Distortion center point identification-based power distribution network fault detection method and system

Abstract

The invention provides a power distribution network fault detection method and system based on distortion center point recognition, and the method comprises the steps: building a nonlinear fault waveform simplified mathematical model through a piecewise function, carrying out the fitting of a nonlinear fault waveform of a power distribution network based on the model, and obtaining a fitted nonlinear fault waveform curve function; solving the fitted non-linear fault waveform curve function through Fourier transform to obtain a characteristic interval in which the phase difference between the fundamental wave and the third harmonic of the non-linear fault waveform at the distortion center point has regular distribution; processing an input fault signal to be recognized based on a similarity principle, and recognizing a nonlinear fault waveform distortion center point; processing a nonlinear fault waveform distortion center point to obtain a fundamental wave and third harmonic phase difference; designing a fault detection criterion based on the fundamental wave and third harmonic phase difference; and when the continuous set times in the set duration meet the fault detection criterion, determining that the distribution network fault with the nonlinear waveform characteristic occurs.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network fault detection, and in particular relates to a distribution network fault detection method and system based on distortion center point identification. 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 directly facing users and is the key link to ensure power supply quality and improve power grid operation efficiency. Detection and timely handling of distribution network faults are the prerequisites for improving the reliability of distribution network power supply. In my country, about 80% of distribution network faults are single-phase grounding faults, and most of the existing fault handling work is carried out for single-phase grounding faults.

[0004] In single-phase grounding faults, according to the size of the grounding resistance, it can be divided into two categories: low-resistance grounding faults and high-resistance grounding faults. For low-resistance grounding faults, traditional overcurrent protection methods can effectively solve them.

[0005] However, in medium-voltage distribution networks, ground faults are mostly caused by line breakage, tree branches touching the line, or insulator deterioration and breakdown. The ground resistance of such faults is relatively high, so the fault current is very weak, even within 1 ampere, and is classified as a high-resistance ground fault or early fault. The increase in transition resistance brings many difficulties to power system fault analysis and fixed value setting, and can easily lead to false operation or refusal of protection devices.

[0006] Whether it is a high-resistance grounding fault or an early fault, the detection method using electrical quantity characteristics has the disadvantage of insufficient sensitivity.

[0007] At present, many studies at home and abroad have found that the zero-sequence current after the above-mentioned fault has similar nonlinear distortion in the steady-state stage, and proposed detection methods based on the nonlinear characteristics of the waveform. However, most of the characteristics are summarized through experience of field recording, and lack of analysis of widely applicable characteristic laws.

[0008] In this field, zero-sequence current distortion is caused by nonlinear changes in fault resistance. The center of distortion can be regarded as the zero-sequence current point at the moment of the maximum value of the fault resistance, which appears twice in each power frequency cycle. There is no detection method for the center of distortion in existing research.

[0009] This part of the advantage is related to the fault characteristic "phase difference between zero-sequence current fundamental wave and third harmonic". Figure 4 and Figure 7 It can be seen that the phase difference interval at the center of distortion is small, while the phase difference interval at the "zero point" in previous studies is large, which means that extracting features at the center of distortion has higher reliability and applicability.

[0010] In the prior art, there are technical solutions that use algorithms to process zero-sequence current and then detect and analyze distribution network faults. The existing problems are: for the fault characteristics under nonlinear distortion of zero-sequence current, there is no in-depth analysis from the signal level, and the fault judgment criteria are often designed by empirical value thresholds. In addition, the existing method extracts the phase difference characteristics from the current zero-crossing point, and the fault detection reliability under some high-resistance grounding faults is poor. Summary of the invention

[0011] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a distribution network fault detection method based on distortion center point identification, which can continuously extract the phase difference between the fundamental wave and the third harmonic at the distortion center point and detect the occurrence of a fault after continuously triggering the criterion.

