer instructions to cause the multi-standard adaptable power quality evaluation system to perform the methods described in the first aspect and any possible implementation thereof.

[0014] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a multi-standard adapted power quality evaluation system, cause the multi-standard adapted power quality evaluation system to perform the method described in the first aspect and any possible implementation thereof.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a multi-standard adapted power quality evaluation system, cause the multi-standard adapted power quality evaluation system to perform the method described in the first aspect and any possible implementation thereof.

[0016] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By acquiring power quality detection data and detection configuration parameters, validating the detection configuration parameters, automatically correcting abnormal parameters, and determining the dynamic adaptation standards for each power quality indicator based on the corrected parameters, a pass / fail judgment result and evaluation report are finally generated. This solves the problem of limit adaptation deviation caused by data errors when manually looking up tables and manually entering parameters. The parameter verification and correction mechanism is embedded in the front end of the evaluation process to ensure that the input data for subsequent limit calculations is accurate and reliable. This avoids misjudgments caused by directly using incorrect limits due to abnormalities in key parameters such as short-circuit capacity and protocol capacity, thereby improving the accuracy and robustness of power quality evaluation in dynamic detection scenarios.

[0017] 2. By extracting parameters such as voltage level, short-circuit capacity, protocol capacity, single-user status, grid-connected capacity, detection point type, and system grounding method from the detection configuration parameters, a differentiated dynamic adaptation strategy is adopted for seven major power quality indicators, including harmonic current, harmonic voltage, three-phase imbalance, voltage deviation, frequency deviation, overvoltage, and voltage flicker. Among them, harmonic current is dynamically calculated based on short-circuit capacity ratio correction and single-user capacity allocation; voltage deviation selects different evaluation modes according to voltage level and power supply system; and other indicators are matched from the standard library or selected according to the corresponding parameters. The static standard configuration is upgraded to a dynamic adaptation system based on multiple parameters. In particular, the harmonic current limit is calculated through a two-step formula of "common coupling point correction + single-user injection allocation", which can accurately reflect the allowable limit under the actual power grid physical conditions. This solves the problem that the fixed limit in the traditional method cannot adapt to different short-circuit capacities and user capacity ratios, and significantly improves the accuracy and flexibility of the limit adaptation of various power quality indicators.

[0018] 3. By classifying the excessive harmonic currents by phase combination, the system automatically generates natural language conclusion texts for three-phase all-exceeding groups, two-phase exceeding groups, and single-phase exceeding groups. It also automatically generates three-phase composite diagrams, two-phase superimposed diagrams, or single-phase spectrum diagrams based on the grouping type, achieving automated linkage between report conclusions and charts. Simultaneously, by real-time monitoring of operating condition transition nodes, a smooth transition curve is used to replace hard switching limits within the transient data window. Abnormal segments in the steady-state data section undergo dual verification of duration and energy accumulation to eliminate transient disturbances. Furthermore, a standard interpolation curve is constructed during grid-connected and islanded mode switching to achieve a smooth transition of the evaluation benchmark. This solves the technical problems of incomplete descriptions of excessive currents in traditional reports, disconnect between charts and conclusions, evaluation jumps caused by sudden changes in operating conditions, and false reports caused by transient disturbances. This makes the evaluation report more accurate and readable, while improving the continuity and stability of the evaluation results under dynamic operating conditions, avoiding misjudgments caused by mode switching or parameter jumps. Attached Figure Description

[0019] Figure 1This is a flowchart illustrating the multi-standard adaptive power quality evaluation method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the standard-defined process provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the compliance determination process for operating conditions provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a multi-standard adaptive power quality evaluation system provided in an embodiment of this application.

[0020] Explanation of reference numerals in the attached figures: 401, Central Processing Unit; 402, Read-Only Memory; 403, Random Access Memory; 404, Bus; 405, Input / Output Interface; 406, Input Section; 407, Output Section; 408, Storage Section; 409, Communication Section; 410, Driver; 411, Removable Media. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0022] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0023] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0024] This application provides a multi-standard adaptive power quality evaluation method, system, medium, and product to solve the technical problem of how to improve the accuracy of limit adaptation in dynamic scenarios.

[0025] Figure 1This is a flowchart illustrating the multi-standard adaptive power quality evaluation method provided in the embodiments of this application.

[0026] This application discloses a multi-standard adapted power quality evaluation method, such as... Figure 1 As shown, the steps include the following.

[0027] S101. Obtain power quality detection data and detection configuration parameters, and verify the validity of the detection configuration parameters to obtain the verification results.

[0028] Specifically, power quality detection data is first acquired from the data acquisition terminal or storage medium. This data includes continuously sampled voltage and current waveform sequences, as well as characteristic values ​​of various power quality indicators obtained through statistical calculations, such as fundamental RMS value, harmonic content, total distortion rate, frequency, flicker value, and imbalance. Simultaneously, detection configuration parameters are acquired, which at least include voltage level, minimum short-circuit capacity, reference short-circuit capacity, protocol capacity, power supply capacity, single-user status identifier, grid-connected capacity, detection point type, and system grounding method. After acquisition, the validity of each detection configuration parameter is verified, specifically including: checking whether each parameter is within a preset reasonable value range (e.g., the minimum short-circuit capacity should be greater than 0 and not exceed the maximum short-circuit capacity of the power grid, and the protocol capacity should be between 0 and the power supply capacity); and checking whether the logical relationships between parameters are consistent (e.g., when the single-user status identifier is "yes," the protocol capacity cannot be empty or zero, and the ratio of the minimum short-circuit capacity to the reference short-circuit capacity should not exceed the typical engineering experience threshold range, such as 0.2~5.0). For parameters that pass validation, a validation success result is generated directly; for parameters that fail validation, the abnormal parameter type is marked and a validation failure result is given, while triggering subsequent correction steps.

[0029] Power quality monitoring data refers to the raw or pre-processed numerical sequences reflecting the power quality status of the power grid within the evaluation period, including but not limited to instantaneous three-phase voltage values, instantaneous three-phase current values, harmonic amplitude and phase, effective voltage value, frequency value, flicker value, and unbalance value. Monitoring configuration parameters refer to static or semi-static engineering parameters used to define the monitoring scenario and applicable standard conditions, determining the specific values ​​for subsequent dynamic standard adaptation. Verification results refer to Boolean status indicators indicating whether the verification passed or failed; when failing, they also include the location information of abnormal parameters.

[0030] For example, in a wind farm grid connection testing scenario, the acquired power quality testing data consists of three-phase voltage and current waveforms over a continuous 72-hour period, with a sampling rate of 256 points / cycle. The acquired testing configuration parameters are: voltage level 110kV, minimum short-circuit capacity 500MVA, baseline short-circuit capacity 100MVA, protocol capacity 80MVA, power supply capacity 100MVA, single-user status identifier "Yes", grid-connected capacity 100MW, testing point type "wind farm", and system grounding method "direct grounding". Validity verification is performed: First, the minimum short-circuit capacity of 500MVA is checked to be within a reasonable range (>0 and ≤1000MVA); the protocol capacity of 80MVA is less than the power supply capacity of 100MVA, satisfying the logic; the single-user identifier being "Yes" is consistent with the protocol capacity not being empty; the ratio of minimum short-circuit capacity to baseline short-circuit capacity is 5, within the threshold boundary [0.2, 5.0], therefore the verification passes, and the verification result is "Pass". If the user mistakenly inputs the protocol capacity as 120MVA, the verification will find that the protocol capacity is greater than the power supply capacity, and the verification will fail. The verification result will carry the abnormal parameter "protocol capacity" and the reason "exceeds power supply capacity".

[0031] S102. When the verification result indicates that the verification fails, identify the abnormal parameters in the detection configuration parameters, and correct the abnormal parameters based on the preset parameter correction rules to obtain the corrected detection configuration parameters.

[0032] Specifically, when the validity check returns a result indicating a failure, the system analyzes the abnormal parameter location information carried in the check result to identify one or more detection configuration parameters that exceed reasonable thresholds or violate logical constraints. Examples include minimum short-circuit capacity exceeding typical engineering ranges, protocol capacity exceeding power supply capacity, and contradictions between single-user status identifiers and protocol capacity. For each type of abnormal parameter, a preset parameter correction rule library is invoked. This rule library defines corresponding correction strategies and formulas for each abnormality type. For minimum short-circuit capacity anomalies, if it is less than the lower limit of the average typical short-circuit capacity in historical site data at the same voltage level, the lower limit of that average is used instead. If its ratio to the benchmark short-circuit capacity exceeds the threshold range, the benchmark short-circuit capacity is scaled proportionally to bring the ratio into a reasonable range. For anomalies where the protocol capacity exceeds the power supply capacity, correction is performed according to the formula: Protocol Capacity = Power Supply Capacity × Preset Coefficient (e.g., 0.8). For logical contradictions between a single user's status identifier and protocol capacity (e.g., the identifier is "yes" but the protocol capacity is zero), the protocol capacity is automatically estimated based on the voltage level and detection site type. For example, historical protocol capacity values ​​for the same site and voltage level are retrieved from the historical database to complete the calculation. The correction process adopts a sequential execution strategy, first correcting single parameter threshold anomalies, then correcting cross-logical anomalies between parameters. After each correction, the parameter value is updated and related dependencies are re-verified until all anomalies are handled or the maximum number of corrections is reached. Finally, all corrected parameter values ​​are merged with the originally verified parameters to form a complete and self-consistent set of corrected detection configuration parameters, and a correction log containing correction records (original value, corrected value, and correction reason code) is generated for subsequent report traceability.

[0033] Abnormal parameters refer to detection configuration parameters that are determined to be out of range or logically conflicting with other parameters during the verification process. Pre-defined parameter correction rules are pre-defined automatic correction strategies for various abnormal parameters, including substitution rules (replacing abnormal values ​​with statistical benchmark values), scaling rules (adjusting associated parameters proportionally), completion rules (filling in missing values ​​based on historical data or default values), and inference rules (deriving reasonable values ​​based on context such as voltage level and site type). Corrected detection configuration parameters refer to the set of parameters recombined after applying the pre-defined correction rules to all abnormal items in the original detection configuration parameters; this set satisfies all pre-defined validity verification conditions.

[0034] Through the above embodiments, when the verification fails, abnormal parameters are automatically identified and corrected according to preset rules, rather than simply reporting an error or terminating the process. This avoids the tedium of manual intervention and repetitive input, and can intelligently handle common parameter entry errors or data omissions in engineering practice. It automatically generates reasonable and self-consistent corrected detection configuration parameters, thereby ensuring the continuity of subsequent dynamic limit calculation and evaluation processes.

[0035] S103. Based on the corrected detection configuration parameters, or the detection configuration parameters that have passed the verification as indicated by the test results, determine the dynamic adaptation standards corresponding to each power quality index.

[0036] Specifically, after obtaining the verified or corrected detection configuration parameters, the indicators are divided into two categories based on their physical properties: continuously sensitive indicators and discretely sensitive indicators. Different dynamic adaptation standard determination methods are used for each category. Continuously sensitive indicators refer to those whose standard limits exhibit a continuous functional relationship with changes in the detection configuration parameters. The values ​​in the detection configuration parameters are directly substituted into preset physical formulas or analytical expressions for real-time calculation, and the output limits are used as dynamic adaptation standards. This method does not rely on any pre-stored standard configuration library and is entirely driven by parameters to generate continuously variable limits. Discrete-sensitive indicators refer to indicators whose standard limits or evaluation rules are given in the standard text in the form of discrete values, interval divisions, or conditional branches. Key fields in the detection configuration parameters are used as judgment conditions. Through a pre-set multi-level condition judgment tree, corresponding limits or evaluation mode identifiers are output. Although conditional judgment is involved, the limits are derived from discrete values ​​defined in the standard text rather than continuous calculations. Limits are output through interval judgment of the detection configuration parameters, eliminating the need to manually enter limits for each scenario. This is suitable for indicators with discrete branches, such as voltage deviation and overvoltage. Compared to traditional static configuration library solutions that require pre-entering complete limit tables for all indicators under all voltage levels and all detection point types, this embodiment dynamically generates limits through conditional judgment, significantly reducing the workload of standard maintenance (when revising the standard, only expression coefficients or judgment thresholds need to be modified, rather than updating the entire limit table). It also avoids adaptation failures due to incomplete standard configuration libraries or missing parameter combinations, improving the universality and maintainability of the solution.

