Circuit breaker arc extinguish chamber impurity judgment method based on operation wavelength fractal characteristic analysis

Abstract

The invention provides a circuit breaker arc extinguish chamber impurity judgment method based on operation wavelength fractal feature analysis, and the method comprises the following steps: collecting voltage and current waveform data during the operation of a converter station circuit breaker through an oscillograph close to the circuit breaker, and carrying out the preprocessing; performing multi-layer decomposition and reconstruction on the signal waveform by adopting a wavelet packet analysis method to obtain an energy spectrum distribution condition of the signal; comparing signals of different switching processes before and after to obtain a characteristic frequency band; establishing the correlation between the characteristic frequency band energy and the impurity accumulation degree of the arc extinguish chamber by comparing and analyzing the change relation of the characteristic frequency band energy along with the switching times; and according to the change trend of the characteristic frequency band energy, the operation state of the circuit breaker is judged, and early warning is performed on the circuit breaker with relatively high impurity content. The problem that the health state of the arc extinguish chamber is difficult to accurately assess in the prior art is solved, the health state of the arc extinguish chamber can be effectively monitored, and the stability and safety of a power system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of high voltage direct current transmission system maintenance, and in particular to a method for judging impurities in an arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves. Background Art

[0002] In high-voltage direct current transmission projects, when the DC transmission power changes, the AC filter needs to be frequently switched on and off according to the real-time dynamic requirements of the system for reactive power and filtering. The instant the AC filter is switched on, high-frequency, high-amplitude overvoltage and high current will be generated, causing very serious impacts on the AC bus, internal components of the AC filter and lightning arresters. There is even a certain probability that commutation failure will occur, resulting in the DC system being locked. 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 reactive compensation equipment such as capacitor banks and reactors, thereby maintaining system voltage stability and reducing line losses.

[0003] The main differences between AC filter circuit breakers and ordinary circuit breakers are as follows: AC filter circuit breakers need to cut off capacitive loads and have higher requirements for transient recovery voltage; they are frequently switched on and off and have relatively harsh working conditions; the erosion of contacts by closing surge current may produce metal particles; and they need to withstand mixed AC and DC voltages. Since there is a voltage-equalizing capacitor between the two breaks of the circuit breaker, its AC voltage is distributed according to the capacitance of the two breaks, and its unevenness comes from the allowable manufacturing deviation of the capacitor; for DC voltage distribution, due to the leakage current of the contaminated insulation jacket, if the DC resistance is unevenly distributed on the insulation jacket, it will lead to uneven distribution of DC voltage between the two breaks, resulting in a single break with excessive voltage and the risk of severe breakdown. Therefore, the arc extinguishing chamber of the AC filter circuit breaker is more susceptible to ablation than other circuit breakers.

[0004] Based on a comprehensive analysis of the operating characteristics of AC filter circuit breakers and multiple accidents, one of the causes of the accident is the presence of impurities in the arc extinguishing chamber. The impurities will be suspended around the switch due to the frequent switching of the switch and the action of the electric field in the cavity, which will affect the electromagnetic field distribution near the switch, the voltage waveform, etc., and thus affect the power quality of the power system.

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

[0006] The main purpose of the present invention is to provide a method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves, so as to solve the problem that the existing method for detecting impurities in the arc extinguishing chamber of a circuit breaker of reactive compensation equipment affects the safety of the working environment and the stable operation level of electrical equipment.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows: a method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves, comprising the following steps:

[0008] Step 1: Collect voltage and current waveform data during converter station circuit breaker operation and perform data preprocessing;

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

[0010] Step 3: Perform step 2 processing on the waveform data obtained under different switching times of the same circuit breaker, obtain the frequency band with the most obvious energy change by comparison, and use it as the characteristic frequency band to analyze the relationship between the energy of the characteristic frequency band and the switching times, perform correlation analysis and data fitting on the energy of the characteristic frequency band and the switching times of the circuit breaker, and obtain the functional relationship between the energy of the characteristic frequency band and the switching times;

[0011] Step 4: Using the switching times as a medium, analyze the relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc extinguishing chamber. For the same circuit breaker under different switching times before and after operation, use X-ray detection to obtain the relationship between the switching times of the circuit breaker and the degree of impurity accumulation in the arc extinguishing chamber, and use the switching times as a bridge to analyze the functional relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc extinguishing chamber;

[0012] Step 5: Detect the accumulated impurities in the arc extinguishing chamber of the circuit breaker, determine the operating status of the circuit breaker, locate the circuit breaker with high impurity content and issue an early warning.

