Distribution network insulator early fault detection method and system considering interference events

Abstract

The present invention proposes a method and system for detecting early-stage faults of distribution network insulators taking interference events into consideration, comprising: obtaining the zero-sequence current of the distribution network insulator and calculating the zero-sequence current mutation rate based on the zero-sequence current; if the zero-sequence current mutation rate is greater than a set threshold, determining that a fault or disturbance event has occurred in the distribution network; obtaining original recorded wave data of the distribution network insulator and filtering the original recorded wave data based on a wavelet reconstruction algorithm; for the filtered signal, identifying a spike waveform based on the extreme value point of the fault phase voltage; identifying a nonlinear distorted waveform based on sinusoidal fitting of the zero-sequence current, and, based on the identification result, satisfying a set first criterion or a set second criterion, determining that an early-stage insulator fault may have occurred; and then calculating the wavelet energy entropy of the signal; if it is greater than a set entropy threshold, determining that an early-stage insulator fault has occurred in the distribution network; otherwise, determining that an interference event has occurred.

Description

Technical Field

[0001] The present invention belongs to the technical field of early fault detection of insulators in distribution networks, and in particular relates to a method and system for early fault detection of insulators in distribution networks taking interference events into consideration. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The distribution network is a crucial component of the power system, and its power supply reliability is closely linked to the safe production and well-being of electricity users. Improving the fault diagnosis and handling capabilities of the distribution network is an effective way to ensure its reliability, shorten outages, and improve the user experience. Furthermore, as requirements for distribution network reliability and safety increase, the need to shift from post-fault diagnosis to pre-fault warning is growing stronger.

[0004] During the development of distribution network faults, equipment damage typically progresses from quantitative to qualitative. For example, under operating or lightning overvoltage conditions, weak insulation points in equipment can break down, generating arcs. However, due to the small electrical parameters of the fault in low-current grounding systems, they are insufficient to trigger protection. Furthermore, the complex and ever-changing nature of distribution network scenarios, coupled with the characteristics of non-faulty operations and early-stage faults, makes accurate early-stage fault detection more difficult.

[0005] For example, early-stage faults in distribution network insulators occur intermittently, with weak electrical signatures. These signals are often lost amidst numerous disturbances, making them easily overlooked. The high temperatures and thermal stresses generated by intermittent arcing can cause irreversible damage to the porcelain surface. Accumulated damage can cause early-stage faults to gradually transform into permanent failures such as cracking and disconnection, severely impacting both the power grid and personnel. Therefore, it is necessary to analyze the characteristics of early-stage insulator faults to detect and eliminate potential faults before the relay protection system activates, thereby reducing the burden of existing fault handling.

[0006] Early-stage faults in distribution network insulators are subtle, with characteristics varying depending on the degree of insulation degradation and location. These faults can easily be confused with normal distribution network disturbances, such as capacitor switching and magnetizing inrush current. Therefore, the challenge in early-stage fault detection lies in sensitively detecting fault events while distinguishing them from interference events to ensure reliable detection. Summary of the Invention

[0007] To overcome the above-mentioned deficiencies of the prior art, the present invention provides a method for early fault detection of insulators in distribution networks taking interference events into consideration, and performs early detection based on multi-dimensional features to improve the sensitivity and reliability of the detection method.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0009] In a first aspect, a method for detecting early faults of insulators in a distribution network taking interference events into consideration is disclosed, comprising:

[0010] Obtain the zero-sequence current of the distribution network insulator and calculate the zero-sequence current mutation rate based on the zero-sequence current;

[0011] If the zero-sequence current mutation rate is greater than the set threshold, it is determined that a fault or disturbance event has occurred in the distribution network;

[0012] Obtain the original recorded data of the distribution network insulators and filter the original recorded data based on the wavelet reconstruction algorithm;

[0013] For the filtered signal, the spike waveform is identified based on the extreme point of the fault phase voltage; the nonlinear distortion waveform is identified based on the zero-sequence current sine fitting. Based on the identification result, if the set first or second criterion is met, it is determined that an insulator early fault may have occurred;

[0014] Then the wavelet energy entropy of the signal is calculated. If it is greater than the set entropy threshold, it is an early fault of the distribution network insulator, otherwise it is an interference event.

