Power distribution network fault identification method and device, electronic equipment and storage medium

Abstract

The invention discloses a power distribution network fault identification method and device, electronic equipment and a storage medium, and is used for solving the technical problem that an existing power distribution network fault identification method is poor in stability and reliability. The method comprises the following steps: acquiring a power distribution network fault signal through fault traveling wave acquisition equipment; an optimized CEEMDAN algorithm is adopted to decompose the power distribution network fault signal, and intrinsic mode function IMFs signals with different time scales are obtained; the optimized CEEMDAN algorithm is obtained through optimization of a Harlisia eagle optimization algorithm; generating an energy spectrum of the IMFs signal; and carrying out power distribution network fault identification by adopting the energy spectrum.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to a method, device, electronic device and storage medium for identifying distribution network faults. Background Art

[0002] In the power system, the distribution network has a complex structure. These networks usually include a mixed use of overhead lines and underground cables, making them vulnerable to weather changes, physical damage and other external factors. For example, overhead lines are directly exposed to the natural environment and are more easily affected by adverse weather such as wind, rain, snow, hail, lightning, etc., and are also easily disturbed by animal activities and tree growth. Therefore, it is more susceptible to various faults, and the stability and reliability of the distribution network are very important for ensuring continuous power supply and meeting user needs. The fault types in the distribution network are complex, including single-phase grounding faults, two-phase short-circuit faults and three-phase short-circuit faults, etc. These faults may cause power supply interruption and damage to grid equipment. Therefore, when a fault occurs, quickly and accurately identifying the fault is one of the keys to restoring power supply, reducing economic losses and ensuring the safe operation of the power grid.

[0003] Generally, traditional methods for identifying distribution network faults have limitations, including problems such as the accuracy being affected by changes in system parameters, strong dependence on equipment, and limited signal capture ability. With the development of signal processing technology, emerging algorithms such as wavelet transform, Hilbert-Huang transform (HHT) and Teager energy operator have been proposed to improve the processing ability of fault traveling waves. However, these methods still face challenges in actual processing of traveling wave data. For example, wavelet transform requires the selection of appropriate wavelet basis functions and decomposition scales, and has poor adaptability. The EMD algorithm in the HHT method will produce mode mixing phenomena, while the TEO method has a small amount of calculation but does not solve the mode mixing problem of the EMD algorithm itself. The combination method of EEMD and TEO has poor denoising ability in a noisy environment. Summary of the Invention

[0004] The present invention provides a method, device, electronic device and storage medium for identifying distribution network faults, which are used to solve the technical problem that the existing methods for identifying distribution network faults have poor stability and reliability.

[0005] The present invention provides a method for identifying distribution network faults. Fault traveling wave acquisition devices are installed in the branch lines of the distribution network. The method includes:

[0006] Collecting distribution network fault signals through the fault traveling wave acquisition devices;

[0007] Decompose the distribution network fault signal by using the optimized CEEMDAN algorithm to obtain the intrinsic mode function (IMF) signals with different time scales; the optimized CEEMDAN algorithm is optimized by the Harris hawk optimization algorithm;

[0008] Generate the energy spectrum of the IMF signals;

[0009] Use the energy spectrum for distribution network fault identification.

[0010] Optionally, the steps of optimizing and generating the optimized CEEMDAN algorithm by using the Harris hawk optimization algorithm include:

[0011] Collect fault signals caused by different reasons;

[0012] Decompose the fault signal into multiple intrinsic mode functions (IMFs) by using the CEEMDAN algorithm;

[0013] Construct the fitness function of the Harris hawk optimization algorithm by using the IMFs and the fault signal;

[0014] Take the minimum of the fitness function as the optimization goal to obtain the optimal parameters of the CEEMDAN algorithm;

[0015] Generate the optimized CEEMDAN algorithm by using the optimal parameters.

