High-voltage switch operation state evaluation method and system based on intelligent sensing

Abstract

The invention relates to the technical field of high-voltage switch operation state evaluation, and discloses a high-voltage switch operation state evaluation method and system based on intelligent sensing, and the method comprises the steps: obtaining vibration waveform data of a high-voltage switch transmission mechanism in an operation process, and carrying out the noise removal; performing feature extraction on the vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters, and inputting the waveform feature parameters into the mechanism state classification prediction model to obtain a meshing clearance working condition; obtaining a relative amplitude according to the vibration waveform data, obtaining abnormal waveform data according to a comparison relationship between the relative amplitude and a relative amplitude threshold, and extracting abnormal feature parameters from the abnormal waveform data; and inputting the abnormal characteristic parameters into a state scoring model to obtain a state score, and determining the operation state of the high-voltage switch transmission mechanism according to the state score. According to the invention, comprehensive monitoring and accurate evaluation of the operation state of the high-voltage switch transmission mechanism are realized, and safe and stable operation of a power system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage switch operating state evaluation, and in particular to a high-voltage switch operating state evaluation method and system based on intelligent sensing. Background Art

[0002] The reliable operation of high-voltage switches is crucial to the stability of the power system, and the state of its transmission mechanism directly determines the performance of the switch, thus affecting the stability and reliability of the power system. Traditional condition monitoring methods mainly rely on regular maintenance and manual inspection, which is not only inefficient but also difficult to detect potential faults in real time. In recent years, condition monitoring technology based on vibration signal analysis has gradually become a research hotspot.

[0003] In actual engineering applications, the transmission system of the transmission mechanism is subjected to mechanical stress and electromagnetic force for a long time, and the meshing clearance of the transmission shaft will change to a certain extent, resulting in changes in the vibration waveform during the dynamic closing process of the switch. In other words, the vibration waveform data can reflect the operating state of the high-voltage switch transmission mechanism, especially the change of the meshing clearance. Therefore, in-depth analysis of the intrinsic connection between the change of the meshing clearance of the transmission shaft and the characteristics of the vibration waveform is of great significance for accurately evaluating the state of the high-voltage switch transmission mechanism. However, since the vibration signal usually contains a lot of noise, and the accuracy of feature extraction and state classification is limited. In the prior art, the feature extraction method is single, and the complexity and accuracy of the state classification model are insufficient, making it difficult to achieve accurate evaluation of the operating state of the high-voltage switch transmission mechanism. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for evaluating the operating status of a high-voltage switch based on intelligent sensing, so as to solve the problems of low efficiency and difficulty in real-time detection of potential faults in existing methods, and achieve the technical effect of accurately evaluating the operating status of the high-voltage switch transmission mechanism and improving the reliability and safety of the operation of power equipment.

[0005] In a first aspect, the present invention provides a method for evaluating the operating status of a high-voltage switch based on intelligent sensing, the method comprising: Acquire vibration waveform data of the high-voltage switch transmission mechanism during operation, and remove noise from the vibration waveform data to obtain denoised vibration waveform data; Perform feature extraction on the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters, wherein the waveform feature parameters include frequency band feature parameters and wavelet feature parameters; Inputting the waveform characteristic parameters into a pre-constructed mechanism state classification prediction model to obtain the meshing clearance working condition of the high-voltage switch transmission mechanism, wherein the mechanism state classification prediction model is constructed based on a convolutional neural network and a Markov chain; According to the longitudinal component and the transverse component of the denoised vibration waveform data, a relative amplitude is obtained, according to the comparison relationship between the relative amplitude and the relative amplitude threshold, abnormal waveform data is obtained, and abnormal characteristic parameters are extracted from the abnormal waveform data; The abnormal characteristic parameters are input into a state scoring model constructed based on a deep neural network to obtain a state score, and the operating state of the high-voltage switch transmission mechanism under the meshing clearance working condition is determined based on the state score.

[0006] Furthermore, the step of removing noise from the vibration waveform data to obtain the denoised vibration waveform data comprises: A low-pass filter is used to filter a data sequence of the vibration waveform data, wherein the data sequence includes a longitudinal data sequence and a transverse data sequence, and a cutoff frequency of the low-pass filter is determined based on a main frequency of a signal in the vibration waveform data; The vibration amplitude is extracted from the filtered data sequence by using a spectrum analysis method, and the filtered data sequence is marked with data segments according to the comparison relationship between the vibration amplitude and the vibration amplitude threshold, so as to obtain a plurality of marked data segments; Each marked data segment is filtered twice based on an adaptive filter, and the filtered marked data segment is segmented using a sliding window mechanism; In each sliding window, the vector synthesis value of the longitudinal data and the transverse data is calculated to obtain a synthetic vibration waveform curve, and the synthetic vibration waveform curve is fitted to obtain a fitting waveform curve; The vibration waveform data is corrected according to the fitting waveform curve to obtain the denoised vibration waveform data.

[0007] Furthermore, the step of extracting features from the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters comprises: The denoised vibration waveform data is converted into a time-frequency value by Fourier transform to obtain a frequency domain amplitude, and the frequency band characteristic parameters corresponding to each frequency band are extracted from the frequency domain amplitude according to the preset frequency band, wherein the frequency band characteristic parameters include the maximum amplitude, the average amplitude and the frequency bandwidth; The denoised vibration waveform data is decomposed into three layers using wavelet transform to obtain wavelet characteristic parameters, wherein the wavelet characteristic parameters include energy value, standard value and peak value; The frequency band characteristic parameters and the wavelet characteristic parameters are normalized to obtain waveform characteristic parameters.