[0015] As a further technical solution, the filtered signal is evaluated by a curve fitting index, and the signal reconstruction result with the minimum index is used as the required fault feature extraction waveform.

[0016] As a further technical solution, the spike waveform is identified based on the extreme point of the fault phase voltage, specifically including: finding the extreme point of the fault phase voltage near the breakdown moment to identify the arcing spike, and then finding the next voltage extreme point. When the first criterion is met, it is considered to be an arcing spike.

[0017] As a further technical solution, when the first criterion is met, it is considered to be an arc extinction peak, and the criterion is specifically set as follows:

[0018]

[0019] Among them, D1 represents the extreme point of breakdown moment, D h is the hth extreme point after breakdown; T D Corresponding to the extreme point moment, T is the power frequency period; A D is the amplitude of the extreme point; Used to indicate the increasing or decreasing trend of the arc.

[0020] As a further technical solution, the nonlinear distortion waveform is identified based on zero-sequence current sinusoidal fitting, specifically including:

[0021] The zero-sequence current data is divided into time windows of half the power frequency cycle length, and each segment of data takes the zero-sequence current point as the starting point;

[0022] For each point in the time window, a sine function is fitted to the zero-crossing point, and a total of N / 2 sine functions with different amplitudes are obtained;

[0023] The amplitudes of N / 2 sinusoidal functions in the time window tend to the maximum amplitude. When the amplitudes of the sinusoidal functions in the time window meet the second criterion, there is zero-sequence current nonlinear distortion.

[0024] As a further technical solution, the second criterion is specifically:

[0025] k1max(A mp (n)) <M(A mp (n)) <k2max(A mp (n))

[0026] Among them, A mp (n) is the set of amplitudes of each sine function in the time window, M(A mp (n)) is the median of the data, and k1 and k2 are the lower and upper boundary coefficients.

[0027] As a further technical solution, interference event identification is based on wavelet time-frequency energy entropy. The energy entropy index calculation process is as follows:

[0028]

[0029] Among them, p j is the wavelet energy probability of each layer, D is the number of decomposition layers; C2 is the energy entropy index, and THR2 is the threshold obtained according to the experiment.

[0030] In a second aspect, a distribution network insulator early fault detection system considering interference events is disclosed, comprising:

[0031] The zero-sequence current mutation rate calculation module is configured to: obtain the zero-sequence current of the distribution network insulator and calculate the zero-sequence current mutation rate based on the zero-sequence current;

[0032] If the zero-sequence current mutation rate is greater than the set threshold, it is determined that a fault or disturbance event has occurred in the distribution network;

[0033] The recording data filtering module is configured to: obtain the original recording data of the distribution network insulator and filter the original recording data based on the wavelet reconstruction algorithm;

[0034] The waveform recognition module is configured to: for the filtered signal, identify the peak waveform based on the extreme point of the fault phase voltage; identify the nonlinear distortion waveform based on the zero-sequence current sine fitting; and determine that an insulator early fault may have occurred when the identification result meets the first or second criteria set;

[0035] The judgment module is configured to: then calculate the wavelet energy entropy of the signal, if it is greater than the set entropy threshold, it is an early fault of the distribution network insulator, otherwise it is an interference event.

[0036] One or more of the above technical solutions have the following beneficial effects:

[0037] Since the electrical quantity characteristics of early faults are weak, designing detection criteria based on fault waveform characteristics can improve detection sensitivity. In this process, it is necessary not only to filter the recorded data, but also to retain the slight change trend in the original data. Using wavelet transform to decompose the original recorded data into different frequency bands and designing optimal indicators to retain the effective frequency band for reconstruction is a reliable method; considering the need to distinguish between faults and disturbance events, wavelet transform should not be limited to noise reduction processing. Using wavelet transform to perform further time-frequency analysis on the original recorded data and combining the distribution law of time-frequency characteristics to design criteria can effectively distinguish between faults and disturbance events.

[0038] The technical solution of the present invention analyzes the early fault waveforms of distribution network insulators based on measured voltage and current data, and uses waveform characteristics as effective features for early fault detection; considering the influence of short-term and long-term transient interference events, based on wavelet transform processing, the time-frequency characteristic distribution of faults and disturbances is analyzed, and accurate identification of early faults is achieved based on characteristic differences.

