A method for online fault detection and analysis of switchgear

Abstract

The present application relates to the technical field of switch fault detection, and specifically to a method for online fault detection and analysis of switch equipment, the method comprising: determining the dynamic change degree of each type of electrical data in each time period; determining the first fluctuation degree of each type of electrical data in each time period, combining the difference between the dynamic change degree of each type of data in each time period and the minimum dynamic change degree in all time periods, determining the second fluctuation degree of each type of electrical data in each time period; determining the fluctuation coefficient of each type of electrical data in each time period; based on the fluctuation coefficient of each type of electrical data in all time periods after the fault occurrence period, determining the abnormal fluctuation value of each type of electrical data in the circuit breaker, and detecting the fault type of the circuit breaker. The present application can accurately locate the fault occurrence period and improve the accuracy of fault detection of the circuit breaker by comprehensively analyzing the current and voltage data of the circuit breaker switch equipment.

Description

Technical Field

[0001] The present application relates to the technical field of switch fault detection, and in particular to a method for online fault detection and analysis of switch equipment. Background Art

[0002] A circuit breaker is a type of switchgear. A circuit breaker is an electrical safety device that is mainly used to automatically cut off the current when a short circuit or overload occurs in the circuit to protect the circuit and equipment from damage. It can respond quickly to abnormal situations, prevent electrical fires, and ensure the stable operation of the power system. The circuit breaker also has an isolation function, which facilitates safe circuit maintenance and repair.

[0003] During the use of circuit breakers, circuit breakers may fail due to aging and environmental influences, which increases the risk of electrical fires and threatens personnel safety and equipment integrity.

[0004] For circuit breaker detection, the current and voltage of the circuit breaker are generally used to quickly determine whether the circuit breaker has a fault. Since there are many types of circuit breaker faults, such as circuit breaker tripping faults, short circuit faults, insulation faults (leakage), and poor contact faults, different faults have similar current and voltage characteristics. Directly identifying the fault type of the circuit breaker through current and voltage is prone to electrical fault identification errors, reducing the accuracy of circuit breaker fault detection. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a method for online fault detection and analysis of a switching device to solve the existing problems.

[0006] A method for online fault detection and analysis of a switchgear of the present application adopts the following technical solution:

[0007] An embodiment of the present application provides a method for online fault detection and analysis of a switch device, the method comprising the following steps:

[0008] Obtain input and output data of various electrical data of the circuit breaker at all times within a preset time period before the current time;

[0009] Divide the preset time into multiple time periods, and determine the dynamic change degree of each type of electrical data in each time period based on the difference between the input data and the output data of each type of electrical data at any same time in each time period, and the similarity between the input data and the output data of each type of electrical data in the frequency domain;

[0010] Based on the discrete degree of the difference in the dynamic change degree of each type of electrical data between each time period and all previous time periods, combined with the dynamic change degree, determine the first fluctuation degree of each type of electrical data in each time period, and combine the difference between the dynamic change degree of each type of data in each time period and the minimum value of the dynamic change degree of all time periods to determine the second fluctuation degree of each type of electrical data in each time period;

[0011] Determine the fluctuation coefficient of each type of electrical data in each time period based on the difference between the second fluctuation degree of each type of electrical data in each time period and the average distribution of all second fluctuation degrees in its neighborhood;

[0012] Based on the fluctuation coefficient of each type of electrical data in different time periods, combined with the clustering algorithm, the fault period of the circuit breaker is determined; based on the fluctuation coefficient of each type of electrical data in all time periods after the fault period, the abnormal fluctuation value of each type of electrical data in the circuit breaker is determined to detect the fault of the circuit breaker.

[0013] Preferably, the method for determining the dynamic change degree of each type of electrical data in each time period is:

[0014] A frequency domain conversion algorithm is used to convert all input data and output data of each type of electrical data in each time period into the frequency domain, thereby obtaining input frequency domain data and output frequency domain data of each type of electrical data in each time period;

[0015] Based on the difference between the input data and the output data of each electrical data at any same time in each time period, combined with the similarity between the input frequency domain data and the output frequency domain data of each electrical data, the dynamic change degree of each electrical data in each time period is determined.

[0016] Preferably, the expression of the dynamic change degree of each type of electrical data in each time period is: ; In the formula, Indicates the dynamic change degree of the kth type of electrical data in the i-th period; , They represent the kth input and output electrical data at the jth moment in the i-th time period respectively; Indicates the similarity between the set of input frequency domain data of the kth type and the set of output frequency domain data in the i-th time period; represents all moments in the i-th period; Indicates a preset constant greater than 0.