[0008] Furthermore, the mechanism status classification prediction model includes a convolutional neural network model and a hidden Markov model; The convolutional neural network model comprises three convolutional layers and two fully connected layers. A pooling layer is connected behind each convolutional layer. The convolution kernel size of the convolutional layer decreases with the number of layers. The pooling layer adopts the maximum pooling method, and the activation function adopts the rectified linear unit. The input of the convolutional neural network model is the waveform feature parameter, and the output is the gap level probability. The number of states of the hidden Markov model is the same as the number of gap levels, the waveform characteristic parameters in the continuous time window are used as the observation sequence, the gap level probability output by the convolutional neural network model is used as the observation probability matrix, the state transfer matrix is ​​calculated from the training data using maximum likelihood estimation, and the output of the hidden Markov model is the state sequence probability calculated based on the state transfer matrix and the observation probability matrix; The gap level probability output by the convolutional neural network model and the state sequence probability output by the hidden Markov model are weightedly summed, and the meshing gap working condition determined according to the summed probability is used as the output of the mechanism state classification prediction model.

[0009] Furthermore, the step of obtaining a relative amplitude based on the longitudinal component and the transverse component of the denoised vibration waveform data, and obtaining abnormal waveform data based on a comparison relationship between the relative amplitude and a relative amplitude threshold comprises: Based on the sliding window mechanism, the denoised vibration waveform data is segmented, the relative amplitude is calculated according to the longitudinal component and the transverse component in each segment of the data, and the relative amplitude is filtered to obtain the filtered relative amplitude; The filtered relative amplitude is compared with a relative amplitude threshold, and the vibration waveform data exceeding the amplitude threshold is taken as abnormal waveform data. The amplitude threshold is determined based on the mean and variance of a relative amplitude distribution model, and the relative amplitude distribution model is constructed based on historical vibration waveform data.

[0010] Furthermore, the step of extracting abnormal characteristic parameters from the abnormal waveform data includes: Calculating the abnormal waveform data to obtain time domain characteristic parameters, wherein the time domain characteristic parameters include a time domain mean, a time domain variance, and a time domain peak factor; Using Fourier transform to perform time-frequency conversion on abnormal waveform data, and extracting frequency domain characteristic parameters from the converted frequency domain data, wherein the frequency domain characteristic parameters include main frequency amplitude, frequency band energy ratio and harmonic coefficient; Using wavelet transform to perform three-layer decomposition on abnormal waveform data to obtain wavelet characteristic parameters, wherein the wavelet characteristic parameters include energy value, standard value and peak value; The time domain characteristic parameter, the frequency domain characteristic parameter and the wavelet characteristic parameter are used as abnormal characteristic parameters.

[0011] Furthermore, the step of inputting the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score, and determining the operating state of the high-voltage switch under the meshing clearance condition according to the state score includes: Inputting the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score; Performing Gaussian kernel density estimation on the state score to obtain a probability density function, and calculating the time proportion of the state score below the score threshold based on the probability density function; According to the time proportion, the operating state of the high-voltage switch under the meshing gap condition is determined.

[0012] Further, after the step of determining the operating state of the high-voltage switch under the meshing clearance condition, the method further includes: Extract historical clearance changes, historical vibration waveform data and meshing clearance conditions from the transmission mechanism status evaluation history records; According to the historical gap change amount and the historical vibration waveform data, a mapping relationship between the amplitude change rate and the gap change amount is established, and according to the mapping relationship, a gap change critical point is obtained; Extracting waveform characteristic parameters in a time window before and after a critical point of gap change from historical vibration waveform data, and obtaining a parameter change rate based on the extracted waveform characteristic parameters; According to the historical clearance changes and meshing clearance conditions, the evaluation accuracy of the meshing clearance conditions is statistically analyzed, and the accuracy distribution probability density function is established based on the statistical results. The evaluation accuracy mean and the upper and lower limits of the accuracy confidence interval are extracted from the accuracy distribution probability density function. A state assessment accuracy impact table is generated according to the correspondence between the historical gap change amount, the parameter change rate, the assessment accuracy mean value, and the upper and lower limits of the accuracy confidence interval.

[0013] Furthermore, the step of establishing a mapping relationship between the amplitude change rate and the gap change amount according to the historical gap change amount and the historical vibration waveform data, and obtaining the gap change critical point according to the mapping relationship includes: The historical vibration waveform data is processed using a third-order autoregressive algorithm to obtain a relative amplitude change rate sequence; According to the relative amplitude change rate sequence and the historical gap change amount, a support vector regression algorithm of a radial basis kernel function is used to establish a mapping relationship between the amplitude change rate and the gap change amount; The gap change critical point is determined according to the mapping relationship and the slope mutation point of the amplitude change rate curve.

[0014] In a second aspect, the present invention provides a high-voltage switch operating status evaluation system based on intelligent sensing, the system comprising: A data preprocessing module is used to obtain the vibration waveform data of the high-voltage switch transmission mechanism during operation, and remove noise from the vibration waveform data to obtain the denoised vibration waveform data; A feature extraction module, used to extract features from the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters, wherein the waveform feature parameters include frequency band feature parameters and wavelet feature parameters; A clearance working condition prediction module, used for inputting the waveform characteristic parameters into a pre-built mechanism state classification prediction model to obtain the meshing clearance working condition of the high-voltage switch transmission mechanism, wherein the mechanism state classification prediction model is constructed based on a support vector machine model; An abnormality determination module is used to obtain a relative amplitude according to the longitudinal component and the transverse component of the denoised vibration waveform data, obtain abnormal waveform data according to a comparison relationship between the relative amplitude and a relative amplitude threshold, and extract abnormal characteristic parameters from the abnormal waveform data; The state evaluation module is used to input the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score, and determine the operating state of the high-voltage switch transmission mechanism under the meshing clearance working condition based on the state score.