[0012] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0013] In a first aspect, a distribution network fault detection method based on distortion center point identification is disclosed, comprising:

[0014] A simplified mathematical model of nonlinear fault waveform is established through piecewise function, and the nonlinear fault waveform of the distribution network is fitted based on the model to obtain a fitted nonlinear fault waveform curve function;

[0015] The fitted nonlinear fault waveform curve function is solved by Fourier transform, and the characteristic interval of the phase difference between the fundamental wave and the third harmonic of the nonlinear fault waveform at the distortion center point is obtained;

[0016] Based on the similarity principle, the input fault signal to be identified is processed to identify the center point of nonlinear fault waveform distortion;

[0017] The phase difference between the fundamental wave and the third harmonic is obtained by processing the center point of the nonlinear fault waveform distortion;

[0018] Design the fault detection criterion based on the above fundamental wave and third harmonic phase difference;

[0019] When the set number of consecutive times within the set duration meets the fault detection criterion, it is determined that a distribution network fault with nonlinear waveform characteristics has occurred.

[0020] As a further technical solution, the simplified mathematical model of the nonlinear fault waveform includes three parts:

[0021] The first part of the formula is obtained by fitting L line segments with gradually increasing slopes, representing the process of arc resistance decreasing;

[0022] The second part of the formula is a straight line with a slope equal to 0, which indicates the stage when the arc resistance is stable;

[0023] The third part of the formula is fitted using L line segments, and the slope change trend is opposite to the first part of the curve.

[0024] As a further technical solution, the specific process of identifying the center point of the nonlinear fault waveform distortion is:

[0025] Select each half cycle data of the fault signal, take the starting point of each data segment as the starting point coordinate of the sine function, and the absolute value of the data peak point as the amplitude of the sine function, perform sine function fitting, and obtain the fitted fault signal;

[0026] The cosine similarity is used to evaluate the fitted fault signal and the standard sine signal. The similarity index of each sampling point is CS i ;

[0027] When the CS of a certain sampling point i When the set requirements are met, the point is judged to be a distortion point.

[0028] As a further technical solution, after identifying the center point of the nonlinear fault waveform distortion, the following is also included:

[0029] All distortion points are summarized as D L With D R , representing two sets of data located in the left and right intervals of the center position of F.

[0030] As a further technical solution, when the CS of a certain sampling point i When the set requirements are met, the set requirements are specifically the following formula:

[0031]

[0032] Where D is D L or D R Data set; M(D) represents the median of the data; MAD(D) represents the absolute median difference of the data;

[0033] The sampling points that cause the overall similarity CS0 to decrease are identified as signal distortion points, and the threshold is designed using the absolute median difference to remove the data set D L With D R Outliers in ;

[0034] F(n) and S(n) are fault signal and standard sinusoidal signal respectively, and N is the half-cycle sampling data length.

[0035] As a further technical solution, D L With D R The median difference can be obtained by obtaining the phase position D of the distortion center point. M , the calculation formula is:

[0036] DM =M(D L )-(N / 2-D R )

[0037] Among them, when D M When it is greater than zero, the center of distortion is located in the interval [0, N / 4] to the left of F, at position D M ; On the contrary, it is located in the left interval of F [N / 4,N / 2], and the position is N / 2+D M .

[0038] As a further technical solution, the fault detection criterion is specifically:

[0039]

[0040] in, and are the fundamental and third harmonic phases at the center of distortion, respectively.

[0041] In a second aspect, a distribution network fault detection system based on distortion center point identification is disclosed, comprising:

[0042] The model building module is configured to: establish a simplified mathematical model of a nonlinear fault waveform through a piecewise function, fit the nonlinear fault waveform of the distribution network based on the model, and obtain a fitted nonlinear fault waveform curve function;

[0043] The solution analysis module is configured to: solve the fitted nonlinear fault waveform curve function through Fourier transform to obtain a characteristic interval in which the phase difference between the fundamental wave and the third harmonic of the nonlinear fault waveform at the distortion center point has a regular distribution;

[0044] The identification module is configured to: process the input fault signal to be identified based on the similarity principle, and identify the center point of the nonlinear fault waveform distortion;

[0045] The fault detection module is configured to: process the center point of the nonlinear fault waveform distortion to obtain the phase difference between the fundamental wave and the third harmonic;

[0046] Design the fault detection criterion based on the above fundamental wave and third harmonic phase difference;

[0047] When the set number of consecutive times within the set duration meets the fault detection criterion, it is determined that a distribution network fault with nonlinear waveform characteristics has occurred.

