High-precision Detection Method for Circuit Breaker Status Based on Closing and Opening Currents

Abstract

This application relates to the technical field of electrical variable measurement, and specifically relates to a high-precision detection method for the state of a circuit breaker based on closing and opening currents. The method includes: collecting a number of closing and opening current sequences in different states as a training set; for each closing and opening current sequence, obtaining its fundamental frequency sequence, and obtaining the spectral information coefficient of the closing and opening current sequence through the fundamental frequency sequence; dividing all the closing and opening current sequences into four intervals according to the spectral information coefficient, and obtaining the subsequences of the closing and opening current sequences in each interval; obtaining its fault correlation degree through the spectral information coefficient, current and coefficient of variation of the subsequence; constructing attention weights through the fault correlation degree, and combining the logits value as the input to train the model; and completing the detection through the model. This application improves the detection accuracy of the circuit breaker state.

Description

Technical Field

[0001] This application relates to the technical field of electrical variable measurement, and particularly to a high-precision detection method for the state of a circuit breaker based on switching-on and switching-off currents. Background Art

[0002] Switchgear is an important device in the power system, and the reliability of its performance is directly related to the safe operation of the power system. As the core component of the switchgear, the reliability of the switchgear depends to a large extent on the stability of the working state of its circuit breaker. During the operation of the device, the circuit breaker may have equipment failures, which may further cause abnormal switching-on and switching-off of the switchgear, and even serious accidents such as refusal to switch on and refusal to switch off. Therefore, high-precision detection of the circuit breaker state is very important for the operation and maintenance of the switchgear, and is also an important measure to ensure the safe operation of the power system.

[0003] Currently, in the industry, the waveform characteristics of the switching-on and switching-off currents are calculated, and fixed thresholds are set to detect the state of the circuit breaker. Since the fluctuations of the switching-on and switching-off currents of the circuit breaker under different states are different, and the thresholds are set only based on some characteristics of the switching-on and switching-off current data or through the comparison between the switching-on and switching-off current data, the deep information indicating the circuit breaker state cannot be mined from the switching-on and switching-off currents, which affects the detection accuracy of the circuit breaker state. And the published patent No. CN106443433A discloses a circuit breaker state monitoring system and monitoring method based on the switching-on and switching-off coil currents, which identifies the mechanical state of the circuit breaker by calculating the similarity of the current vector sets of the normal operating condition circuit breaker and the monitored circuit breaker current signals. On the one hand, this method simply relies on the similarity between current signals, and it is difficult to further mine the important state information contained in the switching-on and switching-off coil currents during the operation of the circuit breaker, and the detection accuracy for early faults or complex faults is relatively low. On the other hand, the switching-on and switching-off coil currents of the circuit breaker under normal operating conditions will change due to factors such as voltage fluctuations and environmental vibrations. Detecting the circuit breaker state only based on the similarity of the current vector sets of the current signals of the normal operating condition circuit breaker will lead to misjudgment of the circuit breaker state and a decrease in the detection accuracy of the circuit breaker state. Summary of the Invention

[0004] In order to solve the technical problem of relatively low detection accuracy of the circuit breaker, this application provides a high-precision detection method for the state of a circuit breaker based on switching-on and switching-off currents, and the specific technical solutions adopted are as follows:

[0005] This application proposes a high-precision detection method for the state of a circuit breaker based on switching-on and switching-off currents, and this method includes the following steps:

[0006] Collect a number of switching-on and switching-off current sequences in different states as a data set; the different states include normal state and fault state;

[0007] For each closing and opening current sequence, obtain its amplitude sequence; form a fundamental frequency sequence from the amplitude values around the maximum amplitude in the amplitude sequence; obtain the spectral information coefficient of the closing and opening current sequence through the length of the fundamental frequency sequence and all the amplitude values in the amplitude sequence except the fundamental frequency sequence.