[0037] Among them, the dynamic adaptation standard refers to the judgment benchmark used to evaluate whether the corresponding power quality indicators are qualified, which is calculated or selected in real time based on the detection configuration parameters. For harmonic current, it is expressed as a numerical limit; for voltage deviation, it is expressed as an evaluation mode and the limit rules embedded in the mode; and for other indicators, it is expressed as a fixed limit or a range limit.

[0038] Based on the above embodiments, as an optional embodiment, see [link to embodiment]. Figure 2 , Figure 2 This is a schematic diagram of the standard-defined process provided in the embodiments of this application. Power quality indicators include harmonic current, harmonic voltage, three-phase imbalance, voltage deviation, frequency deviation, overvoltage, and voltage flicker. Figure 1 The step S103 shown can be achieved through... Figure 2 The steps S201-S207 are implemented, and will be explained in detail below.

[0039] S201. Extract the voltage level, minimum short-circuit capacity, reference short-circuit capacity, protocol capacity, single-user status identifier, grid-connected capacity, detection point type, and system grounding method from the corrected detection configuration parameters or the detection configuration parameters that have passed the verification by the test results.

[0040] Specifically, after obtaining the verified or corrected set of detection configuration parameters, predefined key parameter fields are parsed and extracted. Power quality indicators cover multiple dimensions, including harmonic current, harmonic voltage, three-phase imbalance, voltage deviation, frequency deviation, overvoltage, and voltage flicker. Each dimension corresponds to one or more evaluation rules. The extraction operation is performed according to a preset field mapping table: the "voltage level" field is read from the parameter set to obtain the nominal voltage value of the detection point; the "minimum short-circuit capacity" and "reference short-circuit capacity" are read to obtain the short-circuit capacity value of the point of common coupling under the minimum operating mode and the reference short-circuit capacity given by the standard, respectively; the "agreement capacity" and "power supply capacity" are read to obtain the power consumption capacity agreed upon by the user and the rated capacity of the transformer or the transmission capacity of the line at the access point; the "single user status identifier" is read to obtain a Boolean value indicating whether the detection object is the only user at the access point; the "grid-connected capacity" is read to obtain the total installed capacity of the new energy power station or power generation unit; the "detection point type" is read to obtain the category identifier of the monitoring point; and the "system grounding method" is read to obtain the neutral point grounding form. After extraction, these parameters are temporarily stored according to their respective index dimensions for use in subsequent steps to determine the dynamic adaptation standards for each power quality index. If a necessary field is found to be missing during the extraction process (e.g., the protocol capacity is empty in single-user mode), an exception handling mechanism that reverts to the previous correction step is triggered.

[0041] Harmonic current refers to the effective value of each frequency component in the non-sinusoidal periodic current flowing into the power grid, typically considering harmonics from the 2nd to the 50th order. Harmonic voltage refers to the effective value of each frequency harmonic component in the power grid voltage waveform. Three-phase imbalance is a measure of the degree of deviation of three-phase voltage or current from symmetry in amplitude or phase, usually expressed as a percentage of negative-sequence components to positive-sequence components. Voltage deviation is the relative deviation between the actual operating voltage and the system nominal voltage, expressed as a percentage. Frequency deviation is the difference between the actual frequency of the power grid and the rated frequency (50Hz). Overvoltage refers to an abnormal voltage rise exceeding the peak value of the normal operating voltage, including power frequency overvoltage and transient overvoltage. Voltage flicker refers to the rapid change in voltage amplitude and the resulting visual flicker effect, expressed as the short-time flicker value P. st and long-term flicker value P ltMeasurement. Voltage level refers to the nominal voltage value of a power system, used to distinguish equipment and standard application conditions at different voltage levels. Values ​​include, but are not limited to, 110kV, 35kV, 10kV, and 0.38kV. Minimum short-circuit capacity represents the short-circuit capacity of the point of common coupling under minimum operating conditions, in MVA, reflecting the maximum short-circuit current capacity the grid can withstand at that point. Reference short-circuit capacity represents the reference short-circuit capacity corresponding to the given limit in the standard, usually taken as 100MVA or determined according to industry practice. Agreement capacity represents the power consumption capacity agreed upon by the user and the power supply company, in kVA or MVA. Power supply capacity represents the rated capacity of the transformer at the connection point or the maximum capacity allowed to be transmitted by the line, in units consistent with the agreement capacity. Single-user status indicator indicates whether the detection point is connected to only a single user, with a value of "yes" or "no," directly affecting the allocation and calculation of harmonic current limits. Grid-connected capacity represents the total installed capacity of new energy power plants or distributed power sources, in MW, used for selecting the frequency deviation limit level. The monitoring point type indicates the functional attributes of the monitoring point, including but not limited to "wind farm", "photovoltaic power station", "industrial user", and "common point of connection". The system grounding method indicates the connection method between the neutral point of the power system and the earth, including direct grounding, no grounding, and grounding through an arc suppression coil, which affects the selection of overvoltage limits.

[0042] S202. Based on the minimum short-circuit capacity, the reference short-circuit capacity, the protocol capacity, and the single-user status identifier, determine the dynamic adaptation standard for harmonic current.

[0043] Specifically, after extracting the minimum short-circuit capacity, reference short-circuit capacity, protocol capacity, power supply capacity, and single-user status identifier, different granularities of harmonic current dynamic adaptation standard determination methods are selected based on the complexity and accuracy requirements of the detection scenario. When the detection point is a rapid evaluation scenario with incomplete detection configuration parameters or low requirements for calculation accuracy, the fixed limit values ​​of each harmonic current are directly read from the standard configuration library according to the voltage level, or a simple linear scaling is performed based on the ratio of the minimum short-circuit capacity to the reference short-circuit capacity. This method does not involve protocol capacity allocation or single-user status judgment. This method is simple to implement and has a fast calculation speed, but its accuracy is relatively limited. The corresponding method can be automatically selected according to the user's preset evaluation mode (rapid evaluation mode or precise reporting mode). When the rapid evaluation mode is selected, a general approximation method is used; when the precise reporting mode is selected, a refined analytical method is used. This can be achieved through the following steps, which are explained in detail below.

[0044] Calculate the ratio of the minimum short-circuit capacity to the reference short-circuit capacity, and multiply this ratio by the preset harmonic baseline limit to obtain the point of common coupling limit for harmonic currents. When the single-user status identifier indicates a single user, obtain the power supply capacity, and calculate the user injection limit based on the power supply capacity and protocol capacity using the following formula, as a dynamic adaptation standard for harmonic currents: I hi=I hp ×(S i / S t ) (1 / α) , among which, I hi Inject limits to users, I hp For the common connection point limit, S i For protocol capacity, S t The power supply capacity is represented by α, which is a preset phase superposition coefficient. When the single-user status identifier indicates a multi-user, the common connection point limit is used as the dynamic adaptation standard for harmonic current.

[0045] Specifically, the preset harmonic baseline limit is read from the standard configuration library, indexed by voltage level and harmonic order. The ratio of the minimum short-circuit capacity to the reference short-circuit capacity is calculated, and this ratio is multiplied by the preset harmonic baseline limit. The product is recorded as the point of common coupling limit I. hp Then, read the single-user status flag. If the flag indicates a single user, obtain the power supply capacity S. t And read the preset phase superposition coefficient α, according to formula I hi =I hp ×(S i / S t )^ (1 / α) Calculate the user injection limit and use it as the dynamic adaptation standard output for harmonic current; if the identifier indicates multiple users, then directly use the point of common coupling limit I. hp This serves as the standard output for dynamic adaptation of harmonic currents. During the calculation process, the value of α is determined from a preset coefficient table based on the voltage level; typically, it is 1 for high-voltage systems and 2 for low-voltage systems. After calculating the harmonic order one by one, a mapping table of harmonic current limits from the 2nd to the 50th harmonics is formed.

[0046] Among them, the common connection point limit I hp This refers to the total permissible harmonic current injected by all users at the point of common coupling after short-circuit capacity correction, expressed in amperes (A). User injection limit I hi This refers to the upper limit of harmonic current that a single user is allowed to inject after being allocated according to their proportion of the protocol capacity, measured in amperes (A). Protocol capacity S i This refers to the electricity capacity agreed upon between the user and the power supply company, measured in kVA or MVA. Power supply capacity (S) t This refers to the rated capacity of the transformer at the point of common coupling or the maximum capacity that the line is allowed to transmit, with the unit consistent with the agreed capacity. The preset phase superposition coefficient α is an exponential parameter used to characterize the phase superposition effect of multi-user harmonic currents. When α is 1, it indicates that the harmonic currents of each user are linearly superimposed; when α is 2, it indicates that the square root of the sum of squares is superimposed, reflecting the synthesis characteristics under different phase conditions.

[0047] For example, consider a single-user access scenario in an industrial park: voltage level 10kV, minimum short-circuit capacity 200MVA, reference short-circuit capacity 100MVA, and second harmonic basic limit 26A. Calculate the ratio = 200 / 100 = 2, I hp =26×2=52A. Single-user identifier is "Yes", protocol capacity S i =5MVA, power supply capacity S t =10MVA, α is taken as 1 at the 10kV level. Calculate the user injection limit I. hi =52×(5 / 10)=52×0.5=26A. If the protocol capacity of another user in the same park is S i =8MVA, then I hi =52×(8 / 10)=41.6A, rounded to 42A. If the access point is shared by multiple users (shared by multiple factories), and the single-user identifier is "No", then Ihp=52A is directly used as the limit. If the scenario switches to a low-voltage 380V system, α is usually taken as 2, assuming I hp =100A, S i =50kVA, S t =100kVA, then I hi =100×(50 / 100) (1 / 2) =100×0.707=70.7A. The above calculation is performed on the 2nd to 50th harmonics one by one, and the final output limit mapping table is {2:26, ​​3:20, 4:13, 5:15, ...}.

[0048] Through the above embodiments, the basic limit given by the standard is transformed into the total limit of the actual access point, which solves the adaptation problem of the difference in the harmonic bearing capacity of the power grid under different short-circuit capacities; the second step is to allocate the total limit to a single user according to the user's electricity consumption rights and responsibilities through the protocol capacity ratio allocation, which solves the problem of fair evaluation of the contribution share of each user when multiple users share the access point.

[0049] S203. Based on the voltage level and the preset standard configuration library, determine the dynamic adaptation standards corresponding to harmonic voltage and three-phase unbalance respectively.

