High-low voltage power distribution cabinet operation monitoring management method and system based on data analysis

Abstract

The application discloses a high-low voltage power distribution cabinet operation monitoring management method and system based on data analysis, and particularly relates to the technical field of power system monitoring and power quality management; the application realizes accurate quantification and root tracing of power quality, solves the defects that traditional monitoring can only qualitatively perceive, calculates steady-state parameters such as harmonic distortion, voltage deviation stability and event quantization indexes by collecting high-precision waveform data, and fuses to generate an electric quality comprehensive health coefficient to comprehensively reflect the equipment operation state. Meanwhile, the steady-state and transient performance coefficients are compared, the root cause type of the abnormal and early warning power distribution cabinet is determined, accurate basis is provided for operation and maintenance, the depth and accuracy of the power distribution system monitoring management are greatly improved, and the fault troubleshooting cost is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of power system monitoring and power quality management technology, and more specifically, to a method and system for monitoring and managing the operation of high and low voltage switchgear based on data analysis. Background Technology

[0002] In the power supply and distribution links of the power system, high and low voltage switchgear is the core equipment for power distribution, control and protection. The stability of its operation and power quality are directly related to the safety and reliability of industrial production, commercial operation and residential electricity use. With the increasing complexity of power load types (such as the access of new energy sources and the popularization of nonlinear loads), the power quality problems faced by the power distribution system are becoming more and more prominent, which puts forward higher requirements for the operation monitoring and management of switchgear.

[0003] However, existing high and low voltage switchgear operation monitoring and management technologies have significant shortcomings:

[0004] On the one hand, the analysis depth is seriously insufficient, and it can only achieve qualitative perception of faults or anomalies. It cannot accurately quantify, trace the root cause, and predict the trend of power quality problems. For example, for common problems such as voltage sag and excessive harmonics, the existing technology can only issue alarm signals, but it is difficult to accurately calculate the depth, duration and impact risk of voltage sag on sensitive loads.

[0005] On the other hand, the early warning mechanisms of existing monitoring and management systems are mostly based on static threshold judgments, which fail to fully consider the dynamic changes in the operating status of distribution cabinets, making it easy to have false or missed early warnings. At the same time, for abnormal problems detected by monitoring, there is a lack of a precise governance strategy matching mechanism based on historical data and actual operating conditions. Maintenance personnel mostly rely on experience to select governance solutions, making it difficult to achieve targeted optimization.

[0006] The existence of these problems seriously affects the operational stability of the power distribution system, increases the risk of failure, and restricts the improvement of power quality. To address this, a data analysis-based method and system for monitoring and managing the operation of high and low voltage switchgear has been developed. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for monitoring and managing the operation of high and low voltage distribution cabinets based on data analysis.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] Data-driven analysis-based methods for monitoring and managing the operation of high and low voltage switchgear include:

[0010] Cloud-based data analytics: Executing steps using pre-edited data analysis logic. to Assess the overall electrical health coefficient of the distribution cabinet within the current monitoring time zone;

[0011] The harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient of the power distribution cabinet in the current monitoring time zone are extracted as steady-state parameters, and weighted and fused to output the steady-state performance coefficient of the power distribution cabinet.

[0012] The transient performance coefficients of the power distribution cabinet are output after identifying transient sag events, transient rise events, duration and frequency of occurrence from the transient events of the power distribution cabinet in the current monitoring time zone, extracting features and fusing them.

[0013] The steady-state and transient performance coefficients of the distribution cabinets within the current monitoring time zone are fused together to output the comprehensive electrical quality health coefficient of the distribution cabinets.

[0014] Screening rule execution: Based on the comprehensive health coefficient of the electrical quality of the distribution cabinet, combined with the auxiliary constraints of steady-state performance coefficient and transient performance coefficient, a screening rule combining basic threshold screening and trend superposition judgment is set to identify abnormal distribution cabinets and early warning distribution cabinets from all distribution cabinets in the power distribution system and output a list of screened distribution cabinets.

[0015] Recommended governance strategies: Based on the list of selected distribution cabinets, the system analyzes the data and uses pre-edited matching rules to retrieve the governance strategies that should be applied to abnormal distribution cabinets and early warning distribution cabinets from the pre-built strategy database. These strategies are then sent to management personnel for review and execution.

[0016] Specifically, the logic for obtaining steady-state parameters;

[0017] The total voltage distortion rate within the current monitoring time zone is sorted from largest to smallest, and the 95th percentile value is taken as the probability assessment value; according to the formula... Calculate harmonic distortion coefficient ;in and These represent the probability assessment value and the preset total harmonic distortion rate limit, respectively.

