Method for judging impurities in circuit breaker arc-extinguishing chamber based on operation wave fractal feature analysis

Abstract

The application provides a circuit breaker arc-extinguishing chamber impurity judgment method based on operation wave fractal feature analysis, comprising the following steps: collecting voltage and current waveform data of a converter station circuit breaker during operation by a recorder close to the circuit breaker and performing pretreatment; using a wavelet packet analysis method to perform multilayer decomposition and reconstruction on the signal waveform to obtain energy spectrum distribution of the signal; obtaining a characteristic frequency band by comparing signals before and after different switching processes; establishing a correlation between the characteristic frequency band energy and the accumulation degree of the arc-extinguishing chamber impurity by comparing and analyzing the change relationship of the characteristic frequency band energy with the switching number; and judging the operation state of the circuit breaker and giving a warning for the circuit breaker with high impurity content according to the change trend of the characteristic frequency band energy. The application solves the problem that the existing monitoring technology cannot accurately evaluate the health state of the arc-extinguishing chamber, can effectively monitor the health state of the arc-extinguishing chamber, and improves the stability and safety of the power system.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of high-voltage direct-current transmission system maintenance, in particular to a circuit breaker arc chamber impurity judgment method based on operating wave fractal feature analysis. BACKGROUND

[0002] In a high-voltage direct-current transmission project, when the direct-current transmission power changes, the real-time dynamic system needs to frequently switch the AC filter according to the demand for reactive power and filtering, and high-frequency high-amplitude overvoltage and large current are generated in the moment when the AC filter is put into operation, which causes a very serious impact on the AC bus, internal elements of the AC filter and the lightning arrester, and even a certain probability of commutation failure leading to the locking of the direct-current system. The switching of the AC filter in the converter station is mainly completed by the AC filter circuit breaker. Therefore, the filter circuit breaker plays a key role in the stable switching of the capacitor bank, the electric reactor and other reactive compensation devices, and the maintenance of system voltage stability and the reduction of line loss.

[0003] The main differences between the AC filter circuit breaker and the ordinary circuit breaker are as follows: the AC filter circuit breaker needs to cut off the capacitive load and has a higher requirement for transient recovery voltage; the switching is frequent and the working condition is relatively poor; the ablation of the contact by the closing inrush current may produce metal particles; and the AC filter circuit breaker needs to withstand the mixed AC / DC voltage. Since the two breaks of the circuit breaker have voltage-sharing capacitors, the AC voltage is distributed according to the capacitance of the two breaks, and the non-uniformity comes from the manufacturing deviation of the capacitor; for the DC voltage distribution, if the DC resistance is unevenly distributed on the insulating sleeve, the leakage current of the contaminated insulating sleeve will cause the uneven distribution of the DC voltage between the two breaks, resulting in the risk of single-break breakdown due to the overvoltage of the single break. Therefore, the arc chamber of the AC filter circuit breaker is more susceptible to ablation than other circuit breakers.

[0004] Based on the comprehensive analysis of the operation characteristics of the AC filter circuit breaker and multiple accidents, one of the causes of the accidents is that impurities exist in the arc chamber, which can be suspended around the switch due to the frequent switching of the switch and the electric field in the cavity, and can affect the electromagnetic field distribution, voltage waveform and other aspects near the switch, and further affect the power quality of the power system.

[0005] At present, the detection method for the impurities in the arc chamber of the circuit breaker is limited to X-ray diffraction (XRD) and other technologies. However, the use of X-ray diffraction will produce radiation, which may affect the safety of the working environment and the stable operation level of the electrical equipment. SUMMARY

[0006] The main purpose of the application is to provide a circuit breaker arc chamber impurity judgment method based on operating wave fractal feature analysis, which solves the problem of the existing reactive compensation device circuit breaker arc chamber impurity detection method affecting the safety of the working environment and the stable operation level of the electrical equipment.

