A method and device for intelligent commissioning of power concentrators

Abstract

This invention relates to the field of power concentrator commissioning, specifically disclosing an intelligent commissioning method and apparatus for power concentrators. The method includes: constructing a dynamic composite test scenario based on rated operating conditions superimposed with multiple disturbance factors, and dividing the scenario into operating condition segments; generating test signals and inputting them to a programmable power supply and load simulator to initiate accuracy and power consumption tests; acquiring the measurement error sequence of the concentrator's basic electrical parameters under each operating condition segment, and evaluating the accuracy level based on this sequence; simultaneously acquiring the power consumption curve of the concentrator performing typical tasks and comparing it with a standard power consumption baseline, identifying whether there is an energy consumption anomaly based on the deviation of energy consumption characteristics; finally, determining whether the concentrator is qualified based on the accuracy level and the energy consumption anomaly identification results, and marking unqualified operating conditions and types. This invention can comprehensively and intelligently evaluate the measurement accuracy and power consumption characteristics of power concentrators under complex operating conditions, improving the realism, accuracy, and efficiency of commissioning.

Description

Technical Field

[0001] This invention relates to the field of concentrator commissioning, and specifically to an intelligent commissioning method and apparatus for power concentrators. Background Technology

[0002] With the continuous advancement of smart grid construction, power concentrators, as key intermediate devices connecting electricity meters and the master station system, undertake important functions such as data acquisition, communication forwarding, and protocol conversion. The stability and measurement accuracy of their electrical performance directly affect the accuracy of electricity metering, line loss analysis, and load regulation. Therefore, comprehensive and reliable performance testing of power concentrators before they leave the factory and during daily operation and maintenance is of great significance.

[0003] Currently, the testing of power concentrators mainly focuses on communication functions, protocol consistency, and basic power metering accuracy. Static and discrete testing methods are usually adopted, such as point-to-point accuracy verification of voltage and current under rated conditions. However, these methods have the following obvious shortcomings: (1) Insufficient coverage of static point inspection and testing scenarios: In actual power grids, there are complex operating conditions such as power factor fluctuations, harmonic pollution, and load transients. Traditional testing methods are difficult to simulate these dynamic conditions, resulting in the inability to fully evaluate the measurement performance of concentrators in real environments.

[0004] (2) Single dimension of electrical performance evaluation: Existing testing focuses on the accuracy of electrical parameter measurement, neglecting the power consumption characteristics of the concentrator in different operating modes. Power consumption characteristics are an important indicator reflecting the stability of the internal circuit, the quality of components and potential defects of the equipment, and there is currently a lack of systematic testing and evaluation methods.

[0005] (3) Simple judgment rules and low level of intelligence: Anomaly judgment mostly relies on manually setting fixed thresholds. For example, if the error exceeds ±0.5%, it is unqualified. This method cannot identify complex fault modes such as intermittent anomalies and transient power consumption spikes. It lacks an intelligent judgment mechanism based on multi-feature fusion, which is prone to missed judgments and misjudgments, affecting debugging efficiency and product reliability. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background art, a method and device for intelligent commissioning of power concentrators are proposed.

[0007] The technical solution adopted by the present invention to solve its technical problem is as follows: Firstly, the present invention provides an intelligent debugging method for power concentrators, including the following steps: S1, taking the rated operating condition as the test benchmark, superimposing multiple different disturbance factors on the test benchmark according to a set time window to build a dynamic composite test scenario, and dividing the test scenario into various operating condition segments.

[0008] S2. Generate a test signal corresponding to the test scenario, and input the test signal into the programmable power supply and load simulator through the test software to start the accuracy test and power consumption test.

[0009] S3. Obtain multiple sets of original sampled values ​​of basic electrical parameters read by the concentrator under each operating condition segment, and compare them with the true values ​​to obtain a measurement error sequence. Evaluate the accuracy level based on the measurement error sequence.

[0010] S4. Obtain the power consumption curves of the concentrator performing typical tasks under various operating conditions, and compare them with the preset standard power consumption baseline. Identify whether there is an energy consumption anomaly based on the deviation between the two in terms of energy consumption characteristics.

[0011] S5. Based on the accuracy level evaluation results and energy consumption anomaly identification results under each working condition segment, determine whether the concentrator is qualified and mark the unqualified working conditions and types, wherein the types are at least one of accuracy deviation and energy consumption anomaly.