[0048] One or more of the above technical solutions have the following beneficial effects:

[0049] The technical solution of the present invention constructs a simplified mathematical model of a nonlinear fault waveform. Based on Fourier transform, the relative relationship between the third harmonic and fundamental phasors in the fault current under different distortion conditions can be theoretically analyzed, and it is concluded that the phase difference between the fundamental and third harmonics of the nonlinear fault waveform at the center of the distortion has a regularly distributed characteristic interval. Compared with the traditional phase comparison method at the zero crossing point, it has higher reliability and wider applicability.

[0050] In order to extract the corresponding fault features, the technical solution of the present invention designs a nonlinear waveform distortion center point identification step, and designs a fault detection criterion through the phase difference between the fundamental wave and the third harmonic at the distortion center point.

[0051] Theoretically, the proposed method is not affected by the fault type, fault resistance and fault occurrence time. As long as a high-resistance grounding fault or an early fault occurs in the distribution network, the proposed method can continuously extract fault features and detect the occurrence of the fault after continuously triggering the judgment criteria.

[0052] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings in the specification, 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.

[0054] Figure 1 Schematic diagram of a nonlinear waveform example of a high-resistance grounding fault in a distribution network of the present invention; wherein (a) is a continuous recording waveform, and (b) is a local enlarged view;

[0055] Figure 2 Schematic diagram of the nonlinear waveform of the early fault of the distribution network of the present invention, wherein (a) is a continuous recording waveform and (b) is a local enlarged diagram;

[0056] Figure 3 It is a schematic diagram of the decomposition of a typical early fault current waveform of the present invention;

[0057] Figure 4 Schematic diagram of the third harmonic characteristic analysis of the nonlinear waveform of the present invention, (a) is the phase difference between the third harmonic and the fundamental wave, (b) is the amplitude ratio of the third harmonic to the fundamental wave;

[0058] Figure 5 Schematic diagram of the fifth harmonic characteristic analysis of the nonlinear waveform of the present invention, (a) is the phase difference between the fifth harmonic and the fundamental wave, (b) is the amplitude ratio of the fifth harmonic to the fundamental wave;

[0059] Figure 6Schematic diagram of the nonlinear curve fitted by the sine function and the amplitude factor function, (a) is the comparison between the measured curve and the fitting curve, (b) is the sine function curve and the amplitude factor function curve used for the fitting curve;

[0060] Figure 7 It is a schematic diagram of the phase difference between the fundamental wave and the third harmonic at the zero-crossing point;

[0061] Figure 8 It is a schematic diagram for identifying the center point of nonlinear waveform distortion;

[0062] Fig. 9 Schematic diagram of insulator early fault detection results. DETAILED DESCRIPTION

[0063] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0064] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0065] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0066] Embodiment 1

[0067] This embodiment discloses a distribution network fault detection method based on distortion center point identification, including:

[0068] Step 1: Analysis of nonlinear fault current waveform characteristics;

[0069] High-resistance grounding faults often occur when overhead lines are broken or sagging, and contact with non-metallic conductors such as concrete, trees, and lawns to form grounding faults. The grounding resistance generally ranges from several hundred ohms to several thousand ohms. Usually, the surface of the grounding medium is not smooth. When the conductor forms an electrical connection with the grounding medium and causes a grounding fault, it will be accompanied by nonlinear air arc breakdown or solid medium breakdown. Figure 1 As shown in Figure 1, the waveform of the overhead line disconnection high-resistance grounding fault in the distribution network is measured. (a) is the continuous recording waveform, and (b) is the local enlarged view.