[0015] The present invention provides a method and system for evaluating the operating status of a high-voltage switch based on intelligent sensing. The present invention combines Fourier transform, wavelet transform, convolutional neural network and Markov chain, which can fully capture the characteristics of vibration signals and improve the accuracy of state classification and scoring; through the calculation of relative amplitude and the rapid extraction of abnormal waveform data, potential faults can be discovered in real time, improving the timeliness of state monitoring; at the same time, through the combination of deep neural network and Markov chain, the complexity and uncertainty of vibration signals can be effectively dealt with, and the robustness of state evaluation can be improved. The present invention can effectively improve the state monitoring level of the high-voltage switch transmission mechanism, thereby reducing the equipment failure rate and ensuring the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a method for evaluating the operating status of a high-voltage switch in an embodiment of the present invention; Figure 2 Schematic diagram of the structure of the high-voltage switch operating status evaluation system in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 The first embodiment of the present invention proposes a method for evaluating the operating status of a high-voltage switch based on intelligent sensing, which includes steps S10 to S50: Step S10, obtaining vibration waveform data of the high-voltage switch transmission mechanism during operation, and removing noise from the vibration waveform data to obtain denoised vibration waveform data; Step S20, extracting features of the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters, wherein the waveform feature parameters include frequency band feature parameters and wavelet feature parameters; Step S30, inputting the waveform characteristic parameters into a pre-built mechanism state classification prediction model to obtain the meshing clearance working condition of the high-voltage switch transmission mechanism, wherein the mechanism state classification prediction model is constructed based on a support vector machine model; Step S40, obtaining a relative amplitude according to the longitudinal component and the transverse component of the denoised vibration waveform data, obtaining abnormal waveform data according to a comparison relationship between the relative amplitude and a relative amplitude threshold, and extracting abnormal characteristic parameters from the abnormal waveform data; Step S50, inputting the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score, and determining the operating state of the high-voltage switch transmission mechanism under the meshing clearance condition according to the state score.

[0019] The present invention evaluates the operation state of the high-voltage switch by analyzing the vibration waveform data of the transmission mechanism of the high-voltage switch. First, the vibration data is collected by installing a vibration sensor array on each key node of the transmission mechanism, wherein the sensor array is arranged orthogonally in the longitudinal and transverse directions, the sampling frequency is set to the number of samples per second, and the sampling time is the entire process of the transmission mechanism. Since the vibration signal of the transmission mechanism of the high-voltage switch has a typical bidirectional component characteristic, the arrangement of the vibration sensor adopts an orthogonal method to better capture the propagation law of the vibration wave. Since the original vibration waveform data collected by the sensor has noise interference, it is also necessary to remove the noise from the collected vibration waveform data. The denoising method can use a conventional filter to remove the noise, or use a method to correct the vibration waveform data based on a calibration curve obtained by the test to remove the noise.

[0020] In a preferred embodiment, in order to improve the denoising effect, the present invention provides a method for denoising vibration waveform data based on a fitting curve, and the specific steps include: A low-pass filter is used to filter a data sequence of the vibration waveform data, wherein the data sequence includes a longitudinal data sequence and a transverse data sequence, and a cutoff frequency of the low-pass filter is determined based on a main frequency of a signal in the vibration waveform data; The vibration amplitude is extracted from the filtered data sequence by using a spectrum analysis method, and the filtered data sequence is marked with data segments according to the comparison relationship between the vibration amplitude and the vibration amplitude threshold, so as to obtain a plurality of marked data segments; Each marked data segment is filtered twice based on an adaptive filter, and the filtered marked data segment is segmented using a sliding window mechanism; In each sliding window, the vector synthesis value of the longitudinal data and the transverse data is calculated to obtain a synthetic vibration waveform curve, and the synthetic vibration waveform curve is fitted to obtain a fitting waveform curve; The vibration waveform data is corrected according to the fitting waveform curve to obtain the denoised vibration waveform data.

[0021] In this embodiment, a longitudinal data sequence and a transverse data sequence are divided from the vibration waveform data, and the two direction data sequences are processed respectively using a Butterworth low-pass filter, wherein the cutoff frequency of the filter is half of the main frequency of the signal, and the main frequency of the signal is obtained by calculating the spectrum of the data sequence through Fourier transform and obtaining it from the spectrum.

[0022] For the filtered longitudinal and transverse data sequences, the spectrum analysis method is used to extract the vibration amplitude from the data sequence. The vibration amplitude is also the absolute amplitude, which is used to describe the intensity or energy of the vibration. It can be characterized by instantaneous amplitude, peak value or root mean square value. When the vibration amplitude exceeds the vibration amplitude threshold, the data segment is marked, where the vibration amplitude threshold is the upper limit of the mean vibration amplitude in the data collected by the sensor at the same position in the historical operation record.

[0023] According to the start and end time of the marked data segment, an adaptive filter is constructed for the data segment, the filter order is set equal to the length of the data segment, the filter coefficient is updated by the recursive least square method, and the data segment is subjected to secondary filtering based on the updated filter coefficient. Then the sliding window method is used to segment the filtered data segment, the window length is set to an integer multiple of the data sampling period, and the overlap rate of adjacent windows is set to 50%. In each sliding window, the vector synthesis value of the longitudinal and lateral vibration data is calculated to obtain the synthetic vibration waveform curve, and the support vector regression is used to fit the synthetic vibration waveform curve in the window, and the abnormal points in the original vibration waveform data that deviate from the fitting curve are corrected according to the fitting curve, and the correction amplitude is limited to no more than 20% of the original data, so as to obtain the final denoised vibration waveform data.

[0024] The denoising method provided in this embodiment can not only remove various abnormal fluctuations, but also retain the true characteristics of the vibration signal to a large extent, thereby providing accurate data support for subsequent waveform data analysis and processing.

[0025] For the denoised vibration waveform data, feature extraction is performed through Fourier transform and wavelet transform. The specific steps include: The denoised vibration waveform data is converted into a time-frequency value by Fourier transform to obtain a frequency domain amplitude, and the frequency band characteristic parameters corresponding to each frequency band are extracted from the frequency domain amplitude according to the preset frequency band, wherein the frequency band characteristic parameters include the maximum amplitude, the average amplitude and the frequency bandwidth; The denoised vibration waveform data is decomposed into three layers using wavelet transform to obtain wavelet characteristic parameters, wherein the wavelet characteristic parameters include energy value, standard value and peak value; The frequency band characteristic parameters and the wavelet characteristic parameters are normalized to obtain waveform characteristic parameters.