[0012] Secondly, the present invention also provides an intelligent debugging device for power concentrators, comprising: a scenario building module, which uses rated operating conditions as a test benchmark, and sequentially superimposes multiple different disturbance factors on the test benchmark according to a set time window to build a dynamic composite test scenario, and divides the test scenario into various operating condition segments.

[0013] The signal application module generates a test signal corresponding to the test scenario and inputs the test signal into the programmable power supply and load simulator through the test software to start the accuracy test and power consumption test.

[0014] The accuracy analysis module acquires multiple sets of original sampled values ​​of basic electrical parameters read by the concentrator under various operating conditions, compares them with the true values ​​to obtain a measurement error sequence, and evaluates the accuracy level based on the measurement error sequence.

[0015] The energy consumption analysis module obtains the power consumption curves of the concentrator performing typical tasks under various operating conditions and compares them with a preset standard power consumption baseline. Based on the deviation of the two in terms of energy consumption characteristics, it identifies whether there is an energy consumption anomaly.

[0016] The comprehensive judgment module determines whether the concentrator is qualified and marks the unqualified working conditions and types based on the accuracy level evaluation results and energy consumption anomaly identification results under each working condition segment. The types are at least one of accuracy deviation and energy consumption anomaly.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention constructs a dynamic composite test scenario based on rated operating conditions superimposed with multiple disturbance factors to simulate complex operating conditions in the real power grid, which can comprehensively evaluate the measurement performance and adaptability of power concentrators under different electrical environments, and solves the problem of insufficient coverage of traditional static test scenarios.

[0018] 2. This invention not only evaluates the measurement accuracy of basic electrical parameters at multiple levels, but also simultaneously analyzes the power consumption curves and energy consumption characteristics of the concentrator under different operating conditions, realizing a comprehensive evaluation of electrical performance and making up for the shortcomings of existing technologies that only focus on accuracy and ignore power consumption characteristics.

[0019] 3. This invention, through measurement error sequence analysis and energy consumption characteristic deviation calculation, combined with trend judgment and multi-feature fusion, can effectively identify complex faults such as intermittent accuracy deviation and transient power consumption anomalies, significantly improving the automation level and judgment accuracy of debugging, and reducing human error and omission. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0022] Figure 2 This is a flowchart of the intelligent commissioning method for power concentrators of the present invention.

[0023] Figure 3 This is a diagram showing the connection of the device modules of the present invention. Detailed Implementation

[0024] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a power concentrator intelligent debugging method and apparatus proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent debugging method and device for power concentrators provided by the present invention.

[0027] Please see Figure 1 and Figure 2As shown, the first aspect of the present invention provides a method for intelligent commissioning of a power concentrator, comprising the following steps: Step S1, taking the rated operating condition as the test benchmark, superimposing multiple different disturbance factors on the test benchmark according to a set time window to build a dynamic composite test scenario, and dividing the test scenario into segments of each operating condition.

[0028] In one embodiment of the present invention, before performing intelligent commissioning of the power concentrator, it is first necessary to construct a dynamic test environment that can simulate the superposition of multiple disturbance factors in actual power grid operation. By building a dynamic composite test scenario, the performance of the power concentrator under different operating conditions can be comprehensively and systematically evaluated, especially its measurement accuracy and power consumption characteristics.

[0029] Given that power grids are subject to various disturbances in actual operation, such as load variations, harmonic pollution, and power factor fluctuations, these factors, acting individually or in combination, can challenge the measurement accuracy and operational stability of power concentrators. To effectively reproduce and assess these effects in a laboratory environment, a systematic approach is needed to program these disturbances.

[0030] Based on this, in a preferred embodiment of the present invention, the method for constructing a dynamic composite test scenario includes: defining operating parameters under rated operating conditions.

[0031] It should be noted that the operating parameters under the rated operating conditions include voltage, current, frequency, power factor, voltage source output waveform, and current source output waveform.

[0032] Select multiple disturbance factors from the preset power grid disturbance factor library and configure the attribute information of each disturbance factor.

[0033] It should be noted that the method for constructing the power grid disturbance factor database is as follows: based on actual power grid operation data and historical fault records, common disturbance factors are collected, each disturbance factor is classified, its attribute information is defined, and stored in the database.