[0050] Specifically, after extracting the voltage level, the system dynamically determines the appropriate standards for two power quality indicators: harmonic voltage and three-phase unbalance. For harmonic voltage, the voltage level is used as the lookup key to retrieve matching harmonic voltage limit records from the preset standard configuration library. These records include the total harmonic distortion rate (THD) limit, odd-order harmonic voltage content limit, and even-order harmonic voltage content limit. When the voltage level precisely matches, the corresponding limit is read directly. When the voltage level falls between two standard levels, the higher limit is selected. When the voltage level exceeds the coverage of the standard configuration library, the limit corresponding to the highest voltage level is used as a substitute. For three-phase unbalance, the voltage level is used as the query key to read the corresponding negative sequence voltage unbalance limit from the preset standard configuration library. Typically, 2% is used for low-voltage systems and 1.3% for high-voltage systems, or it can be determined according to specific standards. After reading, the harmonic voltage limit set (total distortion rate, odd-order limit, even-order limit) and the three-phase unbalance limit are encapsulated into corresponding dynamic adaptation standard data structures for subsequent qualification judgment.

[0051] The preset standard configuration library refers to a pre-built relational database or configuration file that stores standard limits for various power quality indicators, with voltage level as the primary key. It must contain limit data for at least two dimensions: harmonic voltage and three-phase unbalance. The dynamic adaptation standard for harmonic voltage refers to the set of allowable upper limits set for the content rate of each harmonic voltage and the total harmonic distortion rate, including the total harmonic distortion rate limit THDu, the odd harmonic voltage content rate limit HRU (odd), and the even harmonic voltage content rate limit HRU (even). The dynamic adaptation standard for three-phase unbalance refers to the allowable upper limit for negative sequence voltage unbalance, usually expressed as a percentage, denoted as εu.

[0052] S204. Determine the deviation evaluation mode based on the voltage level, and use the deviation evaluation mode as the dynamic adaptation standard for voltage deviation.

[0053] Specifically, after extracting the voltage level and power supply system of the detection point, the deviation evaluation mode corresponding to the voltage deviation index is determined. The voltage deviation evaluation does not use a single fixed limit value, but rather selects different evaluation logics based on the range of the voltage level and the power supply system (three-phase or single-phase). After selecting the evaluation mode, the mode's identifier and the corresponding limit rules (such as deviation and limit value, positive and negative symmetry percentage value, positive and negative asymmetry absolute value) are encapsulated into a dynamic adaptation standard for voltage deviation, which is then used for subsequent compliance judgment. The deviation evaluation modes include a first evaluation mode, a second evaluation mode, and a third evaluation mode. The specific mode selected can be implemented through the following steps, which are explained in detail below.

[0054] When the voltage level is greater than or equal to the first voltage threshold, and the power supply system of the detection point is three phases A, B and C, the first evaluation mode is taken as the dynamic adaptation standard for voltage deviation, and the sum of deviations of the absolute value of positive voltage deviation and the absolute value of negative voltage deviation for three phases A, B and C is calculated respectively. The first evaluation mode is used to characterize that all the sums of deviations are lower than the preset sum-of-deviations limit value.

[0055] Specifically, when determining the voltage deviation evaluation mode, the voltage level and power supply system information of the current detection point are read first, the voltage level is compared with the preset first voltage threshold, and it is judged at the same time whether the power supply system is three phases A, B and C. When the voltage level is greater than or equal to the first voltage threshold and the power supply system is three-phase, the first evaluation mode is selected as the dynamic adaptation standard for voltage deviation. After the mode is selected, the voltage effective value sampling sequences of three phases A, B and C within the evaluation interval are acquired, and the absolute value of positive deviation and the absolute value of negative deviation of each phase are calculated respectively. The absolute value of positive deviation refers to the ratio of the part where the measured maximum voltage of the phase exceeds the nominal voltage to the nominal voltage, which is expressed as a percentage; the absolute value of negative deviation refers to the ratio of the part where the nominal voltage exceeds the measured minimum voltage of the phase to the nominal voltage, which is expressed as a percentage. For each phase, the calculated absolute value of positive deviation and absolute value of negative deviation are added to obtain the sum of deviations of the phase, and the respective sums of deviations of the three phases are compared with the preset sum-of-deviations limit value. Only when the sums of deviations of three phases A, B and C are all lower than the preset sum-of-deviations limit value, can the voltage deviation index be judged as qualified; if the sum of deviations of any phase reaches or exceeds the limit value, it is judged as unqualified. The core judgment rule of the first evaluation mode is that all sums of deviations are lower than the preset sum-of-deviations limit value.

[0056] The first voltage threshold refers to the voltage demarcation value that distinguishes the high-voltage system from the medium and low-voltage systems, which is usually 35kV. When the voltage level is greater than or equal to this value, the first evaluation mode is applicable, and this value can be pre-configured according to national standards or industry specifications. The sum of deviations refers to the sum of the absolute value of positive deviation and the absolute value of negative deviation of the same phase, which is used to characterize the overall degree of deviation of the phase voltage from the nominal value in both positive and negative directions. The calculation formula is sum of deviations = |positive deviation| + |negative deviation|, with the unit %. The absolute value of positive deviation refers to the absolute value obtained by dividing the value derived by subtracting the nominal voltage from the measured maximum voltage by the nominal voltage, which reflects the upward fluctuation amplitude of the voltage. The calculation formula is (|U max -U n | / U n )×100%, where U max is the measured maximum voltage of this phase, U n is the nominal voltage. The absolute value of negative deviation refers to the absolute value obtained by dividing the value derived by subtracting the measured minimum voltage from the nominal voltage by the nominal voltage, which reflects the downward fluctuation amplitude of the voltage. The calculation formula is (|U min -U n | / U n)×100%, where U min This represents the minimum measured voltage for that phase. Preset deviation and limit refer to the pre-defined deviation and maximum allowable value, typically set at 10%. This value can be configured according to different voltage levels and standard requirements. Phases A, B, and C refer to the three phases of a three-phase AC power supply, labeled A, B, and C respectively. The effective voltage value of each phase is calculated and evaluated independently; all three phases must participate in the evaluation under a three-phase power supply system.

[0057] For example, in a 220kV substation testing scenario: the first voltage threshold is preset to 35kV. The current voltage level of 220kV is greater than 35kV, and the power supply system is three-phase; therefore, the first evaluation mode is selected. Nominal voltage U n =220kV. Within the evaluation range, the measured voltage of phase A is 231kV and the minimum is 209kV. The absolute value of the positive deviation is (231-220) / 220×100%=5%, and the absolute value of the negative deviation is (220-209) / 220×100%=5%, with a total deviation of 5%+5%=10%. The maximum value of phase B is 232kV and the minimum is 208kV. The positive deviation is (232-220) / 220=5.45%, and the negative deviation is (220-208) / 220=5.45%, with a total deviation of 10.9%. The maximum value of phase C is 230kV and the minimum is 210kV. The positive deviation is (230-220) / 220=4.55%, and the negative deviation is (220-210) / 220=4.55%, with a total deviation of 9.1%. If the preset deviation and limit are set to 10%, then the deviation of phase B (10.9%) exceeds the limit, and the voltage deviation is deemed unqualified, with phase B being the out-of-range phase. If the sum of the deviations of all three phases is less than 10%, then it is deemed qualified. If the measured voltage of a certain phase is consistently higher than the nominal voltage (e.g., the minimum value is still higher than 220kV), then the absolute value of the negative deviation is 0, and the sum of the deviations equals the absolute value of the positive deviation; conversely, if it is consistently lower than the nominal voltage, then the positive deviation is 0, and the sum of the deviations equals the absolute value of the negative deviation.

[0058] When the voltage level is less than the first voltage threshold and greater than the second voltage threshold, and the power supply system of the detection point is three-phase A, B, and C, the transition evaluation mode is used as the dynamic adaptation standard for voltage deviation. The transition evaluation mode is used to characterize the voltage deviation of the three phases A, B, and C by setting positive and negative asymmetrical maximum limits respectively, and the difference between the absolute values ​​of the positive and negative percentages is dynamically adjusted with the difference between the voltage level and the second voltage threshold.

[0059] Specifically, when the power supply system is three phases A, B, and C, and the voltage level is between the first voltage threshold and the second voltage threshold (i.e., the second threshold < the voltage level < the first threshold), the transition evaluation mode is selected as the dynamic adaptation standard for voltage deviation. This mode is different from both the second evaluation mode (positive and negative symmetric percentage) and the third evaluation mode (single-phase asymmetry). Its characteristics are: setting asymmetric maximum limit values for positive and negative deviations for each of the three phases A, B, and C, that is, the percentage of the allowable positive deviation and the absolute value of the percentage of the allowable negative deviation can be different, and the difference between the two dynamically changes with the gap between the voltage level and the second voltage threshold. When the voltage level is close to the second threshold, the positive and negative limits tend to be symmetric (close to the second evaluation mode); when the voltage level is close to the first threshold, the degree of asymmetry of the positive and negative limits gradually increases, reflecting the physical characteristic that the high-voltage system has lower tolerance to negative deviation (low voltage). According to the difference between the voltage level and the second threshold, the asymmetry coefficient of the positive and negative limits is calculated according to a preset linear or non-linear function, and a unique dynamic adaptation standard for voltage deviation in this scenario is generated.

[0060] Wherein, the transition evaluation mode refers to a voltage deviation evaluation method applicable to a three-phase system in the transition interval from medium voltage to high voltage, which adopts asymmetric maximum limit values for positive and negative deviations, and the degree of asymmetry is dynamically adjusted with the voltage level.

[0061] For example, set the first voltage threshold = 35kV, and the second voltage threshold = 20kV. In a detection scenario for a 25kV three-phase industrial user, the voltage level of 25kV is between 20kV and 35kV, and the transition evaluation mode is selected. Calculate the asymmetry coefficient: the difference between the voltage level and the second threshold = 5kV, the total interval width = 15kV, the ratio = 5 / 15, and the preset maximum asymmetry coefficient is 0.7 (corresponding to a positive / negative limit ratio of 7% / 10% at 35kV), then the current asymmetry coefficient = 1-(1-0.7)×(5 / 15) = 1-0.1 = 0.9, that is, the ratio of the positive limit to the absolute value of the negative limit is 0.9. If the preset negative limit is -10%, the positive limit is +9%. The measured maximum value of phase A is 27.2kV (+8.8%), and the minimum value is 22.5kV (-10%), both of which are within the limits, and it is determined to be qualified. If the voltage level is 30kV, the ratio = 10 / 15 = 0.667, the asymmetry coefficient = 1-0.1×0.667 = 0.933, and the positive limit is +9.33%, which is still qualified.

[0062] When the voltage level is less than or equal to the second voltage threshold, and the power supply system at the detection point is three phases A, B, and C, the second evaluation mode is taken as the dynamic adaptation standard for voltage deviation. The second evaluation mode is used to represent that the maximum positive deviation percentage and the maximum negative deviation percentage are respectively set for the voltage deviations of the three phases A, B, and C, and the absolute values of the positive and negative percentages are the same, and the second voltage threshold is smaller than the first voltage threshold.

[0063] Specifically, when the voltage level is less than or equal to the second voltage threshold and the power supply system is three-phase, the second evaluation mode is selected as the dynamic adaptation standard for voltage deviation, and a preset positive and negative symmetric percentage limit is obtained. This limit is expressed in the form of a percentage of the nominal voltage, and the absolute values of the maximum allowable positive deviation and the maximum allowable negative deviation are the same, for example, ±5% or ±7%. The positive deviation and negative deviation of each of phases A, B and C are calculated respectively: the positive deviation is the ratio of the part by which the maximum measured voltage of the phase exceeds the nominal voltage to the nominal voltage, and the negative deviation is the ratio of the part by which the nominal voltage exceeds the minimum measured voltage of the phase to the nominal voltage. For each phase, the calculated positive deviation is compared with the maximum positive deviation percentage, and the negative deviation is compared with the maximum negative deviation percentage (with the same absolute value). A phase is determined to be qualified only when the positive deviation of the phase does not exceed the maximum positive deviation percentage and the negative deviation of the phase does not exceed the maximum negative deviation percentage.

