CVT error online monitoring method and system based on maximum approximation coefficient growth rate

Abstract

The present invention belongs to the technical field of mutual inductor measurement error identification, and discloses a CVT error online monitoring method and system based on the maximum approximate coefficient growth rate. The method of the present invention comprises: measuring the three-phase voltage data of the secondary side of the capacitor voltage transformer CVT, classifying the three-phase voltage data according to preset requirements, and obtaining training data; calculating the maximum approximate coefficient growth rate MACGR of the training data based on the wavelet analysis method; using the phase with the highest MACGR mean as the error exceeding limit check phase; calculating the error exceeding limit of the error exceeding limit check phase based on the kernel density algorithm; fitting the functional relationship between the error exceeding limit and MACGR to obtain a fitting result; evaluating the fitting result with a preset evaluation index, and monitoring the CVT error online through the evaluation result. The present invention can smoothly collect the CVT secondary side voltage and can identify the error exceeding limit of the CVT.

Description

Technical Field

[0001] The present invention relates to the technical field of mutual inductor measurement error identification, and more specifically, to a CVT error online monitoring method and system based on a maximum approximation coefficient growth rate. Background Art

[0002] Capacitive voltage transformers (CVTs) convert high voltages to low voltages through capacitor voltage division. The voltage is then transformed through an intermediate transformer and supplied to metering, relay protection, automatic control, and signal indication. CVTs are widely used in high-voltage power grids due to their low cost, high reliability, compact size, and simple installation.

[0003] Compared to traditional electromagnetic voltage transformers, CVTs are more complex. The addition of a capacitive voltage divider and compensating reactors makes them less stable than traditional electromagnetic voltage transformers and more susceptible to faults such as measurement error drift. Ratio drift is the most common fault in CVTs after long-term operation. Regulations stipulate that the ratio error of a CVT under normal operating conditions must not exceed ±0.2%. If this exceeds ±0.2%, the CVT must be replaced. CVT ratio drift is detrimental to fair electricity trading. Therefore, fault identification of CVT ratio drift is a topic of considerable research.

[0004] The most commonly used detection technique in power grids is regular offline testing using standard CVT equipment. However, since these tests are typically conducted only once every few years and are time-consuming and costly, regular offline testing is not conducive to timely detection of CVT ratio drift. Furthermore, some studies have shown that actual CVT measurement errors differ significantly from calibration results.

[0005] Current methods for identifying excessive CVT errors include model-based, data-driven, and statistical indicator-based approaches. Model-based approaches identify CVT measurement errors by building precise models of the CVT. This approach offers the advantage of accurately deriving errors. However, during CVT aging, it is difficult to calculate precise CVT parameters. Consequently, these models exhibit significant uncertainty and fail to accurately determine measurement errors. Regarding data-driven approaches, a modified long-short-term memory neural network (LSTM) is used to predict CVT measurement errors, and a Markov model is used to assess CVT reliability, categorizing the CVT into three states: healthy, low-quality, and failed. A gated recursive unit (GRU) is used as a deep learning framework to predict CVT measurement errors. While data-driven approaches offer the advantage of high accuracy, they require a large amount of training data and labeled data, lacking practical applicability. Regarding statistical indicator approaches, researchers have proposed using PCA to calculate the Q value of the secondary voltage as an indicator for assessing CVT error status. This method has achieved good results in CVT error monitoring, but suffers from a high reliance on training data. Summary of the Invention

[0006] In response to the above defects or improvement needs of the prior art, the present invention provides a CVT error online monitoring method and system based on the maximum approximate coefficient growth rate, which aims to improve the accuracy of CVT error online monitoring.

[0007] To achieve the above object, according to a first aspect of the present invention, a CVT error online monitoring method based on maximum approximation coefficient growth rate is provided, comprising:

[0008] Measuring three-phase voltage data on the secondary side of a capacitor voltage transformer (CVT), classifying the three-phase voltage data according to preset requirements, and obtaining training data;

[0009] Calculate the maximum approximate coefficient growth rate MACGR of the training data based on wavelet analysis;

[0010] The phase with the highest MACGR mean is used as the error exceeding limit check phase;

[0011] Calculating the error limit of the error limit check phase based on a kernel density algorithm;

[0012] Fitting the functional relationship between the error limit violation and the MACGR to obtain a fitting result;

[0013] The fitting result is evaluated with a preset evaluation index, and the error of the CVT is monitored online based on the evaluation result.