[0013] In the preferred embodiment, the energy spectrum distribution of the signal is obtained by wavelet packet analysis in step 2, which specifically includes the following sub-steps:

[0014] Step 2.1: Decompose the original signal into n layers of wavelet packets, with 2 j frequency band signals, extract the 2 j signal characteristics;

[0015] Step 2.2: reconstruct the low-frequency coefficients and high-frequency coefficients of each extracted frequency band signal to form new reconstructed signals S respectively;

[0016] Step 2.3: Calculate the energy E of the signal in each frequency band i,j :Let d represent the decomposition coefficient of wavelet packet, S i,j represents the reconstructed signal, E i,j Indicates S i,j The corresponding energy, d j,k Indicates S i,j The wavelet packet reconstruction coefficients are:

[0017]

[0018] Where i = 0, 1, ..., n represents the number of decomposition levels; j = 0, 1, ..., 2 n Indicates the number of nodes in each layer;

[0019] Step 2.4: Construct feature vector:

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

[0021]

[0022] Among them, E i It is represented as the energy of the i-th layer;

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

[0024]

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

[0026]

[0027] Compared with wavelet analysis, wavelet packet analysis can further decompose the details of each layer after wavelet decomposition, that is, the high-frequency part, thereby realizing the analysis of high-frequency signals, thereby realizing the high-frequency signal analysis of circuit breaker operation wave action.

[0028] In the preferred solution, 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 cycles.

[0029] In the preferred solution, in step 2.1, a signal S is given, and the wavelet packet decomposition is performed for the first time to obtain a high-frequency part and a low-frequency part, and then the low-frequency and high-frequency parts are decomposed half at the same time. After a series of decompositions, the wavelet packet decomposition can be expressed as a combination of a series of low-frequency and high-frequency signals.

[0030] In the preferred solution, in step 2, fractal technology is used to experiment on converter station circuit breakers to obtain the degree of impurity accumulation of each circuit breaker, and X-rays are used to detect the components of the arc extinguishing chamber of the circuit breaker to determine the actual degree of impurity accumulation of the circuit breaker and verify the feasibility of the method.

[0031] In the preferred solution, in step 3, when analyzing the characteristic frequency band, correlation analysis and data fitting are performed on the characteristic frequency band energy and the number of switching times of the circuit breaker to obtain a functional relationship between the characteristic frequency band energy and the number of switching times.

[0032] In the preferred embodiment, in step 4, the same circuit breaker is operated under different switching times before and after, and X-ray detection is used to obtain the relationship between the number of switching times of the circuit breaker and the degree of impurity accumulation in the arc extinguishing chamber, and the functional relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc extinguishing chamber is analyzed using the number of switching times as a bridge.

[0033] In the preferred embodiment, in step 5, the impurity content when the arc extinguishing chamber of the circuit breaker explodes is set as 100% impurity accumulation level, and the impurity accumulation level obtained in step 5 is divided into normal state, attention state, abnormal state and severe state, corresponding to 4 different maintenance types A, B, C, and D.

[0034] The present invention provides a method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves. The method uses fractal technology to extract characteristic quantity description parameters of operating waves from signals in a recorder, and studies the relationship between the degree of impurity accumulation in the arc extinguishing chamber of the circuit breaker and the electromagnetic transient process of the voltage and current operating waves of the circuit breaker at the moment of opening and closing.