[0034] The disturbance factors in the power grid disturbance factor database include load changes, harmonic pollution, and power factor fluctuations. Load changes are characterized by their type, amplitude, rate of change, and period; the type can be one or more of step changes, ramp changes, or random fluctuations. Harmonic pollution is characterized by its harmonic order, harmonic content, harmonic phase angle, and total harmonic distortion (THD). Power factor fluctuations are characterized by their type, PF value range, and change pattern; the type can be inductive or capacitive, and the change pattern can be step or gradual.

[0035] In another specific embodiment, the power grid disturbance factor library may also include other disturbance factors, such as voltage fluctuations and sags, frequency deviations, imbalances, etc.

[0036] Determine the superposition order of each disturbance factor and set the time window for superposition between two adjacent disturbance factors, wherein only one disturbance factor is applied for each superposition.

[0037] Using the rated operating conditions as the test benchmark, after the system has been running stably under the rated operating conditions for a first set period of time, the first disturbance factor is added, and after the time window is reached, the second disturbance factor is added, and so on, until all disturbance factors are added.

[0038] After the last disturbance factor is superimposed and continues for a second set duration, the system returns to the rated operating condition, thereby completing the construction of the dynamic composite test scenario.

[0039] It should be noted that the values ​​of the time window, the first set duration, and the second set duration can be the same or different.

[0040] As an example, the operating parameters under the rated conditions are set as follows: voltage 220V, current 5A, frequency 50Hz, power factor PF=1, and the output waveforms of the voltage source and the current source are both pure sine waves.

[0041] The specific process of arranging rated basic operating conditions and disturbance factors based on the timeline to build a dynamic composite test scenario is as follows: Minutes 0 to 2: Maintain rated basic operating conditions to establish a test baseline.

[0042] Minutes 2 to 5: Harmonic pollution is superimposed on the rated base condition, specifically by injecting 20% ​​of the 5th harmonic current and 10% of the 7th harmonic current on the basis of the rated current.

[0043] Minutes 5 to 9: Under the aforementioned harmonic background, a step change in load is further superimposed, causing the load current to change stepwise between 50%, 100%, and 150% of the rated current every 30 seconds.

[0044] Minutes 9 to 12: While keeping the total current effective value constant, a power factor gradual change disturbance is superimposed, causing the power factor to slowly change from 0.95 (inductive) to 0.95 (capacitive).

[0045] Minutes 12 to 14: All the above-mentioned disturbance factors exist simultaneously, forming a comprehensive stress test condition.

[0046] Minutes 14 to 16: Remove all disturbances and restore to rated base conditions.

[0047] After setting up a dynamic composite test scenario, in order to facilitate detailed performance analysis for different test stages, the continuous test process needs to be divided into several independent working condition segments with different test characteristics.

[0048] Based on this, in a preferred embodiment of the present invention, the method for dividing working condition segments includes: identifying the time point corresponding to each working condition switch in the dynamic composite test scenario, and using the time point as the dividing node to divide the entire test process into several continuous working condition segments.

[0049] After dividing the operating conditions into segments, test signals need to be generated and applied to the test system to drive subsequent accuracy and power consumption test processes.

[0050] Step S2: Generate a test signal corresponding to the test scenario, and input the test signal into the programmable power supply and load simulator through the test software to start the accuracy test and power consumption test.

[0051] After the test signal is applied and the accuracy test and power consumption test are started and running, the performance data of the power concentrator under various operating conditions can be collected.

[0052] The first focus is on evaluating the measurement accuracy of the concentrator.

[0053] Step S3: Obtain multiple sets of original sampled values ​​of basic electrical parameters read by the concentrator under each operating condition segment, compare them with the true values ​​to obtain a measurement error sequence, and evaluate the accuracy level based on the measurement error sequence.

[0054] To assess measurement accuracy, it is necessary to acquire and quantify measurement errors. Considering that the test is conducted under multiple different operating conditions, and multiple basic electrical parameters need to be sampled multiple times within each segment, it is necessary to collect measured values ​​and true values ​​separately for each operating condition and electrical parameter, and construct a measurement error sequence by comparing them point by point, so as to provide an accurate data basis for subsequent accuracy level assessment.