[0026] In this embodiment, the vibration waveform data is first converted into frequency domain data through Fourier transform, so as to obtain the frequency domain characteristics, that is, the amplitude spectrum, and the frequency domain amplitude is obtained based on the frequency domain characteristics. At the same time, based on the frequency domain characteristics, the vibration frequency is divided into three frequency bands: low frequency band, medium frequency band and high frequency band according to the vibration frequency range, and the corresponding frequency band characteristic parameters are extracted according to the frequency band. The frequency band characteristic parameters include maximum amplitude, average amplitude and frequency bandwidth.

[0027] Then, the time domain series of the longitudinal component and the transverse component are respectively transformed by wavelet transform, and three-layer decomposition is performed to obtain four coefficient arrays of high frequency, medium frequency, low frequency and lowest frequency. For each layer of decomposition, the energy value, standard value and peak value are extracted as wavelet feature parameters, where the energy value is the square sum of the coefficient array, the standard deviation is the standard deviation of the coefficient array, and the peak value is the absolute maximum value of the coefficient array. Finally, the maximum and minimum linear transformation is used to normalize the above-mentioned frequency band characteristic parameters and wavelet characteristic parameters to obtain the normalized vector, that is, the waveform characteristic parameters.

[0028] The waveform feature parameters will be input into the pre-trained mechanism state classification prediction model for mechanism state prediction. The mechanism state classification prediction model is used to predict the meshing clearance working condition of the high-voltage switch transmission mechanism. In practical applications, the meshing clearance state of the transmission shaft of the high-voltage switch transmission mechanism is closely related to the vibration characteristics of the transmission mechanism. Therefore, by analyzing the vibration characteristics, different clearance working conditions can be effectively identified.

[0029] In this embodiment, the mechanism state classification prediction model can be constructed based on the support vector machine model, the radial basis kernel function is selected as the kernel function, and the kernel function parameters and penalty factors are optimized by the grid search method. The distance from the vibration characteristic parameter to each category hyperplane is calculated by the trained classification prediction model, and the probability value belonging to each type is calculated according to the distance value, and the category corresponding to the maximum probability is selected as the prediction result, so as to obtain the meshing clearance working condition of the high-voltage switch transmission mechanism under the current vibration characteristic parameter.

[0030] In a preferred embodiment, the mechanism status classification prediction model can also be constructed based on convolutional neural networks and Markov chains. In this embodiment, the mechanism status classification prediction model includes a convolutional neural network model and a hidden Markov model, wherein the convolutional neural network model includes three convolutional layers, each convolutional layer is connected to a pooling layer, the convolution kernel size of the convolutional layer decreases with the number of layers, the pooling layer adopts the maximum pooling method, and the activation function adopts the rectified linear unit ReLU. There are two fully connected layers after the pooling layer. The first fully connected layer flattens the output of the convolution layer and connects it to a hidden layer, and the second fully connected layer is connected to the output layer. The number of neurons is equal to the number of gap levels. The output layer uses the Softmax function to output the probability of each gap level.

[0031] When training the convolutional neural network model, the transmission shaft is preset with a meshing clearance range and is evenly divided into five clearance levels from the minimum clearance value to the maximum clearance value. Finite element simulation is used to calculate the vibration response of the transmission shaft at each clearance level. The simulation boundary conditions include the constraint positions and material parameters at both ends of the transmission shaft. The vibration waveform data is extracted from the vibration response, and a training data set is constructed through data processing to train the convolutional neural network model. After the training is completed, the obtained convolutional neural network model can be used as a clearance level predictor to predict the clearance level probability distribution of the input feature vector sequence.

[0032] In order to ensure the accuracy of the prediction, a hidden Markov model is added to the classification prediction model. When the hidden Markov model is constructed, the number of its states is defined as the same as the number of gap levels. Each state corresponds to a gap level. The observation sequence is a sequence of feature vectors in a continuous time window. The initial state probability distribution is defined as a uniform distribution. Based on the maximum likelihood estimation, the state transfer matrix is ​​calculated from the training data, and the gap level probability predicted by the convolutional neural network model is used as the observation probability matrix. Based on the above definition, the hidden Markov model performs forward probability calculation and backward probability calculation according to the state transfer matrix and the observation probability matrix, and obtains the state sequence probability according to the forward probability and backward probability.

[0033] Finally, the gap level probability output by the convolutional neural network model and the state sequence probability output by the hidden Markov model are weighted and summed to obtain the gap operating condition probability, and the current meshing gap operating condition is determined according to the gap operating condition probability, and the meshing gap operating condition is used as the output of the classification prediction model. The mechanism state classification prediction model provided by this embodiment can accurately predict the meshing gap operating condition of the high-voltage switch transmission mechanism. It should be noted that the above embodiment is only a preferred model construction method, and the mechanism state classification prediction model in the present invention can also be constructed based only on the convolutional neural network model, or other neural network models such as deep neural network models, or can be constructed using machine learning algorithms, and no excessive restrictions are made here.

[0034] In the present invention, in addition to evaluating the current meshing clearance working condition, the evaluation of the operating state of the high-voltage switch also needs to evaluate whether the operating state of the transmission mechanism is normal or abnormal under the current meshing clearance working condition. First, based on the comparison relationship between the relative amplitude and the relative amplitude threshold, the abnormal waveform data is extracted from the vibration waveform data. The specific steps include: Based on the sliding window mechanism, the denoised vibration waveform data is segmented, the relative amplitude is calculated according to the longitudinal component and the transverse component in each segment of the data, and the relative amplitude is filtered to obtain the filtered relative amplitude; The filtered relative amplitude is compared with a relative amplitude threshold, and the vibration waveform data exceeding the amplitude threshold is taken as abnormal waveform data. The amplitude threshold is determined based on the mean and variance of a relative amplitude distribution model, and the relative amplitude distribution model is constructed based on historical vibration waveform data.