[0035] The beneficial effects of the present invention are as follows: the present invention can monitor the accumulation degree of impurities in the arc extinguishing chamber of the circuit breaker in real time through real-time analysis of the operating wave waveform, reduce the number of times of using X-rays for detection, and improve the safety of the working environment and the stable operation level of electrical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0037] Figure 1 This is a flow chart of the circuit breaker operation wave characteristic signal acquisition and detection in the present invention;

[0038] Figure 2 This is a three-layer wavelet packet decomposition of an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Example 1

[0040] like Figures 1-2 As shown, a method for judging impurities in an arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves comprises the following steps:

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

[0042] Step 2: Analyze the waveform obtained in step 1 using fractal technology, specifically wavelet packet analysis, to perform wavelet packet decomposition and reconstruction on the signal waveform to obtain the energy spectrum distribution of the signal;

[0043] Step 3: Perform step 2 processing on the waveform data obtained under different switching times of the same circuit breaker, obtain the frequency band with the most obvious energy change by comparison, and use it as the characteristic frequency band to analyze the change relationship between the energy of the characteristic frequency band and the switching times, perform correlation analysis and data fitting on the energy of the characteristic frequency band and the switching times of the circuit breaker, and obtain the functional relationship between the energy of the characteristic frequency band and the switching times;

[0044] Step 4: Using the switching times as a medium, analyze the relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc extinguishing chamber. For the same circuit breaker under different switching times before and after operation, use X-ray detection to obtain the relationship between the switching times of the circuit breaker and the degree of impurity accumulation in the arc extinguishing chamber, and use the switching times as a bridge to analyze the functional relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc extinguishing chamber;

[0045] Step 5: Detect the accumulated impurities in the arc extinguishing chamber of the circuit breaker, determine the operating status of the circuit breaker, locate the circuit breaker with high impurity content and issue an early warning.

[0046] The present invention collects and pre-processes the voltage and current waveform data of the converter station circuit breaker during operation, decomposes and reconstructs the signal using the wavelet packet analysis method, and extracts the energy spectrum distribution of the signal; by comparing and analyzing the recorder signal under different switching times before and after the same circuit breaker, the characteristic frequency band with the most significant energy change is determined, and the relationship between its energy change and the degree of impurity accumulation in the arc extinguishing chamber is explored; finally, the operating state of the circuit breaker is judged according to the change trend of the characteristic frequency band energy, and the circuit breaker with a high impurity content is warned. This method can effectively monitor the health of the circuit breaker arc extinguishing chamber, discover potential problems in time, and thus improve the stability and safety of the power system.

[0047] In order to study the relationship between the degree of impurity accumulation in the arc extinguishing chamber of the AC filter circuit breaker and the electromagnetic transient process of the voltage and current operation wave of the circuit breaker at the moment of opening and closing, it is necessary to extract the characteristic quantity in the electromagnetic transient process. Considering that the characteristic quantity may contain high-frequency signals, in order to decompose and process the high-frequency and low-frequency parts of the signal at the same time, fractal technology is used for analysis, and wavelet packet analysis is specifically selected as a means of feature quantity extraction. The wavelet packet decomposition and reconstruction of the recorder waveform obtained by field operation are performed to obtain the energy distribution frequency band of the signal, thereby obtaining the energy information of the signal in the corresponding time domain.

[0048] When the number of switching times of the circuit breaker changes, the accumulation degree of impurities in its arc extinguishing 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 operation wave signal under different switching times can be constructed. Taking the difference in the number of switching times as the starting point, the frequency band energy of the circuit breaker operation wave signal obtained under different switching times of the same circuit breaker can be reasonably compared, and the frequency band with the most obvious change can be obtained. This is used as the characteristic frequency band to analyze the relationship between the characteristic frequency band energy and the change of the switching times. And with the switching times as the medium, the relationship between the characteristic frequency band energy and the accumulation degree of impurities in the arc extinguishing chamber is obtained. In this way, the connection between the accumulation degree of impurities in the arc extinguishing chamber of the circuit breaker and the electromagnetic transient process of the voltage and current operation wave of the circuit breaker at the moment of opening and closing is found. By inverting the accumulation degree of impurities in the circuit breaker, the operating state of the circuit breaker is judged, and the circuit breaker with high impurity content is located and an early warning is issued.