[0008] Evenly divide the closing and opening current sequences in the dataset into four intervals according to the spectral information coefficient; divide the closing and opening current sequences in different intervals into different numbers of subsequences according to the order of each interval; for each subsequence, obtain the spectral information coefficient of the subsequence through the length of its fundamental frequency sequence and all the amplitude values in the amplitude sequence except the fundamental frequency sequence; calculate the fault correlation degree of the subsequence according to the spectral information coefficient, current maximum value, and coefficient of variation of the subsequence.

[0009] Set a preset number of neurons for the input layer, hidden layer, and output layer of the neural network; use the ratio of the fault correlation degree of the subsequence to all the fault correlation degrees of the closing and opening current sequence where it is located as the attention weight; combine the logits value of the input layer neuron and its corresponding attention weight as the neuron input to complete model training; input the closing and opening current sequence to be detected into the trained model to achieve high-precision detection.

[0010] In the above solution, the present application sequentially performs frequency-domain and time-domain analysis on the closing and opening current sequences, divides the closing and opening current sequences according to the information content contained in the frequency domain, enhances the role of the closing and opening current subsequences with a higher degree of correlation with the circuit breaker fault in the subsequent training process of the circuit breaker state detection model, and improves the detection accuracy of the circuit breaker state; in addition, the present application constructs a circuit breaker state detection model based on a neural network. Using the fault correlation degree of the closing and opening current subsequences, an attention mechanism is introduced to adjust the training process of the circuit breaker state detection model. On the one hand, the calculation efficiency is improved, and the model training time is shortened; on the other hand, the interference of invalid information in the closing and opening current data is avoided, the learning ability of the circuit breaker fault information contained in the closing and opening current data is enhanced, the performance of the circuit breaker state detection model is improved, and high-precision detection of the circuit breaker state is achieved.

[0011] In one embodiment, the fault states include four typical fault states: low coil voltage, fatigue of the closing spring, looseness of the transmission mechanism, and jamming of the opening tripping device.

[0012] In one embodiment, the method of forming a fundamental frequency sequence from the amplitude values around the maximum amplitude in the amplitude sequence is as follows:

[0013] Select the frequencies and their corresponding amplitude values within a preset range of the maximum amplitude in the amplitude sequence, and sort the amplitude values according to the frequency size to obtain the fundamental frequency sequence.

[0014] In one embodiment, the method for obtaining the spectrum information coefficient by the length of the fundamental frequency sequence and all the amplitude values except the fundamental frequency sequence in the amplitude sequence is as follows:

[0015] Denote the ratio of the sum of all the amplitude values except the fundamental frequency sequence in the amplitude sequence to the sum of all the amplitude values in the amplitude sequence as the first amplitude ratio;

[0016] The spectrum information coefficient is positively correlated with the length of the fundamental frequency sequence and the first amplitude ratio.

[0017] In one embodiment, the method for evenly dividing the switching-on and switching-off current sequences in the dataset into four intervals according to the size of the spectrum information coefficient is as follows:

[0018] Sort all the spectrum information coefficients in the dataset in ascending order to form a spectrum information coefficient sequence, and evenly divide the spectrum information coefficient sequence into four intervals by quartiles, denoted as the first interval, the second interval, the third interval, and the fourth interval in ascending order.

[0019] In one embodiment, the method for dividing the switching-on and switching-off current sequences in different intervals into different numbers of subsequences according to the order of each interval is as follows:

[0020] Divide the switching-on and switching-off current sequences in the first interval into 10 subsequences; divide the switching-on and switching-off current sequences in the second interval into 20 subsequences; divide the switching-on and switching-off current sequences in the third interval into 40 subsequences; divide the switching-on and switching-off current sequences in the fourth interval into 80 subsequences.

[0021] In one embodiment, the fault correlation degree is positively correlated with the spectrum information coefficient of the subsequence, the maximum value of the switching-on and switching-off current data, and the coefficient of variation.

[0022] In one embodiment, the number of neurons in the input layer is the same as the number of switching-on and switching-off current data in each switching-on and switching-off current sequence.

[0023] In one embodiment, the attention weight is the ratio of the fault correlation degree of each subsequence to the sum of the fault correlation degrees of all the subsequences in the corresponding switching-on and switching-off current sequence.