[0016] Optionally, the steps of decomposing the distribution network fault signal by using the optimized CEEMDAN algorithm to obtain the intrinsic mode function (IMF) signals with different time scales include:

[0017] Add white noise to the distribution network fault signal to generate multiple noise signals;

[0018] Perform empirical mode decomposition on the noise signals to obtain multiple initial intrinsic mode functions;

[0019] Perform ensemble averaging on all the initial intrinsic mode functions to obtain the initial IMFs;

[0020] Delete the initial IMFs from the distribution network fault signal to obtain an updated distribution network fault signal;

[0021] Judge whether the updated distribution network fault signal meets the iteration termination condition;

[0022] If not, replace the distribution network fault signal with the updated distribution network fault signal and return to the step of adding white noise to the distribution network fault signal to generate multiple noise signals;

[0023] If so, output all the initial IMFs as intrinsic mode function (IMF) signals, and the IMF signals have different time scales.

[0024] Optionally, the step of generating the energy spectrum of the IMF signals includes:

[0025] Calculating the Teager energy values of the IMF signals at multiple sampling points by using the TEO algorithm;

[0026] Generating an energy spectrum by using the Teager energy values of all the sampling points.

[0027] The present invention further provides a distribution network fault identification device. Fault traveling wave acquisition devices are installed in the branch lines of the distribution network. The device includes:

[0028] A distribution network fault signal acquisition module, configured to acquire distribution network fault signals through the fault traveling wave acquisition devices;

[0029] A decomposition module, configured to decompose the distribution network fault signals by using an optimized CEEMDAN algorithm to obtain intrinsic mode function (IMF) signals with different time scales; the optimized CEEMDAN algorithm is optimized by using a Harris hawk optimization algorithm;

[0030] An energy spectrum generation module, configured to generate the energy spectrum of the IMF signals;

[0031] A fault identification module, configured to perform distribution network fault identification by using the energy spectrum.

[0032] Optionally, it further includes:

[0033] A fault signal acquisition module, configured to acquire fault signals caused by different reasons;

[0034] An IMF generation module, configured to decompose the fault signals into multiple intrinsic mode functions (IMFs) by using the CEEMDAN algorithm;

[0035] A fitness function construction module, configured to construct a fitness function of the Harris hawk optimization algorithm by using the IMFs and the fault signals;

[0036] An optimal parameter acquisition module, configured to acquire the optimal parameters of the CEEMDAN algorithm with the minimum of the fitness function as the optimization target;

[0037] An optimized CEEMDAN algorithm generation module, configured to generate an optimized CEEMDAN algorithm by using the optimal parameters.

[0038] Optionally, the decomposition module includes:

[0039] A noise signal generation sub-module, configured to add white noise to the distribution network fault signal to generate a plurality of noise signals;

[0040] An empirical mode decomposition sub-module, configured to perform empirical mode decomposition on the noise signal to obtain a plurality of initial intrinsic mode functions;

[0041] An ensemble average sub-module, configured to perform ensemble average on all the initial intrinsic mode functions to obtain initial IMFs;

[0042] An initial IMFs deletion sub-module, configured to delete the initial IMFs from the distribution network fault signal to obtain an updated distribution network fault signal;

[0043] A termination judgment sub-module, configured to judge whether the updated distribution network fault signal satisfies an iteration termination condition;

[0044] A return sub-module, configured to, if not, replace the distribution network fault signal with the updated distribution network fault signal, and return to the step of adding white noise to the distribution network fault signal to generate a plurality of noise signals;

[0045] An output sub-module, configured to, if so, output all the initial IMFs as intrinsic mode function IMFs signals, and the intrinsic mode function IMFs signals have different time scales.

[0046] Optionally, the energy spectrum generation module includes:

[0047] A Teager energy value acquisition sub-module, configured to calculate Teager energy values of the IMFs signals at a plurality of sampling points by using a TEO algorithm;

[0048] An energy spectrum generation sub-module, configured to generate an energy spectrum by using the Teager energy values of all the sampling points.

[0049] The present invention further provides an electronic device, and the device includes a processor and a memory:

[0050] The memory is configured to store program codes and transmit the program codes to the processor;

[0051] The processor is configured to execute the distribution network fault identification method as described in any one of the above according to the instructions in the program codes.

[0052] The present invention further provides a computer-readable storage medium, and the computer-readable storage medium is configured to store program codes, and the program codes are used to execute the distribution network fault identification method as described in any one of the above.