[0014] Optionally, the three-phase voltage on the secondary side of the CVT is measured by a CVT secondary voltage monitoring device;

[0015] The CVT secondary voltage monitoring device collects the secondary signal quantity of the voltage transformer metering winding in operation through an absolute quantity collection method, and identifies the CVT ratio error exceeding the limit fault.

[0016] Optionally, the training data is less than or equal to a ratio difference threshold.

[0017] Optionally, taking the phase with the highest MACGR mean as the error exceeding limit check phase includes:

[0018] For the approximate coefficients generated after multi-layer wavelet decomposition of the training data, the maximum approximate coefficient is selected, and the growth rate MAGG of the maximum approximate coefficient relative to the maximum approximate coefficient in the normal state is calculated. The MAGG is used as an evaluation index. Based on the evaluation index, the phase with the highest MACGR mean is determined, and the phase with the highest MACGR mean is used as the error limit check phase.

[0019] Optionally, the method further includes: classifying the three-phase voltage data according to preset requirements, obtaining test data, and checking whether the MACGR of the test data exceeds an error limit to verify the online monitoring accuracy.

[0020] Optional, preset evaluation metrics include: correlation coefficient and root mean square error.

[0021] On the other hand, the present invention also proposes a CVT error online monitoring system based on the maximum approximation coefficient growth rate, comprising:

[0022] A data acquisition unit is used to measure the three-phase voltage data of the secondary side of the capacitor voltage transformer (CVT), classify the three-phase voltage data according to preset requirements, and obtain training data;

[0023] a fitting unit, configured to calculate a maximum approximation coefficient growth rate (MACGR) of the training data based on a wavelet analysis method, use a phase with a maximum MACGR mean as an error limit check phase, calculate an error limit of the error limit check phase based on a kernel density algorithm, and fit a functional relationship between the error limit and the MACGR to obtain a fitting result;

[0024] The monitoring unit is used to evaluate the fitting result with a preset evaluation index, and monitor the error of the CVT online based on the evaluation result.

[0025] Optionally, the three-phase voltage on the secondary side of the CVT is measured by a CVT secondary voltage monitoring device;

[0026] The CVT secondary voltage monitoring device collects the secondary signal quantity of the voltage transformer metering winding in operation through an absolute quantity collection method, and identifies the CVT ratio error exceeding the limit fault.

[0027] Optionally, the training data is less than or equal to a ratio difference threshold.

[0028] Optionally, taking the phase with the highest MACGR mean as the error exceeding limit check phase includes:

[0029] For the approximate coefficients generated after multi-layer wavelet decomposition of the training data, the maximum approximate coefficient is selected, and the growth rate MAGG of the maximum approximate coefficient relative to the maximum approximate coefficient in the normal state is calculated. The MAGG is used as an evaluation index. Based on the evaluation index, the phase with the highest MACGR mean is determined, and the phase with the highest MACGR mean is used as the error limit check phase.

[0030] Optionally, the system further includes a testing unit, which is used to classify the three-phase voltage data according to preset requirements, obtain test data, and check whether the MACGR of the test data exceeds an error limit to verify the online monitoring accuracy.

[0031] Optional, preset evaluation metrics include: correlation coefficient and root mean square error.

[0032] In yet another aspect, the present invention further provides a computing device comprising: one or more processors;

[0033] a processor for executing one or more programs;

[0034] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0035] In another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0036] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0037] (1) The present invention provides a CVT error online monitoring method based on maximum approximate coefficient growth rate, comprising: measuring three-phase voltage data on the secondary side of a capacitive voltage transformer (CVT), classifying the three-phase voltage data according to preset requirements, and obtaining training data; calculating the maximum approximate coefficient growth rate (MACGR) of the training data based on a wavelet analysis method; using the phase with the highest MACGR mean as the error overlimit check phase; calculating the error overlimit of the error overlimit check phase based on a kernel density algorithm; fitting the functional relationship between the error overlimit and MACGR to obtain a fitting result; evaluating the fitting result with a preset evaluation index, and monitoring the CVT error online based on the evaluation result. Therefore, compared with traditional monitoring methods, the present invention provides a new online monitoring method, which has the following advantages: the present invention is based on wavelet transform, analyzes the localization of time and frequency of the signal, and gradually refines the signal at multiple scales through telescoping and translation operations, decomposing it into multiple layers of detail coefficients and approximate coefficients. Therefore, the maximum approximate coefficient growth rate MACGR of the training data calculated based on the wavelet analysis method can focus on any detail of the signal, realize the effect of stripping off random errors in the signal, and play a more refined and higher-precision online monitoring of CVT errors, with good real-time performance and no disadvantage of being highly dependent on training data. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the method of the present invention;