[0055] Based on this, in a preferred embodiment of the present invention, the method for obtaining the measurement error sequence includes: setting multiple sampling time points for each working condition segment.

[0056] Multiple sets of original sampled values ​​of basic electrical parameters read from the communication interface of the concentrator under each operating condition segment are obtained, and a dataset of measured electrical parameters corresponding to each operating condition segment is constructed. The basic electrical parameters include voltage, current, power, energy and frequency.

[0057] Simultaneously acquire multiple sets of real values ​​of the corresponding basic electrical parameters output by the programmable power supply and load simulator, and construct a dataset of real electrical parameters corresponding to each operating condition segment.

[0058] The measured electrical parameter datasets for each operating condition segment are compared point by point with the actual electrical parameter datasets. Multiple measurement error values ​​for each basic electrical parameter in each operating condition segment are statistically analyzed and sorted according to the sampling time to form a measurement error sequence for each electrical parameter in each operating condition segment.

[0059] It should be noted that when testing the measurement accuracy of the power concentrator, basic electrical parameters such as voltage, current, power, and energy are selected as the test objects for the following reasons: First, basic electrical parameters are the most fundamental and core parameters acquired by the concentrator, and their measurement accuracy directly affects the subsequent functional implementation and index determination of the concentrator; Second, most advanced electrical parameters and power quality parameters are derived from the basic electrical parameters through corresponding mathematical models, calculation algorithms, or processing methods. Therefore, the measurement accuracy of the basic electrical parameters is the fundamental prerequisite for ensuring the accuracy and reliability of all derived parameters.

[0060] After obtaining the measurement error sequence of each electrical parameter under each operating condition segment, the error sequence can be analyzed and processed according to the preset rules and standards, thereby evaluating the accuracy level of the power concentrator under each operating condition segment.

[0061] Based on this, in a preferred embodiment of the present invention, the method for evaluating the accuracy level includes: D1: extracting the allowable range of measurement error for each basic electrical parameter from a preset database.

[0062] D2: Compare the multiple measurement error values ​​of each basic electrical parameter in each working condition segment with the corresponding allowable measurement error range, count the number of measurement error values ​​of each electrical parameter that exceed the allowable range, and calculate the ratio of this number to the total number of measurement error values ​​of the corresponding electrical parameter to obtain the measurement error rate of the electrical parameter.

[0063] D3: Determine whether the current working condition segment meets any of the following conditions: (1) The measurement error ratio of any electrical parameter is greater than the preset ratio threshold.

[0064] (2) The measurement error sequence of any electrical parameter shows a monotonically increasing trend.

[0065] If the conditions are met, the accuracy level corresponding to the working condition segment is determined to be the preset specified accuracy level, and the subsequent scoring steps are skipped.

[0066] If the condition is not met, proceed to step D4.

[0067] D4: Based on the measurement error sequence of each electrical parameter under each working condition segment, generate the measurement error distribution of each electrical parameter.

[0068] In a preferred embodiment of the present invention, the method for generating a measurement error distribution includes: uniformly dividing the allowable range of measurement error of the electrical parameter into multiple intervals; according to the measurement error sequence of the electrical parameter, counting the number of measurement errors falling into each interval to obtain the measurement error frequency corresponding to each interval; drawing a histogram based on the frequency to generate the measurement error distribution of the electrical parameter.

[0069] Based on the preset mapping relationship between measurement error distribution and measurement accuracy score, the measurement accuracy score corresponding to each electrical parameter is determined.

[0070] The average value of the measurement accuracy scores for each electrical parameter is calculated, and the calculation result is used as the comprehensive measurement accuracy score for that working condition segment.

[0071] Based on the preset comprehensive measurement accuracy scoring range corresponding to different accuracy levels, the accuracy level corresponding to this working condition segment is determined.

[0072] D5: Statistically calculate the accuracy level corresponding to each working condition segment.

[0073] As an example, the preset accuracy levels include high accuracy, standard accuracy, and low accuracy, wherein the specified accuracy level is low accuracy. High accuracy is suitable for scenarios where measurement errors are uniformly distributed and the out-of-tolerance ratio is low; standard accuracy is suitable for scenarios where measurement errors are basically within the allowable range and do not show a significant trend; low accuracy is suitable for scenarios where the out-of-tolerance ratio is high or the measurement error sequence shows a monotonically increasing trend.