[0017] Preferably, the first fluctuation degree of each type of electrical data in each time period is a result of integrating the dynamic change degree of each type of electrical data in each time period with the discrete degree.

[0018] Preferably, the method for determining the second fluctuation degree of each type of electrical data in each time period is:

[0019] Determine the fluctuation index of each type of electrical data in each time period based on the difference between the dynamic change degree of each type of data in each time period and the minimum value of the dynamic change degree of all time periods;

[0020] The second volatility of each type of electrical data in each time period is the result of integrating the first volatility of each type of electrical data in each time period with the volatility index.

[0021] Preferably, the expression of the fluctuation index of each type of electrical data in each time period is: ; In the formula, It represents the fluctuation index of the kth type of electricity data in the i-th period; Indicates the dynamic change degree of the kth type of electrical data in the i-th period; It represents the dynamic change degree of the kth type of electrical data in all time periods in the preset time length; min() represents the minimum value function.

[0022] Preferably, the expression of the fluctuation coefficient of each type of electrical data in each time period is: ; In the formula, It represents the fluctuation coefficient of the kth type of electrical data in the i-th period; represents the second fluctuation of the kth type of electrical data in the zth time period in the neighborhood of the i-th time period; represents the mean value of the second fluctuation of the k-th type of electrical data in all time periods in the neighborhood of the i-th time period; represents the number of all time periods in the neighborhood of the i-th time period; Indicates a preset constant greater than 0.

[0023] Preferably, the process of determining the fault occurrence period of the circuit breaker is:

[0024] Voltage data is one of various electrical data. The fluctuation coefficients of voltage data in all time periods are normalized, and the first preset number of normalized results of the normalized results sorted in ascending order of time are clustered to obtain multiple clusters.

[0025] The number of clusters under different preset numbers is counted. If the number of clusters under any preset number is greater than 1, the circuit breaker fails. The number of clusters is arranged from small to large according to the value of the preset number. The time period corresponding to the first cluster number greater than 1 in the sorting result is taken as the time period when the circuit breaker fails.

[0026] Preferably, the abnormal fluctuation value of each electrical data in the circuit breaker is the average value of the fluctuation coefficient of each electrical data in all time periods after the fault occurrence time period.

[0027] Preferably, the detecting of the fault of the circuit breaker includes:

[0028] Obtain the preset value times of abnormal fluctuation values ​​of each type of electrical data under different fault types from historical data, calculate the average of the preset value times of abnormal fluctuation values ​​of each type of electrical data under the same fault type, record it as the abnormal mean value, and the abnormal means of all different types of electrical data constitute the fault reference data pair of the same fault type; the abnormal fluctuation values ​​of all different types of electrical data of the circuit breaker at the current moment constitute the fault data pair of the circuit breaker;

[0029] The fault type of the circuit breaker is the fault type corresponding to the fault reference data pair that is closest to the fault data pair of the circuit breaker.

[0030] This application has at least the following beneficial effects:

[0031] The present application determines the dynamic change degree of each electrical data in each time period based on the difference between the input data and the output data of each electrical data at any same time in each time period, and the similarity between the input data and the output data of each electrical data in the frequency domain. The beneficial effect is to preliminarily judge whether a circuit breaker has a fault. The present application determines the first fluctuation degree of each electrical data in each time period by analyzing the discrete degree of the difference in the dynamic change degree of each electrical data between each time period and all previous time periods, and combines the dynamic change degree to determine the second fluctuation degree of each electrical data in each time period by combining the difference between the dynamic change degree of each data in each time period and the minimum dynamic change degree in all time periods. , which has the beneficial effect of identifying the potential fault period in the circuit breaker; the present application determines the fluctuation coefficient of each electrical data in each period based on the difference between the second fluctuation degree of each electrical data in each period and the average distribution of all second fluctuation degrees in the neighborhood, and has the beneficial effect of accurately identifying the abnormal change period of electrical data; the present application determines the fault occurrence period of the circuit breaker based on the fluctuation coefficient of each electrical data in different periods, combined with the clustering algorithm; based on the fluctuation coefficient of each electrical data in all periods after the fault occurrence period, the abnormal fluctuation value of each electrical data in the circuit breaker is determined, and the fault type of the circuit breaker is detected, and has the beneficial effect of improving the accuracy of fault identification. The present application improves the accuracy of fault detection of the circuit breaker by comprehensively analyzing the current and voltage data of the circuit breaker switch equipment, accurately locating the fault occurrence period. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1A flowchart of a method for online fault detection and analysis of a switch device provided in one embodiment of the present application;