[0035] In this embodiment, the denoised vibration waveform data is first segmented using a sliding window mechanism, and the continuous vibration data is cut into multiple smaller data segments to facilitate subsequent analysis. Preferably, the fixed length of the sliding window is set to one-fiftieth of the number of sampling points, and the sliding step is set to one-half of the window length. For each data segment, the longitudinal component and the transverse component are extracted respectively. In vibration analysis, the longitudinal component refers to the displacement or acceleration component consistent with the vibration direction, while the transverse component refers to the component perpendicular to the vibration direction. The relative amplitude of the longitudinal component and the transverse component in each segment of data is then calculated. The relative amplitude can be obtained by comparing the absolute value, difference or root mean square value of the two components. In addition, a Kalman filter is used to smooth the relative amplitude, and data smoothing is achieved by reasonably setting the noise covariance.

[0036] The filtered relative amplitude is then compared with the relative amplitude threshold, and the vibration waveform data corresponding to the relative amplitude that exceeds the amplitude threshold is taken as abnormal waveform data. In this embodiment, a normal distribution model of the relative amplitude is constructed based on the historical operation records, and the mean and standard deviation of the relative amplitude under normal operating conditions are calculated, and the mean plus or minus three times the standard deviation is used as the preset relative amplitude threshold range.

[0037] After the abnormal waveform data is extracted, the abnormal waveform data is analyzed to determine whether the operating state of the transmission mechanism is abnormal. In this embodiment, the analysis step includes two parts: extraction of abnormal characteristic parameters and scoring of the operating state. The specific steps of extracting the abnormal characteristic parameters include: Calculating the abnormal waveform data to obtain time domain characteristic parameters, wherein the time domain characteristic parameters include a time domain mean, a time domain variance, and a time domain peak factor; Using Fourier transform to perform time-frequency conversion on abnormal waveform data, and extracting frequency domain characteristic parameters from the converted frequency domain data, wherein the frequency domain characteristic parameters include main frequency amplitude, frequency band energy ratio and harmonic coefficient; Using wavelet transform to perform three-layer decomposition on abnormal waveform data to obtain wavelet characteristic parameters, wherein the wavelet characteristic parameters include energy value, standard value and peak value; The time domain characteristic parameter, the frequency domain characteristic parameter and the wavelet characteristic parameter are used as abnormal characteristic parameters.

[0038] In this embodiment, three types of parameters are used as abnormal characteristic parameters, namely time domain characteristic parameters, frequency domain characteristic parameters and wavelet characteristic parameters. Among them, the time domain feature parameters include three categories: mean, variance, and peak factor. These three types of time domain feature parameters can be obtained by directly calculating the abnormal waveform data; the frequency domain feature parameters include three categories: main frequency amplitude, band energy ratio, and harmonic coefficient. The extraction of these three types of frequency domain feature parameters requires first converting the abnormal waveform data from the time domain to the frequency domain through Fourier transform, and then finding the frequency point with the largest amplitude in the spectrum, whose amplitude is the main frequency amplitude, and dividing the spectrum into several frequency bands such as low frequency, medium frequency, and high frequency. By calculating the energy of each frequency band, the frequency band energy ratio of each frequency band to the total energy is determined, and then the spectrum is analyzed to extract the amplitude of the fundamental frequency and its harmonic frequency, and the ratio of the harmonic amplitude to the fundamental wave amplitude is used as the harmonic coefficient; the wavelet feature parameters include three categories: energy value, standard value, and peak value. These three types of wavelet feature parameters are consistent with the wavelet feature parameters in the extracted waveform feature parameters. The extraction steps can refer to the above embodiment and will not be repeated here.

[0039] For the abnormal characteristic parameters obtained in the above steps, the state scoring model based on the deep neural network is used to perform the state scoring, and the operating state of the high-voltage switch is determined to be normal or abnormal according to the evaluation score. Among them, the state scoring model can be constructed using a deep neural network model or other neural network models, and the operating state of the high-voltage switch can be directly output through the state scoring model.

[0040] In a preferred embodiment, in order to make the evaluation result of the running status more accurate, the present invention also provides a running status determination method based on time proportion, and the specific steps include: Inputting the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score; Performing Gaussian kernel density estimation on the state score to obtain a probability density function, and calculating the time proportion of the state score below the score threshold based on the probability density function; According to the time proportion, the operating state of the high-voltage switch under the meshing gap condition is determined.

[0041] In this embodiment, a state scoring model is first constructed based on a deep neural network model. Preferably, a five-layer deep neural network model is used for construction, the number of nodes in each layer decreases in sequence, the hidden layer uses a ReLU activation function, the output layer uses a linear activation function, and the score is output based on the weighted sum of each feature score. Then, the state evaluation score is estimated by Gaussian kernel density, and the probability density function is calculated. Assuming that the scoring threshold is 80 points, all score data are traversed through the probability density function, and the number of data points with scores lower than the set threshold is counted, and the time proportion of the state score lower than the scoring threshold is obtained by the ratio of the number of data points lower than the set threshold to the total number of data points. Finally, it is determined which category of the preset state threshold the time proportion belongs to, so as to obtain the operating state of the high-voltage switch under the current meshing clearance working condition. Preferably, the state threshold is set to: a time proportion of less than 5% is a normal state, a time proportion of 5% to 15% is a mild abnormality, a time proportion of 15% to 30% is a moderate abnormality, and a time proportion of more than 30% is a moderate abnormality. Assuming that the calculation results show that the score is below the threshold of 80 points for 30% of the time, based on the state threshold, the high-voltage switch is considered to be in a moderately abnormal state. Further, an in-depth analysis can be performed based on the probability density function. Assuming that the longitudinal vibration amplitude is abnormally increased under this working condition while the lateral vibration is basically normal, it indicates that the transmission mechanism may have asymmetric wear.