[0039] Since there is noise in the measuring device and high-frequency arcing is prone to occur during early insulator faults, it is not conducive to the extraction of waveform features. The technical solution of the present invention uses the curve change trend as an indicator, utilizes wavelet reconstruction to denoise the fault phase voltage and zero-sequence current signals, and uses the denoised signals to extract waveform fault features to design the starting judgment criteria.

[0040] The technical solution of the present invention combines the time-frequency distribution characteristics of early faults, extracts the energy of each frequency band after the zero-sequence current wavelet transform, and designs auxiliary criteria using the frequency band energy entropy through differentiated comparison with the time-frequency distribution of interference events; early fault detection is based on the multi-dimensional characteristics of the fault phase voltage, zero-sequence current waveform changes and zero-sequence current time-frequency energy distribution, thereby improving the sensitivity and reliability of the detection method.

[0041] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0043] Figure 1 This is a schematic diagram of recording early faults of insulators in a distribution network according to an embodiment of the present invention;

[0044] Figure 2 Schematic diagram of the waveforms of the fault phase voltage and zero-sequence current after filtering according to an embodiment of the present invention;

[0045] Figure 3 Schematic diagram of zero-sequence current nonlinear distortion characteristics according to an embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram of recorded data of a power distribution system interference event according to an embodiment of the present invention;

[0047] Figure 5 Schematic diagram of time-frequency analysis results of fault and disturbance signals according to an embodiment of the present invention;

[0048] Figure 6 Schematic diagram of time-frequency analysis results of fault and disturbance signals according to an embodiment of the present invention;

[0049] Figure 7 This is a flow chart of early fault detection in a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0051] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0052] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0053] Analysis of early fault characteristics of distribution network insulators based on recorded data:

[0054] The development of early faults in distribution network insulators can be summarized into two common types on site. One is the accumulation of dirt on the surface of the insulator after being exposed to wind and rain, which leads to surface flashover. The other is the appearance of cracks in the insulator under long-term mechanical load and external environment of hot and cold changes. After the intrusion of water vapor, local arcs are generated, which further evolves into flashover.

[0055] Regardless of the type of early-stage fault, the evolution process is similar: due to the degradation of the equipment's insulation performance, a high-resistance area appears in a certain part of the insulator, and the voltage at both ends is applied almost entirely there. When the applied voltage reaches the breakdown voltage, a local arc appears, and as the arc extends toward the two poles, a flashover occurs.

[0056] For fault currents, the instantaneous breakdown current undergoes a sudden change, accompanied by high-frequency arcing. Discharge along the insulator's external surface creates a long arc, and due to the influence of external airflow, the heat dissipation power is high. However, internal cracks in the insulator are narrow, and the creepage distance is much smaller than that of external surface flashover. Heat dissipation relies primarily on the porcelain body, and localized flashover can rapidly develop. When the fault current drops to a certain value, the arc column spreads too rapidly to maintain dynamic equilibrium, leading to arc extinction. Early insulator failures often occur near the voltage peak, often during the rising phase, so the discharge duration is theoretically less than half a cycle.

[0057] It should be noted that in the early stage of the arc, the current change rate of the high-frequency current component is very large at the zero crossing point, and there is a possibility that the arc will not be extinguished even though the current is small; usually the arc current change rate inside the crack is relatively large, and no current interruption will occur during the high-frequency arcing period. After a short oscillation, the fault current is close to the power frequency current; while the external surface arc current change rate is even smaller, and current interruption often occurs when the high-frequency arcing passes through zero.

[0058] like Figure 1 The figure shows the waveform of the insulator early fault simulated on site; Figure 1 (a) and (c) are the zero-sequence current and fault phase voltage of the insulator's external surface discharge, respectively; Figure 1 Figures (b) and (d) show the zero-sequence current and fault phase voltage, respectively, caused by internal crack discharges in the insulator. It can be observed that when the insulator surface breaks down, the initial arcing time is short and the overall current distortion is high. However, after internal crack breakdown, high-frequency arcing occurs, and the fault current exhibits a sinusoidal waveform for a period of time. Furthermore, the fault phase voltage exhibits arcing and extinction spikes during each arcing and extinction event.