[0034] Figure 2 A schematic diagram of normal circuit breaker input and output voltages provided by an embodiment of the present application;

[0035] Figure 3 A schematic diagram of input and output voltages of a fault circuit breaker provided in one embodiment of the present application;

[0036] Figure 4 A schematic diagram of a fluctuation coefficient extraction process provided by an embodiment of the present application;

[0037] Figure 5 A schematic diagram of clustering input data provided by an embodiment of the present application;

[0038] Figure 6 A schematic diagram of a circuit breaker switch device fault detection process provided by an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the online fault detection and analysis method of a switch device proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] The specific scheme of the online fault detection and analysis method for a switch device provided by the present application is described in detail below with reference to the accompanying drawings.

[0042] An embodiment of the present application provides a method for online fault detection and analysis of a switch device. Specifically, the following method for online fault detection and analysis of a switch device is provided. Figure 1 , the method comprises the following steps:

[0043] Step S1: Obtaining input and output data of various electrical data of the circuit breaker at all times within a preset time period before the current time.

[0044] Smart meters are installed at both ends of the circuit breaker, and the smart meters are used to collect input and output data of various electrical data of the circuit breaker at all times within a preset time before the current time, and the data collection frequency is set to f. Among them, various electrical data include current data and voltage data.

[0045] It should be noted that the values ​​of the preset duration and the acquisition frequency f are manually set. In this embodiment, the preset duration is 1s and the acquisition frequency f is 1kHz. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0046] Since the collected current and voltage data contain noise, the input data and output data of various electrical data are used as inputs of the filtering algorithm to perform denoising on the input and output data of various electrical data.

[0047] It should be noted that there are many commonly used filtering algorithms. In this embodiment, a mean filtering algorithm is used to denoise the input and output data of various electrical data respectively. The implementer may also use other noise reduction methods such as Gaussian filtering and median filtering. This embodiment does not impose any special restrictions on the choice of filtering method.

[0048] Among them, the mean filtering algorithm is a well-known technology, and the specific noise reduction process will not be described in detail.

[0049] Step S2: Determine the dynamic change degree of each type of electrical data in each time period based on the difference between the input data and the output data of each type of electrical data at any same time in each time period and the similarity between the input data and the output data of each type of electrical data in the frequency domain.

[0050] Ideally, when the circuit breaker is not faulty, the voltage data between the input and output of the circuit breaker are the same, and the current data are the same. However, due to the presence of various components in the circuit breaker, power loss will occur, and there will be differences in the current and voltage between the input and output of the circuit breaker, but this difference is subtle and not obvious. When the circuit breaker fails, the current and voltage between the input and output of the circuit breaker change significantly.

[0051] Preferably, the normal circuit breaker input and output voltage schematic diagram provided in this embodiment is as follows Figure 2 As shown; the schematic diagram of the input and output voltage of the fault circuit breaker provided in this embodiment is as shown Figure 3 shown.

[0052] In order to analyze the local changes of current and voltage over time and determine whether a circuit breaker fault occurs, the preset time is evenly divided into N time periods, and the changes of current and voltage in each time period are analyzed.

[0053] It should be noted that the value of N is artificially set. In this embodiment, the value of N is 50. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0054] When a circuit breaker fails, the amplitudes of current and voltage will change significantly. In order to detect the periodic changes in the amplitudes of current and voltage and identify the voltage distortion caused by the circuit breaker failure, all input data and output data of each type of electrical data in each time period are used as input of the frequency domain conversion algorithm, and the input frequency domain data and output frequency domain data of each type of electrical data in each time period are output.

[0055] It should be noted that there are many commonly used frequency domain conversion algorithms. In this embodiment, fast Fourier transform is used to obtain input frequency domain data and output frequency domain data of all input data and output data of each type of electrical data in the frequency domain. The implementer may also use other frequency domain conversion algorithms such as wavelet transform. This embodiment does not impose any special restrictions on the selection of frequency domain conversion algorithm.

[0056] The calculation of fast Fourier transform is a well-known technology, and the process of time-frequency conversion will not be described in detail.