[0018] Extract the absolute value sequence of voltage deviation within the current monitoring time zone, calculate its average value and standard deviation; divide the calculated average value by the preset average limit and the standard deviation by the preset standard deviation, sum the two calculated ratios and divide by 2 to output the voltage deviation stability coefficient.

[0019] Divide the current monitoring time zone into x sub-time zone windows, extract the fundamental power factor value sequence of the x sub-time zone windows, calculate the mean and output the fundamental average power of each sub-time zone window, set the weight coefficient of different sub-time zone windows, and calculate the time-weighted average value based on the fundamental average power of each sub-time zone window; divide the preset target value by the time-weighted average value and output the power factor coefficient.

[0020] Specifically, the feature extraction logic for transient events;

[0021] Define a tolerance curve for the voltage amplitude-duration relationship; the curve defines the acceptable and unacceptable boundaries for different combinations of depth and duration.

[0022] For a given depth D and duration of... For transient descent and transient rise events, the corresponding maximum allowable duration is located on the tolerance curve;

[0023] After defining the severity score for a single event, the severity scores of each group of transient descent and transient surge events within the current monitoring time zone are summed to obtain the total transient descent score and the total transient surge score.

[0024] The frequency of occurrence of transient descent and transient rise events is obtained by summing the number of occurrences. The transient performance coefficient is output by weighting and fusing the total transient descent score, total transient rise score, and occurrence frequency in the current monitoring time zone.

[0025] Specifically, the definition logic of severity scores;

[0026] Divide the duration of the temporary landing event by the maximum allowable duration to obtain the temporary landing overshoot factor;

[0027] Divide the duration of the transient rise event by the maximum allowed duration to obtain the transient rise overshoot factor;

[0028] Define the severity score for a single event: Severity Score ; For transient decrease events, the transient decrease excess factor is denoted as , and for transient increase events, the transient increase excess factor is denoted as .

[0029] Specifically, the output logic for filtering the list of power distribution cabinets;

[0030] Distribution cabinets with an overall electrical quality health coefficient higher than the health threshold are marked as abnormal distribution cabinets.

[0031] For abnormal power distribution cabinets, compare the steady-state performance coefficient and the transient performance coefficient. If the steady-state performance coefficient is higher than the transient performance coefficient, the additional identification type is determined to be the steady-state root cause type; otherwise, it is determined to be the transient root cause type.

[0032] Distribution cabinets with an overall electrical quality health coefficient below the health threshold are marked as distribution cabinets to be evaluated.

[0033] The absolute difference between the comprehensive electrical quality health coefficient and the health threshold of the distribution cabinet to be evaluated is calculated and used as the warning distance value; the distribution cabinets to be evaluated whose warning distance value is less than the preset reference distance value are selected as verification distribution cabinets.

[0034] After correcting the comprehensive health coefficient of the power quality of the verification distribution cabinet by combining it with the trend superposition coefficient, the correction result is compared with the health threshold. If the correction result is higher than the health threshold, the verification distribution cabinet is marked as a warning distribution cabinet.

[0035] The comprehensive health coefficient of the power quality of the early warning distribution cabinet is analyzed, and the steady-state performance coefficient and transient performance coefficient are compared. If the steady-state performance coefficient is higher than the transient performance coefficient, the additional identification type is determined to be the steady-state root cause type; otherwise, it is determined to be the transient root cause type.

[0036] Specifically, the logic for obtaining the trend overlay coefficient;

[0037] The comprehensive electrical health coefficients of the power distribution cabinet in the previous five time zones were extracted and verified, and a data sequence was constructed by combining it with the comprehensive electrical health coefficient in the current monitoring time zone.

[0038] After arranging the data sequence in chronological order, the average value of the comprehensive health coefficient of the first three groups of electrical quality is calculated as the average value of the first half of the time zone, and the average value of the comprehensive health coefficient of the last three groups of electrical quality is calculated as the average value of the second half of the time zone. The trend superposition coefficient is obtained by dividing the average value of the second half of the time zone by the average value of the first half of the time zone.

[0039] The trend superposition coefficient is multiplied by the overall electrical health coefficient of the power distribution cabinet in the current monitoring time zone to obtain the correction result.

[0040] Specifically, the retrieval logic of the matching rules;

[0041] Identify the health type of the distribution cabinet and combine it with the additional identification type of the distribution cabinet as a tag pair; health types include abnormal and warning.

[0042] The strategy database includes four strategy sub-libraries, each corresponding to a tag pair. Each strategy sub-library includes historical management strategies for power distribution cabinet applications, comprehensive power quality health coefficients, and application performance parameters. After the tag pairs of the power distribution cabinets are entered into the strategy database for retrieval, each set of historical management strategies is extracted from the corresponding strategy sub-library as candidate strategies.