[0053] As can be seen from the above technical solutions, the present invention has the following advantages: The present invention provides a method for identifying faults in a distribution network, and specifically discloses: collecting distribution network fault signals through a fault traveling wave acquisition device; decomposing the distribution network fault signals by using an optimized CEEMDAN algorithm to obtain intrinsic mode function (IMF) signals with different time scales; the optimized CEEMDAN algorithm is obtained by optimizing the Harris hawk optimization algorithm; generating the energy spectrum of the IMF signals; and using the energy spectrum for identifying faults in the distribution network.

[0054] By performing CEEMDAN decomposition on the distribution network fault data, the present invention can effectively reduce mode mixing, adapt to signals with different characteristics, and adaptively adjust the noise amplitude in each iteration to optimize the decomposition process. At the same time, the Harris hawk optimization algorithm is introduced into CEEMDAN to improve the decomposition accuracy, reduce the generation of false intrinsic mode functions (IMFs), and enhance fault recognition, making the optimized CEEMDAN more effective and accurate in practical applications. Thereby improving the stability and reliability of distribution network fault identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0056] Figure 1 It is a flowchart of the steps of a method for identifying faults in a distribution network provided by an embodiment of the present invention;

[0057] Figure 2 It is a flowchart of the steps of a method for identifying faults in a distribution network provided by another embodiment of the present invention;

[0058] Figure 3 It is an IMF waveform diagram obtained after decomposition;

[0059] Figure 4 It is a structural block diagram of a device for identifying faults in a distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The embodiments of the present invention provide a method, device, electronic device, and storage medium for identifying faults in a distribution network, which are used to solve the technical problem that the existing methods for identifying faults in a distribution network have poor stability and reliability.

[0061] To make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Please refer to Figure 1 , Figure 1 , which is a flowchart of the steps of a method for identifying distribution network faults provided by an embodiment of the present invention.

[0063] A method for identifying distribution network faults provided by the present invention includes fault traveling wave acquisition devices installed in the branch lines of the distribution network. The method may specifically include the following steps:

[0064] Step 101, collecting distribution network fault signals through the fault traveling wave acquisition devices;

[0065] In the embodiments of the present invention, fault traveling wave acquisition devices may be installed in the branch lines of the distribution network to collect distribution network fault signals in the distribution network.

[0066] Step 102, decomposing the distribution network fault signals using the optimized CEEMDAN algorithm to obtain intrinsic mode function (IMF) signals with different time scales; the optimized CEEMDAN algorithm is optimized through the Harris Hawks Optimization algorithm.

[0067] The CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is an improved empirical mode decomposition (EMD) method that can effectively reduce the mode mixing phenomenon of the EMD method and uses an adaptive noise and ensemble averaging strategy to improve the accuracy and stability of signal decomposition.

[0068] The Harris Hawks Optimization (HHO) is a swarm intelligence-based optimization algorithm inspired by the hunting behavior of Harris hawks. HHO achieves a balance between global and local searches by simulating the hunting strategies of Harris hawks, such as tracking, surrounding, attacking, and escaping.

[0069] In the embodiments of the present invention, the Harris hawks algorithm may be used to optimize the CEEMDAN algorithm to improve the decomposition accuracy and reduce the generation of false intrinsic mode functions. Then, the distribution network fault signals are decomposed using the optimized CEEMDAN algorithm to obtain intrinsic mode function (IMF) signals with different time scales.

[0070] Step 103, generate the energy spectrum of the IMFs signal;

[0071] Step 104, perform distribution network fault identification using the energy spectrum.

[0072] In the embodiment of the present invention, after obtaining the IMFs signal, the energy of the IMFs signal can be calculated to obtain the energy spectrum, so as to perform distribution network fault identification according to the energy spectrum.

[0073] In the present invention, by performing CEEMDAN decomposition on the distribution network fault data, modal aliasing can be effectively reduced, signals with different characteristics can be adapted, and the noise amplitude can be adaptively adjusted in each iteration to optimize the decomposition process. At the same time, the Harris hawk optimization algorithm is introduced into CEEMDAN to improve the decomposition accuracy, reduce the generation of false intrinsic mode functions (IMFs), and enhance fault recognition, making the optimized CEEMDAN more effective and accurate in practical applications. Thus, the stability and reliability of distribution network fault identification are improved.