[0064] For example, in a detection scenario of a 10kV distribution station: the second voltage threshold is preset to 20kV, the current voltage level 10kV is less than 20kV, and the power supply system is three-phase, so the second evaluation mode is selected, the preset positive and negative symmetric percentage limit is ±7%, and the nominal voltage U_n=10kV. Within the evaluation interval, the maximum measured voltage of phase A is 10.65kV and the minimum measured voltage is 9.40kV, the positive deviation=(10.65-10) / 10×100%=6.5%, the negative deviation=(9.40-10) / 10×100%=-6.0%, 6.5%<7% and -6.0%>-7%, so phase A is qualified. The maximum measured voltage of phase B is 10.72kV and the minimum measured voltage is 9.35kV, the positive deviation is 7.2%, the negative deviation is -6.5%, and the positive deviation 7.2%>7%, so phase B is unqualified. The maximum measured voltage of phase C is 10.50kV and the minimum measured voltage is 9.30kV, the positive deviation is 5.0%, the negative deviation is -7.0%, the negative deviation exactly reaches -7%, so phase C is qualified. It is finally determined that the voltage deviation is unqualified, and the unqualified phase is phase B.

[0065] When the power supply system at the detection point is single-phase, the third evaluation mode is taken as the dynamic adaptation standard for voltage deviation, and the third evaluation mode is used to represent that positive and negative asymmetric deviation limits are set for the single-phase voltage deviation.

[0066] Specifically, when the power supply system is determined to be single-phase, the third evaluation mode is directly selected as the dynamic adaptation standard for voltage deviation, no longer relying on the comparison results of voltage levels. After selecting the mode, preset positive and negative asymmetric deviation limits are obtained. These limits include two independently set parameters: the maximum permissible percentage for positive deviation and the maximum permissible percentage for negative deviation. The absolute values ​​of these two parameters can be different. The positive and negative deviations of the single phase are calculated: the positive deviation is the ratio of the portion of the measured maximum voltage exceeding the nominal voltage to the nominal voltage; the negative deviation is the ratio of the portion of the nominal voltage exceeding the minimum measured voltage to the nominal voltage. The calculated positive deviation is compared with the maximum positive deviation percentage, and the negative deviation is compared with the maximum negative deviation percentage. Only when the positive deviation does not exceed the maximum positive deviation percentage, and the negative deviation does not exceed the maximum negative deviation percentage, is the voltage deviation of the single phase deemed acceptable.

[0067] Example: Power quality testing scenario for a 220V residential user: The power supply system at the testing point is single-phase, therefore the third evaluation mode is selected. Preset positive and negative asymmetry deviation limits: maximum positive +7%, maximum negative -10%. Nominal voltage U n =220V. Within the evaluation range, the measured maximum voltage is 235V and the minimum is 198V. Positive deviation = (235-220) / 220×100% = 6.8%, negative deviation = (198-220) / 220×100% = -10.0%. 6.8% < 7% and -10.0% ≥ -10%, the positive deviation does not exceed the limit, and the negative deviation just reaches the limit. The voltage deviation is judged to be qualified but in a boundary state, and the system can trigger an early warning. If the measured maximum voltage is 238V (+8.2%), the positive deviation 8.2% > 7%, and it is judged to be unqualified; if the measured minimum voltage is 195V (-11.4%), the negative deviation -11.4% < 10%, it is also judged to be unqualified. If user equipment is more sensitive to low voltage in a certain scenario, the negative limit can be tightened to -7%, while the positive limit remains at +10%. In the -7% scenario, the aforementioned 198V (-10%) will be deemed unacceptable. For other national or regional standards, such as the US 120V single-phase system, the nominal voltage is 120V, and the preset positive and negative asymmetric limits can be configured according to local standards, such as +6% and -13%.

[0068] Through the above embodiments, the corresponding voltage deviation evaluation mode is automatically selected according to different voltage levels and power supply systems, instead of using a uniform and fixed deviation limit. The differentiated design of multiple modes makes the voltage deviation evaluation more in line with the actual engineering situation of different voltage levels and energy use scenarios, avoiding the problem of overly strict or overly lenient evaluation caused by uniform limits, and improving the scenario adaptability and accuracy of power quality evaluation.

[0069] S205. Based on grid-connected capacity, determine the dynamic adaptation standard corresponding to frequency deviation.

[0070] Specifically, after extracting the grid-connected capacity, the dynamic adaptation standard corresponding to the frequency deviation index is determined. The frequency deviation limit is not fixed but is set according to the grid-connected capacity of the system connected to the detection point. The grid-connected capacity is compared with multiple preset capacity thresholds to determine the capacity level to which the current grid-connected capacity belongs. Each level corresponds to a frequency deviation limit. When the grid-connected capacity is greater than or equal to the first capacity threshold, it indicates that the detection point is connected to a large-capacity power system with strong frequency support capabilities, and the frequency deviation limit is set to a stricter value, usually ±0.2Hz. When the grid-connected capacity is less than the first capacity threshold but greater than or equal to the second capacity threshold, it indicates that the system is connected to a medium-capacity system, and the frequency deviation limit is appropriately relaxed, usually ±0.5Hz. When the grid-connected capacity is less than the second capacity threshold, it indicates that the system is connected to a small-capacity island or microgrid system, and the frequency deviation limit is further relaxed, usually ±1.0Hz. Based on the level of the grid-connected capacity, the corresponding frequency deviation limit is selected as the dynamic adaptation standard output. If grid-connected capacity information is missing or exceeds the preset range, the default setting can be used or a conservative setting can be made according to the strictest standards.

[0071] The first capacity threshold is the dividing line between large-capacity and medium-capacity systems, typically set at 200MW or configured according to the actual conditions of the regional power grid. The second capacity threshold is the dividing line between medium-capacity and small-capacity systems, typically set at 20MW or configured according to the standards for microgrids and islanded operation.

[0072] For example, a grid-connected detection scenario of a large-scale photovoltaic power station has a grid-connected capacity of 300MW. This capacity is compared with preset thresholds: the first capacity threshold is 200MW, and the second capacity threshold is 20MW. Since 300MW is greater than 200MW, it falls into the large-capacity system gear, and the selected frequency deviation limit is ±0.2Hz. Within the evaluation interval, the detected maximum power grid frequency is 50.18Hz and the minimum is 49.85Hz, with deviations of +0.18Hz and -0.15Hz respectively, both within the range of ±0.2Hz, so the result is determined to be qualified. A grid-connected detection scenario of a medium-scale wind farm has a grid-connected capacity of 50MW, which is between 20MW and 200MW, belonging to the medium-capacity system gear with a frequency deviation limit of ±0.5Hz. The detected maximum frequency is 50.35Hz (+0.35Hz) and the minimum is 49.60Hz (-0.40Hz), both within the limit, so the result is determined to be qualified. An island operation scenario of an industrial park microgrid has a grid-connected capacity of 5MW, which is less than 20MW, belonging to the small-capacity system gear with a frequency deviation limit of ±1.0Hz. The detected maximum frequency is 50.80Hz (+0.8Hz) and the minimum is 49.30Hz (-0.7Hz), so the result is determined to be qualified. If the grid-connected capacity field is missing, the system defaults to the strictest limit of ±0.2Hz to ensure the safety redundancy of the evaluation.

[0073] S206. Based on the type of the detection point, determine the short-term flicker limit and the long-term flicker limit corresponding to the detection point, so as to obtain the dynamically adaptive standard corresponding to voltage flicker.

[0074] Specifically, the limit of voltage flicker is not consistent in all detection scenarios, but is set differentially according to the load type or station category to which the detection point belongs. Using the detection point type as a query key, the short-term flicker limit P matching the type is retrieved in the preset flicker limit configuration library st and the long-term flicker limit P lt . The configuration library pre-associates a corresponding flicker limit pair for each type of detection point. For conventional public connection points or general industrial users, the short-term flicker limit P st is set to 1.0, and the long-term flicker limit P lt is set to 0.8. For new energy stations (including wind farms and photovoltaic power stations), due to the volatility characteristics of new energy power generation, the standards usually require their flicker limits to be consistent with or slightly adjusted from those of conventional users, and P is still adopted st =1.0, P lt =0.8, but the superposition effect of multiple units needs to be considered in actual calculation. For impact loads such as electric arc furnaces and rolling mills, the short-term flicker limit may be relaxed to 1.2 or calculated separately according to specific standards. For residential users or detection points sensitive to flicker, the limit may be tightened. When there is no exact match for the detection point type in the configuration library, the default limit or the limit of the nearest type is used as a substitute. After the limit is determined, Pst and P lt It is packaged as a dynamic adaptation standard for voltage flicker, for use in subsequent conformity assessment.

[0075] Example: A wind farm grid connection monitoring scenario: The monitoring point type is "wind farm". Using "wind farm" as the key, the flicker limit configuration library is queried, and the short-time flicker limit P is retrieved. st =1.0, long-term flicker limit P lt =0.8. Within the evaluation interval, the system continuously measures the voltage fluctuations output by the wind farm and calculates P for each 10-minute period. st Value, maximum P st =0.85, less than 1.0, short-term flicker is acceptable; then calculate P for the two-hour period. lt Value, P lt =0.72, less than 0.8, long-term flicker is qualified, and the voltage flicker index is judged to be qualified. A certain point of common coupling detection scenario: the detection point type is "point of common coupling", and P is also taken as... st =1.0, P lt =0.8. A load monitoring scenario for an electric arc furnace: The monitoring point type is "electric arc furnace," and the standard's flicker limit requirements are consistent with those for conventional users, so P is still taken. st =1.0, P lt =0.8, but due to the typically large voltage fluctuations generated during the operation of the electric arc furnace, the actual measured value may exceed the standard. In this case, further analysis or remedial measures are required. If the detection point type is "sensitive user" and P is preset in the configuration library... st =0.8, P lt If the value is 0.6, a stricter limit will be applied. If the detection point type has no matching item in the configuration library, such as "charging station" which is not predefined, the system can use the default value P. st =1.0, P lt =0.8 or prompt the user to configure manually.

[0076] Through the above embodiments, a dynamic adaptation standard for voltage flicker is determined differently based on the type of detection point, replacing the traditional fixed mode of using Pst=1.0 and Plt=0.8 for all detection points. This differentiated design considers the differences in flicker sensitivity and flicker generation capacity of different load types: although new energy power plants are more likely to experience fluctuations, their flicker impact is evaluated using the same benchmark as conventional users, ensuring fairness; impulsive loads such as electric arc furnaces may face stricter scrutiny in actual evaluations; sensitive users such as hospitals and data centers have higher requirements for power quality and can be configured with stricter limits. Through automatic identification of detection point types and flexible expansion of the configuration library, this mechanism can adapt to the needs of different standards and application scenarios, improving the scenario adaptability and flexibility of voltage flicker evaluation. When standards are updated or new detection point types are added, only the configuration library needs to be updated without modifying the core algorithm, enhancing the maintainability and scalability of the system.

[0077] S207. Based on the voltage level and system grounding method, determine the transient overvoltage multiple limit and the power frequency overvoltage limit to obtain the dynamic adaptation standard for overvoltage.