[0043] Specifically, the application's governance strategy selection logic;

[0044] For each group of candidate strategies retrieved from the power distribution cabinet, the single similarity value and strategy evaluation value are extracted, weighted and fused, and the effectiveness coefficient of each group of candidate strategies is output. The candidate strategy with the highest effectiveness coefficient is selected as the governance strategy to be applied.

[0045] Specifically, the calculation logic for singleton similarity and policy evaluation value;

[0046] The parameters of the additional identification type corresponding to each group of candidate strategies are analyzed. If the additional identification type is the steady-state root cause type, the harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient are obtained.

[0047] The similarity between the values ​​and the harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient calculated in real time in the current monitoring time zone is calculated using Euclidean distance, and used as the single similarity value for each group of candidate strategies. If it is a transient root cause type, the total sag score, total rise score, and occurrence frequency are obtained, and the similarity between these values ​​and the values ​​and occurrence frequency calculated in real time in the current monitoring time zone is calculated using Euclidean distance, and used as the single similarity value for each group of candidate strategies.

[0048] Application performance parameters include strategy effectiveness and cost-effectiveness.

[0049] Strategy effectiveness is the rate of change of the overall electrical quality health coefficient before and after the application of the strategy; cost-effectiveness ratio is obtained by calculating the difference between the overall electrical quality health coefficient before and after the application of the strategy and dividing it by the total cost of implementing the strategy; the strategy evaluation value is output after weighted fusion of the strategy effectiveness and cost-effectiveness ratio of each group of candidate strategies.

[0050] The high and low voltage switchgear operation monitoring and management system based on data analysis includes:

[0051] The cloud-based data analysis module is used to execute data analysis logic, calculate the steady-state performance coefficient and transient performance coefficient of the power distribution cabinet, and integrate and output the comprehensive health coefficient of power quality.

[0052] The filtering rule execution module is used to identify abnormal power distribution cabinets and early warning power distribution cabinets by using the comprehensive electrical quality health coefficient as the core judgment criterion, combined with the auxiliary constraints of steady-state performance coefficient and transient performance coefficient, and through the rule of combining basic threshold filtering and trend superposition judgment, and outputting a list of filtered power distribution cabinets.

[0053] The governance strategy recommendation module is used to retrieve the optimal governance strategy from the strategy database based on the health type and additional identification type in the list of power distribution cabinets, and then send it to the management personnel for review and execution.

[0054] The technical effects and advantages of this invention are as follows:

[0055] (1) To achieve precise quantification and root cause tracing of power quality, and to solve the shortcomings of traditional monitoring which can only qualitatively perceive power quality, by collecting high-precision waveform data, calculating steady-state parameters such as harmonic distortion and voltage deviation stability, as well as event quantification indicators, and integrating them to generate a comprehensive power quality health coefficient, which fully reflects the operating status of equipment. At the same time, by comparing steady-state and transient performance coefficients, the root cause types of abnormal and early warning distribution cabinets are identified, providing accurate basis for operation and maintenance, greatly improving the depth and accuracy of power distribution system monitoring and management, and effectively reducing the cost of fault diagnosis;

[0056] (2) Adopt a screening rule that combines basic threshold and trend superposition judgment, calculate the trend superposition coefficient through historical data, correct the current health coefficient, accurately identify the early warning device with deteriorating trend, and avoid misjudgment and missed judgment;

[0057] (3) Relying on the strategy database and tag pair matching mechanism, combined with Euclidean distance similarity, strategy effectiveness and cost-effectiveness ratio, the optimal governance strategy is selected to break the governance conflict and waste caused by "information silos", achieve targeted optimization, improve governance efficiency and return on investment, and ensure the stable operation of the power distribution system. Attached Figure Description

[0058] Figure 1 This is a flowchart of the high and low voltage switchgear operation monitoring and management method based on data analysis according to the present invention;

[0059] Figure 2 This is a schematic diagram of the high and low voltage switchgear operation monitoring and management system based on data analysis, which is the subject of this invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Example 1

[0062] like Figure 1 As shown, the specific method for monitoring and managing the operation of high and low voltage switchgear based on data analysis is as follows:

[0063] Cloud-based data analytics: Executing steps using pre-edited data analysis logic. to Assess the overall electrical health coefficient of the distribution cabinet within the current monitoring time zone;

[0064] Multifunctional power quality monitoring terminals (IMUs) are deployed at key high and low voltage distribution cabinet nodes (incoming cabinets, outgoing cabinets, important load branches, etc.) in the power distribution system. All IMUs are time-synchronized based on a unified IEEE1588 (PTP) precision clock protocol or GPS, and are used to synchronously collect instantaneous waveform data of three-phase voltage and three-phase current at a rate of no less than 128 points / cycle (for a 50Hz system, i.e., a sampling rate of 6.4kHz).