[0074] After evaluating the measurement accuracy under each operating condition segment, this embodiment of the invention will further evaluate the energy consumption performance of the power concentrator under the same test scenario.

[0075] Step S4: Obtain the power consumption curves of the concentrator performing typical tasks under various operating conditions, and compare them with the preset standard power consumption baseline. Identify whether there is an energy consumption anomaly based on the deviation between the two in terms of energy consumption characteristics.

[0076] The method for determining the preset standard power consumption baseline is as follows: based on historical test data and the power consumption characteristics of typical tasks, a standard power consumption baseline is established for each typical task. For example, the standard power consumption baseline for meter reading tasks is an average power consumption of 0.5W, the standard power consumption baseline for communication tasks is an average power consumption of 1.2W, and the standard power consumption baseline for data processing tasks is an average power consumption of 0.8W.

[0077] To accurately identify energy consumption anomalies, firstly, curve data that accurately reflects the change of concentrator power consumption over time is collected; then, energy consumption characteristic parameters are extracted from these curve data; and finally, the energy consumption characteristic parameters are compared with standard energy consumption characteristic parameters to determine whether the energy consumption is abnormal.

[0078] Based on this, in a preferred embodiment of the present invention, the method for identifying whether there is abnormal energy consumption includes: using a high-precision power analyzer connected in series in the power supply circuit of the concentrator to collect the curve of the power consumption of the concentrator as a function of time when performing each typical task under each operating condition segment, and obtaining the corresponding power consumption curve.

[0079] It should be noted that the typical tasks mentioned include, but are not limited to, meter reading, communication, and data processing.

[0080] Based on the power consumption curve, the actual energy consumption characteristics are extracted, including the steady-state average power consumption, the transient peak power consumption, and the power consumption fluctuation frequency.

[0081] Extract the corresponding energy consumption characteristics from the standard power consumption baselines corresponding to each typical task, and use them as standard energy consumption characteristics.

[0082] The actual energy consumption characteristics of the concentrator performing typical tasks under various operating conditions are compared with the standard energy consumption characteristics, and the deviation of the energy consumption characteristics is calculated.

[0083] If the energy consumption characteristic deviation during the execution of a typical task under a certain working condition segment exceeds the set deviation threshold, the energy consumption of that working condition segment is determined to be abnormal; otherwise, the energy consumption is determined to be normal.

[0084] It should be noted that the deviation threshold is dynamically set based on the concentrator model, task type, and industry standards.

[0085] Summarize the energy consumption anomaly identification results for all operating conditions.

[0086] Since different energy consumption characteristics may contribute differently to the overall energy consumption performance, the impact of their deviation from the normal value may also vary.

[0087] Based on this, in a preferred embodiment of the present invention, the method for calculating the deviation of energy consumption characteristics includes: obtaining the actual values ​​of various energy consumption characteristics and their corresponding standard values ​​when performing typical tasks, calculating the difference between each actual value and the corresponding standard value, and obtaining the individual deviation of each energy consumption characteristic based on the ratio of the difference to the standard value.

[0088] Based on the preset weights of each energy consumption characteristic, a linear weighted fusion analysis is performed on each individual deviation to obtain a comprehensive energy consumption characteristic deviation.

[0089] After obtaining the accuracy level assessment results and energy consumption anomaly identification results for each operating condition segment, it is necessary to make a final judgment on the overall commissioning results of the power concentrator.

[0090] Step S5: Based on the accuracy level evaluation results and energy consumption anomaly identification results under each working condition segment, determine whether the concentrator is qualified and mark the unqualified working conditions and types. The types are at least one of accuracy deviation and energy consumption anomaly.

[0091] Based on this, in a preferred embodiment of the present invention, the method for determining whether the concentrator is qualified includes: if the accuracy level of the concentrator is not lower than the preset expected accuracy level in all operating conditions, and the energy consumption in all operating conditions is identified as normal, then the concentrator is determined to be qualified; otherwise, the concentrator is determined to be unqualified.

[0092] It should be noted that the desired accuracy level is set according to the testing requirements.

[0093] It should be noted that if the accuracy level of a certain operating condition segment is lower than the expected accuracy level, or if the operating condition segment is identified as having abnormal energy consumption, then the operating condition segment will be marked as an unqualified operating condition.