[0078] Specifically, overvoltage includes two types: power frequency overvoltage and transient overvoltage. The limits for both are closely related to the voltage level and the system neutral grounding method. Using the voltage level and system grounding method as a joint lookup key, matching limit records are retrieved from a preset overvoltage limit configuration library. For power frequency overvoltage, the limit is usually expressed as a multiple of the nominal voltage. When the system grounding method is direct grounding, the power frequency overvoltage multiple is generally controlled within 1.3 times the nominal voltage. When the system grounding method is ungrounded or grounded through an arc suppression coil, the power frequency overvoltage multiple may be relaxed to 1.4 times or higher, depending on the voltage level and standard regulations. For transient overvoltage multiple limits, the same applies to the voltage level and grounding method: in a directly grounded system, the transient overvoltage multiple is usually controlled within 2.0 times the nominal voltage; in an ungrounded or arc suppression coil grounded system, the transient overvoltage multiple may reach 3.0 times or more. Based on the joint query results, the power frequency overvoltage multiple limit and the transient overvoltage multiple limit are determined, and both are encapsulated into a dynamic overvoltage adaptation standard for use in subsequent compliance assessment. When the voltage level or grounding method does not have an exact match in the configuration library, the conservative limit of the nearest condition is used as a substitute.

[0079] Power frequency overvoltage refers to a continuous or short-duration overvoltage with a frequency of 50Hz, usually caused by the capacitive effect of an unloaded long line, asymmetrical grounding faults, or load shedding. Its multiple refers to the ratio of the peak overvoltage to the peak nominal voltage. Transient overvoltage refers to an impulsive overvoltage with a short duration (milliseconds), including switching overvoltage and lightning overvoltage. Its multiple refers to the ratio of the peak impulse voltage to the peak nominal voltage.

[0080] For example, consider the testing scenario of a 110kV substation: voltage level 110kV, system grounding method direct grounding. Using {voltage level = 110kV, grounding method = direct grounding} as the joint key, the overvoltage limit configuration library is queried. The retrieved value shows a power frequency overvoltage multiple limit of 1.3 pu (pu represents the nominal voltage peak multiple) and a transient overvoltage multiple limit of 2.0 pu. Within the evaluation range, the detected maximum peak power frequency overvoltage is 1.25 pu, less than 1.3 pu, thus the power frequency overvoltage is acceptable; the maximum peak transient overvoltage is 1.8 pu, less than 2.0 pu, thus the transient overvoltage is acceptable, and the overvoltage index is deemed acceptable. Consider the testing scenario of a 35kV ungrounded system: voltage level 35kV, system grounding method ungrounded. The configuration library shows a power frequency overvoltage multiple limit of 1.4 pu and a transient overvoltage multiple limit of 3.0 pu. The measured peak power frequency overvoltage is 1.35 pu (acceptable), and the peak transient overvoltage is 2.8 pu (acceptable). A testing scenario for a 10kV arc-suppression coil grounding system: Voltage level 10kV, grounding method via arc-suppression coil. The query yielded a power frequency overvoltage limit of 1.4 pu and a transient overvoltage limit of 3.2 pu, both of which met the measured requirements. If the voltage level is 220kV and directly grounded, the query yielded a power frequency overvoltage limit of 1.3 pu and a transient overvoltage limit of 1.8 pu (which may be more stringent according to standards). If the voltage level or grounding method combination does not have an exact match in the configuration library, such as 35kV direct grounding (less common), the system can refer to the limit of 110kV direct grounding (1.3 pu) as a conservative alternative.

[0081] S104. Based on power quality detection data and various dynamic adaptation standards, generate the pass / fail judgment results for each power quality indicator, and generate a power quality evaluation report based on the pass / fail judgment results.

[0082] Specifically, statistical characteristic values ​​are calculated for power quality monitoring data according to the evaluation period, including the maximum, minimum, average, and probability statistics (such as P95 value) of each power quality indicator within the evaluation period. For harmonic current indicators, the maximum value or P95 value is extracted from the test data sequentially (2 to 50 times) and phase by phase (A, B, and C), and compared with the corresponding user injection limit or point of common coupling limit in the dynamic adaptation standard. If the detected value exceeds the limit, the phase is deemed unqualified; otherwise, it is qualified. For harmonic voltage, the harmonic voltage content rate and total harmonic distortion rate are extracted and compared with the limit in the dynamic adaptation standard to determine qualification. For three-phase unbalance, the maximum value or P95 value of negative sequence voltage unbalance is extracted and compared with the limit in the dynamic adaptation standard. For voltage deviation, the judgment is performed phase by phase according to the judgment rules of the selected evaluation mode (the first evaluation mode compares the sum of deviations, the second evaluation mode compares the percentage of positive and negative symmetry, and the third evaluation mode compares the positive and negative asymmetry limits). For frequency deviation, the maximum absolute value of frequency deviation within the evaluation interval is extracted and compared with the limit in the dynamic adaptation standard. For voltage flicker, the short-time flicker P value is extracted. st Value and long-term flicker P lt The values ​​are compared with the limits in the dynamic adaptation standard. For overvoltage, the peak multiple of power frequency overvoltage and peak multiple of transient overvoltage are extracted and compared with the multiple limit in the dynamic adaptation standard. The pass / fail judgment results of all indicators are stored in a multi-dimensional structure of indicator-phase-number, forming a complete evaluation result dataset. Then, the report generation engine is called to read the preset report template, and the pass / fail judgment results, the detection statistics of each power quality indicator, the corresponding dynamic adaptation standard, and the detection configuration parameters from the evaluation result dataset are filled into the specified positions of the template to generate a power quality evaluation report containing complete evaluation conclusions, data tables, and chart attachments. Evaluation reports in multiple formats can be generated according to requirements.

[0083] The pass / fail determination result refers to the conclusion drawn after comparing the statistical characteristic value of the test data with the dynamic adaptation standard, including two states: pass and fail. For multiple indicators such as harmonics, the specific phase and number of times the test data exceeds the standard must also be recorded.

[0084] Through the above embodiments, power quality testing data is systematically compared to generate standardized compliance judgment results, and a complete evaluation report is automatically output. The report generation engine adopts a template-based design, supports multiple output formats such as Word, PDF, and Excel, and can automatically adjust the report content according to the compliance judgment results (such as adding analysis explanations and inserting charts when standards are exceeded). This achieves automated linkage between evaluation conclusions and report content, ensuring the consistency and professionalism of the test reports. At the same time, the reports are generated from unified data compliance results, ensuring the consistency and accuracy of reports in different formats.

[0085] Based on the above embodiments, as an optional embodiment, for Figure 1 The step of generating a power quality assessment report in step S104 shown can be achieved for harmonic currents through the following steps, which are explained in detail below.

[0086] Based on the determination results of harmonic currents in each phase, the frequencies and corresponding phases of the excessive harmonics are selected. Each excessive harmonic is then divided into three-phase all-exceeding groups, two-phase exceeding groups, and single-phase exceeding groups. Based on the total frequencies and phases in each exceeding group, an excessive harmonic conclusion text is generated, containing phase combination, harmonic frequency, detection value, and limit information. Corresponding spectrum diagrams are generated according to the grouping type: three-phase all-exceeding groups correspond to a three-phase composite diagram, two-phase exceeding groups correspond to a two-phase superimposed diagram, and single-phase exceeding groups correspond to a single-phase diagram. All spectrum diagrams and excessive harmonic conclusion texts are automatically populated into the power quality evaluation report.

[0087] Specifically, the system iterates through the judgment results for each harmonic in each phase, filtering out all records judged as exceeding the limit. Each record includes the harmonic number, phase (one or more phases from A, B, and C), detection value, and corresponding limit value. Next, the exceeding harmonics are grouped according to phase combination rules: if a harmonic exceeds the limit in all three phases (A, B, and C), it is classified into the "Three-Phase Exceeding Limit Group"; if it exceeds the limit in two phases (e.g., A and B, B and C, or A and C), it is classified into the corresponding "Two-Phase Exceeding Limit Group"; if it exceeds the limit in only one phase, it is classified into the "Single-Phase Exceeding Limit Group." After grouping, a conclusion text is generated for each exceeding group. This text is filled with a preset sentence template and includes a phase combination description, a list of harmonic numbers, and the corresponding detection values ​​and limit values ​​for each harmonic. Simultaneously, corresponding spectrum diagrams are generated based on the grouping type: For the group with all three phases exceeding the standard, a composite spectrum diagram is generated containing bar charts for phases A, B, and C, with bars for the same harmonic order displayed side-by-side for comparison; for the group with two phases exceeding the standard, a composite spectrum diagram is generated containing only the bar charts for those two phases; and for the group with one phase exceeding the standard, a single-phase spectrum diagram is generated containing only the bar chart for that single phase. The horizontal axis of all spectrum diagrams represents the harmonic order, and the vertical axis represents the current value (in A). Limit locations can be marked on the graphs (e.g., using a red horizontal dashed line). Finally, the generated conclusion text and spectrum diagrams are automatically filled into the designated chapters of the power quality assessment report in the order they were generated. The conclusion text is placed in the text description section, and the spectrum diagrams are inserted as appendices with figure numbers and titles.

[0088] Among them, excessive harmonics refer to the harmonic number whose detected value in a certain phase exceeds the corresponding limit in the dynamic adaptation standard. A phase refers to the three phases A, B, and C of a three-phase AC power supply, representing three different lines. A three-phase excessive group refers to the set where a certain harmonic is determined to be excessive in all three phases (A, B, and C), and this group should highlight the consistency of the three-phase problem. A two-phase excessive group refers to the set where a certain harmonic is excessive in two phases of the three-phase system, and the other phase is within acceptable limits; depending on which two phases are excessive, it can be further divided into AB phase group, BC phase group, and AC phase group. A single-phase excessive group refers to the set where a certain harmonic is excessive in only one phase (A, B, or C), and the other two phases are within acceptable limits. A three-phase composite chart is a bar chart that simultaneously displays the data for phases A, B, and C in the same graph, usually using grouped or stacked bar charts for easy and intuitive comparison of three-phase differences. A two-phase overlay chart is a bar chart that only displays the data for the two phases that are excessive, showing the two phase bar charts side-by-side or overlaid. A single-phase diagram is a bar chart that displays only single-phase data.

[0089] Through the above embodiments, harmonic current exceeding the standard items are structurally classified according to phase combination, and targeted conclusion text and spectrum diagrams are automatically generated based on the classification results, realizing refined and differentiated report generation: the group with all three phases exceeding the standard generates a three-phase composite diagram, which is convenient for observing the consistency of the three phases; the group with two phases exceeding the standard only shows the two phases exceeding the standard, avoiding redundant information; the group with one phase exceeding the standard only shows a single-phase diagram, focusing on the problem. At the same time, the conclusion text and the diagram are linked, with the text clearly listing the detected value and the limit value, and the limit value position marked with a red dotted line in the diagram. The two corroborate each other, improving the readability and professionalism of the report. Through the full-process automation of automatic classification, automatic generation, and automatic filling, the problems of low efficiency, easy error, and inconsistency between text and diagram caused by manual screening of exceeding the standard items, manual drawing, and manual filling of conclusions in the traditional method are solved, which significantly improves the efficiency and quality of power quality assessment report preparation.

[0090] Based on the above embodiments, as an optional embodiment, refer to... Figure 3 , Figure 3 This is a flowchart illustrating the compliance determination process for operating conditions provided in an embodiment of this application. Figure 1 The step S104 shown, which generates the pass / fail determination results for each power quality indicator, may further include... Figure 3 Steps S301-S304 are explained in detail below.

[0091] S301. Monitor the status characteristics of the power grid operation in real time. When a condition change node that triggers the dynamic adaptation standard switching is identified, extract the transient data segment of the preset time window centered on the condition change node.