[0007] To solve the above technical problems, the technical scheme adopted by the present application is as follows: A circuit breaker arc chamber impurity judgment method based on operation wave fractal feature analysis, comprising the following steps:

[0008] Step 1: Collecting voltage and current waveform data when the circuit breaker of the converter station is operated, and performing data preprocessing;

[0009] Step 2: Using fractal technology to analyze the waveform obtained in step 1, specifically using wavelet packet analysis method, wavelet packet decomposition and reconstruction are performed on the signal waveform to obtain the energy spectrum distribution of the signal;

[0010] Step 3: The waveform data obtained under different switching times of the same circuit breaker is processed in step 2, through comparison, the frequency band with the most obvious energy change is obtained as the characteristic frequency band, and the change relationship between the characteristic frequency band energy and the switching times is analyzed, the correlation analysis and data fitting between the characteristic frequency band energy and the switching times are performed, and the functional relationship between the characteristic frequency band energy and the switching times is obtained;

[0011] Step 4: Using switching times as a medium, the relationship between characteristic frequency band energy and arc chamber impurity accumulation degree is analyzed. For the same circuit breaker under different switching times, the relationship between switching times and arc chamber impurity accumulation degree is obtained by using x-ray detection, and the functional relationship between characteristic frequency band energy and arc chamber impurity accumulation degree is analyzed by using switching times as a bridge;

[0012] Step 5: Detecting the arc chamber impurity accumulation degree of the circuit breaker, judging the running state of the circuit breaker, positioning the circuit breaker with high impurity content and issuing a warning.

[0013] In the preferred scheme, the wavelet packet analysis method in step 2 obtains the energy spectrum distribution of the signal, which specifically includes the following sub-steps:

[0014] Step 2.1: Decomposing the original signal by n layers of wavelet packet, the jth layer has 2 j band signals, and the 2 j signal features of the nth layer are extracted;

[0015] Step 2.2: Reconstructing the low frequency coefficient and the high frequency coefficient of each extracted frequency band signal to form new reconstructed signals S;

[0016] Step 2.3: Calculating the energy E i,j of each frequency band signal: let d represent the decomposition coefficient of the wavelet packet, S i,j represent the reconstructed signal, E i,j represent the energy corresponding to S i,j , and d j,k represent the wavelet packet reconstruction coefficient of S i,j , then:

[0017]

[0018] where i = 0, 1,..., n represents the number of decomposition layers; j = 0, 1,..., 2 n represents the number of nodes of each layer;

[0019] Step 2.4: Constructing a feature vector:

[0020] The wavelet packet decomposition process follows the principle of energy conservation, and the energy of each layer is equal to the total energy. The total energy of the signal is defined as:

[0021]

[0022] where, E i represents the energy of the ith layer;

[0023] The relative wavelet packet energy of a certain frequency band is defined as:

[0024]

[0025] The selected relative wavelet packet energy feature vector of the nth layer is:

[0026]

[0027] Compared with wavelet analysis, wavelet packet analysis can realize the re-decomposition of the detail part of each layer after wavelet decomposition, i.e. the high-frequency part, thereby realizing the analysis of high-frequency signals, and thus realizing the analysis of high-frequency signals of circuit breaker operation fluctuations.

[0028] In the preferred scheme, when measuring and analyzing power grid signals using the wavelet packet decomposition method, the measurement window length is set to 10 power frequency periods.

[0029] In the preferred scheme, in step 2.1, given a signal S, the wavelet packet decomposition is first decomposed into high-frequency and low-frequency parts, and then the low-frequency and high-frequency parts are decomposed again to obtain a half-decomposition. Through a series of decompositions, the wavelet packet decomposition can be represented as a combination of a series of low-frequency and high-frequency signals.

[0030] In the preferred scheme, in step 2, the fractal technology is used to conduct experiments on the circuit breakers of the converter station to obtain the impurity accumulation degree of each circuit breaker, and X-ray is used to detect the composition of the arc chamber of the circuit breaker to determine the actual impurity accumulation degree of the circuit breaker and verify the feasibility of the method.

[0031] In the preferred scheme, in step 3, when analyzing the characteristic frequency band, the characteristic frequency band energy and the number of circuit breaker switching are analyzed for correlation and data fitting to obtain the functional relationship between the characteristic frequency band energy and the number of switching.

[0032] In the preferred scheme, in step 4, the relationship between the switching times of the same circuit breaker and the impurity accumulation degree of the arc extinguishing chamber is obtained by using X-ray detection under the operating conditions of different switching times of the circuit breaker before and after, and the functional relationship between the characteristic band energy and the impurity accumulation degree of the arc extinguishing chamber is analyzed by taking the switching times as a bridge.