[0059] Example 1

[0060] This embodiment discloses a method for detecting early faults of insulators in a distribution network taking interference events into consideration. Figure 7 As shown, including:

[0061] Step S1: using a fault recording device to collect fault phase voltage and zero-sequence current data;

[0062] Step S2: The collected data is judged using the start criterion. If the start criterion is not met, the process returns to step S1. If the start criterion is met, the process proceeds to step S3.

[0063] Step S3: Determine whether the collected data meets the voltage criterion or the current criterion. If not, return to step S1. If so, proceed to step S4.

[0064] Step S4: Calculate the wavelet energy entropy. If it is greater than a set threshold, it is determined to be an early fault of the distribution network insulator; if it is less than a threshold, it is determined to be a disturbance event.

[0065] In this implementation, steps S2, S3, and S4 extract multiple fault features to achieve early fault detection and identification, improving detection reliability through multi-level processing. Step 3 designs voltage and current criteria based on the waveform characteristics of early faults. These criteria are not conventional but are adjusted based on the waveform characteristics. Step 4 designs criteria based on the time-frequency energy distribution of early faults and system disturbances, also adaptively adjusted. These three steps resemble the sequential detection process in the flowchart. When all criteria are met, it is determined to be an early fault. Previous detection methods were mostly based on a single feature, but this implementation extracts multiple fault features to jointly design detection criteria to improve detection reliability.

[0066] In step S2, the sub-technical solution of this embodiment uses the zero-sequence current mutation rate as the starting criterion. When the zero-sequence current mutation rate is greater than the threshold at a certain moment, it is considered that a fault or disturbance event has occurred in the distribution network, and the subsequent diagnosis process is started:

[0067] C1=di0 / dt>THR1 (1)

[0068] Among them, C1 is the mutation rate indicator, i0 is the zero-sequence current, and THR1 is the threshold value obtained according to the experiment.

[0069] The manifestation of early faults of insulators varies according to the degree of insulation degradation. When the insulation is acceptable, the early fault is mainly intermittent discharge with a short duration; as the degree of insulation damage deepens, the early fault is similar to a high-resistance grounding fault, and the fault current is a periodic waveform with distortion at the zero crossing point. For example Figure 1 In (b), a destructive test is conducted on an insulator. The insulator is drilled vertically to the hardware. When the weather is clear, the insulator will not break down and discharge. However, when water invades the insulator, the early fault develops rapidly. After multiple breakdown discharges, a stable discharge channel is formed. Due to the continuous evaporation and invasion of water during this process, high-frequency arcing is exhibited at each zero-rest moment in the early stage of the fault.

[0070] For intermittent arcs, the sub-technical solution of this embodiment uses the current mutation value to detect the breakdown moment. When the fault phase voltage has both arcing peaks and arcing peaks within half a cycle, it is considered to be a valid fault feature. For early faults with fast and stable discharge, the zero-off period caused by the nonlinear distortion characteristics of the zero-sequence current is quantified and extracted as a fault feature.

[0071] In step S3: before determining whether the collected data meets the voltage criterion or the current criterion, it is necessary to extract the fault waveform features based on wavelet reconstruction:

[0072] The above method uses the current mutation rate during breakdown arcing as the starting criterion, but high-frequency components and system noise will affect the subsequent extraction of waveform features such as the arcing peak, arcing peak, and zero-sequence current zero-off period of the fault phase voltage. The fault waveform feature extraction process of the present invention is as follows:

[0073] Step S3-1: Filter the original recorded data based on the wavelet reconstruction algorithm considering target optimization. It consists of two parts: wavelet decomposition and target optimization. The wavelet filtering algorithm is:

[0074]

[0075] Among them, S(t) is the original data, c j (t) is the jth component after wavelet transform, S F (t) is the filtered signal, obtained by optimizing the components to be filtered out. Since the subsequent fault characteristics are the quantization of the fault phase voltage spike waveform and the zero-sequence current zero-off period waveform, the filtered signal should focus on reflecting the changing trend of the original data and establish the evaluation index:

[0076]

[0077] Among them, E r is the curve fitting index, N is the number of sampling points of one cycle data; ΔS(t n ) and ΔS F (t n ) is the original signal and the filtered signal at each time t n Due to the presence of intermittent high-frequency noise in the early fault waveform, the degree of change is calculated by taking the difference of the data mean within a short time window; the signal reconstruction result with the minimum index is used as the required fault feature extraction waveform. Figure 2 As shown in Figure 2, the fault phase voltage waveform and zero-sequence current waveform before and after filtering are shown. After filtering, slight changes in the waveform can be seen.