[0039] Figure 2 This is a flow chart of Case 1 of the method of the present invention;

[0040] Figure 3 This is a signal decomposition process diagram of Case 1 of the method of the present invention;

[0041] Figure 4 Schematic diagram of specific parameters of each layer decomposition of the signal in Case 1 of the method of the present invention;

[0042] Figure 5 This is a schematic diagram of the three-phase voltage after the ratio difference is gradually added to the A phase of the case 2 of the method of the present invention;

[0043] Figure 6 Schematic diagram of MACGR and over-limit thresholds of each phase of normal operating data of Case 2 of the method of the present invention;

[0044] Figure 7 Schematic diagram of MACGR of each phase during the process of gradually increasing the phase difference of Case 2A of the present invention;

[0045] Figure 8 Schematic diagram of the difference between the values ​​and the MACGR fitting results of Case 2A of the present invention;

[0046] Figure 9This is a schematic diagram of the three-phase voltage amplitudes sampled by the CVT of the substation in Case 3 of the method of the present invention over 200 days;

[0047] Figure 10 This is a voltage amplitude diagram after gradually adding errors to phase A of Case 3 of the method of the present invention;

[0048] Figure 11 This is a schematic diagram of the calculation results of the threshold value of the substation CVT three-phase voltage error exceeding the limit based on wavelet transform in case 3 of the method of the present invention;

[0049] Figure 12 This is a schematic diagram of the three-phase MACGR change of the CVT in the substation of Case 3 of the method of the present invention;

[0050] Figure 13 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0052] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0053] Example 1:

[0054] The present invention proposes a CVT error online monitoring method based on the maximum approximation coefficient growth rate, such as Figure 1 Shown, including:

[0055] Step 1: measuring three-phase voltage data on the secondary side of a capacitor voltage transformer (CVT), classifying the three-phase voltage data according to preset requirements, and obtaining training data;

[0056] Step 2: Calculate the maximum approximate coefficient growth rate MACGR of the training data based on wavelet analysis;

[0057] Step 3: The phase with the highest MACGR mean value is used as the error limit check phase;

[0058] Step 4: Calculate the error limit of the error limit check phase based on a kernel density algorithm;

[0059] Step 5: Fitting the functional relationship between the error limit and MACGR to obtain a fitting result;

[0060] Step 6: Evaluate the fitting result using a preset evaluation index, and monitor the CVT error online based on the evaluation result.

[0061] Among them, the three-phase voltage on the secondary side of the CVT is measured by the CVT secondary voltage monitoring device;

[0062] The CVT secondary voltage monitoring device collects the secondary signal quantity of the voltage transformer metering winding in operation through an absolute quantity collection method, and identifies the CVT ratio error exceeding the limit fault.

[0063] Among them, the training data is less than or equal to the ratio difference threshold.

[0064] The phase with the highest MACGR mean is used as the error limit check phase, including:

[0065] For the approximate coefficients generated after multi-layer wavelet decomposition of the training data, the maximum approximate coefficient is selected, and the growth rate MAGG of the maximum approximate coefficient relative to the maximum approximate coefficient in the normal state is calculated. The MAGG is used as an evaluation index. Based on the evaluation index, the phase with the highest MACGR mean is determined, and the phase with the highest MACGR mean is used as the error limit check phase.

[0066] The method further includes: classifying the three-phase voltage data according to preset requirements, obtaining test data, and checking whether the MACGR of the test data exceeds an error limit to verify the online monitoring accuracy.

[0067] Among them, the preset evaluation indicators include: correlation coefficient and root mean square error.

[0068] The present invention will be further described below with reference to specific cases:

[0069] Case 1:

[0070] Implementation process such as Figure 2 Shown, including:

[0071] S1, CVT secondary voltage monitoring device measures the three-phase voltage on the secondary side of CVT. It is installed in the panel cabinet of the substation control room. It does not require power outage and collects the secondary signal quantity of the voltage transformer metering winding in operation through the absolute quantity acquisition method, and completes the CVT ratio error exceeding limit fault identification in the monitoring system.