[0094] See Figure 3 As shown, a second aspect of the present invention provides an intelligent debugging device for a power concentrator, comprising a scene building module, a signal application module, a precision analysis module, an energy consumption analysis module, and a comprehensive judgment module.

[0095] The scenario building module is connected to the signal application module, the signal application module is connected to the accuracy analysis module and the energy consumption analysis module, and the comprehensive judgment module is connected to the accuracy analysis module and the energy consumption analysis module.

[0096] The scenario building module is used to build a dynamic composite test scenario by using the rated operating condition as the test benchmark and superimposing multiple different disturbance factors on the test benchmark according to a set time window, and to divide the test scenario into various operating condition segments.

[0097] The signal application module is used to generate a test signal corresponding to the test scenario, and input the test signal into the programmable power supply and load simulator through the test software to start the accuracy test and power consumption test.

[0098] The accuracy analysis module is used to obtain multiple sets of original sampled values ​​of basic electrical parameters read by the concentrator under various operating conditions, compare them with the true values ​​to obtain a measurement error sequence, and evaluate the accuracy level based on the measurement error sequence.

[0099] The energy consumption analysis module is used to obtain the power consumption curves of the concentrator performing typical tasks under various operating conditions, and compare them with the preset standard power consumption baseline. Based on the deviation of the two in terms of energy consumption characteristics, it is used to identify whether the energy consumption is abnormal.

[0100] The comprehensive judgment module is used to determine whether the concentrator is qualified and mark the unqualified working conditions and types based on the accuracy level evaluation results and energy consumption anomaly identification results under each working condition segment. The types are at least one of accuracy deviation and energy consumption anomaly.

[0101] In this embodiment, the present invention employs a programmable power supply, a load simulator, and test software to work together to achieve automatic generation and application of test signals, automatic division and analysis of various operating condition segments, ensuring that the test process is standardized and repeatable, and is suitable for batch debugging and long-term performance monitoring.

[0102] In this embodiment, the present invention supports flexible configuration of the power grid disturbance factor library, dynamic division of operating condition segments, and custom settings of accuracy level and energy consumption threshold, which can adapt to the commissioning needs of power concentrators of different models and application scenarios, and improve the versatility and practicality of the method.

[0103] In summary, this invention constructs a dynamic composite test scenario based on rated operating conditions superimposed with multiple disturbance factors, and divides the operating conditions into segments; generates test signals and inputs them to a programmable power supply and load simulator to initiate accuracy and power consumption tests; acquires the measurement error sequence of the concentrator reading basic electrical parameters under each operating segment, and evaluates the accuracy level based on this sequence; simultaneously acquires the power consumption curve of the concentrator performing typical tasks and compares it with a standard power consumption baseline, identifying whether there is an energy consumption anomaly based on the deviation of energy consumption characteristics; finally, it determines whether the concentrator is qualified based on the accuracy level and the energy consumption anomaly identification results, and marks the unqualified operating conditions and types. This invention can comprehensively and intelligently evaluate the measurement accuracy and power consumption characteristics of power concentrators under complex operating conditions, improving the authenticity, accuracy, and efficiency of debugging.

