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

Abstract

The invention belongs to the technical field of mutual inductor metering error identification, and discloses a CVT error online monitoring method and system based on the maximum approximation coefficient growth rate. The method comprises the following steps: measuring three-phase voltage data of a secondary side of a capacitor voltage transformer (CVT), and classifying the three-phase voltage data according to a preset requirement to obtain training data; calculating a maximum approximation coefficient growth rate (MACGR) of the training data based on a wavelet analysis method; taking the phase with the highest mean value of the MACGR as an error overrun check phase; calculating the error overrun of the error overrun check phase based on a kernel density algorithm; fitting a function relationship between the error overrun and the MACGR to obtain a fitting result; and evaluating the fitting result according to a preset evaluation index, and monitoring the error of the CVT on line according to the evaluation result. According to the invention, the secondary side voltage of the CVT can be smoothly collected, and the error overrun of the CVT can be identified.

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 approximate coefficient growth rate. Background Art

[0002] Capacitive voltage transformer (CVT) converts high voltage into low voltage through capacitor voltage division, and then transforms it through the intermediate transformer to provide it for metering, relay protection, automatic control, signal indication, etc. CVT has the advantages of low cost, high reliability, small size and simple installation. Therefore, CVT has been widely used in high-voltage power grids.

[0003] Compared with the traditional electromagnetic voltage transformer, the CVT structure is more complicated. Capacitor voltage dividers and compensating reactors are added to the CVT structure, so it is not as stable as the traditional electromagnetic voltage transformer and is prone to faults such as measurement error drift. Among them, the ratio difference drift of CVT is the most common fault of CVT after long-term operation. It is stipulated that the ratio difference of CVT shall not exceed ±0.2% when it is in normal working state, and the CVT needs to be replaced when it exceeds ±0.2%. The ratio difference drift of CVT is not conducive to the fairness of electricity trading. Therefore, fault identification of the ratio difference drift of CVT is an issue that many scholars are studying.

[0004] The most commonly used detection technology in power grids is to use standard CVT equipment for regular offline testing. However, since the test is generally conducted once every few years and has the disadvantages of being time-consuming and costly, regular offline testing is not conducive to timely detection of CVT ratio drift. At the same time, some studies have shown that there is a significant difference between the actual measurement error of CVT and the calibration result.

[0005] The current CVT error over-limit identification methods are divided into model-based methods, data-driven methods, and statistical indicator methods. The model method identifies the measurement error of CVT by establishing an accurate model for CVT. The advantage of this method is that it can obtain accurate errors. However, it is difficult to calculate accurate CVT parameters during the aging process of CVT. Therefore, these models show great uncertainty and cannot obtain accurate measurement errors. Regarding the data-driven method, an improved long short-term memory neural network (LSTM) is used for CVT measurement error prediction, and a Markov model is used to evaluate the reliability of CVT. The CVT is divided into a three-state model of healthy state, low-quality state, and failure state. The gate recursive unit is used as a deep learning framework to predict the measurement error of its CVT. The data-driven method has the advantage of high accuracy, but it requires a large amount of training data and label data for training, and lacks practicality. Regarding the statistical indicator method, some scholars have proposed a method of using the PCA method to calculate the Q value of the secondary side voltage as an indicator for evaluating the error state of CVT. This method has achieved good results in CVT error monitoring, but has the disadvantage of being highly dependent on training data. Summary of the invention

[0006] In view of 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 a maximum approximation coefficient growth rate is provided, comprising:

[0008] Measuring 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;

[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 value is used as the error exceeding limit inspection 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 and 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 through 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, and 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 inspection phase.

[0019] Optionally, 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.

[0020] Optional, preset evaluation indicators 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 approximate coefficient growth rate, comprising:

[0022] A data acquisition unit, 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 acquire training data;

[0023] A fitting unit is used to calculate the maximum approximate coefficient growth rate MACGR of the training data based on a wavelet analysis method, take the phase with the highest MACGR mean as an error exceeding limit inspection phase, calculate the error exceeding limit of the error exceeding limit inspection phase based on a kernel density algorithm, fit the functional relationship between the error exceeding limit and MACGR, and 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 through 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, and 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 inspection phase.