[0070] Similarly, for equipment such as cable joints, insulators and lightning arresters, arcs will be generated when weak insulation points are broken down due to lightning strikes, operating overvoltages, etc. The high temperature of the arc will corrode the line insulation, causing irreversible losses and further reducing the insulation capacity of the equipment. After multiple intermittent arc processes, the distribution equipment will maintain a stable breakdown discharge phenomenon, as shown in the figure, which is the measured insulator early fault waveform of the distribution network, (a) is the continuous recording waveform, and (b) is a local enlarged view.

[0071] It can be seen that both high-resistance grounding faults and early faults have continuous and stable nonlinear distortion in the steady-state stage. It is essentially caused by the nonlinearity of the fault arc resistance and is related to the exchange of electrical and thermal energy, which will lead to an increase in the harmonic content of the current. However, under different grounding media and distribution network equipment, the degree of distortion, distortion duration and distortion offset of nonlinear distortion are different. From the perspective of solving equivalent circuits, after the nonlinear resistor is connected, it is difficult to obtain an analytical solution in time and frequency for the differential equation established by the circuit. Therefore, the fault characteristics are derived from the waveform perspective, and the process is as follows:

[0072] Step 1-1: Establish a simplified mathematical model of nonlinear fault waveform through piecewise function.

[0073] As shown in the figure, it is a typical nonlinear fault waveform. According to the change of arc resistance, the fault current waveform can be regarded as the superposition of N micro-elements, and each micro-element belongs to a part of a certain amplitude power frequency cycle:

[0074] L n (t) = m n sin(ω0t) (1)

[0075] Among them, m n is the amplitude of the nth infinitesimal element; ω0 is the angular frequency of the power frequency.

[0076] Therefore, the nonlinear fault waveform can be regarded as a sine wave with time-varying amplitude, which is defined as the synthesis of the amplitude factor function A(t) and the standard sine wave function sin(ω0t), and is expressed in a piecewise fitting manner. The mathematical expression is:

[0077]

[0078] Among them, k l1 、b l1 ,t l1 , k l2 、b l2 ,t l2 are the parameters of each line segment, l1 and l2 are the segmented line segments of the first half cycle and the second half cycle of the current peak point, with L segments respectively; T is the power frequency period.

[0079] Thus, within each half power frequency cycle, the amplitude factor function curve A(t) can be divided into three parts. The first part is fitted with L line segments with gradually increasing slopes, representing the process of the arc resistance decreasing; the second part is a straight line with a slope equal to 0, indicating the stage where the arc resistance is stable; the third part is also fitted with L line segments, but the slope change trend is opposite to that of the first part curve.

[0080] Step 1-2: Solve the fitted non-linear fault waveform curve function f(t) through Fourier transform. The fitted non-linear fault waveform curve function: the multiplication of Formula 1 and the sine function (after considering the offset angle), that is

[0081] To simplify the analysis, take L = 2 in the amplitude factor function A(t) and perform Fourier transform on the fitted non-linear fault waveform curve. Considering the different degrees of offset in the non-linear fault waveform, the solution formula is:

[0082]

[0083] In the formula, ω0 is the power frequency angular frequency; is the offset angle. The part within the integral of Formula (3) F(w) is the fault waveform.

[0084] The solution can obtain the complex expressions of each frequency current component:

[0085]

[0086] In the formula, k1, b1, t1 and k2, b2, t2 are the parameters of the first and second segments respectively and satisfy k1 = (b2 - b1) / t1 and k2 = (1 - b2) / (t2 - t1).