[0024] In one embodiment, the input of the hidden layer is the product of the logits value of the neurons in the input layer and its corresponding attention weight.

[0025] The beneficial effects of this application are as follows:

[0026] This application sequentially performs frequency-domain and time-domain analyses on the switching-on and switching-off current sequences, divides the switching-on and switching-off current sequences according to the amount of information contained in the frequency domain, enhances the role of the switching-on and switching-off current subsequences with a higher degree of association with breaker faults in the subsequent training process of the breaker state detection model, and improves the detection accuracy of the breaker state. In addition, based on a neural network, this application constructs a breaker state detection model. Using the fault correlation degree of the switching-on and switching-off current subsequences, an attention mechanism is introduced to adjust the training process of the breaker state detection model. On the one hand, the calculation efficiency is improved and the model training time is shortened. On the other hand, the interference of invalid information in the switching-on and switching-off current data is avoided, the learning ability of the breaker fault information contained in the switching-on and switching-off current data is enhanced, the performance of the breaker state detection model is improved, and high-precision detection of the breaker state is achieved. Description of the Drawings

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is a flowchart of a method for high-precision detection of breaker state based on switching-on and switching-off currents provided by an embodiment of the present application. Detailed Embodiments

[0029] To further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following will, in conjunction with the drawings and preferred embodiments, detail the specific embodiments, structures, features, and effects of a method for high-precision detection of breaker state based on switching-on and switching-off currents proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0031] An embodiment of a method for high-precision detection of breaker state based on switching-on and switching-off currents:

[0032] The following will specifically describe the specific solution of a method for high-precision detection of breaker state based on switching-on and switching-off currents provided by the present application with reference to the drawings.

[0033] Please refer to Figure 1, which shows a flowchart of a high-precision detection method for the state of a circuit breaker based on switching-on and switching-off currents. The method includes the following steps:

[0034] Step S001, collect a number of switching-on and switching-off current sequences in different states as a data set.

[0035] The switching-on and switching-off current data of the circuit breaker contains rich information. According to the change characteristics of the switching-on and switching-off current data, the operating conditions of the switching-on and switching-off mechanisms of the circuit breaker can be obtained, so as to judge whether there are faults such as low voltage, fatigue of the closing spring, looseness of the transmission mechanism, etc. in the circuit breaker, in order to detect the state of the circuit breaker.

[0036] Directly using the collected switching-on and switching-off current data to judge the fault state of the circuit breaker is highly subjective, and there are deviations in the constructed data set labels, which directly affect the subsequent training of the circuit breaker state detection model.

[0037] To avoid the deviation introduced by the artificial labels of the data set, the present invention constructs the data set through multiple repeated experiments. The specific process is as follows:

[0038] Install the switching-on and switching-off current acquisition device on the switching-on and switching-off operation power line to collect the switching-on and switching-off current data of the circuit breaker. In this embodiment, the test object for constructing the data set is a 35 kV outdoor high-voltage SF6 circuit breaker, and the switching-on and switching-off current acquisition device uses a Hall current sensor. The acquisition frequency of the switching-on and switching-off current data is 10 kHz.

[0039] Collect the switching-on and switching-off current data for a preset time and sort it in chronological order to form a switching-on and switching-off current sequence. In this embodiment, considering the action time of the circuit breaker for switching off and switching on, the acquisition time is set to 200 ms, that is, the number of switching-on and switching-off current data in each switching-on and switching-off current sequence is 2000.

[0040] The present invention constructs the data set by using the simulated fault method, and respectively collects the switching-on and switching-off current sequences in the normal working state and the fault state. In this embodiment, the fault states include four typical fault states: low coil voltage, fatigue of the closing spring, looseness of the transmission mechanism, and jamming of the switching-off tripping device, and the proportion of the collected data is 3:2:2:2:1.

[0041] Specifically, in the experiment, the fault of low coil voltage of the circuit breaker is realized by lowering the coil voltage; the fatigue of the closing spring is realized by loosening the fixing bolt of the closing spring; the looseness of the transmission mechanism is realized by loosening the base bolt; and the jamming of the switching-off lock device is realized by using a card to block the switching-off lock device.