[0104] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for intelligent commissioning of a power concentrator, characterized in that, include: Using the rated operating condition as the test benchmark, multiple different disturbance factors are superimposed on the test benchmark according to the set time window to build a dynamic composite test scenario, and the test scenario is divided into various operating condition segments. The method for dividing the test scenario into various working condition segments includes: identifying the time point corresponding to each working condition switch in the dynamic composite test scenario, and using the time point as the dividing node to divide the entire test process into several continuous working condition segments. Generate test signals corresponding to the test scenario, and input the test signals into the programmable power supply and load simulator through test software to start accuracy test and power consumption test; Multiple sets of original sampled values ​​of basic electrical parameters read by the concentrator under various operating conditions are obtained and compared with the true values ​​to obtain a measurement error sequence. The accuracy level is evaluated based on the measurement error sequence. The method for assessing the accuracy level includes: D1: Extract the allowable measurement error range of each basic electrical parameter from the preset database; D2: Compare the multiple measurement error values ​​of each basic electrical parameter in each working condition segment with the corresponding allowable measurement error range, count the number of measurement error values ​​of each electrical parameter that exceed the allowable range, and calculate the ratio of this number to the total number of measurement error values ​​of the corresponding electrical parameter to obtain the measurement deviation ratio of the electrical parameter. D3: Determine whether the current operating condition segment meets any of the following conditions: (1) The measurement error rate of any electrical parameter is greater than the preset threshold. (2) The measurement error sequence of any electrical parameter shows a monotonically increasing trend. If the conditions are met, the accuracy level corresponding to the working condition segment is determined to be the preset specified accuracy level, and the subsequent scoring steps are skipped. If not satisfied, proceed to step D4; D4: Based on the measurement error sequence of each electrical parameter under each working condition segment, generate the measurement error distribution of each electrical parameter, and determine the measurement accuracy score corresponding to each electrical parameter according to the preset mapping relationship between the measurement error distribution and the measurement accuracy score. The average value of the measurement accuracy scores for each electrical parameter is calculated, and the calculation result is used as the comprehensive measurement accuracy score for this working condition segment. The accuracy level corresponding to this working condition segment is determined based on the preset comprehensive measurement accuracy scoring range corresponding to different accuracy levels. D5: Statistics on the accuracy level corresponding to each working condition segment; The power consumption curves of the concentrator performing typical tasks under various operating conditions are obtained and compared with the preset standard power consumption baseline. The deviation between the two in terms of energy consumption characteristics is used to identify whether there is an energy consumption anomaly. Based on the accuracy level assessment results and energy consumption anomaly identification results under each operating condition segment, determine whether the concentrator is qualified, and mark the unqualified operating conditions and types.

2. The intelligent commissioning method for a power concentrator according to claim 1, characterized in that: The method for constructing dynamic composite test scenarios includes: Define the operating parameters under rated operating conditions; Select multiple disturbance factors from the preset power grid disturbance factor library and configure the attribute information of each disturbance factor; Determine the superposition order of each disturbance factor and set the time window for superposition between two adjacent disturbance factors, wherein only one disturbance factor is applied in each superposition; Using the rated operating conditions as the test benchmark, after the first set time of stable operation under the rated operating conditions, the first disturbance factor is superimposed, and after the time window is reached, the second disturbance factor is superimposed, and so on, until all disturbance factors are superimposed. After the last disturbance factor is superimposed and continues for a second set duration, the system returns to the rated operating condition, thereby completing the construction of the dynamic composite test scenario.

3. The intelligent commissioning method for a power concentrator according to claim 1, characterized in that: The method for obtaining the measurement error sequence includes: Multiple sampling time points were set for each working condition segment; Multiple sets of original sampled values ​​of basic electrical parameters read from the communication interface of the concentrator under each operating condition segment are obtained, and a dataset of measured electrical parameters corresponding to each operating condition segment is constructed. The basic electrical parameters include voltage, current, power, energy and frequency. Simultaneously acquire multiple sets of real values ​​of the corresponding basic electrical parameters output by the programmable power supply and load simulator, and construct a dataset of real electrical parameters corresponding to each operating condition segment; The measured electrical parameter datasets for each operating condition segment are compared point by point with the actual electrical parameter datasets. Multiple measurement error values ​​for each basic electrical parameter in each operating condition segment are statistically analyzed and sorted according to the sampling time to form a measurement error sequence for each electrical parameter in each operating condition segment.

4. The intelligent commissioning method for a power concentrator according to claim 1, characterized in that: The method for generating the measurement error distribution includes: The allowable range of measurement error for electrical parameters is evenly divided into multiple intervals; Based on the measurement error sequence of electrical parameters, the number of measurement errors falling into each interval is counted to obtain the measurement error frequency corresponding to each interval. A histogram is plotted based on the frequency count to generate the measurement error distribution of the electrical parameters.