[0087] At the same time, stipulate the parameter boundaries in Formula (3), set 0 < t1 < t2 < 5ms and 0 < b1 < b2 < 1, representing the changes of the amplitude factor function under different distortion durations and distortion degrees. Due to different distortion offsets, the offset angle is not a fixed value. Combining with on-site tests, stipulate

[0088] After solving the above formula, it is found that the nonlinear fault current only contains the fundamental wave and odd harmonics, while the even harmonic content is 0. After solving formula (3), the phase difference and amplitude ratio between the third harmonic and the fundamental wave within the boundary range are shown in the figure, (a) is the phase difference between the third harmonic and the fundamental wave, and (b) is the amplitude ratio of the third harmonic to the fundamental wave. Among them, from the overall perspective of Figure (a), the phase difference between the two is in the range of 120° to 240°. When there is no distortion offset, the phase of the third harmonic is completely opposite to that of the fundamental wave. In addition, when the degree of distortion increases, it also approaches this characteristic. Similarly, the phase difference and amplitude ratio of the fifth harmonic to the fundamental wave are calculated, and the results are as follows: Figure 5 As shown, (a) is the phase difference between the fifth harmonic and the fundamental wave, and (b) is the amplitude ratio of the fifth harmonic to the fundamental wave.

[0089] By comparison, it can be seen that when the nonlinear fault waveform undergoes different distortions, the phase difference between the third harmonic and the fundamental wave is distributed in a wider range, generally in the range of 0° to 360°, and when there is no distortion offset, the phase difference exists in two scales of 180° and 0°. In addition, through the analysis of the change in amplitude ratio, as the degree of distortion increases, the amplitude of the fundamental wave decreases while the amplitude of each harmonic increases, and the third harmonic accounts for the largest proportion.

[0090] The above analysis clarifies the harmonic change characteristics caused by the nonlinearity of the fault waveform, among which the third harmonic content is the largest and the phase difference with the fundamental wave is more concentrated. For harmonics above the third harmonic, on the one hand, their amplitude is small, and it is difficult to accurately detect them in high-resistance grounding faults or early fault scenarios; on the other hand, there is no obvious phase characteristic interval under different fault scenarios. In the fault detection method based on harmonic characteristics, the third harmonic components and above will limit the applicability of the method to a certain extent.

[0091] Step 2: Fault detection step based on identification of the center point of nonlinear fault waveform distortion;

[0092] In the previous analysis, the core fault feature of the nonlinear fault waveform can be reflected as the relative relationship between the fundamental wave and the third harmonic phase, and according to the construction characteristics of formula (3), it is actually the relative relationship at the center point of the nonlinear fault waveform distortion. As shown in the figure, the nonlinear curve is fitted using the sine function and the amplitude factor function. (a) is the comparison between the measured curve and the fitting curve, and (b) is the sine function curve and the amplitude factor function curve used for fitting the curve. It can be seen that due to the existence of distortion offset, formula (3) will solve the phasor of each frequency signal at the center of distortion.

[0093] For the phase comparison method of the signal, traditional research believes that the fundamental wave and the third harmonic are in opposite phase at the current zero crossing point. This conclusion is based on the experience of field tests and is not universal. In fact, by offsetting the fundamental wave and the third harmonic obtained by equation (3), the theoretical distribution of the phase difference of the fault waveform at the zero crossing point can be obtained, as shown in the figure. It can be seen that under different distortions of the nonlinear fault waveform, the phase difference at the zero crossing point is distributed in the range of 45° to 315°, and the detection reliability is poor when used as a fault feature.

[0094] Therefore, the phase difference between the fundamental wave and the third harmonic at the center point of the nonlinear fault waveform distortion is selected as the feature quantity. The key lies in confirming the position of the distortion center point of the fault signal and extracting the phase difference between the fundamental wave and the third harmonic at this point. The specific steps are as follows:

[0095] Step 2-1: Identify the center point of nonlinear fault waveform distortion based on the similarity principle.

[0096] The zero-sequence current is continuously recorded as a fault signal, and a time window of half a cycle length is set to extract the fault characteristics. The data F in each time window takes the zero-crossing point as the starting point.