[0092] Specifically, during the power quality assessment process, the state characteristics of the power grid operation are monitored in real time. These state characteristics include, but are not limited to, physical quantities that reflect changes in the power grid operation mode, such as short-circuit capacity at the point of common coupling, system frequency, effective voltage value, grid-connected switch status, and line switching status. These state characteristics are continuously collected at a preset sampling period and constructed into a state characteristic sequence in chronological order. The state characteristics at the current moment are compared with the state characteristics at the previous moment, and the degree of difference between the two is calculated. When the difference of a certain state characteristic exceeds a preset jump threshold, that moment is identified as a state condition jump node that triggers the dynamic adaptation standard switching. After identifying a transition node in the operating condition, a preset time length is extended in both directions before and after the corresponding timestamp of the node to form a continuous transient data segment. The start time of this transient data segment is the timestamp of the transition node minus the preset preceding time length, and the end time is the timestamp of the transition node plus the preset following time length. The length of the preset time window can be configured according to the dynamic characteristics of the power grid and the detection accuracy requirements. Usually, the preceding and following time lengths are equal or asymmetrically set according to actual needs. After the segmentation is completed, the power quality detection data in the transient data segment is marked and isolated from the data of the preceding and following steady-state periods for subsequent smooth transition processing.

[0093] The operating condition characteristics of the power grid refer to a set of measurable physical quantities that characterize the current operating mode and state of the power system, including but not limited to the on / off status of grid-connected switches, the connection and disconnection status of lines, the tap position of transformers, the start / stop status of generators, changes in short-circuit capacity, and fluctuations in system frequency. A condition jump node refers to the moment in the state characteristic sequence where the state characteristics change significantly compared to the previous moment. This node signifies a switch in the power grid operating mode, which may require a change in dynamic adaptation standards. A preset time window refers to a continuous time period extending forward and backward from the condition jump node. The length of this window should be reasonably set according to the duration of the power grid transient process, typically from 0.5 seconds before to 2.5 seconds after the condition jump node, or configured according to actual detection needs. A transient data segment refers to a subset of power quality detection data located within the preset time window. This data segment includes the complete transient process before, during, and after the jump.

[0094] S302. Construct a smooth transition curve within the transient data segment window, and compare the power quality detection data with the smooth transition curve to generate the compliance judgment result of the transient data segment.

[0095] Specifically, after the transient data segment is intercepted, the dynamic adaptation standard adopted before the occurrence of the working condition jump node (referred to as the historical adaptation standard) and the dynamic adaptation standard to be adopted after the jump node (referred to as the target adaptation standard) are respectively acquired. In the case where no smoothing processing is adopted, there will be a hard switch of the standard limit before and after the jump node, that is, the limit jumps from the historical value to the target value at the instant of the jump node. To avoid the misjudgment of detection data in the transient process caused by such hard switch, a smooth transition curve is constructed within the time window of the transient data segment to replace the original hard switch limit. The smooth transition curve takes time as the independent variable, is equal to the historical adaptation standard limit at the start moment of the transient data segment, is equal to the target adaptation standard limit at the end moment of the transient data segment, and changes continuously in the middle period according to a preset interpolation function (such as linear interpolation, cubic spline interpolation or S-shaped curve). After the construction is completed, the detection data at each moment in the transient data segment is compared with the corresponding limit on the smooth transition curve at that moment: if the detection data does not exceed the limit at that moment, the moment is judged to be qualified; if the detection data exceeds the limit at that moment, the moment is judged to be unqualified. Finally, the judgment results of all moments in the transient data segment are summarized to generate the compliance judgment result of the transient data segment, including information such as the proportion of qualified moments, the distribution of over-limit moments, and the statistics of over-limit amplitude.

[0096] Wherein, the smooth transition curve refers to a limit curve that changes continuously with time in the transient data segment, and is used to replace the instantaneous hard switch at the jump node, and its function form may be a linear function, a polynomial function, an exponential function or an S-shaped logic function.

[0097] For example, suppose the second harmonic current limit in the historical adaptation standard is 52A (before the jump), and the second harmonic current limit in the target adaptation standard is 31.2A (after the jump). The transient data segment time window is from t0-0.5 seconds to t0+2.5 seconds, with a total duration of 3 seconds. If smoothing is not used, the limit will jump instantly from 52A to 31.2A at time t0. Assuming the harmonic current detected at t0+0.2 seconds is 35A, this value is less than the historical limit of 52A but greater than the target limit of 31.2A. If a hard handover were used, it would be considered unqualified. However, this 35A is actually a normal response during the system's transition from the 52A limit to the 31.2A limit. A smooth transition curve is constructed using linear interpolation: the limit at the initial time (t0-0.5 seconds) is 52A, the limit at the end time (t0+2.5 seconds) is 31.2A, and the limit at intermediate times is calculated using the linear formula L(t)=52-(52-31.2) / 3×(t-(t0-0.5)). At t0+0.2 seconds, t differs from the initial time by 0.7 seconds. The limit = 52-20.8 / 3×0.7 = 52-4.85 = 47.15A. Since 35A < 47.15A, it is considered qualified. If cubic spline interpolation is used, the curve changes more gently near the abrupt change node, and the first derivative is zero at both ends, simulating the inertial characteristics of the physical process and further reducing transient misjudgments. If an S-curve is used, the limit change shows a trend of slow at first, fast then slow again, which is closer to the physical law of actual power grid parameter changes. The comparison results of all sampling points within the transient data segment are summarized, and the pass rate is calculated to be 98.5%. The recorded times of exceeding the limit are concentrated between t0+1.8 seconds and t0+2.1 seconds, with a maximum exceedance of 8%, generating the compliance judgment result of the transient data segment.

[0098] Through the above embodiments, a smooth transition curve is used to replace the hard switching limit within the transient data segment, effectively avoiding evaluation misjudgments caused by the instantaneous jump in standard limit values ​​during operational condition transitions. In actual engineering practice, changes in grid parameters (such as short-circuit capacity) are not instantaneous but involve an inertial transition process. The corresponding dynamic adaptation standard should also change continuously rather than jump. Introducing a smooth transition curve matches the change in limit values ​​with the transient characteristics of the grid's physical process. Comparing the detected data with the continuously changing limit values ​​more realistically reflects the power quality status during the transient process. Compared to the hard switching method, which misjudges originally qualified data as unqualified during the transient process, or requires manual removal of transient data leading to incomplete evaluation, this application achieves automated and rational evaluation of the transient data segment, improving the accuracy and continuity of qualification determination in dynamic scenarios. The optional configuration of various smoothing functions such as linear interpolation, cubic spline, and S-curve allows this mechanism to adapt to the transient characteristics of different grid scenarios, enhancing the flexibility and applicability of the solution.

[0099] S303. For the steady-state data segments other than the transient data segments, extract the abnormal segments that exceed each dynamic adaptation standard from the steady-state data segments, and record the duration and energy accumulation value of the abnormal segments.

[0100] Specifically, after completing the smooth transition processing of the transient data segment, the evaluation focus shifts to the steady-state data segment outside the transient data segment. The steady-state data segment refers to all continuous data parts in the entire evaluation interval other than the transient data segment. The power grid operating conditions corresponding to this part of the data are relatively stable, and the dynamic adaptation standard remains unchanged. The power quality detection data in the steady-state data segment is traversed, and each point is compared with the currently applicable dynamic adaptation standard. All data points whose detection values ​​exceed the corresponding limits are screened out. Then, these continuous out-of-limit data points are aggregated in chronological order to form several abnormal segments. Each abnormal segment consists of a continuous sequence of out-of-limit data points, including information such as start time, end time, and out-of-limit amplitude sequence. For each abnormal segment, two key quantitative indicators are recorded: duration and energy accumulation value. The duration refers to the length of time from the start time to the end time of the abnormal segment, usually in milliseconds or seconds. The cumulative energy value refers to the value obtained by integrating or summing the excess amplitude (the difference between the detected value and the limit value) of each data point within the abnormal segment along the time axis. The calculation formula is: Cumulative Energy Value = ∫(Detected Value - Limit Value) dt. For discrete sampled data, an approximate summation method is used. After recording, the start time, end time, duration, cumulative energy value, and corresponding indicator name, phase, and harmonic order of each abnormal segment are stored as an abnormal segment record for subsequent determination of real violations.

[0101] S304. When the duration exceeds the preset duration and the energy accumulation value exceeds the preset energy threshold, the abnormal segment is determined as a real violation event, and the compliance determination result of the steady-state data segment is obtained.

[0102] Specifically, after recording the duration and cumulative energy value of each abnormal segment, a dual threshold determination is performed on each segment to distinguish between genuine violations and exemptable transient disturbances. Preset duration and energy thresholds are read. These thresholds are pre-configured based on factors such as the type of power quality indicator, voltage level, and equipment sensitivity; different thresholds can be set for different indicators. The duration of the abnormal segment is compared with the preset duration threshold, and the cumulative energy value is also compared with the preset energy threshold. Only when both the duration and energy value exceed the preset duration and energy thresholds is the abnormal segment determined to be a genuine violation. If the duration exceeds the threshold but the energy value does not, or the energy value exceeds the threshold but the duration does not, or neither exceeds the threshold, the abnormal segment is determined to be an exemptable transient disturbance and is not included in the final non-compliance statistics. For abnormal segments determined to be genuine violations, detailed information such as indicator name, phase, harmonic order, start time, end time, maximum exceedance amplitude, duration, and cumulative energy value are recorded, and the compliance determination results for the steady-state data segment are generated. The compliance determination result is combined with the compliance determination result of the aforementioned transient data segment to form a complete compliance determination result, which is used for subsequent report generation. For abnormal segments of transient disturbances that are determined to be exempt, exemption information can be recorded in the log for traceability, but they are not marked as non-compliant.

[0103] The preset duration is a time boundary value used to distinguish between transient disturbances and persistent violations. Different power quality indicators can be configured with different duration thresholds. For example, for voltage deviation, the duration threshold can be set to 0.5 seconds; voltage fluctuations shorter than this value are considered transient disturbances. For harmonic current, the duration threshold can be set to 3 seconds. The preset energy threshold is an energy accumulation boundary value used to distinguish between small energy disturbances and large energy violations. Different indicators can be configured with different energy thresholds. The unit of the energy threshold depends on the specific indicator. For example, the energy threshold unit for voltage deviation is %·s (percentage per second), and the energy threshold unit for harmonic current is A·s (ampere per second). A true violation event refers to an abnormal segment with a relatively long duration and large energy accumulation. This event reflects a substantial power quality problem in the power grid or equipment and needs to be listed as a non-compliance item in the report.

[0104] Through the above embodiments, abnormal segments in the steady-state data segment are finely screened, distinguishing between substantial real violations and exemptable transient disturbances. Taking voltage deviation as an example, a voltage fluctuation lasting 0.25 seconds with very small energy accumulation would be judged as unqualified in traditional evaluations, but this fluctuation has almost no impact on most equipment. This solution reasonably exempts it through dual thresholds, reducing unnecessary rectification notices. Conversely, a voltage deviation lasting 5 seconds with large energy accumulation reflects substantial problems such as insufficient system voltage regulation capability, and this solution accurately identifies it as a real violation. The joint judgment logic of duration and energy accumulation value conforms to the basic principle of "small disturbances are acceptable, large disturbances need to be handled" in power quality engineering practice, and has higher accuracy and rationality compared to single threshold judgment (only looking at the duration or only looking at the exceedance amplitude). This mechanism significantly reduces the false alarm rate and improves the credibility and engineering practicality of the evaluation results.

[0105] Based on the above embodiments, as an optional embodiment, for Figure 1 The multi-standard adaptive power quality evaluation method shown may also include the following steps, which are explained in detail below.