[0074] Please refer to Figure 2 , Figure 2 which is the step flowchart of a distribution network fault identification method provided by another embodiment of the present invention. Specifically, it may include the following steps:

[0075] Step 201, collect the distribution network fault signal through the fault traveling wave acquisition device;

[0076] Step 202, decompose the distribution network fault signal using the optimized CEEMDAN algorithm to obtain the intrinsic mode function IMFs signals with different time scales; the optimized CEEMDAN algorithm is optimized through the Harris hawk optimization algorithm;

[0077] In the embodiment of the present invention, the steps of optimizing the CEEMDAN algorithm through the Harris hawk algorithm include:

[0078] S11, collect the fault signals caused by different reasons;

[0079] S12, decompose the fault signal into multiple intrinsic mode functions IMFs using the CEEMDAN algorithm;

[0080] S13, construct the fitness function of the Harris hawk optimization algorithm using the IMFs and the fault signal;

[0081] S14, taking the minimum of the fitness function as the optimization goal, obtain the optimal parameters of the CEEMDAN algorithm;

[0082] S15, generate the optimized CEEMDAN algorithm using the optimal parameters.

[0083] The IMF is a signal component that meets specific conditions and can represent the local characteristics and oscillation modes of the signal. Each IMF represents the oscillation components at different time scales in the signal.

[0084] In the specific implementation, the Harris hawk algorithm mainly consists of three parts: the search phase, the transition phase, and the exploitation phase. In the embodiments of the present invention, the Harris hawk algorithm is used to optimize the initial amplitude σ0, the number of iterations N, and the average number I of the CEEMDAN algorithm.

[0085] Set the fitness function as:

[0086]

[0087]

[0088]

[0089] where X is the original fault signal, α is the reconstruction error, β is the mode mixing index, T is the total duration of the signal, n is the total number of IMFs, k ∈ [1, n], x(t) is the original signal, and IMF k (t) is the k-th intrinsic mode function, and C ij is the correlation coefficient between the i-th and j-th IMFs, and the calculation formula is:

[0090]

[0091] In the global search phase, the Harris hawk randomly updates its position to search for prey. Its mathematical expression is:

[0092]

[0093]

[0094] where is the position of the hawk at the t-th iteration; is a random individual; is the prey position; is the average position; and ~ are random numbers belonging to [0, 1]; G is the population size; is the maximum value of the value range of the search space parameter; is the minimum value of the value range of the search space parameter.

[0095] In the transition phase, the algorithm determines whether it is in the exploration phase or the exploitation phase according to the escape energy of the prey. The position update of the Harris hawk depends on the comparison result between the value of the escape energy E and the random number r. Its initial escape energy is set at within the range

[0096] The escape energy E is expressed as:

[0097]

[0098] where t is the current iteration number and T is the maximum iteration number

[0099] In the development stage, a variety of different predation strategies are designed according to the escape behavior of the prey. Here, three classic strategies are introduced. When 0.5 < |E| ≤ 1.0 and r ≥ 0.5, the prey has sufficient energy and has a probability of escaping. At this time, the Harris hawk adopts a soft siege strategy to gradually consume the energy of the prey, and the expression is:

[0100]

[0101] where is the distance between the optimal individual and the current individual; J is a random number in [0, 2].

[0102] When |E| < 0.5 and r > 0.5, the prey is considered exhausted. At this time, the Harris hawk adopts a hard siege strategy to quickly dive and capture the prey, and the position update expression is:

[0103]

[0104] When |E| > 0.5 and r < 0.5, the Harris hawk first conducts a soft siege before the final raid.

[0105] After optimizing the CEEMDAN algorithm, the optimized CEEMDAN algorithm can be used to decompose the distribution network fault signal to obtain the intrinsic mode function IMFs signals with different time scales.