[0078] Step S3-2: Identify the peak waveform based on the extreme point of the fault phase voltage.

[0079] After the fault detection program is started, the fault phase voltage extreme point is searched for near the moment of zero-sequence current mutation to identify the arcing spike. Then the next voltage extreme point is searched for. When the following criteria are met, it is considered to be an arcing spike:

[0080]

[0081] Among them, D1 represents the extreme point of breakdown moment, D h is the hth extreme point after breakdown; T Dh 、T D1 is the breakdown moment and the moment of the hth extreme point after breakdown; AD1, ADh is the amplitude of the hth extreme point after breakdown at the breakdown moment; T is the power frequency period; It is used to express the increasing or decreasing trend of the arc. Formula (4) improves the accuracy of arc extinction peak identification through the combined judgment method.

[0082] Step S3-3: Identify the nonlinear distortion waveform based on the zero-sequence current sinusoidal fitting.

[0083] After the fault detection program is started, the zero-sequence current data is divided into time windows of half the length of the power frequency cycle, and each segment of data takes the zero-sequence current point as the starting point. For each point in the time window, a sine function is fitted with the zero-crossing point, and a total of N / 2 sine functions with different amplitudes are obtained, where N is the number of sampling points in one cycle of data. According to the high-resistance fault resistance change curve, the equivalent fault resistance of the fault current is the smallest at the peak point and the largest near the zero-crossing point, and the fault resistance changes rapidly during the zero rest period. Therefore, the amplitudes corresponding to the N / 2 sine functions in the time window have an obvious distribution pattern, which can be attributed to the fact that the amplitudes of most sine functions tend to the maximum amplitude, such as Figure 3 shown.

[0084] When the amplitudes of the sine functions within the time window meet the following criteria, it is considered that zero-sequence current nonlinear distortion exists:

[0085] k1max(A mp (n)) <M(A mp (n)) <k2max(A mp (n)) (5)

[0086] Among them, A mp (n) is the set of amplitudes of each sine function in the time window, M(A mp (n)) is the median of the data, k1 and k2 are the lower and upper boundary coefficients, and this criterion is a targeted criterion proposed by the present invention based on the above nonlinear characteristics.

[0087] When either the voltage criterion (4) or the current criterion (5) is met, it is considered that an insulator early failure may have occurred.

[0088] In step S4, interference events are identified based on wavelet time-frequency energy entropy.

[0089] The early fault of the insulator in the distribution network is self-recovering and hidden. At the signal level, it is similar to some system interference events. After the disturbance event occurs, it may cause the false triggering of the detection criteria. Figure 4 As shown, Figure 4 (a) is the on-site capacitor switching. Figure 4The waveforms of the magnetizing inrush current recording in (b) belong to instantaneous transient interference and long-term transient interference respectively. The former has instantaneous discharge phenomenon, which is easily confused with intermittent arc, while the latter also has nonlinear distortion.

[0090] In order to ensure the safety of fault detection, the present invention uses wavelet transform to analyze the time-frequency characteristics of the fault waveform and the disturbance waveform, and designs a criterion to distinguish between faults and interference from the perspective of frequency band energy distribution. The process is as follows:

[0091] Step S4-1: Signal time-frequency difference analysis.

[0092] The time-frequency analysis of zero-sequence current of fault and disturbance signals is performed using wavelet transform. The results are as follows: Figure 5 As shown, Figure 5 (a) is the analysis result of the zero-sequence current signal of the early fault. Figure 5 (b) is the analysis result of the capacitor switching zero-sequence current signal. Figure 5 (c) in the figure is the analysis result of the zero-sequence current signal of the excitation inrush current.