[0030] Optionally, the system also 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 the error limit to verify the online monitoring accuracy.

[0031] Optional, preset evaluation indicators 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 yet 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; taking 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 through the evaluation result. Therefore, compared with the traditional monitoring method, the present invention provides a new online monitoring method, which has the following advantages: the present invention is based on wavelet transform, and analyzes the localization of time and frequency of the signal. It gradually refines the signal at multiple scales through telescoping and translation operations, and decomposes 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, and realize the effect of stripping off the random errors in the signal, which plays a more refined and higher-precision online monitoring of CVT errors, and has good real-time performance, and does not have the 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 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 A schematic diagram of specific parameters of each layer decomposition of a 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 This is a schematic diagram of the MACGR and over-limit threshold values ​​of each phase of normal operating data of Case 2 of the method of the present invention;

[0044] Figure 7 This is a schematic diagram of the MACGR of each phase during the process in which the difference in phase value gradually increases in Case 2A of the method of the present invention;

[0045] Figure 8 This is a schematic diagram of the difference in relative value and the MACGR fitting results of Case 2A of the method of the present invention;

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

[0047] Fig.10 This is a voltage amplitude diagram after the error is gradually added to phase A of case 3 of the method of the present invention;

[0048] Fig.11 This is a schematic diagram of the calculation result 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] Fig.12 This is a schematic diagram of the three-phase MACGR change of the CVT of the substation in case 3 of the method of the present invention;

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

[0051] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0052] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the 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] Embodiment 1:

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

[0055] Step 1, 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;

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

[0057] Step 3: taking the phase with the highest MACGR mean as the error exceeding 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 with a preset evaluation index, and monitor the error of CVT online through the evaluation result.

[0061] Among them, the three-phase voltage on the secondary side of the CVT is measured by a 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 exceeding 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, and 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 inspection phase.

[0066] 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 the 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 is further described below with reference to specific cases:

[0069] Case 1:

[0070] Implementation process such as Figure 2 As 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. It 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 the ratio difference threshold, for example, set the ratio difference threshold greater than or equal to 0.2%, the ratio difference of the training data needs to be less than or equal to 0.2%, that is, the data 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, and wavelet analysis is performed to calculate the maximum approximate coefficient growth rate MACGR of the training data.

[0073] S3, after multi-layer wavelet decomposition, approximate coefficients and detail coefficients are formed. 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 of the normal state is calculated as a difference evaluation index to determine the phase in which the error may exceed the limit among the three phases of the CVT.

[0074] S4, the phase with the highest MACGR mean value among the three phases is used as the error over-limit inspection phase. The error over-limit fault identification experiment is carried out for this phase. The threshold of the error over-limit is calculated using the kernel density statistical method to determine whether an error over-limit fault occurs.

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

[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, that is, 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 “FPGA-based CVT secondary voltage monitoring system” in step S1 is as follows:

[0079] The core circuit 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. The three modules are powered by power modules. A maximum of four signal conversion modules can be connected in parallel in the system to collect 24 sets of CVT voltage signals.

[0080] The voltage signal acquisition module uses TV54A71 CVT, which can collect the secondary signals of 6 CVTs at the same time. Each system can include up to four voltage signal acquisition modules, and a total of 24 voltage transformer secondary signals can be connected. The voltage signal acquisition module uses a voltage transformer with a ratio error of less than or equal to 0.02%. The voltage transformer collects the secondary signal of the substation high-voltage transformer and connects it 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 ZU3EG chip from XILINX as the control core. The chip integrates the quad-core ARM Cortex-A53 and FPGA programmable logic using Processing System (PS) + Programmable Logic (PL) technology to realize the analysis function of CVT signal data.