[0106] In an example, the steps of using the optimized CEEMDAN algorithm to decompose the distribution network fault signal to obtain the intrinsic mode function IMFs signals with different time scales may include the following sub-steps:

[0107] S21, adding white noise to the distribution network fault signal to generate multiple noise signals;

[0108] S22, performing empirical mode decomposition on the noise signals to obtain multiple initial intrinsic mode functions;

[0109] S23, performing ensemble averaging on all the initial intrinsic mode functions to obtain the initial IMFs;

[0110] S24, deleting the initial IMFs from the distribution network fault signal to obtain the updated distribution network fault signal;

[0111] S25. Determine whether the updated distribution network fault signal meets the iteration termination condition;

[0112] S26. If not, replace the distribution network fault signal with the updated distribution network fault signal, and return to the step of adding white noise to the distribution network fault signal to generate multiple noise signals;

[0113] S27. If so, output all the initial IMFs as the intrinsic mode function IMFs signal, and the intrinsic mode function IMFs signal has different time scales.

[0114] In specific implementation, first, set the initial amplitude σ0 of the white noise, the corresponding iteration number N, and the average number I according to the parameters obtained by the above optimization, input the original distribution network fault signal X, add white noise ω with different amplitudes respectively, and perform EMD decomposition on the signal after adding white noise to obtain the corresponding IMF components:

[0115]

[0116]

[0117] Among them, is the mean value of the IMF component, that is, the initial IMFs, is the residual.

[0118] Then delete the initial IMFs from the original distribution network fault signal, and re-perform empirical mode decomposition and calculate the initial IMFs and the residual until the iteration number reaches the preset k times.

[0119]

[0120] Among them, is the residual after k iterations.

[0121] That is:

[0122]

[0123] It can be seen from the above formula that the original distribution network fault signal X is the sum of all calculated IMF components and the final residual.

[0124] Finally, output all the initial IMFs.

[0125] Step 203. Calculate the Teager energy values of the IMFs signal at multiple sampling points using the TEO algorithm;

[0126] Step 204. Generate an energy spectrum using the Teager energy values of all sampling points;

[0127] In specific implementation, for a continuous signal, the definition of TEO is:

[0128]

[0129] In the embodiments of the present invention, the output IMFs are generally discrete signals. The TEO algorithm can obtain the Teager energy values of the IMFs at the sampling points by calculating the first-order difference and the second-order difference of the discrete signals. These values reflect the energy change of the signal at this point, and an energy spectrum can be obtained.

[0130] Step 205: Use the energy spectrum for distribution network fault identification.

[0131] Through the energy spectrum, the wavefront of the fault traveling wave can be accurately calibrated, and the highest energy point is the first wavefront of the traveling wave.

[0132] In an example, the fault identification results under different transition resistances and fault distances through the simulation analysis of the embodiments of the present invention are shown in Tables 1 and 2. The parameter settings of HHO are shown in Table 3, and the parameter settings of CEEMDAN are shown in Table 4. The IMFs obtained after decomposition are as Figure 3 shown. It can be seen from the simulation results that under the change of fault types (AG, AB, ABC) and transition resistance values (10, 50, 200), the effectiveness of this method is verified. It can accurately identify faults under different conditions, has high stability and reliability, and is of great significance for ensuring the safety management of the power grid.

[0133] Table 1 Typical fault identification results for different fault types and transition resistances

[0134]

[0135] Table 2 Typical fault identification results for different fault types and fault distances

[0136]

[0137] Table 3 Parameter settings of HHO

[0138]

[0139] Table 4 Parameter settings of CEEMDAN

[0140]

[0141] Through the CEEMDAN decomposition of the distribution network fault data, the present invention can effectively reduce modal aliasing, adapt to signals with different characteristics, and adaptively adjust the noise amplitude in each iteration to optimize the decomposition process. At the same time, the Harris hawk optimization algorithm is introduced into CEEMDAN to improve the decomposition accuracy, reduce the generation of false intrinsic mode functions (IMFs), and enhance fault identification, making the optimized CEEMDAN more effective and accurate in practical applications. Thereby improving the stability and reliability of distribution network fault identification.

[0142] Please refer to Figure 4 , Figure 4 which is the structural block diagram of a distribution network fault identification device provided by an embodiment of the present invention.