[0057] Furthermore, in order to determine whether the circuit breaker has a fault, based on the difference between the input data and the output data of each type of electrical data at any same time in each time period, combined with the similarity between the input frequency domain data and the output frequency domain data of each type of electrical data, the dynamic change degree of each type of electrical data in each time period is determined to quantify the degree of change of the electrical data over time, thereby identifying whether the circuit breaker has an abnormal fault. The specific determination process of the dynamic change degree of each type of electrical data in each time period is as follows:

[0058] The expression of the dynamic change degree of each type of electrical data in each period is: ; In the formula, Indicates the dynamic change degree of the kth type of electrical data in the i-th period; , They represent the kth input and output electrical data at the jth moment in the i-th time period respectively; Indicates the similarity between the set of input frequency domain data of the kth type and the set of output frequency domain data in the i-th time period; represents all moments in the i-th period; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is set manually. The value of is 0.01. The implementer can set it according to the specific situation, provided that the denominator is not 0 and does not excessively affect the calculation result. This embodiment does not impose any special restrictions.

[0059] It should be noted that there are many methods for measuring the differences between sets. In this embodiment, the cosine similarity between the set consisting of the input frequency domain data and the set consisting of the output frequency domain data of each type of electrical data in each time period is calculated to measure the similarity between the input data and the output data of each type of electrical data in the frequency domain. The implementer may also use other methods for measuring similarity, such as the inverse of the Euclidean distance, and this embodiment does not impose any special restrictions.

[0060] The calculation process of cosine similarity is a well-known technology, and the specific calculation process will not be repeated here.

[0061] Further, according to the dynamic change degree of each electrical data in each time period, it can be understood that when the circuit breaker is in a normal working state, the difference between the current and the output end of the circuit breaker and the voltage between the input end and the output end in the same time period is small, and the amplitude change of the current and the voltage is small. Therefore, the input frequency domain data and the output frequency domain data obtained by time-frequency conversion of the input current and the output current are the same or similar, the greater the similarity between the set composed of the input frequency domain data and the set composed of the output frequency domain data, the greater the similarity between the same input voltage and the output voltage in the frequency domain, the smaller the dynamic change degree of each electrical data when the circuit breaker is in normal working; on the contrary, when the circuit breaker fails, the difference between the current and the voltage between the input end and the output end of the circuit breaker in the same time period is large, and the amplitude change of the current and the voltage is large. Therefore, the difference between the input frequency domain data and the output frequency domain data obtained by time-frequency conversion of the input current and the output current is large, the smaller the similarity between the set composed of the input frequency domain data and the set composed of the output frequency domain data, the smaller the similarity between the same input voltage and the output voltage in the frequency domain, the greater the dynamic change degree of each electrical data when the circuit breaker is in normal working.

[0062] Step S3: Based on the discrete degree of the difference in the dynamic change degree of each electrical data between each time period and all previous time periods, combined with the dynamic change degree, determine the first fluctuation degree of each electrical data in each time period, and combine the difference between the dynamic change degree of each data in each time period and the minimum value of the dynamic change degree of all time periods to determine the second fluctuation degree of each electrical data in each time period.

[0063] There is a certain degree of repetitiveness in the current and voltage data of different periods during the normal operation of the circuit breaker. There are situations where the dynamic change degree of the current data of the circuit breaker in different periods is the same and the value is small, and the dynamic change degree of the voltage data is the same and the value is small. For the current detection period, there are two situations when the circuit breaker fails. One is that the failure occurs before the current detection period, and the other is that the failure occurs during the current detection period.

[0064] For the first case, since the circuit breaker has failed, the voltage dynamic change sequence data has greater volatility during detection; for the second case, the circuit breaker failure occurred during detection, the first part of the data in the voltage dynamic change sequence is normal with less volatility, and the second part of the data is abnormal with greater volatility.

[0065] S301: Determine a first fluctuation degree of each type of electrical data in each time period based on the degree of discreteness of the difference in the dynamic change degree of each type of electrical data between each time period and all previous time periods and in combination with the dynamic change degree.

[0066] Therefore, in order to determine whether the circuit breaker is tending to a fault state, based on the discrete degree of the difference in the dynamic change degree of each electrical data between each time period and all previous time periods, combined with the dynamic change degree, the first fluctuation degree of each electrical data in each time period is determined to evaluate the abnormal degree of electrical data fluctuation in a specific time period, and then quantify the fault risk. The determination process of the first fluctuation degree of each electrical data in each time period is as follows:

[0067] Analyze the discrete degree of the difference in the dynamic change degree of each type of electrical data between each time period and all previous time periods;

[0068] It should be noted that there are many ways to measure differences. In this embodiment, the difference in the dynamic change degree of each electrical data between each time period and all previous time periods is measured by calculating the absolute value of the difference in the dynamic change degree of each electrical data between each time period and all previous time periods. As other implementation methods, implementers can also use other methods of measuring differences such as ratios, and this embodiment does not impose any special restrictions.