[0097] The starting point of each data segment is used as the starting point coordinate of the sine function, and the peak point of the data is used as the amplitude of the sine function to perform sine function fitting:

[0098]

[0099] In the formula, p S is the peak point of data F, N is the half-cycle sampling data length, and T is the power frequency period.

[0100] And use cosine similarity to evaluate the similarity of the two signals:

[0101]

[0102] Among them, F(n) and S(n) are the fault signal and the standard sinusoidal signal respectively, N is the half-cycle sampling data length, and CS0 is the overall similarity between the fault signal and the standard sinusoidal signal.

[0103] In each half-cycle time window data, a short time window W is set, W is centered at sampling point n, and the length is selected as N / 20, and the similarity of the remaining part is calculated using formula 6; the part belonging to W in the fault signal and the standard sine signal is removed, and the similarity of the remaining parts of the two signals is calculated. If the calculated similarity increases after removal, it means that the removed part W belongs to the distortion point. The same operation is performed on each sampling point, and all distortion points can be identified; the same processing is performed on each sampling point to obtain N similarity indicators CS i , when the CS of a certain sampling point iWhen equation (7) is satisfied, the point is judged to be a distortion point; the positions of all distortion points are summarized as D L With D R , representing two sets of data located in the left and right intervals of the center position of F.

[0104]

[0105] Where D is D L or D R Data set, when n is in the left interval of the center of F, take D L , otherwise take D R ; M(D) represents the median of the data; MAD(D) represents the absolute median difference of the data. The sampling points that cause the overall similarity CS0 to decrease are identified as signal distortion points. Then, the absolute median difference of the position distribution of the distortion points is used to design the threshold and remove the data set D L With D R Outliers in .

[0106] The median is the number in the middle of a set of data arranged in order. The absolute median difference is the median of the absolute deviations of each data point in the data set from the median.

[0107] It should be noted that similarity calculation and distortion point identification do not require separate threshold design. After calculating the overall similarity CS0, CS0 is the "threshold" used to determine the distortion point.

[0108] Then all the distortion point positions are divided into DL and DR parts. The adaptive threshold can be calculated through the absolute median difference. The distortion points that do not meet the criteria are regarded as abnormal points and removed. Finally, the remaining distortion point data set is used to calculate the distortion neutral point.

[0109] Since the amplitude factor function is symmetrical, by L With D R The median difference can be obtained by obtaining the position of the distortion center point D M , the calculation formula is:

[0110] D M =M(D L )-(N / 2-D R ) (8)

[0111] Among them, when D M When it is greater than zero, the center of distortion is located in the interval [0, N / 4] to the left of F, at position D M ; On the contrary, it is located in the left interval of F [N / 4,N / 2], and the position is N / 2+D M As shown in the figure, this is the distortion center point identified under a half-cycle nonlinear fault waveform.

[0112] Step 2-2: Design fault detection criteria using the phase difference between the fundamental wave and the third harmonic;

[0113] Based on the identification of the center point of nonlinear waveform distortion, the fault detection criterion can be written as:

[0114]

[0115] in, and are the fundamental and third harmonic phases at the center of distortion, respectively.

[0116] When the detection device (such as PMU, fault recorder, etc.) stores data of a certain period length, the distortion center point is identified for each half cycle, and the fundamental wave and third harmonic phase of the point are extracted using FFT. When the phase difference is in the interval of [120°, 240°], it is considered to be a valid fault detection criterion trigger. When the criterion is continuously met within a duration (for example, three to five cycles, triggered twice per cycle, corresponding to six to ten times for the above duration), it is considered that a distribution network fault with nonlinear waveform characteristics has occurred.

[0117] As shown in the figure, this is the detection result of the early fault recording data of the insulator. After the fault occurs, the phase difference between the fundamental wave and the third harmonic at the center point of the distortion in each half cycle is within the characteristic range.