[0042] Adopt the above method to collect the opening and closing current sequences of the circuit breaker in different states, and jointly form a data set sample with the opening and closing current sequences obtained by the acquisition device and their corresponding circuit breaker states for subsequent training of the circuit breaker state detection model. The data set contains N opening and closing current sequences, and N is 1000 in this embodiment.

[0043] So far, the opening and closing current sequences in the normal state and different fault states have been obtained.

[0044] Step S002: For each opening and closing current sequence, obtain its fundamental frequency sequence, and obtain the spectral information coefficient of the opening and closing current sequence through the fundamental frequency sequence.

[0045] The time-varying nature of the opening and closing currents of the circuit breaker in different states results in differences in the distribution positions of the opening and closing current data containing the key information of the circuit breaker state in the same state. Due to the complex time-domain characteristics of the opening and closing current sequences, if they are directly input into the neural network, it will be difficult for the model to effectively extract the key state information, thereby reducing the detection accuracy.

[0046] To capture the circuit breaker state information contained in the opening and closing current data, it is necessary to obtain the correlation degree between each opening and closing current data and the circuit breaker fault, and estimate the circuit breaker fault information contained in different opening and closing current data.

[0047] Take each opening and closing current sequence as the input, and output its amplitude sequence through the FFT algorithm to estimate the circuit breaker state information contained in the opening and closing current sequence.

[0048] The fundamental frequency of the opening and closing current sequence is the main frequency component, corresponding to the maximum amplitude frequency of the amplitude sequence. The fundamental frequency is usually closely related to the working state of the circuit breaker. When a mechanical fault occurs in the circuit breaker, high-frequency oscillations will appear in the opening and closing current sequence, and the high-frequency components deviating from the fundamental frequency in the amplitude sequence can accurately characterize the corresponding fault.

[0049] Select the frequencies within the preset range of the maximum amplitude in the amplitude sequence and their corresponding amplitude values, and sort the amplitude values according to the frequency size to obtain the fundamental frequency sequence. In this embodiment, the preset range size is 3 dB. By screening the fundamental frequency sequence, the influence of noise and irrelevant information can be reduced, and the frequency domain features closely related to the circuit breaker state can be focused on.

[0050] On the one hand, the frequency components around the fundamental frequency in the normal state of the circuit breaker are relatively concentrated. The longer the fundamental frequency sequence, the more dispersed the fundamental frequency distribution, the more state information contained in the switching current sequence, the greater the probability of phenomena such as vibration during the switching operation of the circuit breaker, and the greater the possibility of a fault state, so it is necessary to strengthen its detection. On the other hand, since the fluctuations of the switching current caused by the circuit breaker fault are usually in the high-frequency part of the amplitude sequence, when the proportion of the amplitude energy outside the fundamental frequency sequence in the amplitude sequence is larger, the probability that the corresponding switching current sequence contains the circuit breaker fault state information is greater, and the greater the role in extracting the key information of the circuit breaker state.

[0051] Based on the above analysis, the spectral information coefficient is obtained through the length of the fundamental frequency sequence and all the amplitude values except the fundamental frequency sequence in the amplitude sequence. The ratio of the sum of all the amplitude values except the fundamental frequency sequence in the amplitude sequence to the sum of all the amplitude values in the amplitude sequence is denoted as the first amplitude ratio.

[0052] The spectral information coefficient is positively correlated with the length of the fundamental frequency sequence and the first amplitude ratio.

[0053] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the change directions of the two variables are the same. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large; the specific relationship is determined by the actual application, and this application does not make special restrictions.

[0054] Preferably, in this embodiment, the expression of the spectral information coefficient is:

[0055] , B represents the length of the fundamental frequency sequence; R represents the first amplitude ratio; F represents the spectral information coefficient of the amplitude sequence. The spectral information coefficient reflects the circuit breaker fault state information contained in the corresponding switching current sequence.