[0033] In the preferred scheme, in step 5, the impurity content when the arc extinguishing chamber of the circuit breaker explodes is set as 100% impurity accumulation degree, and the impurity accumulation degree obtained in step 5 is divided into normal state, attention state, abnormal state and serious state, corresponding to four different maintenance types A, B, C and D.

[0034] The application provides a circuit breaker arc extinguishing chamber impurity judgment method based on operating wave fractal feature analysis.

[0035] The application has the beneficial effects that the accumulation degree of the circuit breaker arc extinguishing chamber impurity can be monitored in real time through real-time analysis of the operating wave form, the number of detection by using X-ray is reduced, and the safety of the working environment and the stable operation level of the electrical equipment are improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] The application will be further described below in combination with the drawings and embodiments:

[0037] Fig. 1 It is a circuit breaker operating wave characteristic signal acquisition and detection flowchart of the application;

[0038] Fig. 2 It is a three-layer wavelet packet decomposition of an embodiment of the application. DETAILED DESCRIPTION

[0039] Embodiment 1

[0040] As shown in the drawings, Figs. 1-2 A circuit breaker arc extinguishing chamber impurity judgment method based on operating wave fractal feature analysis comprises the following steps:

[0041] Step 1: Collect the voltage and current waveform data of the circuit breaker during operation of the converter station, and perform data preprocessing;

[0042] Step 2: The waveform obtained in step 1 is analyzed by using fractal technology, specifically wavelet packet analysis method, wavelet packet decomposition and reconstruction are performed on the signal waveform, and the energy spectrum distribution of the signal is obtained;

[0043] Step 3: The waveform data obtained by the same circuit breaker at different switching times is processed in step 2, and by comparison, the frequency band with the most obvious energy change is obtained as the characteristic frequency band, and the relationship between the characteristic frequency band energy and the switching times is analyzed, the correlation analysis and data fitting of the characteristic frequency band energy and the switching times of the circuit breaker are carried out, and the function relationship of the characteristic frequency band energy and the switching times is obtained;

[0044] Step 4: The relationship between the characteristic frequency band energy and the impurity accumulation degree of the arc chamber is analyzed by using the switching times as the medium. The relationship between the switching times of the circuit breaker and the impurity accumulation degree of the arc chamber is obtained by using X-ray detection under the operation conditions of the same circuit breaker at different switching times before and after, and the function relationship between the characteristic frequency band energy and the impurity accumulation degree of the arc chamber is analyzed by using the switching times as the bridge.

[0045] Step 5: The impurity accumulation degree of the circuit breaker arc chamber is detected, the running state of the circuit breaker is judged, and the circuit breaker with high impurity content is positioned and a warning is issued.

[0046] The present application collects the voltage and current waveform data of the circuit breaker operation in the converter station and pre-processes it, uses wavelet packet analysis method to decompose and reconstruct the signal, and extracts the energy spectrum distribution of the signal; by comparing and analyzing the wave recorder signals of the same circuit breaker at different switching times before and after, the characteristic frequency band with the most obvious energy change is determined, and the relationship between the energy change and the impurity accumulation degree of the arc chamber is explored; finally, according to the change trend of the characteristic frequency band energy, the running state of the circuit breaker is judged, and the circuit breaker with high impurity content is warned. This method can effectively monitor the health status of the circuit breaker arc chamber, find potential problems in time, and improve the stability and safety of the power system.

[0047] In order to study the relationship between the impurity accumulation degree of the circuit breaker arc chamber of the AC filter and the electromagnetic transient process of the circuit breaker voltage and current operation wave at the moment of switching on and off, the characteristic quantity in the electromagnetic transient process needs to be extracted. Considering that the characteristic quantity may contain high frequency signals, in order to simultaneously decompose and process the high frequency part and the low frequency part in the signal, fractal technology is used for analysis, and wavelet packet analysis is specifically selected as the means of characteristic quantity extraction. The wave recorder waveform obtained by field operation is decomposed and reconstructed by wavelet packet, and the energy distribution frequency band of the signal is obtained, so as to obtain the energy information of the signal in the corresponding time domain.

