A method for on-line measurement and evaluation of the metering performance of a current transformer in an operating state

Abstract

The application provides an online measurement and evaluation method for the measurement performance of a current transformer in an operating state, and specifically comprises the following steps: constructing a factor set U of the operating state of the current transformer, a judgment set V, a weight vector P and a membership function; collecting and obtaining parameter data of the current transformer according to the factor set U, and pre-processing and normalizing the parameter data to obtain sample data of each day; constructing an evaluation matrix X based on the obtained sample data of each day; calculating a score vector of the day according to the evaluation matrix X and the weight vector P, continuously obtaining a score vector of a week, and then calculating the final score of the current transformer according to the weekly score vector and a time weight; and substituting the final score of the current transformer into the membership function, and obtaining an evaluation grade of the current transformer according to the maximum membership degree principle. The application can realize the measurement and evaluation of the measurement performance of the current transformer in a non-power-off state, guarantee normal power utilization of users, and avoid economic losses and resource waste.

Description

Technical Field

[0001] This invention relates to the technical field of current transformer safety, and in particular to an online measurement and evaluation method for the metering performance of current transformers under operating conditions. Background Technology

[0002] Current transformers serve as a crucial data source for monitoring and evaluating the operational status of power systems. Their operational reliability and the accuracy of their data reflection are key indicators of their performance. Currently, the method used for error assessment and detection of substation measurement equipment such as current transformers is the standard equipment comparison and calibration method mentioned in the existing Q / GDW424—2010 "Technical Specification for Electronic Current Transformers". However, this existing standard equipment comparison and calibration method can only be used for testing, measurement, and evaluation under power outage conditions, severely impacting users' normal power supply and causing economic losses and resource waste.

[0003] To address this issue, an online measurement and evaluation method for the metering performance of current transformers installed on 0.4kV power lines for metering purposes is proposed. Summary of the Invention

[0004] The purpose of this invention is to propose an online measurement and evaluation method for the metering performance of current transformers under operating conditions, so as to realize the measurement and evaluation of the metering performance of current transformers under uninterrupted power supply conditions, ensuring normal power supply for users while avoiding economic losses and resource waste.

[0005] To achieve the above objectives, the technical solution of the present invention is: an online measurement and evaluation method for the metering performance of a current transformer under operating conditions, which involves data interaction between an online condition evaluation system and the online monitoring unit of the current transformer, and the following steps are executed:

[0006] A factor set U, an evaluation set V, a weight vector P, and a membership function for the operating status of a current transformer are constructed to evaluate the current transformer status using a fuzzy comprehensive evaluation method.

[0007] Based on the constructed factor set U, the parameter data of the current transformer is collected and obtained, and the collected parameter data is preprocessed and normalized to obtain daily sample data.

[0008] An evaluation matrix X is constructed based on the daily sample data obtained.

[0009] The score vector for the day is calculated based on the evaluation matrix X and the weight vector P. After obtaining the score vector for a week, the final score of the current transformer is calculated based on the weekly score vector and the time weight.

[0010] The final score of the current transformer is substituted into the membership function, and the evaluation level of the transformer is obtained according to the principle of maximum membership.

[0011] Preferably, based on the structural characteristics and operating principle of the low-voltage current transformer, representative operating parameters and data are selected as the condition assessment factors for the current transformer, forming a factor set U = {u1, u2, ..., un}. The selected assessment factors include equipment parameters, performance parameters, operating condition parameters, and reliability parameters, specifically:

[0012] The equipment parameters include equipment model, transformer ratio, accuracy class, core material, and rated secondary load.

[0013] The performance parameters include metering point operating error, error consistency, operating calculation error, historical error data, and error stability.

[0014] The operating parameters include voltage fluctuation rate, operating time, historical anomaly frequency, annual temperature difference, and average daily temperature difference.

[0015] The reliability parameters include secondary circuit anomalies, secondary load current, and frequency of historical family failures.

[0016] Preferably, the evaluation set V = {"Excellent", "Good", "Qualified", "Abnormal"} = {v1, v2, v3, v4}; the weight vector P is constructed based on expert experience.

[0017] Preferably, the process of collecting and acquiring parameter data of the current transformer based on the constructed factor set U specifically involves the online status assessment system collecting and acquiring parameter data based on 18 selected assessment factors. The parameter data originates from the online monitoring unit of the current transformer, and the collection frequency is 96 rounds per day.