[0118] The present invention constructs a simplified mathematical model of a nonlinear fault waveform. Based on Fourier transform, it can theoretically analyze the relative relationship between the individual harmonic and fundamental phasors in the fault current under different distortion conditions, and obtains a characteristic interval in which the phase difference between the fundamental and third harmonics of the nonlinear fault waveform at the center of the distortion has a regular distribution. Compared with the traditional phase comparison method at the zero crossing point, it has higher reliability and wider applicability.

[0119] In order to extract the corresponding fault features, the sub-technical solution of this embodiment designs a method for identifying the center point of nonlinear waveform distortion, and designs a fault detection criterion through the phase difference between the fundamental wave and the third harmonic at the center point of distortion. In theory, the proposed method is not affected by the fault type, fault resistance value, and the time of fault occurrence. As long as a high-resistance grounding fault or an early fault occurs in the distribution network, the proposed method can continuously extract fault features and detect the occurrence of a fault after continuously triggering the criterion.

[0120] Embodiment 2

[0121] 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.

[0122] Embodiment 3

[0123] The purpose of this embodiment is to provide a computer-readable storage medium.

[0124] A computer-readable storage medium stores a computer program, which executes the steps of the above method when executed by a processor.

[0125] Embodiment 4

[0126] The purpose of this embodiment is to provide a distribution network fault detection system based on distortion center point identification, including:

[0127] The model building module is configured to: establish a simplified mathematical model of a nonlinear fault waveform through a piecewise function, fit the nonlinear fault waveform of the distribution network based on the model, and obtain a fitted nonlinear fault waveform curve function;

[0128] The solution analysis module is configured to: solve the fitted nonlinear fault waveform curve function through Fourier transform to obtain a characteristic interval in which the phase difference between the fundamental wave and the third harmonic of the nonlinear fault waveform at the distortion center point has a regular distribution;

[0129] The identification module is configured to: process the input fault signal to be identified based on the similarity principle, and identify the center point of the nonlinear fault waveform distortion;

[0130] The fault detection module is configured to: process the center point of the nonlinear fault waveform distortion to obtain the phase difference between the fundamental wave and the third harmonic;

[0131] Design the fault detection criterion based on the above fundamental wave and third harmonic phase difference;

[0132] When the set number of consecutive times within the set duration meets the fault detection criterion, it is determined that a distribution network fault with nonlinear waveform characteristics has occurred.

[0133] The sub-technical solution of this embodiment constructs a simplified nonlinear high-resistance waveform mathematical model to analyze fault characteristics. The nonlinear fault current waveform is regarded as the result of weighted processing of a sine wave by a piecewise function; the theoretical frequency domain distribution characteristics after nonlinear current distortion are obtained through Fourier transform.

[0134] This embodiment of the sub-technical solution proposes a fault detection step based on the identification of the distortion center point. The phase difference distribution interval between the fundamental wave and the third harmonic at the distortion center point of the nonlinear waveform is summarized; a method for identifying the distortion center point is proposed using the similarity principle; and a fault detection criterion is designed based on the phase difference between the fundamental wave and the third harmonic at the distortion center point.

[0135] Embodiment 5

[0136] 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.

[0137] The steps involved in the apparatus of the above embodiment correspond to the method embodiment 1, and the specific implementation method can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0138] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0139] Although the above describes the specific implementation mode 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 creative work are still within the scope of protection of the present invention.

Claims

1. A distribution network fault detection method based on distortion center point identification, characterized in that: include: A simplified mathematical model of nonlinear fault waveform is established through piecewise function, and the nonlinear fault waveform of the distribution network is fitted based on the model to obtain a fitted nonlinear fault waveform curve function; The fitted nonlinear fault waveform curve function is solved by Fourier transform, and the characteristic interval of the phase difference between the fundamental wave and the third harmonic of the nonlinear fault waveform at the distortion center point is obtained; Based on the similarity principle, the input fault signal to be identified is processed to identify the center point of nonlinear fault waveform distortion; The phase difference between the fundamental wave and the third harmonic is obtained by processing the center point of the nonlinear fault waveform distortion; Design the fault detection criterion based on the above fundamental wave and third harmonic phase difference; When the set number of consecutive times within the set duration meets the fault detection criterion, it is determined that a distribution network fault with nonlinear waveform characteristics has occurred.