[0048] When the switching times of circuit breakers change, the accumulation degree of impurities in the arc chamber will also change, and the frequency band energy of the corresponding system current signal will also change. Based on the above analysis, the energy spectrum of the circuit breaker operating wave signal under different switching times can be constructed. Taking the difference in switching times as the starting point, the frequency band energy of the circuit breaker operating wave signal obtained under different switching times of the same circuit breaker is reasonably compared, and the frequency band with the most obvious change is obtained as the characteristic frequency band. The relationship between the energy of the characteristic frequency band and the switching times is analyzed. Taking the switching times as the medium, the relationship between the energy of the characteristic frequency band and the accumulation degree of impurities in the arc chamber is obtained. The relationship between the accumulation degree of impurities in the arc chamber and the electromagnetic transient process of the voltage and current operating wave of the circuit breaker at the moment of switching is found. The accumulation degree of impurities in the circuit breaker is inversely calculated, the running state of the circuit breaker is judged, and the circuit breaker with high impurity content is positioned and a warning is issued.

[0049] In the preferred scheme, the wavelet packet analysis method in step 2 obtains the energy spectrum distribution of the signal, which specifically includes the following sub-steps:

[0050] Step 2.1: The original signal is decomposed by n layers of wavelet packets, and the jth layer has 2 j band signals, and the 2 j signal features of the nth layer are extracted;

[0051] Step 2.2: The low-frequency coefficients and high-frequency coefficients of each extracted frequency band signal are reconstructed to form new reconstructed signals S;

[0052] Step 2.3: The energy E i,j of each frequency band signal is calculated: i,j where d represents the decomposition coefficients of the wavelet packet, S i,j represents the reconstructed signal, E i,j represents the energy corresponding to S j,k , and d i,j represents the wavelet packet reconstruction coefficients of S n , then:

[0053]

[0054] where i=0,1,...,n represents the number of layers of decomposition; j=0,1,...,2 n represents the number of nodes of each layer;

[0055] Step 2.4: Construct a feature vector:

[0056] The wavelet packet decomposition process follows the principle of energy conservation, and the energy of each layer is equal to the total energy. The total energy of the signal is defined as:

[0057]

[0058] where Ei Energy of the i-th layer is represented as Ei;

[0059] The relative wavelet packet energy of a certain frequency band is defined as:

[0060]

[0061] The selected relative wavelet packet energy feature vector of the n-th layer is:

[0062]

[0063] Compared with wavelet analysis, wavelet packet analysis can realize the re-decomposition of the detail part, i.e. high-frequency part, of each layer after wavelet decomposition, thereby realizing the analysis of high-frequency signals, and thus realizing the analysis of high-frequency signals of the circuit breaker operating fluctuation.

[0064] The fractal method is used to extract the characteristic quantity in the signal. The specific implementation is the wavelet packet analysis method, including wavelet packet decomposition analysis, wavelet packet reconstruction analysis, and feature vector extraction.

[0065] The original signal is decomposed by the wavelet packet analysis method, and each layer of decomposition produces multiple frequency band signals, and then the features of the signal are extracted. The original signal is decomposed by the wavelet packet, and signals of different frequency bands are obtained; then the signals are reconstructed to form new reconstructed signals; the energy of each frequency band signal is calculated, and a feature vector is constructed, which includes the relative energy of each frequency band. The above steps can effectively extract the key features in the signal, especially by analyzing the energy changes under different switching times, the degree of impurity accumulation in the arc chamber can be revealed, thereby providing an important basis for the health state monitoring of the circuit breaker. In addition, this method can accurately capture the subtle changes in the signal, improve the sensitivity and accuracy of the monitoring, and ensure the safe and stable operation of the power system.

[0066] 1) Wavelet packet decomposition analysis

[0067] In the multi-resolution analysis of signals, That is, the multi-resolution analysis is to decompose the Hibert space L 2 (R) into the orthogonal direct sum of all subspaces (i.e. W j (j∈Z))) according to different scale factors. Among them, W j is the closure of the wavelet function ψ(t), that is, its subspace.

[0068] The new subspace V is used to unify the scale space V j and the wavelet subspace W j , so that the wavelet subspace W j can be subdivided in binary form according to the frequency, thereby increasing the resolution of the frequency. Let:

[0069]

[0070] In the formula, j∈Z, the orthogonal decomposition of the Hibert space can be achieved through the above formula. Unified The formal decomposition is as follows:

[0071]

[0072] Furthermore, define subspaces It is the function space U n The closure of (t), the function space U n (t) is U again 2n The closure of (t), and U n The two-scale equation satisfied by (t) is as follows:

[0073]

[0074] In the formula, g(k) = (-1) k h(1-k) means that the orthogonality is satisfied; t represents time and k represents the smoothing factor.