[0018] Preferably, the preprocessing specifically involves: using a quartile box plot detection algorithm to filter the parameter data acquired daily through the online monitoring unit in 96 rounds, eliminating invalid data caused by gross measurement errors and communication anomalies.

[0019] Preferably, the evaluation matrix X is constructed as follows: based on an m-by-n matrix formed by daily sample data of the current transformer, the dynamic data related to error in the matrix is ​​retained, and the static data in the matrix other than the error-related data is scored by experts and replaced with the original static data, thereby constructing the evaluation matrix X; where m refers to the number of data groups after 96 rounds of outlier removal, n is the number of parameters, and n=18.

[0020] Preferably, the static data includes equipment model, transformer ratio, accuracy class, core material, and rated secondary load data, while the dynamic data related to the error includes the remaining parameter data excluding equipment model, transformer ratio, accuracy class, core material, and rated secondary load data.

[0021] Preferably, the rating vector for the day is calculated based on the evaluation matrix X and the weight vector P, and the specific calculation is as follows:

[0022]

[0023] In the formula, To evaluate matrix X, p1 to p n The weights of each evaluation factor are y1 to y2. m The scores are for m data sets.

[0024] Preferably, the final score of the current transformer is calculated based on the weekly score vector and the time weight as follows: the weekly score vector is multiplied by the time weight to obtain the final score of the current transformer. The final score is based on an S-percentage system, and the time weight is set according to the principle that the more recent the time, the greater the weight.

[0025] Preferably, the membership function is constructed based on the requirements of CT Level 4 assessment, with a mutual inductor score of 95 or above considered excellent, and 50 or below considered abnormal. The specific membership function is as follows:

[0026]

[0027] In the formula, A corresponds to excellent, B corresponds to good, C corresponds to qualified, D corresponds to abnormal, and x is the final score of the current transformer.

[0028] Compared with the prior art, the present invention has the following advantages: The present invention obtains the operating data of the current transformer by interacting with the online monitoring unit of the current transformer through an online condition assessment system, and uses the fuzzy comprehensive evaluation method to conduct online evaluation of the metering performance of the current transformer under the operating condition based on the obtained data; through the long-term accumulation and storage of feature datasets, and continuous compensation and optimization of the weights of various indicators, a more accurate evaluation of the metering performance of low-voltage current transformers can be achieved. Attached Figure Description

[0029] Figure 1 This is a flowchart of the online measurement and evaluation method for the metering performance of current transformers according to the present invention. Detailed Implementation

[0030] The following is in conjunction with the appendix Figure 1 The technical solution of the present invention will be described in detail below.

[0031] This invention proposes an online measurement and evaluation method for the metering performance of a current transformer under operating conditions. This method involves data interaction between an online condition assessment system and the online monitoring unit of the current transformer, and the following steps are executed:

[0032] A factor set U, an evaluation set V, a weight vector P, and a membership function for the operating status of a current transformer are constructed to evaluate the current transformer status using a fuzzy comprehensive evaluation method.

[0033] Based on the constructed factor set U, the parameter data of the current transformer is collected and obtained, and the collected parameter data is preprocessed and normalized to obtain daily sample data.

[0034] An evaluation matrix X is constructed based on the daily sample data obtained.

[0035] The score vector for the day is calculated based on the evaluation matrix X and the weight vector P. After obtaining the score vector for a week, the final score of the current transformer is calculated based on the weekly score vector and the time weight.

[0036] The final score of the current transformer is substituted into the membership function, and the evaluation level of the transformer is obtained according to the principle of maximum membership.

[0037] In this embodiment, based on the structural characteristics and operating principle of the low-voltage current transformer, representative operating parameters and data are selected as the state evaluation factors of the current transformer, forming a factor set U = {u1, u2, ..., un}. The selected evaluation factors include equipment parameters, performance parameters, operating condition parameters, and reliability parameters, specifically:

[0038] The equipment parameters include equipment model, transformer ratio, accuracy class, core material, and rated secondary load.

[0039] The performance parameters include metering point operating error, error consistency, operating calculation error, historical error data, and error stability.

[0040] The operating parameters include voltage fluctuation rate, operating time, historical anomaly frequency, annual temperature difference, and average daily temperature difference.

[0041] The reliability parameters include secondary circuit anomalies, secondary load current, and frequency of historical family failures.