[0056] The larger the spectral information coefficient, the more circuit breaker fault information contained in the corresponding switching current sequence, the greater the probability that the circuit breaker is in a fault state, and the greater the role in subsequent detection of the circuit breaker state.

[0057] It should be noted that negative correlation means that when one variable increases, the other variable decreases, and the change directions of the two variables are opposite. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small; the specific relationship can be a ratio relationship, a subtraction relationship, etc., and is determined by the actual application, and this application does not make special restrictions.

[0058] So far, the spectral information coefficient of each switching current sequence has been obtained.

[0059] Step S003: Divide all the switching current sequences into four intervals according to the spectrum information coefficient, and obtain the subsequences of the switching current sequences in each interval; obtain the fault correlation degree through the spectrum information coefficient, current, and coefficient of variation of the subsequences.

[0060] Through the above steps, the spectrum information coefficients of each switching current sequence are obtained. Sort all the spectrum information coefficients in the dataset in ascending order to form a spectrum information coefficient sequence. For the spectrum information coefficient sequence, divide it into four intervals evenly by quartiles, denoted as the first interval, the second interval, the third interval, and the fourth interval in ascending order.

[0061] Since each spectrum information coefficient corresponds to a switching current sequence, that is, all the switching current sequences are divided into four intervals. Based on the different intervals where the switching current sequences are located, the switching current sequences are evenly divided into subsequences with different numbers.

[0062] On the one hand, for the switching current sequences with smaller spectrum information coefficients, longer subsequences are generated by division to avoid the influence of their redundant information on the breaker state detection, that is, the number of subsequences divided from the switching current sequences with smaller spectrum information coefficients is less. On the other hand, the larger the spectrum information coefficient of the switching current sequence, the shorter the divided subsequence, which is convenient for more precisely capturing local information, that is, the number of subsequences divided from the switching current sequences with larger spectrum information coefficients is more.

[0063] Therefore, in this embodiment, the switching current sequences in the first interval are divided into 10 subsequences; the switching current sequences in the second interval are divided into 20 subsequences; the switching current sequences in the third interval are divided into 40 subsequences; the switching current sequences in the fourth interval are divided into 80 subsequences.

[0064] The breaker state information contained in the subsequences at different positions of the switching current sequence is different. When the subsequence is near the inflection point of the switching current data, the internal mechanical mechanism of the breaker is in a moving state, and the change in the coil current inside the breaker is relatively large, and the corresponding switching current data contains more state information. At the same time, when the coil current inside the breaker is large, the internal mechanical mechanism of the breaker is close to the start and stop stages of movement, which is a high-incidence stage of faults, and the switching current data at this point is more likely to characterize the subtle fault state of the breaker. Therefore, the more breaker state information is contained in the corresponding subsequence.

[0065] In addition, considering that the spectral information coefficient reflects the breaker fault state information contained in the corresponding switching current data from the frequency domain perspective of the sequence, the spectral information coefficients of different subsequences are calculated in the same way to characterize the breaker fault state information contained in their switching current data from the perspective of frequency domain analysis, and further reflect the correlation degree between the subsequence and the breaker fault state. That is, the spectral information coefficient of the subsequence is obtained through the length of the fundamental frequency sequence corresponding to the subsequence and the first amplitude ratio.

[0066] Moreover, considering the differences in the magnitudes of the switching current data of different subsequences, the coefficient of variation is used to reflect the fluctuation degree of the subsequence in the time domain, eliminating the influence of the magnitude differences of the switching current data on characterizing the data fluctuation degrees of different subsequences.

[0067] Based on the above analysis, the fault correlation degree of the subsequence is calculated through the spectral information coefficient, the maximum current value, and the coefficient of variation of the subsequence.

[0068] The fault correlation degree is positively correlated with the spectral information coefficient of the subsequence, the maximum value of the switching current data, and the coefficient of variation.

[0069] Preferably, in this embodiment, the expression of the fault correlation degree is:

[0070] , represents the spectral information coefficient of the k-th subsequence in the switching current sequence; represents the maximum value of the switching current data in the k-th subsequence, represents the coefficient of variation of the k-th subsequence, represents the fault correlation degree of the k-th subsequence.