5. The intelligent commissioning method for a power concentrator according to claim 1, characterized in that: The method for identifying whether energy consumption is abnormal includes: By using a high-precision power analyzer connected in series in the power supply circuit of the concentrator, the curves of the power change of the concentrator over time when performing typical tasks under various operating conditions are collected, and the corresponding power consumption curves are obtained. Based on the power consumption curve, the actual energy consumption characteristics are extracted, including the steady-state average power consumption, the transient peak power consumption, and the power consumption fluctuation frequency. Extract the corresponding energy consumption characteristics from the standard power consumption baselines corresponding to each typical task, and use them as standard energy consumption characteristics. The actual energy consumption characteristics of the concentrator performing each typical task under each operating condition segment are compared with the standard energy consumption characteristics, and the deviation of the energy consumption characteristics is calculated. If the energy consumption characteristic deviation during the execution of a typical task under a certain working condition segment is greater than the set deviation threshold, the energy consumption of that working condition segment is determined to be abnormal; otherwise, the energy consumption is determined to be normal. Summarize the energy consumption anomaly identification results for all operating conditions.

6. The intelligent commissioning method for a power concentrator according to claim 5, characterized in that: The method for calculating the deviation of energy consumption characteristics includes: Obtain the actual values ​​of various energy consumption characteristics and their corresponding standard values ​​when performing typical tasks, calculate the difference between each actual value and the corresponding standard value, and obtain the individual deviation of each energy consumption characteristic based on the ratio of the difference to the standard value. Based on the preset weights of each energy consumption characteristic, a linear weighted fusion analysis is performed on each individual deviation to obtain a comprehensive energy consumption characteristic deviation.

7. The intelligent commissioning method for a power concentrator according to claim 1, characterized in that: The method for determining whether a concentrator is qualified includes: If the accuracy level of the concentrator is not lower than the preset expected accuracy level in all operating conditions, and the energy consumption in all operating conditions is identified as normal, then the concentrator is deemed qualified; otherwise, the concentrator is deemed unqualified.

8. A smart debugging device for a power concentrator, characterized in that, include: The scenario building module uses the rated working condition as the test benchmark. Multiple different disturbance factors are superimposed on the test benchmark according to the set time window to build a dynamic composite test scenario and divide the test scenario into various working condition segments. The method for dividing the test scenario into various working condition segments includes: identifying the time point corresponding to each working condition switch in the dynamic composite test scenario, and using the time point as the dividing node to divide the entire test process into several continuous working condition segments. The signal application module generates a test signal corresponding to the test scenario and inputs the test signal into the programmable power supply and load simulator through the test software to start the accuracy test and power consumption test. The accuracy analysis module acquires multiple sets of original sampled values ​​of basic electrical parameters read by the concentrator under various operating conditions, compares them with the true values ​​to obtain a measurement error sequence, and evaluates the accuracy level based on the measurement error sequence. The method for assessing the accuracy level includes: D1: Extract the allowable measurement error range of each basic electrical parameter from the preset database; D2: Compare the multiple measurement error values ​​of each basic electrical parameter in each working condition segment with the corresponding allowable measurement error range, count the number of measurement error values ​​of each electrical parameter that exceed the allowable range, and calculate the ratio of this number to the total number of measurement error values ​​of the corresponding electrical parameter to obtain the measurement deviation ratio of the electrical parameter. D3: Determine whether the current operating condition segment meets any of the following conditions: (1) The measurement error rate of any electrical parameter is greater than the preset threshold. (2) The measurement error sequence of any electrical parameter shows a monotonically increasing trend. If the conditions are met, the accuracy level corresponding to the working condition segment is determined to be the preset specified accuracy level, and the subsequent scoring steps are skipped. If not satisfied, proceed to step D4; D4: Based on the measurement error sequence of each electrical parameter under each working condition segment, generate the measurement error distribution of each electrical parameter, and determine the measurement accuracy score corresponding to each electrical parameter according to the preset mapping relationship between the measurement error distribution and the measurement accuracy score. The average value of the measurement accuracy scores for each electrical parameter is calculated, and the calculation result is used as the comprehensive measurement accuracy score for this working condition segment. The accuracy level corresponding to this working condition segment is determined based on the preset comprehensive measurement accuracy scoring range corresponding to different accuracy levels. D5: Statistics on the accuracy level corresponding to each working condition segment; The energy consumption analysis module obtains the power consumption curves of the concentrator performing typical tasks under various operating conditions and compares them with the preset standard power consumption baseline. Based on the deviation of the two in terms of energy consumption characteristics, it identifies whether the energy consumption is abnormal. The comprehensive judgment module determines whether the concentrator is qualified and marks the unqualified working conditions and types based on the accuracy level evaluation results and energy consumption anomaly identification results under each working condition segment.

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