[0042] Compared with the method of using only the neural network model for status assessment, the method of determining the operating status based on the deep neural network and probability density distribution in this embodiment can provide a more accurate and detailed abnormality degree division, reduce the deviation that may be caused by a single model, and enhance the robustness and reliability of the assessment. In addition, more information about the characteristics of the vibration data can be obtained from the score distribution, which is helpful for further analyzing the cause and type of the fault and providing strong support for subsequent maintenance and management.

[0043] In a preferred embodiment, the present invention further provides a method for analyzing the impact of an evaluation method on accuracy, the specific steps of which include: Extract historical clearance changes, historical vibration waveform data and meshing clearance conditions from the transmission mechanism status evaluation history records; According to the historical gap change amount and the historical vibration waveform data, a mapping relationship between the amplitude change rate and the gap change amount is established, and according to the mapping relationship, a gap change critical point is obtained; Extracting waveform characteristic parameters in a time window before and after a critical point of gap change from historical vibration waveform data, and obtaining a parameter change rate based on the extracted waveform characteristic parameters; According to the historical clearance changes and meshing clearance conditions, the evaluation accuracy of the meshing clearance conditions is statistically analyzed, and the accuracy distribution probability density function is established based on the statistical results. The evaluation accuracy mean and the upper and lower limits of the accuracy confidence interval are extracted from the accuracy distribution probability density function. A state assessment accuracy impact table is generated according to the correspondence between the historical gap change amount, the parameter change rate, the assessment accuracy mean value and the upper and lower limits of the accuracy confidence interval.

[0044] In this embodiment, the relationship between the gap change amount, the waveform characteristic parameters and the evaluation state accuracy is determined by analyzing the recorded data in the transmission mechanism state evaluation history record. Specifically, firstly, a mapping relationship between the amplitude change rate and the gap change amount is established according to the historical gap change amount and the historical vibration waveform data in the historical record, so as to determine the gap change critical point. The specific steps include: The historical vibration waveform data is processed using a third-order autoregressive algorithm to obtain a relative amplitude change rate sequence; According to the relative amplitude change rate sequence and the historical gap change amount, a support vector regression algorithm of a radial basis kernel function is used to establish a mapping relationship between the amplitude change rate and the gap change amount; The gap change critical point is determined according to the mapping relationship and the slope mutation point of the amplitude change rate curve.

[0045] In this embodiment, the gap change and vibration waveform data are first extracted from the mechanism status assessment record, and the amplitude change trend curve is constructed using a third-order autoregressive algorithm. The relative amplitude difference between the longitudinal and transverse components of the vibration waveform is calculated according to the sampling period, and the change rate sequence is obtained by dividing the difference between adjacent sampling points by the time interval.

[0046] Then, based on the relative amplitude change rate sequence of the longitudinal component and the transverse component of the vibration waveform, the support vector regression algorithm of the radial basis kernel function is used to establish the gap change mapping relationship, and the gap change critical point is determined by the slope mutation point of the amplitude change rate curve, such as the point where the slope is greater than the slope threshold. The change of the meshing gap will be displayed in the change of the vibration characteristic parameters. Therefore, the waveform characteristic parameters in the time window before and after the gap change critical point can be extracted, and the parameter change rate can be calculated based on the change of the waveform characteristic parameters before and after.

[0047] Then, random sampling is performed using the Monte Carlo method, and the evaluation accuracy under each gap change is statistically analyzed. The mean and standard deviation of the evaluation accuracy are calculated. Based on the accuracy statistics, a normal distribution probability density function is established, and the accuracy probability distribution corresponding to different gap changes is calculated. The mean of the evaluation accuracy, the upper and lower limits of the accuracy confidence interval are extracted from the distribution function. Finally, a state evaluation accuracy impact table is generated based on the correspondence between the historical gap change, the parameter change rate, the mean of the evaluation accuracy, and the upper and lower limits of the accuracy confidence interval. For example, as the gap change increases, the accuracy gradually decreases, and this accuracy decrease trend shows a clear correlation with the vibration amplitude change rate. Through the impact table provided in this embodiment, the changes in waveform characteristic parameters and the changes in state evaluation accuracy under different gap changes can be accurately displayed, thereby providing an important basis for subsequent equipment maintenance decisions.

[0048] This embodiment provides a method for evaluating the operating status of a high-voltage switch based on intelligent sensing. The present invention combines Fourier transform, wavelet transform, convolutional neural network and Markov chain to fully capture the characteristics of vibration signals and improve the accuracy of state classification and scoring. Through the calculation of relative amplitude and the rapid extraction of abnormal waveform data, potential faults can be discovered in real time, improving the timeliness of state monitoring. At the same time, through the combination of deep neural network and Markov chain, the complexity and uncertainty of vibration signals can be effectively dealt with, and the robustness of state evaluation can be improved. The present invention realizes comprehensive monitoring and accurate evaluation of the operating status of the high-voltage switch transmission mechanism through noise removal, feature extraction, state classification, anomaly detection and state scoring, effectively improving the state monitoring level of the high-voltage switch transmission mechanism, further reducing the equipment failure rate, and ensuring the safe and stable operation of the power system.

[0049] See also Figure 2 Based on the same inventive concept, a high-voltage switch operation status evaluation system based on intelligent sensing is proposed in the second embodiment of the present invention, comprising: The data preprocessing module 10 is used to obtain the vibration waveform data of the high-voltage switch transmission mechanism during operation, and remove noise from the vibration waveform data to obtain the denoised vibration waveform data; A feature extraction module 20 is used to extract features from the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters, wherein the waveform feature parameters include frequency band feature parameters and wavelet feature parameters; A clearance working condition prediction module 30 is used to input the waveform characteristic parameters into a pre-built mechanism state classification prediction model to obtain the meshing clearance working condition of the high-voltage switch transmission mechanism, wherein the mechanism state classification prediction model is constructed based on a support vector machine model; An abnormality determination module is used to obtain a relative amplitude according to the longitudinal component and the transverse component of the denoised vibration waveform data, obtain abnormal waveform data according to a comparison relationship between the relative amplitude and a relative amplitude threshold, and extract abnormal characteristic parameters from the abnormal waveform data; The state evaluation module 40 is used to input the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score, and determine the operating state of the high-voltage switch transmission mechanism under the meshing clearance working condition based on the state score.