[0065] - Steady-state parameter calculation: Extract the harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient of the distribution cabinet in the current monitoring time zone as steady-state parameters, and perform weighted fusion to output the steady-state performance coefficient of the distribution cabinet;

[0066] That is, through the formula The steady-state performance coefficient is calculated. ;in and These represent the voltage deviation stability coefficient and the power factor, respectively. , as well as The weighting coefficients are preset and sum to one. Their values ​​are preset according to the type of load supplied by the distribution cabinet.

[0067] The total voltage distortion rate within the current monitoring time zone is sorted from largest to smallest, and the 95th percentile value is taken as the probability assessment value.

[0068] According to the total harmonic distortion (THD) limit for the corresponding voltage level in the national standard (e.g., 5% for a 0.38kV system), based on the formula... Calculate harmonic distortion coefficient ;in and These represent the probability assessment value and the preset total harmonic distortion rate limit, respectively.

[0069] The lower the value, the better. This means that the harmonic level is far below the national standard for 95% of the time, indicating excellent steady-state harmonic performance. The higher the value, the better. This means that the harmonic level is critical or exceeds the standard.

[0070] Extract the absolute value sequence of voltage deviation within the current monitoring time zone, and calculate its mean and standard deviation;

[0071] Based on the national standard voltage deviation limits, preset average limits and standard deviation limits are set.

[0072] Divide the calculated average value by the preset average limit, divide the standard deviation by the preset standard deviation, sum the two calculated ratios and divide by 2 to get the output voltage deviation stability coefficient.

[0073] Both large average deviation and severe fluctuation are considered to improve the stability and accuracy of voltage assessment.

[0074] Divide the current monitoring time zone into x sub-time zone windows, extract the fundamental power factor value sequence of the x sub-time zone windows, and output the fundamental average power of each sub-time zone window after averaging. Since power is related to time integration, set weight coefficients for different sub-time zone windows, and calculate the time-weighted average value by combining the fundamental average power of each sub-time zone window; the sum of the weight coefficients of different sub-time zone windows is one.

[0075] Divide the preset target value by the time-weighted average value to output the power factor coefficient;

[0076] A lower power factor coefficient indicates that the average power factor has reached or exceeded the target value, while a higher coefficient indicates that there is room for optimization.

[0077] - Transient event feature extraction and quantification: Identify transient descent events, transient rise events, duration and frequency of occurrence from the transient events of the distribution cabinet in the current monitoring time zone, extract features and then fuse them to output the transient performance coefficient of the distribution cabinet;

[0078] Define a tolerance curve for the voltage amplitude-duration relationship; the curve defines the acceptable and unacceptable boundaries for different combinations of depth and duration.

[0079] For a given depth D and duration of... For transient descent and transient rise events, the corresponding maximum allowable duration is located on the tolerance curve;

[0080] Divide the duration of the temporary landing event by the maximum allowable duration to obtain the temporary landing overshoot factor;

[0081] Divide the duration of the transient rise event by the maximum allowed duration to obtain the transient rise overshoot factor;

[0082] Define the severity score for a single event: Severity Score ; For temporary drop events, the excess factor of temporary drop events and the excess factor of temporary rise events;

[0083] If the duration is less than or equal to the maximum allowed duration, then = 0, the event is within the "immune zone" and has no impact;

[0084] If the duration is greater than the maximum allowed duration, then > 0, the larger the value, the more serious the event (far exceeding the device tolerance).

[0085] The total landing score is obtained by summing the severity scores of each group of transient events within the current monitoring time zone;

[0086] The total transient score is obtained by summing the severity scores of each group of transient events within the current monitoring time zone;

[0087] The frequency of occurrence is calculated by summing the number of temporary downtime and temporary uptime events. Even if a single event is not serious, frequent occurrences can still cause problems, so a frequency penalty mechanism is introduced.

[0088] After weighting and fusing the total transient sag score, total transient rise score, and occurrence frequency within the current monitoring time zone, the transient performance coefficient is output.

[0089] That is, through the formula The transient performance coefficient is calculated. ;in , and These represent the total temporary decrease in score, the total temporary increase in score, and the frequency of occurrence, respectively. , as well as The preset total allowable descent score, total allowable scalping score, and maximum acceptable occurrence frequency are defined. , as well as These are preset weighting coefficients, and their sum is one.

[0090] - Output comprehensive electrical quality health coefficient: The steady-state performance coefficient and transient performance coefficient of the distribution cabinet in the current monitoring time zone are fused and processed to output the comprehensive electrical quality health coefficient of the distribution cabinet;

[0091] That is, through the formula The overall electrical health coefficient of the distribution cabinet was calculated. ;in and These are preset weighting coefficients, and their sum is one.