[0072] S2: Set a ratio difference threshold, for example, a threshold of 0.2% or greater. The training data must have a ratio difference of 0.2% or less, i.e., data obtained when the three phases are in a normal state. The collected CVT secondary-side three-phase voltage data is divided into training data and test data. Wavelet analysis is performed to calculate the maximum approximate coefficient growth rate (MACGR) for the training data.

[0073] In step S3, after multi-layer wavelet decomposition, approximate coefficients and detail coefficients are generated. The maximum approximate coefficient is selected from the approximate coefficients, and the growth rate (MAGG) of the maximum approximate coefficient of the CVT relative to the maximum approximate coefficient in the normal state is calculated as a difference evaluation index to determine the phase of the CVT's three phases that may have an error exceeding the limit.

[0074] In step S4, the phase with the highest mean MACGR value among the three phases is selected as the phase for error overlimit detection. An error overlimit fault identification experiment is conducted on this phase. A kernel density statistical method is used to calculate the error overlimit threshold to determine whether an error overlimit fault has occurred.

[0075] S5, based on the MACGR training data of the error exceeding limit check phase, the confidence level is set to 99%, and the kernel density estimation method is used to calculate the error exceeding limit, that is, the threshold value of the ratio difference greater than or equal to 0.2%.

[0076] S6, calculate the MACGR of the test data and check whether the MACGR of the test data exceeds the error limit, that is, the ratio difference is greater than or equal to the 0.2% threshold. In other words, check whether the proposed method meets the evaluation accuracy of 0.2 level.

[0077] S7, based on the polynomial fitting method, fits the functional relationship between the ratio difference and the approximate coefficient growth rate. The correlation coefficient and root mean square error are used as indicators to evaluate the fitting results.

[0078] The details of the "FPGA-based CVT secondary voltage monitoring system" in step S1 are as follows:

[0079] The core circuitry of the FPGA-based CVT secondary voltage monitoring system consists of a voltage signal conversion module, a signal conversion module, and a data analysis module. Each of these modules is powered by a power supply module. Up to four signal conversion modules can be connected in parallel to collect 24 sets of CVT voltage signals.

[0080] The voltage signal acquisition module uses the TV54A71 CVT, capable of simultaneously collecting secondary signals from six CVTs. Each system can include up to four voltage signal acquisition modules, allowing access to a total of 24 voltage transformer secondary signals. The voltage signal acquisition module uses voltage transformers with a ratio error of 0.02% or less. These voltage transformers collect the secondary signals from the substation's high-voltage transformers and connect them to the signal conversion module.

[0081] The signal conversion module uses the 24-bit high-resolution synchronous sampling analog-to-digital conversion chip AD7779, which can digitize 24 CVT analog signals at the same time.

[0082] The data analysis module uses the XILINX ZU3EG chip as the control core. This chip uses Processing System (PS) + Programmable Logic (PL) technology to integrate the quad-core ARM Cortex-A53 and FPGA programmable logic to realize the analysis function of CVT signal data.

[0083] The power module uses a Jinshengyang power module, which is used to convert AC 220VAC into DC12V, and is used to provide working voltage for the voltage signal conversion module, data acquisition module and data analysis module.

[0084] The “wavelet analysis and MACGR error evaluation index” in step S2 is as follows:

[0085] The voltage signal monitored by the FPGA-based CVT secondary voltage monitoring system is decomposed into an approximate signal and a detail signal through wavelet transformation, thereby mining the basic characteristics of the voltage signal. Assuming f(x) is the initial signal, the wavelet transformation formula is:

[0086]

[0087] Among them, ψ(x) is the mother wavelet, a, b and c(a, b) are the scaling parameter, translation parameter and wavelet coefficient of the signal respectively.