[0075] Wavelet packet decomposition decomposes the original signal using a filter bank. A low-pass filter obtained through the scaling function decomposes the initial signal into a low-frequency approximation, while a high-pass filter obtained through the wavelet function decomposes the initial signal into high-frequency detail components. Furthermore, both the scaling function and the wavelet function satisfy the following dual-scaling equation:

[0076]

[0077] In the formula, Ψ(t) is the scaling function, h(k) is the wavelet function, g(k) is the coefficient of the high-pass filter, and g(k) is the coefficient of the low-pass filter.

[0078] Generalization to n∈Z + Then, the equivalent expression of equation (1.3) can be obtained as:

[0079]

[0080] Based on the above derivation, the wavelet packet decomposition algorithm can be obtained as follows:

[0081] Let {u n (t) n∈Z} is about h k The family of small wave packets, at the same time but It can be represented as:

[0082]

[0083] From formula (1.6), the so-called wavelet packet decomposition is decomposed into and namely sought and

[0084] The specific operation is as follows:

[0085]

[0086] In the formula, a k-2l and b k-2l represent the coefficients of the conjugate filter of wavelet decomposition; j, n are wavelet packet nodes.

[0087] Fig. 2 It is a three-layer wavelet packet decomposition process. After the first decomposition of the wavelet packet, two parts (high frequency part and low frequency part) are obtained. When decomposed again, not only the low frequency part is decomposed, but also the low frequency and high frequency parts are simultaneously decomposed. In this way, the wavelet packet decomposition obtains two sequences each time. The decomposition relationship can be expressed by the following formula:

[0088] S = AAA3 + DAA3 + ADA3 + DDA3 + AAD3 + DAD3 + ADD3 + DDD3;

[0089] Wherein, A represents the low frequency part, D represents the high frequency part, and the serial number at the end represents the number of wavelet packet decomposition layers.

[0090] 2) Wavelet packet reconstruction analysis

[0091] After wavelet packet decomposition, the initial signal can be decomposed into a series of sub-bands, and each sub-band is also a decomposition coefficient.

[0092] The wavelet packet reconstruction algorithm is to know and sought The specific solving formula is as follows:

[0093]

[0094] In the formula, h k-2l and g l-2k represent the coefficients of the conjugate filter of wavelet reconstruction; k is the number of decomposition layers; j, n are wavelet packet nodes.

[0095] 3) Feature extraction based on wavelet packet energy method

[0096] Wavelet packet analysis can decompose the signal layer by layer, and each layer has a corresponding frequency band number, and each frequency band contains unique information.

[0097] The energy distribution frequency band of the signal can be obtained through the above decomposition and reconstruction, thus obtaining the energy information of the signal in the corresponding time domain. Based on this, the energy spectrum of the circuit breaker operating wave signal under different switching times is obtained. The frequency band with the most significant changes is extracted as the characteristic frequency band, and the relationship between the characteristic frequency band energy and the number of switching times is analyzed. Furthermore, using the number of switching times as a medium, the relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc-extinguishing chamber is obtained.

[0098] In the preferred scheme, when using the wavelet packet decomposition method to measure and analyze the power grid signal, the measurement window length is set to 10 power frequency cycles.

[0099] This method uses wavelet packet decomposition to analyze power grid signals. By setting the measurement window length to 10 power frequency cycles, it captures key features of the signal, ensuring that the complete signal cycle is captured, improving the accuracy and reliability of the analysis, and thus more accurately monitoring the health status of the arc-extinguishing chamber.

[0100] In the preferred scheme, in step 2.1, given a signal S, the wavelet packet decomposition first decomposes to obtain the high-frequency part and the low-frequency part. The second decomposition simultaneously performs semi-decomposition on both the low-frequency and high-frequency parts. After a series of decompositions, the wavelet packet decomposition can be represented as a combination of a series of low-frequency and high-frequency signals.

[0101] The above-described scheme can meticulously analyze the spectral characteristics of the signal and extract its features in different frequency bands, thereby enabling more accurate monitoring of the arc-extinguishing chamber's status. Through this multi-level decomposition, subtle changes in the signal can be captured, improving monitoring sensitivity and accuracy, and ensuring the safe and stable operation of the power system.