[0042] The evaluation set V = {"Excellent", "Good", "Qualified", "Abnormal"} = {v1, v2, v3, v4};

[0043] The weight vector P is constructed based on expert experience, and the weights of various indicators are compensated and optimized based on the feedback from on-site inspections of the evaluation scores.

[0044] In this embodiment, the collection and acquisition of parameter data of the current transformer based on the constructed factor set U specifically involves the online condition assessment system collecting and acquiring parameter data according to the selected 18 assessment factors. This parameter data originates from the online monitoring unit of the current transformer, with a collection frequency of 96 rounds per day. The definitions and sources of the characteristic parameters are shown in Table 1, where archives and historical data are also obtained through the online monitoring unit.

[0045] Table 1. Definitions and sources of characteristic parameters

[0046]

[0047] The online condition assessment system of this invention has data modification and loss prevention functions, and original data such as operational calculation errors must not be modified. Collected data and assessment results should be retained for at least one year; it has files on the low-voltage current transformers under test, and can statistically query information such as transformer number, installation date, phase, current ratio, power supply unit, inspection date, error value, and metering performance assessment results; it constructs a database of characteristic parameters for assessment, with database management functions, including functions for adding, deleting, querying, and modifying characteristic parameter information of low-voltage current transformers. Characteristic parameters include: performance parameters, reliability parameters, operating condition parameters, and equipment parameters.

[0048] The existing online monitoring unit is used to collect measurement data such as secondary circuit current, secondary circuit voltage, phase angle, secondary impedance, and ambient temperature and humidity of the current transformer. It analyzes and calculates errors based on the measurement data and the transformer's parameter settings, and issues alarms for secondary circuit status monitoring. The online status assessment system of this application interacts with the online monitoring unit, combining the measurement data and error analysis information transmitted by the online monitoring unit with historical data from the current transformer to perform a series of assessments, thereby achieving the function of evaluating the metering performance of the monitored low-voltage current transformer's operating status.

[0049] In this embodiment, the preprocessing specifically involves: filtering the parameter data acquired daily in 96 rounds by the online monitoring unit using a quartile box plot detection algorithm to remove invalid data caused by gross measurement errors and communication anomalies. For example, the aforementioned 18 parameters are divided into static data (e.g., 5 equipment parameter data) and dynamically changing data (e.g., metering point operating errors, operating calculation errors, etc.). For the dynamically changing data in the daily 96 rounds of parameter data, the quartile box plot detection algorithm is used to sort them in ascending order under their corresponding parameter names, and their quartile points are found. The quartile values ​​and interquartile ranges are calculated. Finally, using 1.5 times the interquartile range as a standard, an effective upper and lower limit is set for the parameter, and parameter data corresponding to discrete error values ​​outside the upper and lower limits for that round are removed.

[0050] In addition, to ensure that the final score calculation result is between 0 and 100, the collected parameter data needs to be normalized.

[0051] In this embodiment, the evaluation matrix X is constructed as follows: based on the m-n matrix formed by the daily sample data of the current transformer, the dynamic data related to the error in the matrix is ​​retained, and the static data in the matrix other than the error-related data is scored by experts and replaced with the original static data, thereby constructing the evaluation matrix X; where m refers to the number of data groups after 96 rounds of outlier removal, n is the number of parameters, and n=18.

[0052] The static data includes equipment model, transformer ratio, accuracy class, core material, and rated secondary load data. The dynamic data related to the error includes the remaining parameter data excluding equipment model, transformer ratio, accuracy class, core material, and rated secondary load data.

[0053] In this embodiment, the score vector for the day is calculated based on the evaluation matrix X and the weight vector P. The specific calculation is as follows:

[0054]

[0055] In the formula, To evaluate matrix X, p1 to p n The weights of each evaluation factor are y1 to y2. m The scores are for m data sets.

[0056] In this embodiment, the final score of the current transformer is calculated based on the weekly score vector and the time weight as follows: the weekly score vector is multiplied by the time weight to obtain the final score of the current transformer. The final score is based on a 100-point scale. The time weight is set according to the principle that the closer the time, the greater the weight. The final score is converted from the weekly score vector to a 100-point scale through the time weight.