[0088] Assume {V k} k∈Z For orthogonal multi-resolution analysis, {W k} k∈Z is the associated wavelet space. f(x) in V k The projection onto is as follows:

[0089]

[0090] Where, and They are 2k+1 resolution V k +1 and W k Projection of f(x) onto +1; and Respectively represent the scale coefficient and wavelet coefficient of f(x) with 2k+1 resolution; φ k+1 and ψ k+1 represent the scaling function and wavelet function of 2k+1 resolution respectively. Therefore, c k +1 and d k+1 represent the approximate coefficient and detail coefficient of f(x) at 2k+1 resolution, {V k} k∈Z The breakdown is as follows:

[0091]

[0092] By decomposing the original signal, a series of approximate coefficients and detail coefficients can be formed. After decomposing the original signal into multiple layers, a sequence of approximate coefficients can be obtained. The maximum approximate coefficient is selected from the sequence, and the maximum approximate coefficient growth rate MACGR is calculated using the following formula:

[0093]

[0094] Among them, MAC i represents the maximum approximation coefficient of the calculated test data, and MAC0 represents the maximum approximation coefficient of the normal state.

[0095] When performing wavelet analysis on the CVT secondary-side signal and calculating approximate coefficients, the detail coefficients reflect random errors. Therefore, discarding the detail coefficients can remove random errors from the signal. The approximate coefficients represent a mapping between the true signal and the inherent error on the grid secondary side. Online CVT error estimation aims to calculate the inherent error of the device. Therefore, by calculating the growth rate of the approximate coefficients, the true signal on the secondary side of the voltage can be masked, allowing the variation of the CVT's inherent error to be measured and characterized.

[0096] Therefore, the maximum approximate coefficient growth rate (MACGR) calculated by wavelet analysis is used as a statistical indicator to study whether the CVT has an error exceeding limit fault.

[0097] The process of calculating the maximum approximate coefficient growth rate MACGR of the training data by wavelet analysis in step S2 is as follows:

[0098] For example, the 120 data points of each sample in the training data set are decomposed into multiple layers using the wavelet analysis method, and only the maximum approximation coefficient of each sample is retained. As the ratio difference increases, the growth rate of the maximum approximation coefficient relative to the initial sample is calculated. The 120 data points of each sample point are decomposed into 5 layers to form an approximate signal and 5 detail signals, such as Figure 3 As shown. Taking a random sample from the sample set as an example, its original signal and the approximate coefficients of each layer are displayed graphically, as shown Figure 4 shown.

[0099] The “kernel density statistical method” in step S4 is as follows:

[0100] Assume that the calculated MACGR representation is X, n is the number of features. Since X∈R n, that is, X=[X1,X2,......X n ], and its distribution density is as follows:

[0101]

[0102] Where h is the bandwidth and K is the kernel function. The value of h has a significant impact on the accuracy of the results. The optimal bandwidth value is determined based on the root mean square error combined with the cross-validation method. The probability distribution function of the statistic can be obtained:

[0103]

[0104] P(X i ) is the probability distribution index, and finally the threshold of X is determined under the condition of confidence level α:

[0105] X d =F -1 (α) (7)

[0106] Case 2:

[0107] Based on the data collected on the simulation test platform, the CVT error exceeding limit fault identification scheme of the present invention is verified;

[0108] A phase A error out-of-limit test was conducted on a CVT using the China Electric Power Research Institute's SWCVT-3 online CVT control system. The system includes a HEW99 full-function transformer calibrator and six voltage transformers. The system has a 10 kVA output capacity per phase, is three-phase balanced, and has a phase angle of 120 degrees. The equipment can set the secondary side ratio difference of the CVT and obtain secondary side voltage data corresponding to the ratio difference.

[0109] In the experiment, the ratio difference of phase A was set to gradually increase, while the ratio differences of phases B and C fluctuated within the normal range. The primary and secondary rated voltages of the CVT in the system were 110 kV and 45.7 V, respectively, and the three-phase signal frequency was 50 Hz.

[0110] The voltage ratio difference of phase A was gradually adjusted from -0.028% to 0.627%, while that of phases B and C fluctuated between -0.028% and -0.025%. Due to the adjustment limitations of the online control system, a maximum of 36 samples with different ratio differences could be collected within this range. Each sample was collected once per second for a total of 2 minutes, resulting in 120 data points per sample. This experiment resulted in a total of 4320 data points. The ratio differences of the 36 samples collected are shown in Table 1.