[0092] The steady-state performance coefficient, which reflects steady-state operating quality, and the transient performance coefficient, which reflects impact risk, are combined to generate a comprehensive power quality health coefficient, which comprehensively and intuitively reflects the overall power quality status of the distribution cabinet.

[0093] Screening rule execution: Based on the comprehensive health coefficient of the electrical quality of the distribution cabinet, combined with the auxiliary constraints of steady-state performance coefficient and transient performance coefficient, a screening rule combining basic threshold screening and trend superposition judgment is set to identify abnormal distribution cabinets and early warning distribution cabinets from all distribution cabinets in the power distribution system and output a list of screened distribution cabinets.

[0094] Specifically:

[0095] The health threshold corresponding to the preset comprehensive electrical quality health coefficient is divided into two categories based on the voltage level of the power distribution system and the importance of the load (such as important production load branches and general lighting branches). The threshold for core load branches is higher than that for ordinary branches, and the range of abnormal candidate power distribution cabinets is clearly defined.

[0096] Search all power distribution cabinets in the current monitoring time zone and mark those with an overall electrical quality health coefficient higher than the health threshold as abnormal power distribution cabinets;

[0097] The comprehensive health coefficient of electrical quality of abnormal distribution cabinets is analyzed, and the steady-state performance coefficient and transient performance coefficient are compared. If the steady-state performance coefficient is higher than the transient performance coefficient, the additional identification type is determined to be the steady-state root cause type; otherwise, it is determined to be the transient root cause type.

[0098] Distribution cabinets with an overall electrical quality health coefficient below the health threshold are marked as distribution cabinets to be evaluated.

[0099] The absolute difference between the comprehensive electrical health coefficient and the health threshold of the power distribution cabinet under evaluation is calculated and used as the warning distance value.

[0100] Select power distribution cabinets whose warning distance value is less than the preset reference distance value as verification power distribution cabinets;

[0101] After correcting the comprehensive health coefficient of the power quality of the verification distribution cabinet by combining it with the trend superposition coefficient, the correction result is compared with the health threshold. If the correction result is higher than the health threshold, the verification distribution cabinet is marked as a warning distribution cabinet.

[0102] The comprehensive health coefficient of the power quality of the early warning distribution cabinet is analyzed, and the steady-state performance coefficient and transient performance coefficient are compared. If the steady-state performance coefficient is higher than the transient performance coefficient, the additional identification type is determined to be the steady-state root cause type; otherwise, it is determined to be the transient root cause type.

[0103] Output a list of filtered power distribution cabinets; the list includes the cabinet number, node location, and additional identification type of abnormal power distribution cabinets and early warning power distribution cabinets; the additional identification type includes steady-state root cause type and transient root cause type.

[0104] Using the comprehensive electrical quality health coefficient as the core, combined with steady-state and transient performance coefficients, and pre-setting health thresholds according to load importance, abnormal distribution cabinets are screened out and the root cause type is determined; the warning distance value is calculated for the distribution cabinets to be evaluated, the distribution cabinets are screened and verified, and the trend superposition coefficient is used for correction to determine the warning distribution cabinets and root cause type, and finally a screening list containing key information is output.

[0105] Accurately identify abnormal and potential early warning distribution cabinets in the power distribution system, clarify the root cause of the problem, provide a clear basis for subsequent targeted operation and maintenance optimization, improve the accuracy and efficiency of power distribution system monitoring and management, and reduce the risk of failure.

[0106] The trend superposition coefficient is obtained by extracting and verifying the comprehensive electrical health coefficient of the distribution cabinet in the previous 5 time zones of the current monitoring time zone, and constructing a data sequence with the comprehensive electrical health coefficient of the current monitoring time zone:

[0107] After arranging the data sequence in chronological order, the average value of the comprehensive health coefficient of the first three groups of electrical quality is calculated as the average value of the first half of the time zone, and the average value of the comprehensive health coefficient of the last three groups of electrical quality is calculated as the average value of the second half of the time zone. The trend superposition coefficient is obtained by dividing the average value of the second half of the time zone by the average value of the first half of the time zone.

[0108] The trend superposition coefficient is multiplied by the overall electrical health coefficient of the power distribution cabinet in the current monitoring time zone to obtain the correction result.

[0109] The average of the three sets before and after the breakdown is calculated and compared to obtain the trend superposition coefficient. This coefficient is then multiplied by the current coefficient to complete the correction. The significance lies in quantifying the time-varying trend of the electrical health of the distribution cabinet, capturing the dynamic characteristics of the coefficient's continuous decline / rise, and avoiding the one-sidedness of judging the warning based solely on the current static value. It can accurately identify distribution cabinets whose health status is deteriorating and about to exceed the threshold and include them in the warning, while eliminating misjudgments due to occasional fluctuations. This makes the warning judgment more in line with the dynamic changes in the actual operation of the equipment, improves the accuracy and foresight of the warning screening, and provides a scientific basis for early intervention in operation and maintenance.