[0049] In the preferred embodiment, the energy spectrum distribution of the signal is obtained by wavelet packet analysis in step 2, which specifically includes the following sub-steps:

[0050] Step 2.1: Decompose the original signal into n layers of wavelet packets, with 2 j frequency band signals, extract the 2 j signal characteristics;

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

[0052] Step 2.3: Calculate the energy E of the signal in each frequency band i,j :Let d represent the decomposition coefficient of wavelet packet, S i,j represents the reconstructed signal, E i,j Indicates S i,j The corresponding energy, d j,k Indicates S i,j The wavelet packet reconstruction coefficients are:

[0053]

[0054] Where i = 0, 1, ..., n represents the number of decomposition levels; j = 0, 1, ..., 2 n Indicates the number of nodes in each layer;

[0055] Step 2.4: Construct feature vector:

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

[0057]

[0058] Among them, Ei It is represented as the energy of the i-th layer;

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

[0060]

[0061] The relative wavelet packet energy eigenvector of the selected nth layer is:

[0062]

[0063] Compared with wavelet analysis, wavelet packet analysis can further decompose the details of each layer after wavelet decomposition, that is, the high-frequency part, thereby realizing the analysis of high-frequency signals, thereby realizing the high-frequency signal analysis of circuit breaker operation wave action.

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

[0065] The original signal is decomposed into multiple layers by wavelet packet analysis, and each layer of decomposition produces multiple frequency band signals, thereby extracting the characteristics of the signal. The original signal is decomposed by wavelet packets to obtain signals of different frequency bands; these signals are then 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 of the signal, especially by analyzing the energy changes under different switching times, which can reveal the degree of impurity accumulation in the arc extinguishing chamber, thereby providing an important basis for the health status monitoring of the circuit breaker. In addition, this method can accurately capture subtle changes in the signal, improve the sensitivity and accuracy of 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, multi-resolution analysis is to transform the Hibert space L 2 (R) is decomposed into all subspaces (i.e., W) according to different scale factors. j (j∈Z))) is the orthogonal direct sum. Among them, W j It is the closure of the wavelet function ψ(t), that is, its subspace.

[0068] With the new subspace The scale space V j and the wavelet subspace W j Unify them, so that the wavelet subspace W j Subdivide the frequency in binary form to increase the frequency resolution.

[0069]

[0070] In the formula, j∈Z is satisfied. The above formula can be used to decompose the Hibert space orthogonally. Unified The form is decomposed into:

[0071]

[0072] Furthermore, we define the subspace is the function space U n The closure of (t), the function space U n (t)U again 2n (t) is closed, 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), that is, it satisfies the orthogonal relationship; t represents time, and k represents the smoothing factor.

[0075] Wavelet packet decomposition decomposes the original signal in the form of a filter bank. The low-pass filter obtained by the scaling function decomposes the initial signal into a low-frequency approximation part, and the high-pass filter obtained by the wavelet function decomposes the initial signal into a high-frequency detail part. The scaling function and the wavelet function simultaneously satisfy the following dual-scaling equation:

[0076]

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

[0078] Generalize to n∈Z + When , the equivalent expression of formula (1.3) can be obtained as:

[0079]

[0080] According to the above derivation, the decomposition algorithm of wavelet packet can be obtained as follows:

[0081] Let {u n (t) n∈Z} is about h k The wavelet packet family, but It can be expressed as:

[0082]

[0083] From formula (1.6), we can see that the so-called wavelet packet decomposition is Decompose into and That is beg and

[0084] The specific operations are as follows:

[0085]

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

[0087] Figure 2 It is a three-layer wavelet packet decomposition process. After the first decomposition, the wavelet packet decomposition obtains two parts (high frequency part and low frequency part). When decomposing again, it not only decomposes the low frequency part, but also decomposes the low frequency and high frequency parts at the same time. 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] Among them, A represents the low-frequency part, D represents the high-frequency part, and the serial number at the end represents the number of layers of wavelet packet decomposition.

[0090] 2) Wavelet packet reconstruction analysis

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

[0092] Wavelet packet reconstruction algorithm, that is, and beg The specific solution formula is as follows:

[0093]

[0094] In the formula, h k-2l and g l-2k They all represent the coefficients of the wavelet reconstruction conjugate filter; k is the number of decomposition levels; j and 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, each layer has a corresponding number of frequency bands, and each frequency band contains unique information.