[0111] Table 1 Ratio differences of 36 samples collected in the experiment

[0112] Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 Sample 9 -0.028％ -0.017％ -0.003％ 0.015％ 0.05％ 0.072％ 0.077％ 0.09％ 0.111％ Sample 10 Sample 11 Sample 12 Sample 13 Sample 14 Sample 15 Sample 16 Sample 17 Sample 18 0.132％ 0.158％ 0.179％ 0.191％ 0.0200％ 0.221％ 0.242％ 0.269％ 0.29％ Sample 19 Sample 20 Sample 21 Sample 22 Sample 23 Sample 24 Sample 25 Sample 26 Sample 27 0.302％ 0.311％ 0.334％ 0.349％ 0.381％ 0.403％ 0.413％ 0.426％ 0.446％ Sample 28 Sample 29 Sample 30 Sample 31 Sample 32 Sample 33 Sample 34 Sample 35 Sample 36 0.468％ 0.495％ 0.514％ 0.523％ 0.536％ 0.557％ 0.579％ 0.605％ 0.627％

[0113] The CVT secondary voltage collected by the CVT online control system of the China Electric Power Research Institute is as follows: Figure 5 As shown in the figure, the experimental data show that any increase in the phase A voltage difference will lead to an increase in the voltage amplitude of phase A. Since the voltage ratio difference of phases B and C is within the normal range, their voltage amplitudes remain stable.

[0114] The MACGR of each phase is calculated based on wavelet transform and used as the evaluation index of each phase. The training data is the first 14 samples, a total of 1680 data. The maximum approximate coefficient change rate of each phase training data is as follows: Figure 6 As shown in the figure, the red line represents the ratio difference threshold calculated using kernel density estimation. It can be seen that although the MACGR of phase A exceeds the 0.2% threshold, it has already shown an upward trend. Phases B and C have been operating normally, and their MACGRs remain stable. The average MACGR of phase A is 0.0014, while those of phases B and C are 8.078140132264424e-05 and 2.514559967279445e-04, respectively. Therefore, since phase A's MACGR is significantly greater than those of phases B and C, phase A is selected as the phase for error limit detection.

[0115] The MACGR of all data of phases A, B, and C are as follows: Figure 7 As shown in the figure, the MACGR of phase A shows an increasing trend as the ratio difference increases, and the 0.2% over-limit threshold can be accurately calculated. The MACGR of phases B and C is very stable, with no false alarms. Therefore, the MACGR method proposed in this invention can identify CVT over-limit error faults.

[0116] Figure 8 The figure shows the fitting results of MACGR and ratio difference of phase A in the ratio difference experiment. The horizontal axis represents the ratio difference, and the vertical axis represents MACGR. The expression of the fitting curve is:

[0117] y=-0.00310x^3 + 0.00340x^2 + 0.00939x + 0.00057 (8)

[0118] Where y represents MACGR and x represents the ratio difference.

[0119] The correlation coefficient and RMSE between the fitted values ​​and the measured values ​​are 0.9854057883659882 and 0.00024453788837413437, respectively. The above indicators show that the fitting effect between the fitted values ​​and the measured values ​​of the ratio difference is good.

[0120] Case 3:

[0121] Based on the substation CVT field data, the CVT error exceeding limit fault identification scheme of the present invention is verified;

[0122] In order to further verify the effectiveness of the monitoring system and identification method proposed in this invention, data collected from a single CVT in a substation in Xuzhou, State Grid Corporation of China was used for verification. The FPGA-based monitoring system collected 200 days of CVT monitoring data from the substation. The substation CVT records data once a minute, totaling 1440 data per day. Since the error of the CVT remains relatively constant every day, the first 100 data of each day are taken as experimental data. A total of 20,000 data, such as Figure 9 shown.

[0123] As the CVT ratio difference increases, the phase voltage amplitude increases. Therefore, this experiment artificially increases the error of phase A data for 200 days every day. From the first day to the last day, an additional error of 0.01% to 0.8% is added to the voltage amplitude of the normal signal. Assuming that phase A is out of tolerance, while phases B and C are normal. The voltage data of phase A after adding the error is as follows Figure 10 shown.

[0124] During the test, the MACGR was calculated using the wavelet transform method for the daily collected data. For the experimental data of the first 50 days, a kernel density estimation method was established to determine the threshold with a confidence level of 99%, that is, the over-limit threshold of 0.2%. The online estimation method of measurement error was tested using the test data of all days. The results of the three-phase indicator threshold determination established by the experimental data of the first 50 days are as follows: Figure 11 As shown in the figure, the average MACGR of phase A is 0.002138081906598354, while that of phases B and C is 0.0014830636414601365 and 0.0011783379426657755, respectively. Therefore, phase A is much larger than phases B and C, and is therefore selected as the phase to be checked for excessive errors.