[0110] Recommended governance strategies: Based on the list of selected distribution cabinets, after parsing, the governance strategies to be applied to abnormal distribution cabinets and early warning distribution cabinets are retrieved from the pre-built strategy database using pre-edited matching rules, and then sent to the management personnel for review and execution.

[0111] Specifically:

[0112] Identify the health type of the distribution cabinet and combine it with the additional identification type of the distribution cabinet as a tag pair; health types include abnormal and warning.

[0113] The strategy database includes four strategy sub-libraries, and each strategy sub-library corresponds to a tag pair. Each strategy sub-library includes historical management strategies for power distribution cabinet applications, comprehensive power quality health coefficients, and application performance parameters.

[0114] The four strategy fonts include (abnormal, steady-state root cause type), (abnormal, transient root cause type), (warning, transient root cause type), and (warning, steady-state root cause type).

[0115] After searching the input strategy database for the labels of the power distribution cabinets, the historical governance strategies of each group are extracted from the corresponding strategy character set as candidate strategies.

[0116] The parameters of the additional identification type corresponding to each group of candidate strategies are analyzed. If the additional identification type is the steady-state root cause type, the harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient are obtained.

[0117] The similarity between the harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient calculated in real time in the current monitoring time zone is calculated using Euclidean distance, and used as the single similarity value of each group of candidate strategies.

[0118] If it is a transient root cause type, the total transient decrease score, total transient increase score, and occurrence frequency are obtained, and the similarity between them and the total transient decrease score, total transient increase score, and occurrence frequency calculated in real time in the current monitoring time zone is calculated using Euclidean distance, which is used as the single similarity value of each group of candidate strategies.

[0119] By default, the parameters obtained from the different additional identification types mentioned above are normalized.

[0120] Application performance parameters include strategy effectiveness and cost-effectiveness.

[0121] The effectiveness of a strategy is the rate of change in the overall health coefficient of electrical quality before and after the application of the strategy.

[0122] The result is calculated as (Comprehensive electrical health coefficient before application - Comprehensive electrical health coefficient after application) / Comprehensive electrical health coefficient before application × 100%.

[0123] The cost-benefit ratio is calculated by taking the difference between the overall electrical quality health coefficient before and after the application of the strategy; that is, (overall electrical quality health coefficient before application - overall electrical quality health coefficient after application); and dividing it by the total cost of implementing the application strategy.

[0124] The strategy evaluation value is output after weighted fusion of the strategy effectiveness and cost-effectiveness ratio of each group of candidate strategies;

[0125] That is, by assessing the effectiveness of the strategy Cost-effectiveness ratio After normalization, substitute into the formula The strategy evaluation value of the candidate strategy is calculated. ;in and These are preset weighting coefficients, and their sum is one.

[0126] For each group of candidate strategies retrieved from the power distribution cabinet, the single similarity value and strategy evaluation value are extracted, weighted and fused, and the effectiveness coefficient of each group of candidate strategies is output. The candidate strategy with the highest effectiveness coefficient is selected as the governance strategy to be applied.

[0127] That is, through the formula The stress coefficient of the distribution cabinet is calculated. ;in and These are preset weighting coefficients, and their sum is one. The similarity value calculated for the candidate strategy and the current distribution cabinet.

[0128] Example 2

[0129] Please see Figure 2 As shown, based on the data analysis-based high and low voltage switchgear operation monitoring and management method provided in Embodiment 1 of this application, Embodiment 2 of this application proposes a data analysis-based high and low voltage switchgear operation monitoring and management system. Embodiment 2 is merely a preferred embodiment of Embodiment 1, and its implementation will not affect the individual implementation of Embodiment 1.

[0130] Specifically, the high and low voltage switchgear operation monitoring and management system based on data analysis provided in Embodiment 2 of this application includes:

[0131] The cloud-based data analysis module is used to execute data analysis logic, calculate the steady-state performance coefficient and transient performance coefficient of the power distribution cabinet, and integrate and output the comprehensive health coefficient of power quality.

[0132] The filtering rule execution module is used to identify abnormal power distribution cabinets and early warning power distribution cabinets by using the comprehensive electrical quality health coefficient as the core judgment criterion, combined with the auxiliary constraints of steady-state performance coefficient and transient performance coefficient, and through the rule of combining basic threshold filtering and trend superposition judgment, and outputting a list of filtered power distribution cabinets.

[0133] The governance strategy recommendation module is used to retrieve the optimal governance strategy from the strategy database based on the health type and additional identification type in the list of power distribution cabinets, and then send it to the management personnel for review and execution.