[0143] An embodiment of the present invention provides a distribution network fault identification device. Fault traveling wave acquisition devices are installed in the branch lines of the distribution network. The device includes:

[0144] A distribution network fault signal acquisition module 401, configured to acquire distribution network fault signals through the fault traveling wave acquisition device;

[0145] A decomposition module 402, configured to decompose the distribution network fault signals by using an optimized CEEMDAN algorithm to obtain intrinsic mode function IMFs signals with different time scales; the optimized CEEMDAN algorithm is optimized by the Harris hawk optimization algorithm;

[0146] An energy spectrum generation module 403, configured to generate the energy spectrum of the IMFs signals;

[0147] A fault identification module 404, configured to perform distribution network fault identification by using the energy spectrum.

[0148] In the embodiment of the present invention, it further includes:

[0149] A fault signal acquisition module, configured to acquire fault signals caused by different reasons;

[0150] An IMF generation module, configured to decompose the fault signals into multiple intrinsic mode functions IMFs by using the CEEMDAN algorithm;

[0151] A fitness function construction module, configured to construct a fitness function of the Harris hawk optimization algorithm by using the IMFs and the fault signals;

[0152] An optimal parameter acquisition module, configured to take the minimum of the fitness function as the optimization goal to acquire the optimal parameters of the CEEMDAN algorithm;

[0153] An optimized CEEMDAN algorithm generation module, configured to generate an optimized CEEMDAN algorithm by using the optimal parameters.

[0154] In an embodiment of the present invention, the decomposition module 402 includes:

[0155] A noise signal generation sub-module, configured to add white noise to the distribution network fault signal to generate a plurality of noise signals;

[0156] An empirical mode decomposition sub-module, configured to perform empirical mode decomposition on the noise signal to obtain a plurality of initial intrinsic mode functions;

[0157] An ensemble average sub-module, configured to perform ensemble average on all the initial intrinsic mode functions to obtain initial IMFs;

[0158] An initial IMFs deletion sub-module, configured to delete the initial IMFs from the distribution network fault signal to obtain an updated distribution network fault signal;

[0159] A termination judgment sub-module, configured to judge whether the updated distribution network fault signal satisfies the iteration termination condition;

[0160] A return sub-module, configured to, if not, replace the distribution network fault signal with the updated distribution network fault signal, and return to the step of adding white noise to the distribution network fault signal to generate a plurality of noise signals;

[0161] An output sub-module, configured to, if so, output all the initial IMFs as the intrinsic mode function IMFs signal, and the intrinsic mode function IMFs signal has different time scales.

[0162] In an embodiment of the present invention, the energy spectrum generation module 403 includes:

[0163] A Teager energy value acquisition sub-module, configured to calculate the Teager energy values of the IMFs signal at a plurality of sampling points by using the TEO algorithm;

[0164] An energy spectrum generation sub-module, configured to generate an energy spectrum by using the Teager energy values of all the sampling points.

[0165] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory:

[0166] The memory is used to store program codes and transmit the program codes to the processor;

[0167] The processor is used to execute the distribution network fault identification method according to the instructions in the program codes in an embodiment of the present invention.

[0168] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the distribution network fault identification method according to an embodiment of the present invention.

[0169] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0170] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0171] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention can take the form of all-hardware embodiments, all-software embodiments, or embodiments combining software and hardware aspects. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0172] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing terminal devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing terminal devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0173] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0174] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable terminal device provide for realizing the functions in Figure 1 one process or multiple processes and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0175] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0176] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the element.

[0177] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distribution network fault identification method, characterized in that: A fault traveling wave acquisition device is installed in the branch line of the distribution network, and the method comprises: Collect distribution network fault signals through fault traveling wave acquisition equipment; The distribution network fault signal is decomposed by using an optimized CEEMDAN algorithm to obtain intrinsic mode function IMFs signals with different time scales; the optimized CEEMDAN algorithm is optimized by the Harris Eagle optimization algorithm; generating an energy spectrum of the IMFs signal; The energy spectrum is used to identify distribution network faults.