[0125] Figure 12 The following table shows the statistical results of the full-process variation of the three-phase voltage maximum approximation coefficient growth rate (MACGR). The results show that as the error increases, the MACGR of phase A shows an increasing trend and steadily exceeds the 0.2% ratio difference threshold after approximately 70 days. The MACGR of phases B and C, on the other hand, remains relatively stable. Therefore, the proposed method can meet the online estimation accuracy requirement of level 0.2.

[0126] This paper designs an FPGA-based online monitoring system consisting of a voltage signal conversion module, a data acquisition module, and a data analysis module. The voltage signal conversion module can simultaneously acquire secondary signals from six CVTs. The data acquisition module utilizes the AD7779 analog-to-digital converter chip. The data analysis module uses the XILINX ZU3EG chip as its core control. This chip integrates a quad-core ARM Cortex-A53 processor and an FPGA programmable logic device using Processing System (PS) + Programmable Logic (PL) technology.

[0127] Wavelet analysis is performed on the CVT secondary voltage measured online by an FPGA-based monitoring device. Multi-layer wavelet decomposition generates approximate and detail coefficients. The maximum approximate coefficient is selected from these approximate coefficients, and the growth rate of the maximum approximate coefficient relative to the maximum approximate coefficient in the normal state is calculated as a statistical indicator. This is used to identify the phase of the CVT's three phases where an error exceeding the limit may occur. Kernel density statistics are then used to calculate the error exceeding limit threshold to determine whether an error exceeding limit fault has occurred.

[0128] The proposed MACGR method was tested using the SWCVT-3 CVT online test system of the China Electric Power Research Institute to collect data showing gradually increasing ratio differences. The maximum approximation coefficient growth rate method proposed in this paper accurately identified ratio differences as small as 0.2%. Subsequently, the FPGA-based monitoring system and the MACGR method were tested using actual CVT secondary voltage data from a State Grid Xuzhou substation. During the tests, the FPGA-based monitoring system designed in this paper successfully acquired CVT secondary voltage and identified CVT errors exceeding the specified limit.

[0129] Example 2:

[0130] The present invention also proposes a CVT error online monitoring system 200 based on the maximum approximation coefficient growth rate, such as Figure 13 Shown, including:

[0131] The data acquisition unit 201 is used to measure the three-phase voltage data of the secondary side of the capacitor voltage transformer (CVT), classify the three-phase voltage data according to preset requirements, and obtain training data;

[0132] A fitting unit 202 is configured to calculate a maximum approximation coefficient growth rate (MACGR) of the training data based on a wavelet analysis method, use a phase with the highest MACGR mean as an error limit check phase, calculate an error limit of the error limit check phase based on a kernel density algorithm, and fit a functional relationship between the error limit and the MACGR to obtain a fitting result.

[0133] The monitoring unit 203 is used to evaluate the fitting result using a preset evaluation index, and monitor the CVT error online based on the evaluation result.

[0134] Among them, the three-phase voltage on the secondary side of the CVT is measured by the CVT secondary voltage monitoring device;

[0135] The CVT secondary voltage monitoring device collects the secondary signal quantity of the voltage transformer metering winding in operation through an absolute quantity collection method, and identifies the CVT ratio error exceeding the limit fault.

[0136] Among them, the training data is less than or equal to the ratio difference threshold.

[0137] The phase with the highest MACGR mean is used as the error limit check phase, including:

[0138] For the approximate coefficients generated after multi-layer wavelet decomposition of the training data, the maximum approximate coefficient is selected, and the growth rate MAGG of the maximum approximate coefficient relative to the maximum approximate coefficient in the normal state is calculated. The MAGG is used as an evaluation index. Based on the evaluation index, the phase with the highest MACGR mean is determined, and the phase with the highest MACGR mean is used as the error limit check phase.

[0139] The system further includes a testing unit 204, which is used to classify the three-phase voltage data according to preset requirements, obtain test data, and check whether the MACGR of the test data exceeds the error limit to verify the online monitoring accuracy.