[0083] The power module uses a Goldensun 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 approximate signals and detail signals through wavelet transformation, and the basic characteristics of the voltage signal are mined. 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 on is as follows:

[0089]

[0090] In the formula, 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 This breaks down as follows:

[0091]

[0092] By decomposing, a series of approximate coefficients and detail coefficients can be formed. After decomposing the original signal in 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 calculated maximum approximation coefficient of the test data, and MAC0 represents the maximum approximation coefficient of the normal state.

[0095] In the process of wavelet analysis of CVT secondary side acquisition signal and calculation of approximate coefficient, since the detail coefficient reflects the random error, discarding the detail coefficient can remove the random error in the acquisition signal. The approximate coefficient is the mapping between the real signal and the inherent error of the secondary side of the power grid. The online error estimation of CVT is to calculate the inherent error of the equipment. Therefore, the real signal of the secondary side of the voltage can be shielded by calculating the growth rate of the approximate coefficient, and the change of the inherent error of CVT can 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 "using wavelet analysis to calculate the maximum approximate coefficient growth rate MACGR of training data" in step S2 is as follows:

[0098] For example, the 120 data of each sample of 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 of the sample at the initial moment is calculated. The 120 data of each sample point are decomposed into 5 layers to form an approximate signal and 5 detail signals, such as Figure 3 Take a random sample from the sample set as an example, and display its original signal and the approximate coefficients of each layer in a graphical way, as shown in Figure 4 shown.

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

[0100] Assume that the calculated MACGR is represented by 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] Among them, h is the bandwidth and K is the kernel function. The value of h has a great influence on the accuracy of the result. The optimal value of the bandwidth 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 over-limit fault identification scheme of the present invention is verified;

[0108] Based on the CVT online control system SWCVT-3 of China Electric Power Research Institute, the A-phase error over-limit test of CVT is carried out. The system includes HEW99 full-function transformer tester and six voltage transformers. The output capacity of each phase of this system is 10KVA, three-phase balance, and the angle between the two phases is 120 degrees. The equipment can set the ratio difference of the secondary side of the self-contained CVT and obtain the secondary side voltage data of the corresponding ratio difference.

[0109] In the experiment, the ratio difference of phase A is set to increase gradually, while the ratio difference of phase B and phase C fluctuates within the normal range. The primary side rated voltage and secondary side rated voltage of CVT in the system are 110kV and 45.7V respectively, and the three-phase signal frequency is 50HZ.

[0110] The ratio difference of phase A voltage is gradually adjusted from -0.028% to 0.627%, and phase B and phase C fluctuate between -0.028% and -0.025%. Due to the adjustment limit of the online control system, a maximum of 36 samples with different ratio differences can be collected in this interval. Each sample collects 1 data point per second for a total of 2 minutes, so each sample point includes 120 data points. A total of 4320 data points were collected in this experiment. The ratio differences of the 36 samples collected in the experiment 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 China Electric Power Research Institute is as follows: Figure 5 As shown in the figure, it can be observed from the experimental data that the increase of the ratio difference of phase A will lead to the increase of the voltage amplitude of phase A. Since the ratio difference of phase B voltage and phase C voltage is within the normal range, their voltage amplitudes have also been 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. The red line is the ratio difference threshold calculated using the kernel density estimation method. It can be seen that although the MACGR of phase A exceeds the threshold of 0.2%, it has shown an upward trend. Since phases B and C have been in normal working conditions, the MACGR remains stable. The average MACGR of phase A is 0.0014, and the MACGRs of phases B and C are 8.078140132264424e-05 and 2.514559967279445e-04, respectively. Therefore, since the MACGR of phase A is much greater than the MACGR of phases B and C, phase A is selected as the error limit inspection phase.

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

[0116] Figure 8 The figure is the fitting result 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 value and the measured value are 0.9854057883659882 and 0.00024453788837413437 respectively. The above indicators can show that the fitting effect between the fitted value and the measured value of the ratio difference is good.

[0120] Case 3:

[0121] Based on the substation CVT field data, the CVT error over-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, a single CVT in a substation in Xuzhou, State Grid Corporation of China is used to collect data for verification. The FPGA-based monitoring system collected 200 days of CVT monitoring data in 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 Fig. 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, phases B and C are normal. The voltage data of phase A after adding the error is as follows: Fig.10 shown.