[0134] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0135] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0136] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0137] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0140] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0141] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0142] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data analysis-based method for monitoring and managing the operation of high and low voltage switchgear, characterized in that: include: Cloud-based data analytics: Executing steps using pre-edited data analysis logic. to Assess the overall electrical health coefficient of the distribution cabinet within the current monitoring time zone; The harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient of the power distribution cabinet in the current monitoring time zone are extracted as steady-state parameters, and weighted and fused to output the steady-state performance coefficient of the power distribution cabinet. The transient performance coefficients of the power distribution cabinet are output after identifying transient sag events, transient rise events, duration and frequency of occurrence from the transient events of the power distribution cabinet in the current monitoring time zone, extracting features and fusing them. The steady-state and transient performance coefficients of the distribution cabinets within the current monitoring time zone are fused together to output the comprehensive electrical quality health coefficient of the distribution cabinets. Screening rule execution: Based on the comprehensive health coefficient of the electrical quality of the distribution cabinet, combined with the auxiliary constraints of steady-state performance coefficient and transient performance coefficient, a screening rule combining basic threshold screening and trend superposition judgment is set to identify abnormal distribution cabinets and early warning distribution cabinets from all distribution cabinets in the power distribution system and output a list of screened distribution cabinets. Recommended governance strategies: Based on the list of selected distribution cabinets, the system analyzes the data and uses pre-edited matching rules to retrieve the governance strategies that should be applied to abnormal distribution cabinets and early warning distribution cabinets from the pre-built strategy database. These strategies are then sent to management personnel for review and execution.

2. The high and low voltage switchgear operation monitoring and management method based on data analysis according to claim 1, characterized in that: Logic for obtaining steady-state parameters; The total voltage distortion rate within the current monitoring time zone is sorted from largest to smallest, and the 95th percentile value is taken as the probability assessment value. According to the formula Calculate harmonic distortion coefficient ;in and These represent the probability assessment value and the preset total harmonic distortion rate limit, respectively. Extract the absolute value sequence of voltage deviation within the current monitoring time zone, calculate its average value and standard deviation; divide the calculated average value by the preset average limit and the standard deviation by the preset standard deviation, sum the two calculated ratios and divide by 2 to output the voltage deviation stability coefficient. Divide the current monitoring time zone into x sub-time zone windows, extract the fundamental power factor value sequence of the x sub-time zone windows, calculate the mean and output the fundamental average power of each sub-time zone window, set the weight coefficient of different sub-time zone windows, and calculate the time-weighted average value based on the fundamental average power of each sub-time zone window; divide the preset target value by the time-weighted average value and output the power factor coefficient.

3. The high and low voltage switchgear operation monitoring and management method based on data analysis according to claim 1, characterized in that: Logic for feature extraction of transient events; Define a tolerance curve for the voltage amplitude-duration relationship; the curve defines the acceptable and unacceptable boundaries for different combinations of depth and duration. For a given depth D and duration of... For transient descent and transient rise events, the corresponding maximum allowable duration is located on the tolerance curve; After defining the severity score for a single event, the severity scores of each group of transient descent and transient surge events within the current monitoring time zone are summed to obtain the total transient descent score and the total transient surge score. The frequency of occurrence of transient descent and transient rise events is obtained by summing the number of occurrences. The transient performance coefficient is output by weighting and fusing the total transient descent score, total transient rise score, and occurrence frequency in the current monitoring time zone.

4. The high and low voltage switchgear operation monitoring and management method and system based on data analysis according to claim 3, characterized in that: The definition logic of severity score; Divide the duration of the temporary landing event by the maximum allowable duration to obtain the temporary landing overshoot factor; Divide the duration of the transient rise event by the maximum allowed duration to obtain the transient rise overshoot factor; Define the severity score for a single event: Severity Score ; For transient decrease events, the transient decrease excess factor is denoted as , and for transient increase events, the transient increase excess factor is denoted as .

5. The high and low voltage switchgear operation monitoring and management method based on data analysis according to claim 1, characterized in that: Output logic for filtering the list of power distribution cabinets; Distribution cabinets with an overall electrical quality health coefficient higher than the health threshold are marked as abnormal distribution cabinets. For abnormal power distribution cabinets, compare the steady-state performance coefficient and the transient performance coefficient. If the steady-state performance coefficient is higher than the transient performance coefficient, the additional identification type is determined to be the steady-state root cause type; otherwise, it is determined to be the transient root cause type. Distribution cabinets with an overall electrical quality health coefficient below the health threshold are marked as distribution cabinets to be evaluated. The absolute difference between the comprehensive electrical health coefficient and the health threshold of the power distribution cabinet under evaluation is calculated and used as the warning distance value. Select power distribution cabinets whose warning distance value is less than the preset reference distance value as verification power distribution cabinets; After correcting the comprehensive health coefficient of the power quality of the verification distribution cabinet by combining it with the trend superposition coefficient, the correction result is compared with the health threshold. If the correction result is higher than the health threshold, the verification distribution cabinet is marked as a warning distribution cabinet. The comprehensive health coefficient of the power quality of the early warning distribution cabinet is analyzed, and the steady-state performance coefficient and transient performance coefficient are compared. If the steady-state performance coefficient is higher than the transient performance coefficient, the additional identification type is determined to be the steady-state root cause type; otherwise, it is determined to be the transient root cause type.