2. A distribution network fault detection method based on distortion center point identification as claimed in claim 1, characterized in that: The simplified mathematical model of the nonlinear fault waveform includes three parts: The first part of the formula is obtained by fitting L line segments with gradually increasing slopes, representing the process of arc resistance decreasing; The second part of the formula is a straight line with a slope equal to 0, which indicates the stage when the arc resistance is stable; The third part of the formula is fitted using L line segments, and the slope change trend is opposite to the first part of the curve.

3. A distribution network fault detection method based on distortion center point identification as claimed in claim 1, characterized in that: The specific process of identifying the center point of the nonlinear fault waveform distortion is as follows: Select the data of each half cycle of the fault signal, take the starting point of each segment of data as the starting point coordinate of the sine function, and the absolute value of the data peak point as the amplitude of the sine function, perform sine function fitting, and obtain the fitted fault signal; The cosine similarity is used to evaluate the fitted fault signal and the standard sine signal. The similarity index of each sampling point is CS i ; When the CS of a certain sampling point i When the set requirements are met, the point is judged to be a distortion point.

4. A distribution network fault detection method based on distortion center point identification as claimed in claim 1, characterized in that: After identifying the center point of the nonlinear fault waveform distortion, it also includes: All distortion points are summarized as D L With D R , representing two sets of data located in the left and right intervals of the center position of F.

5. A distribution network fault detection method based on distortion center point identification as claimed in claim 1, characterized in that when CS of a sampling point i When the set requirements are met, the set requirements are specifically the following formula: Where D is D L or D R Data set; M(D) represents the median of the data; MAD(D) represents the absolute median difference of the data; The sampling points that cause the overall similarity CS0 to decrease are identified as signal distortion points, and the threshold is designed using the absolute median difference to remove the data set D L With D R Outliers in ; F(n) and S(n) are fault signal and standard sinusoidal signal respectively, and N is the half-cycle sampling data length.

6. A distribution network fault detection method based on distortion center point identification as claimed in claim 1, characterized in that: Right D L With D R The median difference can be obtained by obtaining the phase position D of the distortion center point. M , the calculation formula is: D M =M(D L )-(N / 2-D R ) Among them, when D M When it is greater than zero, the center of distortion is located in the interval [0, N / 4] to the left of F, at position D M ; On the contrary, it is located in the left interval of F [N / 4,N / 2], and the position is N / 2+D M ; Preferably, the fault detection criterion is specifically: in, and are the fundamental and third harmonic phases at the center of distortion, respectively.

7. A distribution network fault detection system based on distortion center point identification, characterized in that: include: The model building module is configured to: establish a simplified mathematical model of a nonlinear fault waveform through a piecewise function, fit the nonlinear fault waveform of the distribution network based on the model, and obtain a fitted nonlinear fault waveform curve function; The solution analysis module is configured to: solve the fitted nonlinear fault waveform curve function through Fourier transform to obtain a characteristic interval in which the phase difference between the fundamental wave and the third harmonic of the nonlinear fault waveform at the distortion center point has a regular distribution; The identification module is configured to: process the input fault signal to be identified based on the similarity principle, and identify the center point of the nonlinear fault waveform distortion; The fault detection module is configured to: process the center point of the nonlinear fault waveform distortion to obtain the phase difference between the fundamental wave and the third harmonic; Design the fault detection criterion based on the above fundamental wave and third harmonic phase difference; When the set number of consecutive times within the set duration meets the fault detection criterion, it is determined that a distribution network fault with nonlinear waveform characteristics has occurred.

8. 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 6 is implemented.

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

10. 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 described in any one of claims 1 to 6 are performed.

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