[0050] The technical features and technical effects of the high-voltage switch operation status assessment system based on intelligent sensing proposed in the embodiment of the present invention are the same as the method proposed in the embodiment of the present invention, and will not be repeated here. Each module in the above-mentioned high-voltage switch operation status assessment system based on intelligent sensing can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0051] In summary, an embodiment of the present invention proposes a method and system for evaluating the operating state of a high-voltage switch based on intelligent sensing. The method obtains vibration waveform data of a high-voltage switch transmission mechanism during operation, and removes noise from the vibration waveform data to obtain denoised vibration waveform data; extracts features from the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters, wherein the waveform feature parameters include frequency band feature parameters and wavelet feature parameters; inputs the waveform feature parameters into a pre-constructed mechanism state classification prediction model to obtain the meshing clearance working condition of the high-voltage switch transmission mechanism, wherein the mechanism state classification prediction model is constructed based on a convolutional neural network and a Markov chain; obtains a relative amplitude according to the longitudinal component and the transverse component of the denoised vibration waveform data, obtains abnormal waveform data according to the comparison relationship between the relative amplitude and the relative amplitude threshold, and extracts abnormal feature parameters from the abnormal waveform data; inputs the abnormal feature parameters into a state scoring model constructed based on a deep neural network to obtain a state score, and determines the operating state of the high-voltage switch transmission mechanism under the meshing clearance working condition according to the state score. The present invention combines Fourier transform, wavelet transform, convolutional neural network and Markov chain, which can fully capture the characteristics of vibration signals and improve the accuracy of state classification and scoring; through the calculation of relative amplitude and the rapid extraction of abnormal waveform data, potential faults can be discovered in real time, improving the timeliness of state monitoring; at the same time, through the combination of deep neural network and Markov chain, it can effectively deal with the complexity and uncertainty of vibration signals and improve the robustness of state assessment. The present invention realizes comprehensive monitoring and accurate evaluation of the operating state of the high-voltage switch transmission mechanism through noise removal, feature extraction, state classification, anomaly detection and state scoring, effectively improving the state monitoring level of the high-voltage switch transmission mechanism, thereby reducing the equipment failure rate and ensuring the safe and stable operation of the power system.

[0052] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for evaluating the operating status of a high-voltage switch based on intelligent sensing, characterized in that: include: Acquire vibration waveform data of the high-voltage switch transmission mechanism during operation, and remove noise from the vibration waveform data to obtain denoised vibration waveform data; Perform feature extraction on the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters, wherein the waveform feature parameters include frequency band feature parameters and wavelet feature parameters; Inputting the waveform characteristic parameters into a pre-constructed mechanism state classification prediction model to obtain the meshing clearance working condition of the high-voltage switch transmission mechanism, wherein the mechanism state classification prediction model is constructed based on a convolutional neural network and a Markov chain; According to the longitudinal component and the transverse component of the denoised vibration waveform data, a relative amplitude is obtained, according to the comparison relationship between the relative amplitude and the relative amplitude threshold, abnormal waveform data is obtained, and abnormal characteristic parameters are extracted from the abnormal waveform data; The abnormal characteristic parameters are input into a state scoring model constructed based on a deep neural network to obtain a state score, and the operating state of the high-voltage switch transmission mechanism under the meshing clearance working condition is determined based on the state score.

2. The method for evaluating the operating status of a high-voltage switch based on intelligent sensing according to claim 1 is characterized in that: The step of removing noise from the vibration waveform data to obtain the denoised vibration waveform data comprises: A low-pass filter is used to filter a data sequence of the vibration waveform data, wherein the data sequence includes a longitudinal data sequence and a transverse data sequence, and a cutoff frequency of the low-pass filter is determined based on a main frequency of a signal in the vibration waveform data; The vibration amplitude is extracted from the filtered data sequence by using a spectrum analysis method, and the filtered data sequence is marked with data segments according to the comparison relationship between the vibration amplitude and the vibration amplitude threshold, so as to obtain a plurality of marked data segments; Perform secondary filtering on each marked data segment based on an adaptive filter, and segment the filtered marked data segment using a sliding window mechanism; In each sliding window, the vector synthesis value of the longitudinal data and the transverse data is calculated to obtain a synthetic vibration waveform curve, and the synthetic vibration waveform curve is fitted to obtain a fitting waveform curve; The vibration waveform data is corrected according to the fitting waveform curve to obtain the denoised vibration waveform data.

3. The method for evaluating the operating status of a high-voltage switch based on intelligent sensing according to claim 1 is characterized in that: The step of extracting features from the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters comprises: The denoised vibration waveform data is converted into a time-frequency value by Fourier transform to obtain a frequency domain amplitude, and the frequency band characteristic parameters corresponding to each frequency band are extracted from the frequency domain amplitude according to the preset frequency band, wherein the frequency band characteristic parameters include the maximum amplitude, the average amplitude and the frequency bandwidth; The denoised vibration waveform data is decomposed into three layers using wavelet transform to obtain wavelet characteristic parameters, wherein the wavelet characteristic parameters include energy value, standard value and peak value; The frequency band characteristic parameters and the wavelet characteristic parameters are normalized to obtain waveform characteristic parameters.