[0106] When the target of the test is a new energy power station with both grid-connected and islanded operation modes, the status of the grid-connected switch of the power station is monitored in real time. When a switch from grid-connected mode to islanded mode or vice versa is detected, the start time and the completion time of the switch are recorded. The first dynamic standard set at the start time of the switch and the second dynamic standard set at the completion time of the switch are extracted. Within the transition time window between the start time and the completion time of the switch, multiple standard interpolation curves that change continuously with time are constructed. The value of each standard interpolation curve at the start time of the switch is equal to the corresponding limit value of the first dynamic standard set, and the value at the completion time of the switch is equal to the corresponding limit value of the second dynamic standard set. Within the transition time window, the power quality test data is compared with each standard interpolation curve to generate a transition period judgment result with time stamp. The start time of the switch, the completion time of the switch, and the transition period judgment result are automatically filled into the power quality evaluation report.

[0107] Specifically, when the target of the monitoring is a new energy power station with both grid-connected and islanded operation modes (such as an island microgrid with energy storage, a photovoltaic power station or wind farm with islanded operation capability), the status of the grid-connected switch is monitored in real time during the power quality assessment process. The grid-connected switch is a circuit breaker connecting the new energy power station to the main power grid, and its status includes "closed" (grid-connected mode) and "open" (islanded mode). The status signal of the grid-connected switch is collected at a preset sampling period, and the status time series is maintained. When the switch status is detected to change from closed to open, or from open to closed, the time series is recorded. The switching start time is marked, and the switch status is continuously monitored until the new status stabilizes and no longer changes. This time is marked as the switching completion time. The first dynamic standard set used before the switching start time (in grid-connected mode) and the second dynamic standard set to be used after the switching completion time (in islanded mode) are extracted. Both standard sets are generated according to the dynamic adaptation standard determination method in the aforementioned claims, but may have significant differences due to different operating modes. For example, in islanded mode, the voltage deviation limit may be relaxed, the frequency deviation limit may be relaxed, and the harmonic current limit may be calculated using different bases. Within the transition time window from the start of the switchover to the completion of the switchover, a continuously varying standard interpolation curve is constructed for each type of power quality indicator in the standard set. The value of each standard interpolation curve at the start of the switchover is equal to the corresponding indicator limit in the first dynamic standard set, and the value at the completion of the switchover is equal to the corresponding indicator limit in the second dynamic standard set. During the intermediate period, the curve continuously varies according to a preset interpolation method (linear interpolation, cubic spline interpolation, or S-curve). For power quality detection data within the transition time window, it is compared moment-by-moment with the limit value on the corresponding standard interpolation curve to generate a pass / fail judgment for that moment, and these are summarized to form a transition period judgment result with a time stamp. Finally, the switchover start time, switchover completion time, and transition period judgment result are automatically filled into a designated section of the power quality evaluation report, enabling report users to clearly understand the operation mode switchover event and its impact on the evaluation benchmark.

[0108] Among them, grid-connected mode refers to the state in which a renewable energy power station operates in parallel with the main power grid through a grid-connected switch. In this mode, the power station follows the frequency and voltage support of the main power grid, and the power quality evaluation adopts the grid-connected standard. Islanded mode refers to the operating state in which a renewable energy power station is disconnected from the main grid and independently supplies power to local loads. In this mode, the power station needs to maintain voltage and frequency stability on its own, and the evaluation standards are usually more relaxed than those for grid-connected mode. The switching start time is the first time point when a state jump of the grid-connected switch is detected, marking the start of the switching process. The switching completion time is the time point when the switch state stabilizes again after a jump and does not jump again for a certain period of time, marking the end of the switching process. The first dynamic standard set refers to the complete set of dynamic adaptation standards for each power quality indicator in grid-connected or islanded mode before the switching start time. The second dynamic standard set refers to the complete set of dynamic adaptation standards for each power quality indicator in islanded or grid-connected mode after the switching completion time. The transition period judgment result refers to the set of qualified conclusions generated by comparing the detection data with the standard interpolation curve limit at the corresponding time moment within the transition time window, including the judgment distribution in the time dimension.

[0109] Example: Monitoring scenario for a renewable energy power station in an island microgrid: This power station normally operates in grid-connected mode, but may switch to islanded mode during typhoon season. The status of the grid-connected switch is monitored in real time. At time t0=08:00:00:000, the switch is detected to change from closed to open, recorded as the switching start time. After the switch contacts complete their mechanical travel, the switch is fully open and stable at time t1=08:00:00:500, recorded as the switching completion time. The first dynamic standard set (grid-connected mode) before the switching start time is extracted: voltage deviation limit is ±5% (second evaluation mode, symmetrical percentage), frequency deviation limit is ±0.2Hz, and total harmonic voltage distortion rate limit is 2.0%. The second dynamic standard set (islanded mode) after the switching completion time is extracted: voltage deviation limit is ±7%, frequency deviation limit is ±1.0Hz, and total harmonic voltage distortion rate limit is 3.0%. The transition time window is from 08:00:00:000 to 08:00:00:500, with a duration of 0.5 seconds. For voltage deviation limits, a linear interpolation curve is constructed: the initial limit is 5%, the ending limit is 7%, and the intermediate time is calculated as L(t) = 5% + (7% - 5%) × (t - t0) / (t1 - t0). At t0 + 0.2 seconds, L(t0 + 0.2) = 5% + 2% × 0.2 / 0.5 = 5.8%. For frequency deviation limits, a similar linear interpolation curve is constructed: the initial value is 0.2Hz, the ending value is 1.0Hz, and the limit at t0 + 0.2 seconds is 0.2 + 0.8 × 0.2 / 0.5 = 0.52Hz. The voltage deviation detected within the transition time window (e.g., measured +5.5% at t0 + 0.2 seconds) is compared with the corresponding interpolation limit of 5.8%. If 5.5% < 5.8%, it is considered acceptable. The measured frequency deviation of +0.6Hz is compared with the interpolation limit of 0.52Hz; if 0.6 > 0.52, it is considered unacceptable. All judgment results within 0.5 seconds are summarized to generate a transition period judgment result with a time stamp, including the percentage of qualified results and the first time the standard was exceeded. Finally, the switch start time "08:00:00.000", switch completion time "08:00:00.500", and transition period judgment results (such as "frequency deviation exceeded the standard at 0.2 seconds") are automatically filled into the "Operating Mode Switching Event" section of the power quality evaluation report.

[0110] Through the above embodiments, for renewable energy power plants with both grid-connected and islanded operation modes, a standard interpolation curve is constructed to replace instantaneous standard jumps during mode switching, achieving a smooth transition of evaluation benchmarks. Renewable energy power plants use different power quality evaluation standards in grid-connected and islanded modes. If the standard is switched directly at the moment of switching, the same test data will be evaluated by completely different benchmarks before and after the switch, easily leading to misjudgments. For example, in grid-connected mode, a frequency deviation of ±0.2Hz is a strict limit, while in islanded mode, ±1.0Hz is a relaxed limit. If the frequency jumps from 0.2Hz to 1.0Hz at the moment of switching, the same value might be judged as unqualified before the switch due to a frequency deviation of 0.3Hz, but qualified after the switch, causing a break in the evaluation logic. This solution uses a standard interpolation curve to make the limit change continuously within the transition window. The test data is compared with the continuously changing limit, and the evaluation results transition naturally, avoiding contradictions caused by jumps. At the same time, the judgment results during the transition period are time-stamped, reflecting the specific distribution of qualification during the switching process, providing maintenance personnel with more refined diagnostic information. This mechanism fills the gap in the existing technology for power quality evaluation in the scenario of switching modes at new energy power plants, and significantly improves the continuity and scientific nature of the evaluation.

[0111] Based on the above embodiments, as an optional embodiment, for Figure 1 The multi-standard adaptive power quality evaluation method shown may also include the following steps, which are explained in detail below.

[0112] When the target being detected is an electric vehicle charging station, the charging request parameters of the charging objects connected to the charging station are obtained. These parameters include the current remaining battery power, target battery power, expected dwell time, and the minimum battery power required for the destination. During periods without charging tasks, the standard limits of various power quality indicators are maintained as dynamic adaptation standards. When a charging request is received, the minimum total battery power to be replenished is calculated based on the difference between the current remaining battery power and the target battery power, and the charging power demand range is determined in conjunction with the expected dwell time. Based on the relationship between the minimum battery power required for the destination and the current remaining battery power, the charging demand is divided into emergency charging and regular charging. The system offers three charging levels: emergency charging, flexible charging, and rechargeable charging. Within the limits of various power quality indicators specified in national or industry standards, corresponding allowable deviation relaxation coefficients are set for each charging level. Specifically, the voltage deviation limit, harmonic current limit, and three-phase imbalance limit for the emergency charging level are increased by a preset proportion of the standard limit, while the rechargeable charging level maintains the standard limit unchanged. The relaxed limits are compared with the real-time detection data of the charging pile access point. When the detection data exceeds the relaxed limit but does not exceed the standard limit, it is judged as qualified and charging continues. When the detection data exceeds the standard limit, a protection action of power reduction or charging interruption is triggered.

[0113] The charging request parameters refer to the information reported by the electric vehicle when it connects to the charging pile via charging communication protocols (such as ISO15118 and GB / T 27930). This includes the current remaining battery level (SoC, expressed as a percentage), the user-set target battery level (e.g., charging to 80% or 100%), the expected dwell time (the user's estimated charging time, in minutes), and the minimum battery level required for the destination (calculated based on navigation distance and vehicle energy consumption, expressed as a percentage). The total battery level to be replenished is the difference between the target battery level and the current remaining battery level, expressed in kWh or as a percentage. The charging power demand range refers to the average charging power range calculated based on the required replenishment and the expected dwell time, used to guide the power output settings of the charging pile. Emergency charging refers to charging when the current remaining battery level is below or about to fall below the minimum battery level required to reach the destination, and the user faces range anxiety, requiring the battery to be replenished as quickly as possible. Regular charging refers to charging performed by the user according to their daily habits without urgent travel needs, and with sufficient remaining battery power. Flexible charging refers to charging scenarios where the user has ample dwell time, no immediate need for vehicle use, and can accept lower charging power or even interruptions. The relaxation factor refers to the magnification factor relative to the standard limit. For example, during emergency charging, the voltage deviation limit is relaxed from ±7% to ±8.4% (1.2 times), and the harmonic current limit is relaxed from 20A to 24A (1.2 times). The "absolute standard limit" refers to the rigid limit stipulated by national or industry standards that cannot be exceeded. Even during emergency charging, this limit cannot be exceeded to ensure the safety of the power grid and equipment.

[0114] For example, in a city's public charging station, an electric vehicle connects to a DC fast charging pile. Charging request parameters are obtained via the charging communication protocol: current remaining battery level 30%, user-set target battery level 80%, expected stay duration 30 minutes, and navigation indicating a minimum battery level of 25% required to reach the destination. First, the basic dynamic adaptation standards are calculated using standard methods: voltage deviation limit ±7% at 10kV voltage level, 5th harmonic current limit 24A, and three-phase imbalance limit 2%. During periods without charging tasks (e.g., 3:00 AM - 5:00 AM), the charging pile is idle, maintaining the standard limits unchanged. The measured voltage deviation in the area is +2%, the 5th harmonic current is 8A, and the three-phase imbalance is 0.5%, all within acceptable limits. When the above charging request is received, the total battery level to be replenished is calculated as 80% - 30% = 50%. Considering the expected stay duration of 30 minutes, the charging power requirement is determined to be approximately 50% × battery capacity / 0.5 hours. Comparing the current remaining battery level of 30% with the minimum battery level required at the destination (25%), the current remaining battery level is still higher than the minimum requirement (30% > 25%), but the difference is only 5%, indicating some range anxiety. Therefore, the charging demand is classified as "regular charging" (not urgent but not flexible either), and the standard limit remains unchanged. During charging, a 5th harmonic current of 23A (less than 24A) was detected, and the voltage deviation was +6.5% (less than +7%), which is acceptable. Charging continues at normal power.