[0069] In addition, it should be understood that there are many commonly used methods for measuring the degree of discreteness. In this embodiment, the degree of discreteness of the difference in the degree of dynamic change of each type of electrical data between each time period and all previous time periods is measured by calculating the average absolute deviation of the difference in the degree of dynamic change of each type of electrical data between each time period and all previous time periods. As other implementation methods, implementers may also use other methods for measuring the degree of discreteness, such as variance and dispersion coefficient, and this embodiment does not impose any special restrictions.

[0070] The calculation process of the mean absolute deviation is a well-known technique, and the specific calculation process will not be described in detail.

[0071] Furthermore, the first fluctuation degree of each electrical data in each time period is determined based on the discrete degree and the dynamic change degree, and the first fluctuation degree of each electrical data in each time period is the result of the fusion of the dynamic change degree of each electrical data in each time period and the discrete degree.

[0072] It should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0073] Preferably, in this embodiment, the expression of the first fluctuation degree of each type of electrical data in each time period is: ; In the formula, Indicates the first fluctuation of the kth type of electrical data in the i-th period; Indicates the dynamic change degree of the kth type of electrical data in the i-th period; Indicates the degree of discreteness of the difference in the dynamic change degree of the kth type of electrical data between the ith time period and all previous time periods. In actual application, as another implementation, the first fluctuation degree of each type of electrical data in each time period is an exponential function value with a natural constant as the base and the sum of the dynamic change degree of each type of electrical data in each time period and the discreteness as the independent variable.

[0074] According to the first volatility of each type of electrical data in each time period, it can be understood that during the detection process, when the circuit breaker is in a period without a fault, the dynamic change of the electrical data in all time periods before the current period is small, and the volatility of the electrical data in the corresponding period and the previous period is small. Therefore, the smaller the discrete degree of the difference in the dynamic change of the electrical data between the corresponding period and all previous time periods, the smaller the first volatility of the electrical data in the corresponding period, indicating that the risk of circuit breaker failure is smaller; conversely, when the circuit breaker fails, the dynamic change of the electrical data in all time periods before the current period is large, and the volatility of the electrical data in the corresponding period and the previous period is large. Therefore, the greater the discrete degree of the difference in the dynamic change of the electrical data between the corresponding period and all previous time periods, the greater the first volatility of the electrical data in the corresponding period, indicating that the risk of circuit breaker failure is greater.

[0075] S302: Determine a second fluctuation degree of each type of electrical data in each time period based on the first fluctuation degree of each type of electrical data in each time period and the difference between the dynamic change degree of each type of data in each time period and the minimum dynamic change degree in all time periods.

[0076] The dynamic change degree of each electrical data calculated for different time periods indicates that within the corresponding time period, the more significant the fluctuation of the current difference and voltage difference between the input and output ends of the circuit breaker, the more likely it is that the circuit breaker has poor contact, aging components or other faults.

[0077] Therefore, in order to analyze the difference between the dynamic change degree of the electrical data and the minimum dynamic change degree under normal conditions, thereby identifying potential fault periods, the second fluctuation degree of each electrical data in each period is determined based on the difference between the dynamic change degree of each data in each period and the minimum value of the dynamic change degree of all periods. The specific process is as follows:

[0078] The expression of the fluctuation index of each type of electricity data in each time period is: ; In the formula, It represents the fluctuation index of the kth type of electricity data in the i-th period; Indicates the dynamic change degree of the kth type of electrical data in the i-th period; It represents the dynamic change degree of the kth type of electrical data in all time periods in the preset time length; min() represents the minimum value function.

[0079] Furthermore, according to the fluctuation index of each type of electrical data in each time period, it can be understood that when the circuit breaker is in a normal working state, the dynamic change degree of the electrical data in different time periods is small and the volatility of the electrical data is small. Therefore, the difference in the dynamic change degree of the electrical data between different time periods is very small. The smaller the fluctuation index of the electrical data, the smaller the fault occurrence intensity of the circuit breaker in the corresponding time period; conversely, when a circuit breaker fails, the dynamic change degree of the electrical data in different time periods is small and the volatility of the electrical data is large. Therefore, the difference in the dynamic change degree of the electrical data between different time periods is very large. The larger the fluctuation index of the electrical data, the greater the fault occurrence intensity of the circuit breaker in the corresponding time period.

[0080] Furthermore, in order to evaluate the severity of the circuit breaker fault and more accurately identify the time period when the fault occurs, the second volatility of each type of electrical data in each time period is determined based on the volatility index and the first volatility, and the second volatility of each type of electrical data in each time period is the result of the fusion of the first volatility of each type of electrical data in each time period and the volatility index.