[0124] During the test, the wavelet transform method was used to calculate the MACGR for the daily collected data. For the experimental data of the first 50 days, a method combining kernel density estimation 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 three-phase indicator threshold determination results established by the experimental data of the first 50 days are as follows: Fig.11 As shown in the figure, the average MACGR of phase A is 0.002138081906598354, and 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 phase A is selected as the error limit check phase.

[0125] Fig.12 The results of the full-process indicator statistics of the maximum approximate coefficient growth rate MACGR of the three-phase voltage are shown in the figure. It can be observed from the result graph that with the increase of the error, the MACGR of phase A shows an increasing trend and stably exceeds the ratio difference threshold of 0.2% in about 70 days. The MACGR of phases B and C has always been in a relatively stable state. Therefore, the method proposed in the present invention can meet the online estimation accuracy requirement of level 0.2.

[0126] The present invention designs an FPGA-based online monitoring system, which is composed of a voltage signal conversion module, a data acquisition module, and a data analysis module. The designed voltage signal conversion module can simultaneously collect the secondary signals of 6 CVTs. The data acquisition module uses the analog-to-digital conversion chip AD7779. The data analysis module uses the ZU3EG chip of XILINX as the control core, which integrates the quad-core ARMCortex-A53 and FPGA programmable logic devices using Processing System (PS) + Programmable Logic (PL) technology.

[0127] Wavelet analysis is performed on the CVT secondary voltage measured online by the FPGA-based monitoring device. Approximate coefficients and detail coefficients are formed after multi-layer wavelet decomposition. The maximum approximate coefficient is selected from the approximate coefficients, and the growth rate of the maximum approximate coefficient of the CVT relative to the maximum approximate coefficient of the normal state is calculated as a statistical indicator to determine the phase in which the error may exceed the limit among the three phases of the CVT, and the kernel density statistical method is used to calculate the threshold of the error exceeding the limit to determine whether an error exceeding the limit fault occurs.

[0128] The SWCVT-3 CVT online test system of the China Electric Power Research Institute was used to collect data on the gradually increasing ratio difference, and the proposed MACGR method was tested. In the test, the maximum approximation coefficient growth rate method proposed by the present invention can accurately identify a ratio difference of 0.2%. Subsequently, the FPGA-based monitoring system and the MACGR method were tested using the actual measured data of the CVT secondary side of a substation in Xuzhou, State Grid. In the test, the FPGA-based monitoring system designed by the present invention can smoothly collect the CVT secondary side voltage and identify the CVT error exceeding the limit.

[0129] Embodiment 2:

[0130] The present invention also proposes a CVT error online monitoring system 200 based on the maximum approximate coefficient growth rate, such as Fig.13 As 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 acquire training data;

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

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

[0134] Among them, the three-phase voltage on the secondary side of the CVT is measured by a 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 exceeding 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, and 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 inspection phase.

[0139] The system further includes a test 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 over-limit error of the CVT.

[0142] Embodiment 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 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, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0144] Embodiment 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 is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and 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 appreciated by those skilled in the art that 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. Moreover, 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 codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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 can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

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 of the secondary side of the 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 value is used as the error exceeding limit inspection 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 and 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 through 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, characterized in that The step of taking the phase with the highest MACGR mean 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, and 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 inspection phase.

5. The method according to claim 1, characterized in that 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, 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 acquire training data; A fitting unit is used to calculate the maximum approximate coefficient growth rate MACGR of the training data based on a wavelet analysis method, take the phase with the highest MACGR mean as an error exceeding limit inspection phase, calculate the error exceeding limit of the error exceeding limit inspection phase based on a kernel density algorithm, fit the functional relationship between the error exceeding limit and MACGR, and 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 through 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, characterized in that The training data is less than or equal to a ratio difference threshold.

10. The system according to claim 7, characterized in that The step of taking the phase with the highest MACGR mean 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, and 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 inspection phase.

11. The system according to claim 7, characterized in that 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.

12. The system according to claim 7, characterized in that 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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