[0097] Through the above decomposition and reconstruction, the energy distribution band of the signal can be obtained, thereby obtaining the energy information of the signal in the corresponding time domain. Based on this, the energy spectrum of the circuit breaker operation wave signal under different switching times is obtained. The frequency band with the most obvious change is extracted as the characteristic frequency band, and the relationship between the characteristic frequency band energy and the switching times is analyzed. And with the switching times as the 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 solution, 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 cycles.

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

[0100] In the preferred solution, in step 2.1, a signal S is given, and the wavelet packet decomposition is performed for the first time to obtain a high-frequency part and a low-frequency part, and then the low-frequency and high-frequency parts are decomposed half at the same time. After a series of decompositions, the wavelet packet decomposition can be expressed as a combination of a series of low-frequency and high-frequency signals.

[0101] The above scheme can carefully analyze the spectral characteristics of the signal and extract the characteristics of the signal in different frequency bands, so as to more accurately monitor the state of the arc extinguishing chamber. Through such multi-level decomposition, subtle changes in the signal can be captured, the sensitivity and accuracy of monitoring can be improved, and the safe and stable operation of the power system can be ensured.

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

[0103] Fractal technology was used to conduct experiments on converter station circuit breakers, and the degree of impurity accumulation of each circuit breaker was obtained by analyzing the signal waveform. In addition, X-ray detection technology was used to detect the composition of the circuit breaker arc extinguishing chamber to determine the actual accumulation degree of impurities, thereby verifying the feasibility of the monitoring method. This method combines signal processing technology and physical detection means, can effectively monitor the health of the arc extinguishing chamber, 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, fractal technology is used to experiment on converter station circuit breakers to obtain the degree of impurity accumulation of each circuit breaker, and X-rays are used to detect the components of the arc extinguishing chamber of the circuit breaker to determine the actual degree of impurity accumulation 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, correlation analysis and data fitting are performed on the characteristic frequency band energy and the number of switching times of the circuit breaker to obtain a functional relationship between the characteristic frequency band energy and the number of switching times.

[0106] In step 3, by performing correlation analysis and data fitting between the characteristic frequency band energy and the number of switching times of the circuit breaker, the functional relationship between the two can be accurately established, which is helpful for quantitatively evaluating the degree of aging or degradation of the circuit breaker under different operating frequencies. It not only provides a dynamic monitoring method for the accumulation of impurities inside the circuit breaker, but also provides a scientific basis for predictive maintenance, so that operation and maintenance personnel can timely discover potential faults according to the changing 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 faults.

[0107] In the preferred embodiment, in step 4, the same circuit breaker is operated under different switching times before and after, and X-ray detection is used to obtain the relationship between the number of switching times of the circuit breaker and the degree of impurity accumulation in the arc extinguishing chamber, and the functional relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc extinguishing chamber is analyzed using the number of switching times as a bridge.

[0108] In step 4, by analyzing the functional relationship between the energy of the characteristic frequency band and the degree of impurity accumulation in the arc extinguishing chamber, a direct connection between the change in the electrical characteristics of the circuit breaker and the degradation of the physical state can be established. Using the number of switching times as an associated variable can not only quantitatively describe the impact of impurity accumulation on circuit breaker performance, but also provide data support for predictive maintenance strategies to ensure that measures are taken before impurities reach a level that affects the safe operation of the equipment, thereby improving the reliability of the circuit breaker and extending 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 bursts is set to 100% impurity accumulation. According to the state maintenance guideline for sulfur hexafluoride high-voltage circuit breakers, the circuit breaker state is divided into normal state, attention state, abnormal state and severe state, corresponding to 4 different maintenance types A, B, C, and D. 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 status

[0111]

[0112] Combining the degree of impurity accumulation with the circuit breaker status maintenance classification can accurately determine the operating status of the circuit breaker based on the impurity content, and clearly mark the normal to severe status. It provides a quantitative evaluation standard, allowing operation and maintenance personnel to implement a graded maintenance strategy based on the specific degree of impurity accumulation, thereby effectively preventing equipment failures caused by arc chamber contamination, ensuring the safe and stable operation of the power system, and optimizing the allocation of maintenance resources to reduce unnecessary maintenance costs.