6. The high and low voltage switchgear operation monitoring and management method based on data analysis according to claim 5, characterized in that: The logic for obtaining the trend overlay coefficient; The comprehensive electrical health coefficients of the power distribution cabinet in the previous five time zones were extracted and verified, and a data sequence was constructed by combining it with the comprehensive electrical health coefficient in the current monitoring time zone. After arranging the data sequence in chronological order, the average value of the comprehensive health coefficient of the first three groups of electrical quality is calculated as the average value of the first half of the time zone, and the average value of the comprehensive health coefficient of the last three groups of electrical quality is calculated as the average value of the second half of the time zone. The trend superposition coefficient is obtained by dividing the average value of the second half of the time zone by the average value of the first half of the time zone. The trend superposition coefficient is multiplied by the overall electrical health coefficient of the power distribution cabinet in the current monitoring time zone to obtain the correction result.

7. The high and low voltage switchgear operation monitoring and management method based on data analysis according to claim 1, characterized in that: The retrieval logic for matching rules; Identify the health type of the distribution cabinet and combine it with the additional identification type of the distribution cabinet as a tag pair; health types include abnormal and warning. The strategy database includes four strategy sub-libraries, each corresponding to a tag pair. Each strategy sub-library includes historical management strategies for power distribution cabinet applications, comprehensive power quality health coefficients, and application performance parameters. After the tag pairs of the power distribution cabinets are entered into the strategy database for retrieval, each set of historical management strategies is extracted from the corresponding strategy sub-library as candidate strategies.

8. The high and low voltage switchgear operation monitoring and management method based on data analysis according to claim 7, characterized in that: Application governance strategy selection logic; For each group of candidate strategies retrieved from the power distribution cabinet, the single similarity value and strategy evaluation value are extracted, weighted and fused, and the effectiveness coefficient of each group of candidate strategies is output. The candidate strategy with the highest effectiveness coefficient is selected as the governance strategy to be applied.

9. The high and low voltage switchgear operation monitoring and management method based on data analysis according to claim 8, characterized in that: The calculation logic for singlet value and strategy evaluation value; The parameters of the additional identification type corresponding to each group of candidate strategies are analyzed. If the additional identification type is the steady-state root cause type, the harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient are obtained. The similarity between the harmonic distortion coefficient, voltage deviation stability coefficient, and power factor coefficient calculated in real time in the current monitoring time zone is calculated using Euclidean distance, and used as the single similarity value of each group of candidate strategies. If it is a transient root cause type, the total transient decrease score, total transient increase score, and occurrence frequency are obtained, and the similarity between them and the total transient decrease score, total transient increase score, and occurrence frequency calculated in real time in the current monitoring time zone is calculated using Euclidean distance, which is used as the single similarity value of each group of candidate strategies. Application performance parameters include strategy effectiveness and cost-effectiveness. Strategy effectiveness is the rate of change of the overall electrical quality health coefficient before and after the application of the strategy; cost-effectiveness ratio is obtained by calculating the difference between the overall electrical quality health coefficient before and after the application of the strategy and dividing it by the total cost of implementing the strategy. The strategy evaluation value is output after weighted fusion of the strategy effectiveness and cost-effectiveness ratio of each group of candidate strategies.

10. A high- and low-voltage switchgear operation monitoring and management system based on data analysis, applied to any one of the above-mentioned high- and low-voltage switchgear operation monitoring and management methods based on data analysis, characterized in that: The cloud-based data analysis module is used to execute data analysis logic, calculate the steady-state performance coefficient and transient performance coefficient of the power distribution cabinet, and integrate and output the comprehensive health coefficient of power quality. The filtering rule execution module is used to identify abnormal power distribution cabinets and early warning power distribution cabinets by using the comprehensive electrical quality health coefficient as the core judgment criterion, combined with the auxiliary constraints of steady-state performance coefficient and transient performance coefficient, and through the rule of combining basic threshold filtering and trend superposition judgment, and outputting a list of filtered power distribution cabinets. The governance strategy recommendation module is used to retrieve the optimal governance strategy from the strategy database based on the health type and additional identification type in the list of power distribution cabinets, and then send it to the management personnel for review and execution.

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