[0071] The fault correlation degree reflects the correlation degree between the information contained in the switching current data within the subsequence and the breaker fault state. On the one hand, the fault correlation degree characterizes the role of the position of the subsequence in detecting the breaker state. The greater the fault correlation degree, the greater the subsequent role in detecting the breaker state. On the other hand, the fault correlation degree reflects the amount of breaker fault information contained in the current subsequence. The greater the fault correlation degree of the subsequence, the more attention needs to be paid to its switching current data in subsequent detections to improve the detection accuracy of the breaker state.

[0072] So far, the fault correlation degrees of all subsequences in the switching current sequence have been obtained.

[0073] Step S004, constructing attention weights through the fault correlation degree and using the logits value as the input to train the model; the detection is completed through the model.

[0074] This application constructs a circuit breaker status detection model based on a neural network. Specifically, in this embodiment, a BP neural network is selected as the detection model.

[0075] There is a lot of redundant closing and opening current data in each data sample. These data points may be noise, background interference, or current data that has no direct correlation with the circuit breaker status. Under normal conditions of the circuit breaker, due to factors such as vibration during equipment operation and voltage changes, there are certain normal fluctuations in the closing and opening current sequences.

[0076] After the closing and opening current sequences are input into the circuit breaker status detection model, the neural network will try to learn all input features, including those that have nothing to do with the circuit breaker status. On the one hand, this leads to an increase in network training time, and the model may overfit irrelevant features, thereby reducing its generalization ability and detection accuracy. On the other hand, the model will overly focus on the fluctuation features under normal conditions of the circuit breaker during the training process, masking the true fault features, reducing the model's ability to extract effective features, and affecting the detection performance of the model.

[0077] To overcome the above defects, this application introduces an attention mechanism into the circuit breaker status detection model, enabling the model to focus on the parts of the input sequence that contain more circuit breaker status information, thereby improving the performance and robustness of the model.

[0078] Specifically, the BP neural network in this application is divided into an input layer, a hidden layer, and an output layer. The number of neurons in the input layer is the same as the number of closing and opening current data in each closing and opening current sequence; then a hidden layer and an output layer with a preset number of neurons are set. In this embodiment, the number of neurons is 2000, the number of neurons in the hidden layer is 512, and the number of neurons in the output layer is 1; the neuron in the output layer represents the two states of normal and faulty of the circuit breaker.

[0079] Considering that the fault correlation degree of each subsequence characterizes the amount of circuit breaker fault information it contains, the larger its value, the greater the role in detecting the circuit breaker status. Therefore, the ratio of the fault correlation degree of each subsequence to the sum of all fault correlation degrees in the closing and opening current sequence is used as the attention weight.

[0080] In this embodiment, the expression of the attention weight is:

[0081] , where K represents the number of subsequences in the closing and opening current sequence; and respectively represent the fault correlation degrees of the i-th and k-th subsequences in the closing and opening current sequence; represents the attention weight of the closing and opening current data in the k-th subsequence of the closing and opening current sequence.

[0082] The core role of the attention mechanism is to dynamically allocate weights to different input features, thereby highlighting important features and suppressing irrelevant features. Subsequences with a high fault correlation degree have a greater impact on detecting the circuit breaker status. Therefore, a higher weight is assigned to them through the attention mechanism to enhance the model's learning ability of the circuit breaker status information and reduce the interference of irrelevant information.

[0083] The product of the logits value of the input layer neuron and its corresponding attention weight is used as the input of the hidden layer neuron, and the BP neural network model is trained based on the constructed dataset according to the error backpropagation algorithm.

[0084] The on-off current sequence to be detected acquired by the acquisition device is input into the trained circuit breaker status detection model, and the corresponding circuit breaker status is output to achieve high-precision detection of the circuit breaker status.

[0085] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

[0086] Each embodiment in this specification is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. The key points of each embodiment are the differences from other embodiments.