[0102] In the preferred embodiment, in step 2, fractal technology is used to conduct experiments on the circuit breakers of the converter station to obtain the degree of impurity accumulation in each circuit breaker, and X-rays are used to detect the composition of the arc-extinguishing chamber of the circuit breaker to determine the actual degree of impurity accumulation in the circuit breaker and verify the feasibility of the method.

[0103] Fractal technology was used to conduct experiments on the circuit breakers of the converter station, and the degree of impurity accumulation in each circuit breaker was obtained by analyzing the signal waveforms. In addition, X-ray detection technology was used to detect the composition of the arc-extinguishing chambers of the circuit breakers, thereby determining the actual degree of impurity accumulation and verifying the feasibility of the monitoring method. This method combines signal processing technology and physical detection methods, which can effectively monitor the health status of the arc-extinguishing chambers, improve the accuracy and reliability of monitoring, and ensure the safe and stable operation of the power system.

[0104] In the preferred solution, in step 2, the fractal technology is used to experiment on the circuit breakers of the converter station to obtain the impurity accumulation degree of each circuit breaker, and X-ray is used to detect the composition of the arc extinguishing chamber of the circuit breaker to determine the actual impurity accumulation degree of the circuit breaker and verify the feasibility of the method.

[0105] In the preferred solution, in step 3, when analyzing the characteristic frequency band, the correlation analysis and data fitting are performed on the characteristic frequency band energy and the switching times of the circuit breaker to obtain the functional relationship between the characteristic frequency band energy and the switching times.

[0106] In step 3, by performing correlation analysis and data fitting on the characteristic frequency band energy and the switching times of the circuit breaker, the functional relationship between the two can be accurately established, which helps to quantitatively evaluate the aging or degradation of the circuit breaker under different usage frequencies. Not only can it provide a dynamic monitoring method for the impurity accumulation inside the circuit breaker, but also provide a scientific basis for predictive maintenance, so that the potential failure can be discovered in time according to the change trend of the characteristic frequency band energy, thereby effectively improving the reliability and economy of the power system and avoiding large-scale power outages caused by sudden failures.

[0107] In the preferred solution, in step 4, for the same circuit breaker under different switching times, the relationship between the switching times and the impurity accumulation degree of the arc extinguishing chamber is obtained by X-ray detection, and the functional relationship between the characteristic frequency band energy and the impurity accumulation degree of the arc extinguishing chamber is analyzed by using the switching times as a bridge.

[0108] In step 4, by analyzing the functional relationship between the characteristic frequency band energy and the impurity accumulation degree of the arc extinguishing chamber, the direct relationship between the change of the electrical characteristics of the circuit breaker and the physical state degradation can be established. By using the switching times as a correlation variable, not only can the influence of impurity accumulation on the performance of the circuit breaker be quantitatively described, but also data support can be provided for the predictive maintenance strategy to ensure that measures are taken before the impurity reaches a level that affects the safe operation of the equipment, thereby improving the reliability of the circuit breaker and prolonging its service life, reducing unplanned downtime, and ensuring the stable operation of the power system.

[0109] In the preferred solution, in step 5, the impurity content when the arc extinguishing chamber of the circuit breaker explodes is set as 100% impurity accumulation degree. According to the state maintenance guide for sulfur hexafluoride high-voltage circuit breakers, the state of the circuit breaker is divided into normal state, attention state, abnormal state and serious state, corresponding to four different maintenance types A, B, C and D. Therefore, the impurity accumulation degree obtained in step 5 is divided to represent the operating state of the circuit breaker, as shown in Table 1.

[0110] Table 1 Classification of circuit breaker operating state

[0111]

[0112] Combining the impurity accumulation degree with the condition maintenance classification of the circuit breaker can accurately determine the operation state of the circuit breaker according to the impurity content, and clearly indicate the state from normal to serious. A quantitative evaluation standard is provided, so that the operation and maintenance personnel can implement a grading maintenance strategy based on the specific impurity accumulation degree, thereby effectively preventing equipment failure caused by arc extinguishing chamber pollution, ensuring the safe and stable operation of the power system, optimizing the allocation of maintenance resources, and reducing unnecessary maintenance costs.