4. The method for evaluating the operating status of a high-voltage switch based on intelligent sensing according to claim 1 is characterized in that: The institution status classification prediction model includes a convolutional neural network model and a hidden Markov model; The convolutional neural network model comprises three convolutional layers and two fully connected layers. A pooling layer is connected behind each convolutional layer. The convolution kernel size of the convolutional layer decreases with the number of layers. The pooling layer adopts the maximum pooling method, and the activation function adopts the rectified linear unit. The input of the convolutional neural network model is the waveform feature parameter, and the output is the gap level probability. The number of states of the hidden Markov model is the same as the number of gap levels, the waveform characteristic parameters in the continuous time window are used as the observation sequence, the gap level probability output by the convolutional neural network model is used as the observation probability matrix, the state transfer matrix is ​​calculated from the training data using maximum likelihood estimation, and the output of the hidden Markov model is the state sequence probability calculated based on the state transfer matrix and the observation probability matrix; The gap level probability output by the convolutional neural network model and the state sequence probability output by the hidden Markov model are weightedly summed, and the meshing gap working condition determined according to the summed probability is used as the output of the mechanism state classification prediction model.

5. The method for evaluating the operating status of a high-voltage switch based on intelligent sensing according to claim 1, characterized in that: The step of obtaining the relative amplitude according to the longitudinal component and the transverse component of the denoised vibration waveform data, and obtaining the abnormal waveform data according to the comparison relationship between the relative amplitude and the relative amplitude threshold comprises: Based on the sliding window mechanism, the denoised vibration waveform data is segmented, the relative amplitude is calculated according to the longitudinal component and the transverse component in each segment of the data, and the relative amplitude is filtered to obtain the filtered relative amplitude; The filtered relative amplitude is compared with a relative amplitude threshold, and the vibration waveform data exceeding the amplitude threshold is taken as abnormal waveform data. The amplitude threshold is determined based on the mean and variance of a relative amplitude distribution model, and the relative amplitude distribution model is constructed based on historical vibration waveform data.

6. The method for evaluating the operating status of a high-voltage switch based on intelligent sensing according to claim 1, characterized in that: The step of extracting abnormal characteristic parameters from abnormal waveform data comprises: Calculating the abnormal waveform data to obtain time domain characteristic parameters, wherein the time domain characteristic parameters include a time domain mean, a time domain variance, and a time domain peak factor; Using Fourier transform to perform time-frequency conversion on abnormal waveform data, and extracting frequency domain characteristic parameters from the converted frequency domain data, wherein the frequency domain characteristic parameters include main frequency amplitude, frequency band energy ratio and harmonic coefficient; Using wavelet transform to perform three-layer decomposition on abnormal waveform data to obtain wavelet characteristic parameters, wherein the wavelet characteristic parameters include energy value, standard value and peak value; The time domain characteristic parameter, the frequency domain characteristic parameter and the wavelet characteristic parameter are used as abnormal characteristic parameters.

7. The method for evaluating the operating status of a high-voltage switch based on intelligent sensing according to claim 6 is characterized in that: The step of inputting the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score, and determining the operating state of the high-voltage switch under the meshing clearance condition according to the state score comprises: Inputting the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score; Performing Gaussian kernel density estimation on the state score to obtain a probability density function, and calculating the time proportion of the state score below the score threshold based on the probability density function; According to the time proportion, the operating state of the high-voltage switch under the meshing gap condition is determined.

8. The method for evaluating the operating status of a high-voltage switch based on intelligent sensing according to claim 1, characterized in that: After the step of determining the operating state of the high-voltage switch under the meshing clearance condition, the method further includes: Extract historical clearance changes, historical vibration waveform data and meshing clearance conditions from the transmission mechanism status evaluation history records; According to the historical gap change amount and the historical vibration waveform data, a mapping relationship between the amplitude change rate and the gap change amount is established, and according to the mapping relationship, a gap change critical point is obtained; Extracting waveform characteristic parameters in a time window before and after a critical point of gap change from historical vibration waveform data, and obtaining a parameter change rate based on the extracted waveform characteristic parameters; According to the historical clearance changes and meshing clearance conditions, the evaluation accuracy of the meshing clearance conditions is statistically analyzed, and the accuracy distribution probability density function is established based on the statistical results. The evaluation accuracy mean and the upper and lower limits of the accuracy confidence interval are extracted from the accuracy distribution probability density function. A state assessment accuracy impact table is generated according to the correspondence between the historical gap change amount, the parameter change rate, the assessment accuracy mean value, and the upper and lower limits of the accuracy confidence interval.

9. The method for evaluating the operating status of a high-voltage switch based on intelligent sensing according to claim 8, characterized in that: The step of establishing a mapping relationship between the amplitude change rate and the gap change amount according to the historical gap change amount and the historical vibration waveform data, and obtaining the gap change critical point according to the mapping relationship comprises: The historical vibration waveform data is processed using a third-order autoregressive algorithm to obtain a relative amplitude change rate sequence; According to the relative amplitude change rate sequence and the historical gap change amount, a support vector regression algorithm of a radial basis kernel function is used to establish a mapping relationship between the amplitude change rate and the gap change amount; The gap change critical point is determined according to the mapping relationship and the slope mutation point of the amplitude change rate curve.

10. A high-voltage switch operating status assessment system based on intelligent sensing, characterized in that: include: A data preprocessing module is used to obtain the vibration waveform data of the high-voltage switch transmission mechanism during operation, and remove noise from the vibration waveform data to obtain the denoised vibration waveform data; A feature extraction module, used to extract features from the denoised vibration waveform data according to Fourier transform and wavelet transform to obtain waveform feature parameters, wherein the waveform feature parameters include frequency band feature parameters and wavelet feature parameters; A clearance working condition prediction module, used for inputting the waveform characteristic parameters into a pre-built mechanism state classification prediction model to obtain the meshing clearance working condition of the high-voltage switch transmission mechanism, wherein the mechanism state classification prediction model is constructed based on a support vector machine model; An abnormality determination module is used to obtain a relative amplitude according to the longitudinal component and the transverse component of the denoised vibration waveform data, obtain abnormal waveform data according to a comparison relationship between the relative amplitude and a relative amplitude threshold, and extract abnormal characteristic parameters from the abnormal waveform data; The state evaluation module is used to input the abnormal characteristic parameters into a state scoring model constructed based on a deep neural network to obtain a state score, and determine the operating state of the high-voltage switch transmission mechanism under the meshing clearance working condition based on the state score.

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