[0115] If another vehicle connects with only 20% battery remaining, which is less than the minimum 25% required for the destination (20% < 25%), the user faces the risk of not being able to reach the destination, and the charging demand is classified as "emergency charging." Within the absolute limits of the national standard, adjustments are made by a relaxation factor of 1.2: the voltage deviation limit is relaxed from ±7% to ±8.4%, the 5th harmonic current limit is relaxed from 24A to 28.8A, and the three-phase imbalance limit is relaxed from 2% to 2.4%. During emergency charging, a measured voltage deviation of +8.0% (exceeding the standard ±7% but not exceeding the relaxed ±8.4%) is considered qualified and charging continues; a measured 5th harmonic current of 26A (exceeding the standard 24A but not exceeding the relaxed 28.8A) is also considered qualified. When the detected data exceeds the relaxed limit but still does not exceed the absolute standard limit (e.g., voltage deviation +8.5%), the system still determines it to be qualified but issues a warning. When the detected data exceeds the absolute standard limit (e.g., voltage deviation +9.0%), the system triggers power reduction protection, reducing the charging power from 120kW to 60kW. If the voltage deviation falls back, the power will be restored; otherwise, charging will be further interrupted. If the user expects to stay for up to 8 hours (e.g., charging at night), and the current remaining battery is 40%, while the destination only requires 20%, the charging demand is classified as "flexible charging." The standard limit remains unchanged, but it can be appropriately tightened (e.g., the voltage deviation limit is narrowed to ±5%) to ensure the power quality margin of the area during long-term charging.

[0116] Through the above embodiments, a dynamic limit adjustment mechanism driven by charging demand is introduced into the electric vehicle charging pile scenario. Based on the user's range anxiety level (the relationship between current battery level and the required battery level at the destination) and dwell time, charging demand is divided into different levels, corresponding to different limit relaxation strategies. During emergency charging, power quality limits are moderately relaxed, allowing the charging pile to operate at higher power for a short period to meet the user's urgent energy replenishment needs; regular charging maintains standard limits; and flexible charging can tighten limits. All relaxation operations are carried out within the absolute limits of national standards, without exceeding the safety red lines of the power grid and equipment.

[0117] The multi-standard adaptive power quality evaluation system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic diagram of the structure of a multi-standard adaptable power quality evaluation system provided in an embodiment of this application.

[0118] It should be noted that, Figure 4 The structure of the multi-standard adaptive power quality evaluation system shown is merely an example and should not impose any limitations on the functionality and scope of application of the embodiments of this application.

[0119] like Figure 4As shown, the multi-standard adaptable power quality evaluation system includes a central processing unit 401, which can perform various appropriate actions and processes based on a program stored in a read-only memory 402 or a program loaded from a storage section 408 into a random access memory 403, such as executing the methods described in the above embodiments. The random access memory 403 also stores various programs and data required for system operation. The central processing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0120] The following components are connected to the input / output interface 405: an input section 406 including audio input devices, push-button switches, etc.; an output section 407 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0121] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs the various functions defined in the present invention. It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0122] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0123] Specifically, the multi-standard adaptable power quality evaluation system of this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the multi-standard adaptable power quality evaluation method provided in the above embodiment.

[0124] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the multi-standard adapted power quality assessment system described in the above embodiments; or it may exist independently and not assembled into the multi-standard adapted power quality assessment system. The storage medium carries one or more computer programs, which, when executed by a processor of the multi-standard adapted power quality assessment system, cause the multi-standard adapted power quality assessment system to implement the multi-standard adapted power quality assessment method provided in the above embodiments.

[0125] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A multi-standard adaptive power quality evaluation method, characterized in that, include: Acquire power quality detection data and detection configuration parameters, and verify the validity of the detection configuration parameters to obtain the verification results; When the verification result indicates that the verification fails, the abnormal parameters in the detection configuration parameters are identified, and the abnormal parameters are corrected based on the preset parameter correction rules to obtain the corrected detection configuration parameters. Based on the corrected detection configuration parameters, or the detection configuration parameters that have passed the verification as indicated by the test results, the dynamic adaptation standards corresponding to each power quality index are determined respectively. Based on the power quality detection data and each of the dynamic adaptation standards, a pass / fail determination result for each of the power quality indicators is generated, and a power quality evaluation report is generated based on the pass / fail determination result.

2. The method according to claim 1, characterized in that, The power quality indicators include harmonic current, harmonic voltage, three-phase unbalance, voltage deviation, frequency deviation, overvoltage, and voltage flicker. The dynamic adaptation standards corresponding to each power quality indicator are determined based on the corrected detection configuration parameters, or the detection configuration parameters verified by the test results, including: The voltage level, minimum short-circuit capacity, reference short-circuit capacity, protocol capacity, single-user status identifier, grid-connected capacity, detection point type, and system grounding method are extracted from the modified detection configuration parameters or the detection configuration parameters that have passed the verification as indicated by the test results. Based on the minimum short-circuit capacity, the reference short-circuit capacity, the protocol capacity, and the single-user status identifier, the dynamic adaptation standard of the harmonic current is determined; Based on the voltage level and the preset standard configuration library, determine the dynamic adaptation standards corresponding to the harmonic voltage and the three-phase unbalance, respectively. The deviation evaluation mode is determined based on the voltage level, and the deviation evaluation mode is used as the dynamic adaptation standard for the voltage deviation. Based on the grid-connected capacity, determine the dynamic adaptation standard corresponding to the frequency deviation; Based on the detection point type, the short-time flicker limit and long-time flicker limit corresponding to the detection point are determined, and the dynamic adaptation standard corresponding to the voltage flicker is obtained. Based on the voltage level and the system grounding method, the transient overvoltage multiple limit and the power frequency overvoltage limit are determined to obtain the dynamic adaptation standard of the overvoltage.

3. The method according to claim 2, characterized in that, The determination of the dynamic adaptation standard for the harmonic current based on the minimum short-circuit capacity, the reference short-circuit capacity, the protocol capacity, and the single-user status identifier includes: Calculate the ratio of the minimum short-circuit capacity to the reference short-circuit capacity, and then calculate the product of this ratio and the preset harmonic baseline limit to obtain the common coupling point limit for the harmonic current: When the single-user status identifier indicates a single user, the power supply capacity is obtained, and based on the power supply capacity and the protocol capacity, the user injection limit is calculated using the following formula as the dynamic adaptation standard for the harmonic current: I hi =I hp ×(S i / S t ) (1 / α) , Among them, I hi Inject limits into the user, I hp S is the limit value for the common connection point. i For the protocol capacity, S t The power supply capacity is given by α, which is a preset phase superposition coefficient. When the single-user status identifier indicates a multi-user state, the common connection point limit is used as the dynamic adaptation standard for the harmonic current.

4. The method according to claim 2, characterized in that, The deviation evaluation mode includes a first evaluation mode, a second evaluation mode, and a third evaluation mode. The step of determining the deviation evaluation mode based on the voltage level as a dynamic adaptation standard for the voltage deviation includes: When the voltage level is greater than or equal to the first voltage threshold, and the power supply system of the detection point is three-phase A, B, and C, the first evaluation mode is used as the dynamic adaptation standard for the voltage deviation. The absolute values ​​of the positive voltage deviation and the absolute values ​​of the negative voltage deviation of the three phases A, B, and C are calculated respectively. The first evaluation mode is used to characterize that all the deviations are lower than the preset deviation limit. When the voltage level is less than or equal to the second voltage threshold, and the power supply mode of the detection point is the three-phase A, B, and C, the second evaluation mode is used as the dynamic adaptation standard for the voltage deviation. The second evaluation mode is used to characterize the setting of the positive maximum deviation percentage and the negative maximum deviation percentage for the voltage deviation of the three phases A, B, and C, respectively, and the absolute values ​​of the positive and negative percentages are the same. The second voltage threshold is less than the first voltage threshold. When the power supply system of the detection point is single-phase, the third evaluation mode is used as the dynamic adaptation standard for the voltage deviation. The third evaluation mode is used to characterize the setting of positive and negative asymmetric deviation limits for the voltage deviation of the single phase.

5. The method according to claim 1, characterized in that, Regarding the harmonic current, the qualification determination result includes the determination result of the harmonic current in each phase. The generation of a power quality evaluation report based on the qualification determination result includes: Based on the determination results of the harmonic current in each phase, the number of the excessive harmonics and their corresponding phases are selected. The aforementioned excessive harmonics are divided into three-phase excessive groups, two-phase excessive groups, and single-phase excessive groups according to the phase of the excessive harmonics. Based on the total number and phase of each exceeding group, an exceeding harmonic conclusion text is generated, which includes phase combination, harmonic number, detection value and limit information. A corresponding spectrum diagram is generated based on the grouping type, wherein the group with all three phases exceeding the standard corresponds to a three-phase composite diagram, the group with two phases exceeding the standard corresponds to a two-phase superimposed diagram, and the group with one phase exceeding the standard corresponds to a single-phase diagram; All the aforementioned spectrum diagrams and the text of the conclusions regarding the excessive harmonics will be automatically populated into the power quality assessment report.

6. The method according to claim 1, characterized in that, The step of generating a pass / fail judgment result for each power quality indicator based on the power quality detection data and each of the dynamic adaptation standards also includes: Real-time monitoring of the state characteristics of power grid operation conditions; when a condition transition node that triggers dynamic adaptation standard switching is identified, a transient data segment with a preset time window is extracted centered on the condition transition node. A smooth transition curve is constructed within the transient data segment window, and the power quality detection data is compared with the smooth transition curve to generate a compliance determination result for the transient data segment. For steady-state data segments other than the transient data segments, extract abnormal segments that exceed each of the dynamic adaptation criteria from the steady-state data segments, and record the duration and energy accumulation value of the abnormal segments; When the duration exceeds a preset duration and the energy accumulation value exceeds a preset energy threshold, the abnormal segment is determined as a real violation event, and the compliance determination result of the steady-state data segment is obtained.

7. The method according to claim 1, characterized in that, The method further includes: When the object of detection is a new energy power station with both grid-connected and islanded operation modes, the status of the grid-connected switch of the power station is monitored in real time. When the switch from grid-connected mode to islanded mode or the reverse switch is detected, the start time and the completion time of the switch are recorded. Extract the first dynamic standard set at the start time of the handover and the second dynamic standard set at the completion time of the handover; Within the transition time window between the start of the handover and the completion of the handover, multiple standard interpolation curves that change continuously with time are constructed. The value of each standard interpolation curve at the start of the handover is equal to the corresponding limit value of the first dynamic standard set, and the value at the completion of the handover is equal to the corresponding limit value of the second dynamic standard set. Within the transition time window, the power quality detection data is compared with each of the standard interpolation curves to generate a transition period determination result with a time stamp. The switching start time, the switching completion time, and the transition period determination result are automatically filled into the power quality evaluation report.

8. A multi-standard adaptive power quality evaluation system, characterized in that, The multi-standard adaptable power quality assessment system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the multi-standard adaptable power quality assessment system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is run on a multi-standard adapted power quality assessment system, the multi-standard adapted power quality assessment system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the multi-standard adapted power quality evaluation system, the multi-standard adapted power quality evaluation system performs the method as described in any one of claims 1-7.

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