[0081] Preferably, in this embodiment, the expression of the second fluctuation degree of each type of electrical data in each time period is: ; In the formula, Indicates the second fluctuation of the kth type of electrical data in the i-th period; Indicates the first fluctuation of the kth type of electrical data in the i-th period; It represents the fluctuation index of the kth type of electrical data in the i-th time period. In practical application, the second fluctuation degree of each type of electrical data in each time period is an exponential function value with a natural constant as the base and the sum of the first fluctuation degree and the fluctuation index of each type of electrical data in each time period as the independent variable.

[0082] Furthermore, according to the second volatility of each type of electrical data in each time period, it can be understood that when a circuit breaker fails, the greater the second volatility of the electrical data and the greater the volatility index, the more obvious the failure of the circuit breaker is, and the circuit breaker needs to be repaired or replaced to ensure the safe and stable operation of the power system and prevent further damage; conversely, when the circuit breaker does not fail, the smaller the second volatility of the electrical data and the smaller the volatility index are, which means that the failure of the circuit breaker is less obvious.

[0083] Step S4: Determine the fluctuation coefficient of each type of electrical data in each time period based on the difference between the second fluctuation degree of each type of electrical data in each time period and the average distribution of all second fluctuation degrees in its neighborhood; determine the fault occurrence time period of the circuit breaker based on the fluctuation coefficient of each type of electrical data in different time periods combined with a clustering algorithm; determine the abnormal fluctuation value of each type of electrical data in the circuit breaker based on the fluctuation coefficient of each type of electrical data in all time periods after the fault occurrence time period.

[0084] S401: Determine the fluctuation coefficient of each type of electrical data in each time period based on the difference between the second fluctuation degree of each type of electrical data in each time period and the average distribution of all second fluctuation degrees in its neighborhood.

[0085] In order to detect abnormal conditions of circuit breakers in a local period, a neighborhood W is divided with each period as the center. The value of the neighborhood W is set manually. In this embodiment, the value of the neighborhood W is 4, and the implementer can also set it according to the specific situation.

[0086] Furthermore, by comparing the average fluctuation of the electric data in the neighborhood, and based on the difference between the second fluctuation of each electric data in each time period and the average distribution of all the second fluctuations in its neighborhood, the fluctuation coefficient of each electric data in each time period is determined to accurately identify which time periods have abnormal changes in the electric data, thereby improving the accuracy of fault identification. The specific determination process of the fluctuation coefficient of each electric data in each time period is as follows:

[0087] The expression of the fluctuation coefficient of each type of electrical data in each period is: ; In the formula, It represents the fluctuation coefficient of the kth type of electrical data in the i-th period; represents the second fluctuation of the kth type of electrical data in the zth time period in the neighborhood of the i-th time period; represents the mean value of the second fluctuation of the k-th type of electrical data in all time periods in the neighborhood of the i-th time period; represents the number of all time periods in the neighborhood of the i-th time period; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is set manually. The value of is 0.01. The implementer can set it according to the specific situation, provided that the denominator is not 0 and does not excessively affect the calculation result. This embodiment does not impose any special restrictions.

[0088] Furthermore, according to the fluctuation coefficient of each type of electrical data in each time period, it can be understood that if the difference between the second volatility of the electrical data in a certain time period and the average of all second fluctuations in the neighborhood is large, the larger the fluctuation coefficient of the electrical data is, the more likely the circuit breaker is to be in a faulty state in the corresponding time period; conversely, if the difference between the second volatility of the electrical data and the average of all second fluctuations in the neighborhood is smaller, the smaller the second volatility of the electrical data in the corresponding time period is, and the smaller the possibility of circuit breaker failure is.

[0089] Preferably, the schematic diagram of the circuit breaker switch device fault detection process provided in this embodiment is as follows Figure 4 shown.

[0090] S402: Determine the fault occurrence period of the circuit breaker based on the fluctuation coefficient of each type of electrical data in different time periods in combination with a clustering algorithm, and determine the abnormal fluctuation value of each type of electrical data in the circuit breaker based on the fluctuation coefficient of each type of electrical data in all time periods after the fault occurrence period.

[0091] When a circuit breaker fails before or during the detection process, the fault classification coefficients of the circuit breakers have obvious differences, so there is an obvious classification between the fault classification coefficients of the circuit breakers.

