Low-voltage transformer area fault positioning method and system

Abstract

The invention discloses a low-voltage transformer area fault positioning method and system, and relates to the technical field of transformer area fault analysis, and the method comprises the steps: constructing a network frame topology in a low-voltage transformer area; performing low-voltage fault simulation, and determining a preliminary fault area; performing electromagnetic, sound wave and critical fluctuation detection on the preliminary fault area to obtain an electromagnetic wave reflection signal, a secondary electromagnetic signal and critical effect fluctuation data; fusing the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data; and performing multi-modal analysis on the fusion signal to generate a fault report. By combining electromagnetism, sound waves and critical fluctuation detection, various signal characteristics generated when a fault occurs can be effectively captured, meanwhile, by means of multi-modal information fusion analysis, the fault positioning accuracy is remarkably improved, the operation and maintenance time is shortened, and more detailed fault level evaluation is provided for system operation and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer substation fault analysis, and in particular to a method and system for locating a low-voltage transformer substation fault. Background Art

[0002] With the rapid development of the power system and the acceleration of urbanization, the importance of low-voltage distribution networks in the power system has become increasingly prominent. As the terminal area of ​​power transmission, the low-voltage substation is directly connected to the end user, and its operating status and reliability are directly related to the user's power quality. However, due to the wide coverage, complex branch lines, and interference from various external environmental factors, various faults often occur in low-voltage substations, including short circuits, grounding, overloads, etc. These faults may not only cause power outages, but also affect the stability of the entire power system. For this reason, fault location technology has become the key to ensuring the stable operation of low-voltage substations.

[0003] Traditional fault location mainly relies on the monitoring of a single electrical parameter, such as voltage drop, current mutation, etc. These methods can only work in obvious fault scenarios, and have weak positioning capabilities for hidden faults, especially intermittent faults. In addition, since low-voltage substations usually have complex distributed structures, signal transmission between nodes is often affected by large impedance and interference. The accuracy of traditional fault detection based on electrical signals is significantly reduced in long-distance, multi-branch networks. Secondly, most existing fault location technologies lack the comprehensive use of multimodal information, resulting in incomplete fault feature information. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is to solve the problem that the prior art is difficult to locate highly concealed faults and lacks comprehensive utilization of multi-modal information.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for locating a low voltage station fault, which comprises the following steps:

[0007] Construct a grid topology in the low-voltage substation area; conduct low-voltage fault simulation to determine the preliminary fault area; conduct electromagnetic, acoustic and critical fluctuation detection on the preliminary fault area to obtain electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data; fuse the electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data; perform multimodal analysis on the fused signals and generate a fault report.

[0008] As a preferred solution of the low-voltage substation fault location method described in the present invention, the low-voltage fault simulation includes collecting electrical parameters of each node before and after the simulation, and the electrical parameters include voltage, current and power factor.

[0009] The electrical parameters before and after the simulation are compared and analyzed, and the affected nodes are located in combination with the simulated fault types, which include short circuit simulation, ground fault simulation, and leakage simulation.

[0010] The fault area is divided according to the electrical changes between the affected nodes.

[0011] As a preferred solution of the low-voltage substation fault location method described in the present invention, the electromagnetic and acoustic wave detection in the electromagnetic, acoustic and critical fluctuation detection includes emitting electromagnetic waves through the electromagnetic wave detection device to penetrate the fault object, and penetrating the electromagnetic waves in the preliminary fault area through the quantum tunneling effect, performing signal amplification processing, and generating an electromagnetic wave reflection signal.

[0012] By controlling the vibration of the piezoelectric crystal, sound waves are generated. The sound waves penetrate the faulty object to produce an acoustic-electric coupling effect and generate secondary electromagnetic wave signals.

[0013] As a preferred solution of a low-voltage substation fault location method described in the present invention, the critical fluctuation detection in the electromagnetic, acoustic and critical fluctuation detection includes deploying a self-organized critical detection device in the preliminary fault area, and the self-organized critical detection device includes a stress sensor network circuit, a vibrator detection circuit and a signal processing circuit.

[0014] The stress sensor network circuit includes a plurality of stress sensors based on the piezoresistive effect, which are used to detect stress signals in the preliminary fault area. The stress signals are transmitted to the signal processing circuit through a front-end amplifier.

[0015] The vibrator detection circuit is used to capture the vibration signal in the preliminary fault area, and convert the vibration signal into an electrical signal and transmit it to the signal processing circuit. The signal processing circuit amplifies and filters the stress signal in the preliminary fault area and the vibration signal in the preliminary fault area to generate the critical effect fluctuation data.

[0016] As a preferred solution of the low-voltage substation fault location method described in the present invention, the multimodal analysis includes extracting features from the electromagnetic wave reflection signal, secondary electromagnetic signal and critical effect fluctuation data, integrating the confidence of each feature, and generating a fused feature vector.

[0017] The fused feature vector is input into a trained fault diagnosis model for multimodal analysis, and the fault location, fault level and fault type are output.

[0018] Another object of the present invention is to provide a low-voltage area fault location system, which can solve the problems of low efficiency and low accuracy of existing low-voltage area fault location through intelligent data analysis and remote monitoring technology.

[0019] To solve the above technical problems, the present invention provides the following technical solutions: a low voltage substation fault location system, comprising a topology construction module, a low voltage fault simulation module, a signal detection module, a signal fusion module and a fault location module.

[0020] The topology building module is used to build a grid topology in a low-voltage area, and the grid topology includes multiple nodes.

[0021] The low voltage fault simulation module is used to perform low voltage fault simulation at the multiple nodes to determine a preliminary fault area.

[0022] The signal detection module is used to perform electromagnetic, acoustic and critical fluctuation detection on the preliminary fault area to obtain electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data.