[0057] In this embodiment, the membership function is constructed based on the requirements of CT Level IV assessment. A score of 95 or above for the mutual inductor is considered excellent, while a score of 50 or below is considered abnormal. The specific membership function is as follows:

[0058]

[0059] In the formula, A corresponds to excellent, B corresponds to good, C corresponds to qualified, D corresponds to abnormal, and x is the final score of the current transformer.

[0060] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for online measurement and evaluation of the metering performance of a current transformer under operating conditions, characterized in that, This is achieved by interacting with the online monitoring unit of the current transformer through an online condition assessment system and performing the following steps: A factor set U, an evaluation set V, a weight vector P, and a membership function for the operating status of a current transformer are constructed to evaluate the current transformer status using a fuzzy comprehensive evaluation method. Based on the constructed factor set U, the parameter data of the current transformer is collected and obtained, and the collected parameter data is preprocessed and normalized to obtain daily sample data. An evaluation matrix X is constructed based on the daily sample data obtained. The score vector for the day is calculated based on the evaluation matrix X and the weight vector P. After obtaining the score vector for a week, the final score of the current transformer is calculated based on the weekly score vector and the time weight. The final score of the current transformer is substituted into the membership function, and the evaluation level of the transformer is obtained according to the principle of maximum membership.

2. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 1, characterized in that, Based on the structural characteristics and operating principle of low-voltage current transformers, representative operating parameters and data are selected as condition assessment factors for the current transformers, forming a factor set U = {u1, u2, ..., un}. The selected assessment factors include equipment parameters, performance parameters, operating condition parameters, and reliability parameters, specifically: The equipment parameters include equipment model, transformer ratio, accuracy class, core material, and rated secondary load. The performance parameters include metering point operating error, error consistency, operating calculation error, historical error data, and error stability. The operating parameters include voltage fluctuation rate, operating time, historical anomaly frequency, annual temperature difference, and average daily temperature difference. The reliability parameters include secondary circuit anomalies, secondary load current, and frequency of historical family failures.

3. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 1, characterized in that, The evaluation set V = {"Excellent", "Good", "Qualified", "Abnormal"} = {v1, v2, v3, v4}; the weight vector P is constructed based on expert experience.

4. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 2, characterized in that, The online condition assessment system collects and acquires parameter data of the current transformer based on the constructed factor set U. Specifically, the online condition assessment system collects and acquires parameter data based on the selected 18 assessment factors. The parameter data comes from the online monitoring unit of the current transformer, and the collection frequency is 96 rounds per day.

5. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 4, characterized in that, The preprocessing specifically involves: using a quartile box plot detection algorithm to filter the parameter data acquired daily through the online monitoring unit in 96 rounds, eliminating invalid data caused by gross measurement errors and communication anomalies.

6. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 5, characterized in that, The evaluation matrix X is constructed as follows: based on an m-n matrix formed by daily sample data of the current transformer, the dynamic data related to error in the matrix is ​​retained, and the static data in the matrix other than the error-related data are scored by experts and replaced with the original static data, thereby constructing the evaluation matrix X; where m refers to the number of data groups after 96 rounds of outlier removal, n is the number of parameters, and n=18.

7. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 6, characterized in that, The static data includes equipment model, transformer ratio, accuracy class, core material, and rated secondary load data. The dynamic data related to error are the remaining parameter data excluding equipment model, transformer ratio, accuracy class, core material, and rated secondary load data.

8. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 1, characterized in that, The rating vector for the day is calculated based on the evaluation matrix X and the weight vector P. The specific calculation is as follows: In the formula, m refers to the number of data sets after 96 rounds of outlier removal, and n is the number of evaluation factors, where n = 18. To evaluate matrix X, p1 to p n The weights of each evaluation factor are y1 to y2. m The scores are for m data sets.

9. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 1, characterized in that, The final score of the current transformer is calculated based on the weekly score vector and the time weight. Specifically, the weekly score vector is multiplied by the time weight to obtain the final score of the current transformer. The final score is based on an S-percentage system, and the time weight is set according to the principle that the more recent the time, the greater the weight.

10. The online measurement and evaluation method for the metering performance of a current transformer under operating conditions according to claim 1, characterized in that, The membership function is constructed based on the requirements of CT Level 4 assessment. A mutual inductor score of 95 or above is considered excellent, and a score of 50 or below is considered abnormal. The specific membership function is as follows: In the formula, A corresponds to excellent, B corresponds to good, C corresponds to qualified, D corresponds to abnormal, and x is the final score of the current transformer.

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