[0113] The above embodiments are only preferred technical solutions of the present application, and should not be regarded as limitations of the present application. The protection scope of the present application should be based on the technical solutions recited in the claims, including equivalent replacement solutions of the technical features recited in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present application.

Claims

1. A circuit breaker arc chute impurity judgment method based on operating wave fractal feature analysis, characterized by, The method comprises the following steps: Step 1: Collecting voltage and current waveform data when the circuit breaker of the converter station is operated, and performing data preprocessing; Step 2: Processing the waveform obtained in step 1 by using a fractal technology, and the specific method is a wavelet packet analysis method, wavelet packet decomposition and reconstruction are performed on the signal waveform, so that the energy spectrum distribution of the signal is obtained, and the specific steps include the following sub-steps: Step 2.1: n-level wavelet packet decomposition is performed on the original signal, the jth level has 2 j band signals, and 2 j signals are extracted from the nth level Step 2.2: Reconstructing the low-frequency coefficients and the high-frequency coefficients of each frequency band signal extracted to form new reconstructed signals S respectively; Step 2.3: Find the energy E of each band signal i,j Let d denote the decomposition coefficients of the wavelet packet, S i,j denote the reconstructed signal, E i,j denote the energy of S i,j corresponding to d j,k denote the wavelet packet reconstruction coefficients of S i,j , then we have: ; where i = 0, 1,..., n represents the number of decomposition layers; j = 0, 1,..., 2 n represents the number of nodes of each layer; Step 2.4: Constructing a feature vector: The wavelet packet decomposition process follows the principle of energy conservation, and the energy of each layer is equal to the total energy, and the total energy of the signal is defined as: ； where E i represents the energy of the i-th layer; The relative wavelet packet energy of a certain frequency band is defined as: ； The relative wavelet packet energy feature vector of the selected nth layer is: ； Step 3: Processing the waveform data obtained under different switching times of the same circuit breaker by step 2, comparing and obtaining the frequency band with the most obvious energy change, and taking the frequency band as a characteristic frequency band, and analyzing the change relationship between the characteristic frequency band energy and the switching times; Step 4: Taking the switching times as a medium to obtain the relationship between the characteristic frequency band energy and the impurity accumulation degree of the arc chamber; Step 5: Detecting the impurity accumulation degree of the arc chamber of the circuit breaker, judging the operating state of the circuit breaker, positioning the circuit breaker with high impurity content and issuing a warning.

2. The method of claim 1, wherein the method is characterized by: When the wavelet packet decomposition method is used to measure and analyze the power grid signal, the measurement window length is set to 10 power frequency periods.

3. The method of claim 1, wherein the method is characterized by: In step 2.1, a signal is given, the first wavelet packet decomposition obtains a high-frequency part and a low-frequency part, and the second wavelet packet decomposition simultaneously decomposes the low-frequency and high-frequency parts to obtain a half-decomposition, and through a series of decompositions, the wavelet packet decomposition is represented as a combination of a series of low-frequency and high-frequency signals.

4. The method for determining the impurities in the arc chute of a circuit breaker based on the analysis of the operational wave fractal characteristics according to claim 1, characterized in that, In step 2, the fractal technology is used to test the circuit breaker of the converter station, the impurity accumulation degree of each circuit breaker is obtained, the composition of the arc chamber of the circuit breaker is detected by using X-ray, the actual impurity accumulation degree of the circuit breaker is judged, and the feasibility of the method is verified.

5. The method of claim 1, wherein the method is characterized by: In step 3, when analyzing the characteristic frequency band, the characteristic frequency band energy and the switching times of the circuit breaker are analyzed and data fitting is performed, and a functional relationship between the characteristic frequency band energy and the switching times is obtained.

6. The method of claim 1, wherein the method is characterized by: In step 4, under the operating conditions of the same circuit breaker under different switching times before and after, the relationship between the switching times of the circuit breaker and the impurity accumulation degree of the arc chamber is obtained by using X-ray detection, and the functional relationship between the characteristic frequency band energy and the impurity accumulation degree of the arc chamber is analyzed by taking the switching times as a bridge.

7. The method of claim 1, wherein the method is characterized by: In step 5, the impurity content of the arc chamber of the circuit breaker when the arc chamber bursts is set to 100% impurity accumulation degree, and the impurity accumulation degree obtained in step 5 is divided into normal state, attention state, abnormal state and serious state, corresponding to A, B, C and D four different maintenance.

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