2. The method according to claim 1, characterized in that The step of using the Harris Eagle optimization algorithm to optimize and generate the CEEMDAN algorithm includes: Collect fault signals caused by different reasons; Decomposing the fault signal into a plurality of intrinsic mode functions IMFs by using the CEEMDAN algorithm; Constructing a fitness function of a Harris Hawk optimization algorithm using the IMF and the fault signal; Taking the minimization of the fitness function as the optimization goal, obtaining the optimal parameters of the CEEMDAN algorithm; The optimal parameters are used to generate an optimized CEEMDAN algorithm.

3. The method according to claim 1, characterized in that The step of using the optimized CEEMDAN algorithm to decompose the distribution network fault signal to obtain intrinsic mode function IMFs signals with different time scales includes: Adding white noise to the distribution network fault signal to generate multiple noise signals; Performing empirical mode decomposition on the noise signal to obtain a plurality of initial intrinsic mode functions; Performing ensemble averaging on all the initial intrinsic mode functions to obtain initial IMFs; Deleting the initial IMFs from the distribution network fault signal to obtain an updated distribution network fault signal; Determining whether the updated distribution network fault signal satisfies an iteration termination condition; If not, replacing the distribution network fault signal with the updated distribution network fault signal, and returning to the step of adding white noise to the distribution network fault signal to generate a plurality of noise signals; If yes, all the initial IMFs are output as intrinsic mode function IMFs signals, and the intrinsic mode function IMFs signals have different time scales.

4. The method according to claim 1, characterized in that: The step of generating the energy spectrum of the IMFs signal comprises: The Teager energy value of the IMFs signal at multiple sampling points is calculated using the TEO algorithm; The energy spectrum is generated using the Teager energy values ​​of all the sampling points.

5. A distribution network fault identification device, characterized in that: A fault traveling wave acquisition device is installed in the branch line of the distribution network, and the device includes: A distribution network fault signal acquisition module, used to collect distribution network fault signals through a fault traveling wave acquisition device; A decomposition module, used for decomposing the distribution network fault signal by using an optimized CEEMDAN algorithm to obtain intrinsic mode function IMFs signals with different time scales; the optimized CEEMDAN algorithm is obtained by optimizing the Harris Eagle optimization algorithm; An energy spectrum generating module, used for generating an energy spectrum of the IMFs signal; A fault identification module is used to identify distribution network faults using the energy spectrum.

6. The device according to claim 5, characterized in that Also includes: Fault signal acquisition module, used to collect fault signals caused by different reasons; An IMF generation module is used to decompose the fault signal into multiple intrinsic mode functions IMFs by using the CEEMDAN algorithm; A fitness function building module, used for building a fitness function of a Harris Hawk optimization algorithm using the IMF and the fault signal; An optimal parameter acquisition module, used to obtain the optimal parameters of the CEEMDAN algorithm by taking the minimum fitness function as the optimization goal; The optimized CEEMDAN algorithm generation module is used to generate an optimized CEEMDAN algorithm using the optimal parameters.

7. The device according to claim 5, characterized in that The decomposition module comprises: A noise signal generating submodule, used for adding white noise to the distribution network fault signal to generate multiple noise signals; An empirical mode decomposition submodule, used for performing empirical mode decomposition on the noise signal to obtain a plurality of initial intrinsic mode functions; An ensemble averaging submodule, used for ensemble averaging all the initial intrinsic mode functions to obtain initial IMFs; An initial IMFs deletion submodule, used to delete the initial IMFs from the distribution network fault signal to obtain an updated distribution network fault signal; A termination judgment submodule, used to judge whether the updated distribution network fault signal meets the iteration termination condition; a return submodule, for, if not, replacing the distribution network fault signal with the updated distribution network fault signal, and returning to the step of adding white noise to the distribution network fault signal to generate a plurality of noise signals; The output submodule is used for outputting all the initial IMFs as intrinsic mode function IMFs signals, wherein the intrinsic mode function IMFs signals have different time scales.

8. The device according to claim 5, characterized in that The energy spectrum generation module comprises: Teager energy value acquisition submodule, used for calculating the Teager energy value of the IMFs signal at multiple sampling points using the TEO algorithm; The energy spectrum generation submodule is used to generate an energy spectrum using the Teager energy values ​​of all the sampling points.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the distribution network fault identification method according to any one of claims 1-4 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the distribution network fault identification method according to any one of claims 1-4.

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