[0093] It can be seen that at the moment of insulator early fault breakdown, the zero-sequence current shows a broadband distribution in the frequency domain, which is related to the arc burning and extinction in the early stage of the early fault; while the zero-sequence current of capacitor switching and excitation inrush current is more concentrated in the frequency domain distribution.

[0094] Step S4-2: Design an energy entropy criterion to distinguish between faults and disturbances.

[0095] According to the above analysis, we can extract features from the energy distribution law to distinguish fault signals from disturbance signals, such as Figure 6 As shown in Figure 2, the energy distribution of early faults in the frequency domain is more chaotic.

[0096] The wavelet energy entropy index is designed based on information entropy. The more chaotic the information, the greater the information entropy value. In order to further amplify the energy entropy of early faults, a distribution coefficient is set for the energy of each frequency band. For system interference, since the energy distribution is relatively concentrated, the distribution coefficient has little effect on the calculation of energy entropy. However, for early faults, an uneven distribution coefficient will further increase the degree of energy distribution chaos, which is beneficial for distinguishing between faults and disturbances:

[0097]

[0098] Among them, p j is the wavelet energy probability of each layer; E j is the wavelet energy of each layer; i jis the j-th wavelet coefficient of the zero-sequence current; D is the number of wavelet coefficient layers. DB4 is selected as the mother wavelet. The zero-sequence current is decomposed into 7 layers to obtain 7 detail coefficients and 1 approximate coefficient; λ(j) is the set distribution coefficient, which can be manually set through field tests and used to combine with the time-frequency characteristic distribution characteristics of the early fault of the present invention; C2 is the energy entropy index, and THR2 is the threshold value obtained according to the experiment.

[0099] It should be noted that although wavelet energy and information entropy methods are commonly used in fault detection, the present invention performs time-frequency analysis on the field recorded waveforms of early faults and system disturbance events, and specifically designs wavelet energy entropy criteria; among them, the time-frequency energy difference analysis between early faults and other disturbances is not available in previous studies.

[0100] Overall, the flow chart of the early fault detection method for distribution network insulators is as follows: Figure 7 As shown in the figure, a multi-level fault diagnosis method is adopted to sensitively detect the occurrence of early faults while avoiding noise and interference events.

[0101] The present invention analyzes the on-site recording data of early-stage faults of insulators in the distribution network, extracts fault characteristics from the waveform angle, and designs detection criteria. It uses the current mutation rate as the starting criterion, and quantitatively extracts the arcing and arcing peak waveforms of the fault phase voltage and the nonlinear waveform characteristics of the zero-sequence current to identify possible early-stage faults.

[0102] The present invention analyzes the time-frequency characteristics of fault and disturbance signals through wavelet transformation, designs an energy entropy index from the perspective of energy distribution law, and is used to distinguish between fault and disturbance events; combines multi-dimensional fault characteristics to design a multi-level fault diagnosis method to ensure the sensitivity and reliability of detecting weak fault signals when early faults occur.

[0103] Example 2

[0104] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0105] Example 3

[0106] The purpose of this embodiment is to provide a computer-readable storage medium.

[0107] A computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the above method.

[0108] Example 4

[0109] The purpose of this embodiment is to provide a distribution network insulator early fault detection system that takes interference events into consideration, including:

[0110] The zero-sequence current mutation rate calculation module is configured to: obtain the zero-sequence current of the distribution network insulator and calculate the zero-sequence current mutation rate based on the zero-sequence current;

[0111] If the zero-sequence current mutation rate is greater than the set threshold, it is determined that a fault or disturbance event has occurred in the distribution network;

[0112] The recording data filtering module is configured to: obtain the original recording data of the distribution network insulator and filter the original recording data based on the wavelet reconstruction algorithm;

[0113] The waveform recognition module is configured to: identify the peak waveform of the filtered signal based on the extreme point of the fault phase voltage; identify the nonlinear distortion waveform based on the zero-sequence current sine fitting; and determine that an insulator early fault may have occurred when the identification result meets the first or second criteria set;

[0114] The judgment module is configured to: then calculate the wavelet energy entropy of the signal, if it is greater than the set entropy threshold, it is an early fault of the distribution network insulator, otherwise it is an interference event.