Claims

1. A high-precision detection method for the state of a circuit breaker based on the closing and opening currents, characterized in that, The method includes the following steps: Collect a number of switching current sequences in different states as a data set; the different states include normal state and fault state; For each switching current sequence, obtain its amplitude sequence; form a fundamental frequency sequence with the amplitude values around the maximum amplitude in the amplitude sequence; obtain the spectral information coefficient of the switching current sequence through the length of the fundamental frequency sequence and all the amplitude values in the amplitude sequence except the fundamental frequency sequence; Evenly divide the switching current sequences in the data set into four intervals according to the magnitude of the spectral information coefficient; divide the switching current sequences in different intervals into different numbers of subsequences according to the order of each interval; for each subsequence, obtain the spectral information coefficient of the subsequence through the length of its fundamental frequency sequence and all the amplitude values in the amplitude sequence except the fundamental frequency sequence; calculate the fault correlation degree of the subsequence according to the spectral information coefficient, current maximum value and coefficient of variation of the subsequence; Set a preset number of neurons for the input layer, hidden layer and output layer of the neural network; the number of neurons in the input layer is the same as the number of switching current data in each switching current sequence; use the ratio of the fault correlation degree of the subsequence to all the fault correlation degrees of its corresponding switching current sequence as the attention weight; combine the logits value of the input layer neuron and its corresponding attention weight as the input of the hidden layer neuron to complete model training; input the switching current sequence to be detected into the trained model to achieve high-precision detection.

2. The high-precision detection method for the breaker state based on the switching current according to claim 1, characterized in that The fault state includes four typical fault states: low coil voltage, fatigue of closing spring, looseness of transmission mechanism and jamming of opening tripping device.

3. The high-precision detection method for the circuit breaker state based on the opening and closing current according to claim 1, characterized in that The method of forming a fundamental frequency sequence with the amplitude values around the maximum amplitude in the amplitude sequence is as follows: Select the frequencies and their corresponding amplitude values within a preset range of the maximum amplitude in the amplitude sequence, and sort the amplitude values according to the frequency size to obtain the fundamental frequency sequence.

4. The high-precision detection method for the breaker state based on the closing and opening currents according to claim 1, characterized in that, The method of obtaining the spectral information coefficient of the switching current sequence through the length of the fundamental frequency sequence and all the amplitude values in the amplitude sequence except the fundamental frequency sequence is as follows: Denote the ratio of the sum of all the amplitude values in the amplitude sequence except the fundamental frequency sequence to the sum of all the amplitude values in the amplitude sequence as the first amplitude ratio; The spectral information coefficient is positively correlated with the length of the fundamental frequency sequence and the first amplitude ratio.

5. The high-precision detection method for the circuit breaker state based on the closing and opening currents according to claim 1, characterized in that, The method of evenly dividing the switching current sequences in the data set into four intervals according to the magnitude of the spectral information coefficient is as follows: Sort all the spectral information coefficients in the data set in ascending order to form a spectral information coefficient sequence, and evenly divide the spectral information coefficient sequence into four intervals through quartiles, denoted as the first interval, the second interval, the third interval and the fourth interval from small to large.

6. The high-precision detection method for the breaker state based on the closing and opening currents according to claim 1, wherein The method of dividing the switching current sequences in different intervals into different numbers of subsequences according to the order of each interval is as follows: Divide the switching current sequences in the first interval into 10 subsequences; divide the switching current sequences in the second interval into 20 subsequences; divide the switching current sequences in the third interval into 40 subsequences; divide the switching current sequences in the fourth interval into 80 subsequences.

7. The high-precision breaker status detection method based on closing and opening currents according to claim 1, characterized in that The fault correlation degree is positively correlated with the spectral information coefficient of the subsequence, the maximum value of the switching current data, and the coefficient of variation.

8. The high-precision detection method for the breaker state based on the closing and opening currents according to claim 1, characterized in that The attention weight is the ratio of the fault correlation degree of each subsequence to the sum of the fault correlation degrees of all subsequences in its corresponding switching current sequence.

9. The high-precision detection method for the breaker state based on the closing and opening currents according to claim 1, wherein The input of the hidden layer is the product of the logits value of the input layer neuron and its corresponding attention weight.

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