[0092] Therefore, the voltage fluctuation coefficients in all time periods are normalized, and the first preset number of normalized results in the results of the normalized results sorted in ascending order of time are clustered to obtain multiple clusters. The preset number value is artificially set. In this embodiment, the preset number value range is set from 4 to 50. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0093] Preferably, the clustering input data schematic diagram provided in this embodiment is as follows: Figure 5 As shown, 1-50 represents the sequence number of the voltage fluctuation coefficient in 50 time periods.

[0094] Furthermore, the number of clusters under different preset numbers is counted. If the number of clusters under any preset number is greater than 1, the circuit breaker fails, indicating that there are outlier data in all fluctuation coefficients, that is, there are differences in the data in different time periods of the circuit breaker, and the voltage change is unstable. Therefore, the number of clusters is arranged from small to large according to the value of the preset number, and the time period corresponding to the first cluster number greater than 1 in the sorting result is taken as the time period when the circuit breaker fails; conversely, if the number of clusters under different preset numbers is less than or equal to 1, it means that the circuit breaker has not failed.

[0095] Furthermore, the average value of the fluctuation coefficient of each type of electrical data in all time periods after the fault occurrence period is analyzed and recorded as the abnormal fluctuation value of each type of electrical data in the circuit breaker.

[0096] Step S5: Detecting the fault of the circuit breaker based on the abnormal current fluctuation value and the abnormal voltage fluctuation value in each fault occurrence period.

[0097] (1) In the historical data of the circuit breaker, for different fault types, according to the data collection method in step S1, a preset value of data collection is performed for each fault type, where the different fault types include circuit breaker tripping fault, short circuit fault, insulation fault (leakage), and poor contact fault.

[0098] It should be noted that the value of the preset numerical value is manually set. In this embodiment, the value of the preset numerical value is 100. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0099] (2) Further, according to steps S2-S4, under each fault type, the preset numerical values ​​of the abnormal fluctuation values ​​of various electrical data are obtained respectively, and the mean of the preset numerical values ​​of the abnormal fluctuation values ​​of each type of electrical data is calculated respectively, recorded as the abnormal mean, and the abnormal means of all different types of electrical data are combined to form a fault reference data pair corresponding to the fault type.

[0100] (3) Further, the abnormal fluctuation values ​​of all different types of electrical data of the circuit breaker at the current moment obtained in step S4 are combined into a fault data pair of the circuit breaker.

[0101] (4) The fault type of the circuit breaker is the fault type corresponding to the fault reference data pair that is closest to the fault data pair of the circuit breaker.

[0102] It should be noted that there are many methods for calculating the distance between a fault data pair and a fault reference data pair. In this embodiment, Euclidean distance is used. The implementer may also use other distance calculation methods such as Manhattan distance and Chebyshev distance. This embodiment does not impose any special restrictions.

[0103] The calculation process of the Euclidean distance is a well-known technique, and the specific calculation process will not be described in detail.

[0104] Preferably, the schematic diagram of the circuit breaker switch device fault detection process provided in this embodiment is as follows Figure 6 shown.

[0105] So far, this embodiment analyzes the voltage and voltage at the input and output ends of the circuit breaker, constructs the fluctuation coefficient in different time periods, uses the fluctuation coefficient of the circuit breaker to determine whether the circuit breaker has a fault, and calculates the fault data pair. By comparing with the fault benchmark data pairs of different fault types, the fault type of the circuit breaker is identified, thereby improving the accuracy of identifying the fault type of the circuit breaker switch device.

[0106] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0108] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for online fault detection and analysis of a switchgear, characterized in that: The method comprises the following steps: Obtain input and output data of various electrical data of the circuit breaker at all times within a preset time period before the current time; Divide the preset time into multiple time periods, and determine the dynamic change degree of each type of electrical data in each time period based on the difference between the input data and the output data of each type of electrical data at any same time in each time period, and the similarity between the input data and the output data of each type of electrical data in the frequency domain; Based on the discrete degree of the difference in the dynamic change degree of each type of electrical data between each time period and all previous time periods, combined with the dynamic change degree, determine the first fluctuation degree of each type of electrical data in each time period, and combine the difference between the dynamic change degree of each type of data in each time period and the minimum value of the dynamic change degree of all time periods to determine the second fluctuation degree of each type of electrical data in each time period; Determine the fluctuation coefficient of each type of electrical data in each time period based on the difference between the second fluctuation degree of each type of electrical data in each time period and the average distribution of all second fluctuation degrees in its neighborhood; Based on the fluctuation coefficient of each type of electrical data in different time periods, combined with the clustering algorithm, the fault period of the circuit breaker is determined; based on the fluctuation coefficient of each type of electrical data in all time periods after the fault period, the abnormal fluctuation value of each type of electrical data in the circuit breaker is determined to detect the fault of the circuit breaker.