[0115] Example 5

[0116] The purpose of this embodiment is to provide a computer program product containing instructions, which, when running on a computer, enables the computer to execute the methods and functions involved in any of the above embodiments.

[0117] The steps involved in the apparatus of the above embodiment correspond to those of the method embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0118] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0119] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for early fault detection of insulators in distribution networks considering interference events, characterized by: include: Obtain the zero-sequence current of the distribution network insulator and calculate the zero-sequence current mutation rate based on the zero-sequence current; If the zero-sequence current mutation rate is greater than the set threshold, it is determined that a fault or disturbance event has occurred in the distribution network; Obtain the original recorded data of the distribution network insulators and filter the original recorded data based on the wavelet reconstruction algorithm; For the filtered signal, the spike waveform is identified based on the extreme value point of the fault phase voltage; Identify the nonlinear distortion waveform based on zero-sequence current sinusoidal fitting, and determine that an insulator early fault may have occurred when the identification result meets the first or second criteria set; Then the wavelet energy entropy of the signal is calculated. If it is greater than the set entropy threshold, it is an early fault of the distribution network insulator, otherwise it is an interference event. When the first criterion is met, it is considered to be an arc extinction peak. The specific criterion is set as follows: in, D 1 represents the extreme point at the breakdown moment, D h After the breakdown h extreme points; T D At the moment of the extreme point, T is the power frequency period; A D is the amplitude of the extreme point; Used to indicate the increasing or decreasing trend of the arc; Identify nonlinear distortion waveforms based on zero-sequence current sinusoidal fitting, specifically including: The zero-sequence current data is divided into time windows of half the power frequency cycle length, and each segment of data takes the zero-sequence current point as the starting point; For each point in the time window, a sine function is fitted to the zero-crossing point, and the total N / 2 sine functions with different amplitudes; In the time window N / 2 The amplitudes of the sine functions tend to the maximum amplitude. When the amplitudes of the sine functions meet the second criterion within the time window, there is zero-sequence current nonlinear distortion; The second criterion is specifically: in, A mp ( n ) is the set of amplitudes of each sine function in the time window, M ( A mp ( n ))The median of the data, k 1 and k 2 is the lower and upper boundary coefficients; Interference event identification based on wavelet time-frequency energy entropy, the energy entropy index calculation process is: in, p j is the wavelet energy probability of each layer, D is the number of decomposition layers; C 2Energy entropy index, THR 2 is the threshold value obtained from the experiment.

2. The method for detecting early faults of insulators in a distribution network considering interference events according to claim 1, wherein: The filtered signal is evaluated by the curve fitting index, and the signal reconstruction result with the minimum index is used as the required fault feature extraction waveform.

3. The method for detecting early faults of insulators in a distribution network considering interference events according to claim 1, wherein: The spike waveform is identified based on the extreme point of the fault phase voltage, specifically including: finding the extreme point of the fault phase voltage near the breakdown moment to identify the arcing spike, and then finding the next voltage extreme point. When the first criterion is met, it is considered to be an arcing spike.

4. A distribution network insulator early fault detection system considering interference events, applying the distribution network insulator early fault detection method considering interference events according to any one of claims 1 to 3, characterized in that: include: The zero-sequence current mutation rate calculation module is configured to: obtain the zero-sequence current of the distribution network insulator and calculate the zero-sequence current mutation rate based on the zero-sequence current; If the zero-sequence current mutation rate is greater than the set threshold, it is determined that a fault or disturbance event has occurred in the distribution network; The recording data filtering module is configured to: obtain the original recording data of the distribution network insulator and filter the original recording data based on the wavelet reconstruction algorithm; The waveform recognition module is configured to: for the filtered signal, identify the peak waveform based on the extreme value point of the fault phase voltage; Identify the nonlinear distortion waveform based on zero-sequence current sinusoidal fitting, and determine that an insulator early fault may have occurred when the identification result meets the first or second criteria set; The judgment module is configured to: then calculate the wavelet energy entropy of the signal, if it is greater than the set entropy threshold, it is an early fault of the distribution network insulator, otherwise it is an interference event.

5. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method described in any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are performed.

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