[0140] Among them, the preset evaluation indicators include: correlation coefficient and root mean square error.

[0141] The present invention can smoothly collect the secondary side voltage of the CVT and can identify the error exceeding the limit of the CVT.

[0142] Example 3:

[0143] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the method in the above embodiment.

[0144] Example 4:

[0145] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0146] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0147] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0150] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0151] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A CVT error online monitoring method based on the maximum approximation coefficient growth rate, characterized in that: include: Measuring three-phase voltage data on the secondary side of a capacitor voltage transformer (CVT), classifying the three-phase voltage data according to preset requirements, and obtaining training data; Calculate the maximum approximate coefficient growth rate MACGR of the training data based on wavelet analysis; The phase with the highest MACGR mean is used as the error exceeding limit check phase; Calculating the error limit of the error limit check phase based on a kernel density algorithm; Fitting the functional relationship between the error limit violation and the MACGR to obtain a fitting result; The fitting result is evaluated with a preset evaluation index, and the error of the CVT is monitored online based on the evaluation result.

2. The method according to claim 1, characterized in that The three-phase voltage on the secondary side of the CVT is measured by a CVT secondary voltage monitoring device; The CVT secondary voltage monitoring device collects the secondary signal quantity of the voltage transformer metering winding in operation through an absolute quantity collection method, and identifies the CVT ratio error exceeding the limit fault.

3. The method according to claim 1, characterized in that The training data is less than or equal to a ratio difference threshold.

4. The method according to claim 1, wherein The step of taking the phase with the highest MACGR mean value as the error exceeding limit check phase includes: For the approximate coefficients generated after multi-layer wavelet decomposition of the training data, the maximum approximate coefficient is selected, and the growth rate MAGG of the maximum approximate coefficient relative to the maximum approximate coefficient in the normal state is calculated. The MAGG is used as an evaluation index. Based on the evaluation index, the phase with the highest MACGR mean is determined, and the phase with the highest MACGR mean is used as the error limit check phase.

5. The method according to claim 1, wherein The method further includes: classifying the three-phase voltage data according to preset requirements, acquiring test data, and checking whether the MACGR of the test data exceeds an error limit to verify the online monitoring accuracy.

6. The method according to claim 1, characterized in that The preset evaluation indicators include: correlation coefficient and root mean square error.

7. A CVT error online monitoring system based on maximum approximation coefficient growth rate, characterized in that: include: A data acquisition unit is used to measure the three-phase voltage data of the secondary side of the capacitor voltage transformer (CVT), classify the three-phase voltage data according to preset requirements, and obtain training data; a fitting unit, configured to calculate a maximum approximation coefficient growth rate (MACGR) of the training data based on a wavelet analysis method, use a phase with a maximum MACGR mean as an error limit check phase, calculate an error limit of the error limit check phase based on a kernel density algorithm, and fit a functional relationship between the error limit and the MACGR to obtain a fitting result; The monitoring unit is used to evaluate the fitting result with a preset evaluation index, and monitor the error of the CVT online based on the evaluation result.

8. The system according to claim 7, characterized in that The three-phase voltage on the secondary side of the CVT is measured by a CVT secondary voltage monitoring device; The CVT secondary voltage monitoring device collects the secondary signal quantity of the voltage transformer metering winding in operation through an absolute quantity collection method, and identifies the CVT ratio error exceeding the limit fault.

9. The system according to claim 7, wherein: The training data is less than or equal to a ratio difference threshold.

10. The system according to claim 7, wherein: The step of taking the phase with the highest MACGR mean value as the error exceeding limit check phase includes: For the approximate coefficients generated after multi-layer wavelet decomposition of the training data, the maximum approximate coefficient is selected, and the growth rate MAGG of the maximum approximate coefficient relative to the maximum approximate coefficient in the normal state is calculated. The MAGG is used as an evaluation index. Based on the evaluation index, the phase with the highest MACGR mean is determined, and the phase with the highest MACGR mean is used as the error limit check phase.

11. The system according to claim 7, wherein: The system further includes a testing unit configured to classify the three-phase voltage data according to preset requirements, obtain test data, and check whether the MACGR of the test data exceeds an error limit, so as to verify the accuracy of online monitoring.

12. The system according to claim 7, wherein: The preset evaluation indicators include: correlation coefficient and root mean square error.

13. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 6 is implemented.

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