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

Claims

1. A method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves, characterized in that: The following steps are involved: Step 1: Collect voltage and current waveform data during converter station circuit breaker operation and perform data preprocessing; Step 2: Use fractal technology to process the waveform obtained in step 1. The specific method is wavelet packet analysis. Perform wavelet packet decomposition and reconstruction on the signal waveform to obtain the energy spectrum distribution of the signal. Step 3: Process the waveform data obtained under different switching times of the same circuit breaker in step 2, and obtain the frequency band with the most obvious energy change by comparison, and use it as the characteristic frequency band to analyze the relationship between the energy of the characteristic frequency band and the switching times; Step 4: Using the switching times as a medium, obtain the relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc extinguishing chamber; Step 5: Detect the accumulated impurities in the arc extinguishing chamber of the circuit breaker, determine the operating status of the circuit breaker, locate the circuit breaker with high impurity content and issue an early warning.

2. The method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves according to claim 1, characterized in that: In step 2, the energy spectrum distribution of the signal is obtained by wavelet packet analysis, which specifically includes the following sub-steps: Step 2.1: Decompose the original signal into n layers of wavelet packets, with 2 j frequency band signals, extract the 2 j signal characteristics; Step 2.2: reconstruct the low-frequency coefficients and high-frequency coefficients of each extracted frequency band signal to form new reconstructed signals S respectively; Step 2.3: Calculate the energy E of the signal in each frequency band i,j :Let d represent the decomposition coefficient of wavelet packet, S i,j represents the reconstructed signal, E i,j Indicates S i,j The corresponding energy, d j,k Indicates S i,j The wavelet packet reconstruction coefficients are: Where i = 0, 1, ..., n represents the number of decomposition levels; j = 0, 1, ..., 2 n Indicates the number of nodes in each layer; Step 2.4: Construct feature vector: The wavelet packet decomposition process follows the principle of energy conservation. The energy of each layer is equal to the total energy. The total energy of the signal is defined as: Among them, E i It is represented as 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 eigenvector of the selected nth layer is:

3. The method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves according to claim 2, characterized in that: When wavelet packet decomposition method is used to measure and analyze power grid signals, the measurement window length is set to 10 power frequency cycles.

4. The method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves according to claim 2, characterized in that: In step 2.1, given a signal, the wavelet packet decomposition is first decomposed to obtain the high-frequency part and the low-frequency part, and then the low-frequency and high-frequency parts are decomposed half at the same time. After a series of decompositions, the wavelet packet decomposition is represented as a combination of a series of low-frequency and high-frequency signals.

5. The method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves according to claim 1, characterized in that: In step 2, fractal technology is used to experiment on converter station circuit breakers to obtain the degree of impurity accumulation of each circuit breaker, and X-rays are used to detect the components of the arc extinguishing chamber of the circuit breaker to determine the actual degree of impurity accumulation of the circuit breaker and verify the feasibility of the method.

6. The method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves according to claim 1, characterized in that: In step 3, when analyzing the characteristic frequency band, correlation analysis and data fitting are performed on the characteristic frequency band energy and the number of switching times of the circuit breaker to obtain a functional relationship between the characteristic frequency band energy and the number of switching times.

7. The method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves according to claim 1, characterized in that: In step 4, the same circuit breaker is operated under different switching times before and after, and X-ray detection is used to obtain the relationship between the number of switching times of the circuit breaker and the degree of impurity accumulation in the arc extinguishing chamber, and the functional relationship between the characteristic frequency band energy and the degree of impurity accumulation in the arc extinguishing chamber is analyzed using the number of switching times as a bridge.

8. The method for judging impurities in the arc extinguishing chamber of a circuit breaker based on fractal characteristic analysis of operating waves according to claim 1, characterized in that: In step 5, the impurity content when the arc extinguishing chamber of the circuit breaker bursts 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 severe state, corresponding to 4 different maintenance types A, B, C and D.

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