2. A method for online fault detection and analysis of a switch device as claimed in claim 1, characterized in that: The method for determining the dynamic change degree of each type of electrical data in each time period is as follows: A frequency domain conversion algorithm is used to convert all input data and output data of each type of electrical data in each time period into the frequency domain, thereby obtaining input frequency domain data and output frequency domain data of each type of electrical data in each time period; Based on the difference between the input data and the output data of each electrical data at any same time in each time period, combined with the similarity between the input frequency domain data and the output frequency domain data of each electrical data, the dynamic change degree of each electrical data in each time period is determined.

3. A method for online fault detection and analysis of a switch device as claimed in claim 2, characterized in that: The expression of the dynamic change degree of each type of electrical data in each time period is: ; In the formula, Indicates the dynamic change degree of the kth type of electrical data in the i-th period; , They represent the kth input and output electrical data at the jth moment in the i-th time period respectively; Indicates the similarity between the set of input frequency domain data of the kth type and the set of output frequency domain data in the i-th time period; represents all moments in the i-th period; Indicates a preset constant greater than 0.

4. A method for online fault detection and analysis of a switch device according to claim 1, characterized in that: The first fluctuation degree of each type of electrical data in each time period is a result of integrating the dynamic change degree of each type of electrical data in each time period with the discrete degree.

5. A method for online fault detection and analysis of a switch device according to claim 1, characterized in that: The method for determining the second fluctuation degree of each type of electrical data in each time period is as follows: Determine the fluctuation index of each type of electrical data in each time period based on the difference between the dynamic change degree of each type of data in each time period and the minimum value of the dynamic change degree of all time periods; The second volatility of each type of electrical data in each time period is the result of integrating the first volatility of each type of electrical data in each time period with the volatility index.

6. A method for online fault detection and analysis of a switch device as claimed in claim 5, characterized in that: The expression of the fluctuation index of each type of electrical data in each time period is: ; In the formula, It represents the fluctuation index of the kth type of electricity data in the i-th period; Indicates the dynamic change degree of the kth type of electrical data in the i-th period; Indicates the dynamic change degree of the kth type of electrical data in all time periods within the preset time period; min() represents the minimum function.

7. A method for online fault detection and analysis of a switch device according to claim 1, characterized in that: The expression of the fluctuation coefficient of each type of electrical data in each time period is: ; In the formula, It represents the fluctuation coefficient of the kth type of electrical data in the i-th period; represents the second fluctuation of the kth type of electrical data in the zth time period in the neighborhood of the i-th time period; represents the mean value of the second fluctuation of the k-th type of electrical data in all time periods in the neighborhood of the i-th time period; represents the number of all time periods in the neighborhood of the i-th time period; Indicates a preset constant greater than 0.

8. A method for online fault detection and analysis of a switch device as claimed in claim 1, characterized in that: The process of determining the fault occurrence period of the circuit breaker is as follows: Voltage data is one of various electrical data. The fluctuation coefficients of voltage data in all time periods are normalized, and the first preset number of normalized results of the normalized results sorted in ascending order of time are clustered to obtain multiple clusters. The number of clusters under different preset numbers is counted. If the number of clusters under any preset number is greater than 1, the circuit breaker fails. The number of clusters is arranged from small to large according to the value of the preset number. The time period corresponding to the first cluster number greater than 1 in the sorting result is taken as the time period when the circuit breaker fails.

9. A method for online fault detection and analysis of a switch device as claimed in claim 1, characterized in that: The abnormal fluctuation value of each type of electrical data in the circuit breaker is the average value of the fluctuation coefficient of each type of electrical data in all time periods after the fault occurrence period.

10. A method for online fault detection and analysis of a switch device according to claim 1, characterized in that: The detecting of the fault of the circuit breaker comprises: The abnormal fluctuation values ​​of the preset numerical values ​​of each type of electrical data under different fault types are obtained from the historical data respectively; the mean of the abnormal fluctuation values ​​of the preset numerical values ​​of each type of electrical data under the same fault type is calculated respectively, which is recorded as the abnormal mean value; the abnormal means of all different types of electrical data constitute the fault reference data pair of the same fault type; the abnormal fluctuation values ​​of all different types of electrical data of the circuit breaker at the current moment constitute the fault data pair of the circuit breaker; the fault type of the circuit breaker is the fault type corresponding to the fault reference data pair that is closest to the fault data pair of the circuit breaker.

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