[0023] The signal fusion module is used to fuse the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data.

[0024] The fault location module is used to perform multi-modal analysis on the fused signal and generate a fault report including the fault location, fault level and fault type.

[0025] As a preferred solution of the low voltage area fault location system described in the present invention, wherein:

[0026] The low voltage fault simulation module is specifically used to collect electrical parameters of each node before and after simulation, and the electrical parameters include voltage, current and power factor.

[0027] The electrical parameters before and after the simulation are compared and analyzed, and the affected nodes are located based on the simulated fault type.

[0028] The simulated fault types include short circuit simulation, ground fault simulation, and leakage simulation.

[0029] The fault area is divided according to the electrical changes between the affected nodes.

[0030] The signal detection module is specifically used to transmit electromagnetic waves through the electromagnetic wave detection device to penetrate the fault object, and penetrate the electromagnetic waves in the preliminary fault area through the quantum tunneling effect, and then perform signal amplification processing to generate an electromagnetic wave reflection signal.

[0031] By controlling the vibration of the piezoelectric crystal, sound waves are generated. The sound waves penetrate the faulty object to produce an acoustic-electric coupling effect and generate secondary electromagnetic wave signals.

[0032] As a preferred solution of the low voltage area fault location system described in the present invention, wherein:

[0033] The signal detection module is specifically used to deploy a self-organized criticality detection device in the preliminary fault area, and the self-organized criticality detection device includes a stress sensor network circuit, a vibrator detection circuit and a signal processing circuit.

[0034] The stress sensor network circuit includes a plurality of stress sensors based on the piezoresistive effect, which are used to detect stress signals in the preliminary fault area. The stress signals are transmitted to the signal processing circuit through a front-end amplifier.

[0035] The vibrator detection circuit is used to capture the vibration signal in the preliminary fault area, and convert the vibration signal into an electrical signal and transmit it to the signal processing circuit. The signal processing circuit amplifies and filters the stress signal in the preliminary fault area and the vibration signal in the preliminary fault area to generate the critical effect fluctuation data.

[0036] The fault location module is specifically used to extract features from the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data, and to generate a fused feature vector by integrating the confidence of each feature.

[0037] The fused feature vector is input into a trained fault diagnosis model for multimodal analysis, and the fault location, fault level and fault type are output.

[0038] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the low-voltage substation fault location method as described above when executing the computer program.

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a low-voltage station area fault location method as described above.

[0040] Beneficial effects of the present invention: By combining electromagnetic, acoustic and critical fluctuation detection, the present invention can effectively capture the various signal characteristics generated when a fault occurs. At the same time, through multimodal information fusion analysis, the accuracy of fault location is significantly improved, the operation and maintenance time is reduced, and a more detailed fault level assessment is provided for system operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 An overall flow chart of a low voltage substation fault location method provided for the first embodiment of the present invention.

[0043] Figure 2 A schematic flow chart of simulating multiple fault types in a low voltage substation fault location method provided in the first embodiment of the present invention.

[0044] Figure 3 A schematic diagram of the data fusion process of a low voltage substation fault location method provided in the first embodiment of the present invention.

[0045] Figure 4 An overall framework diagram of a low voltage substation fault location system provided for the second embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0047] Example 1, reference Figure 1 to Figure 3 , is an embodiment of the present invention, and provides a low voltage area fault location method, characterized in that:

[0048] S1: Construct grid topology in low voltage area.

[0049] Furthermore, the grid topology includes a plurality of nodes.

[0050] Node definition: Based on the actual situation of the low-voltage substation, multiple nodes are defined, including transformers, distribution switches, feeders and load points. Each node represents an important location in the power grid.

[0051] Connection planning: Determine the electrical connection relationship between nodes, simulate the actual electrical connection of the power grid, and build a complete grid topology.

[0052] Furthermore, the nodes are divided into key nodes and ordinary nodes. Key nodes are the key points of power distribution in the system, and ordinary nodes are ordinary loads and equipment.

[0053] S2: Perform low voltage fault simulation to determine the preliminary fault area.

[0054] The electrical parameters of each node before and after simulation are collected, including voltage, current and power factor.

[0055] The electrical parameters before and after the simulation are compared and analyzed, and the affected nodes are located in combination with the simulated fault types; the simulated fault types include short circuit simulation, ground fault simulation, and leakage simulation.

[0056] Among them, (1) short circuit simulation includes single-phase short circuit, two-phase short circuit and three-phase short circuit, and the relevant parameter settings are as follows:

[0057] ①Single-phase short circuit

[0058] Current setting: Adjust the current source of the simulator and set the current amplitude of the single-phase short circuit to make it higher than the normal operating current.

[0059] Duration setting: Set the short circuit duration, which can be an instantaneous short circuit or a short circuit that lasts for a certain period of time.

[0060] Measurement and feedback: Monitor current and voltage fluctuations in real time and generate data logs to verify the impact of short circuit faults.

[0061] ②Two-phase short circuit

[0062] Current setting: Set the two-phase short-circuit current and select the two-phase short-circuit current value through a switch or current regulator.

[0063] Duration setting: Set the duration of the short circuit to simulate the impact of short circuit in different scenarios.

[0064] Data acquisition: monitor two-phase current and other parameters to assess the impact on the system.

[0065] ③Three-phase short circuit

[0066] Current setting: Configure the three-phase short-circuit current amplitude, generally set to high current to truly simulate the impact of a full short circuit.

[0067] Duration Setting: Provides adjustment options to simulate momentary or long-term three-phase short circuit conditions.

[0068] Test and Analysis: Record current and voltage changes and analyze the system’s response to a three-phase short circuit.

[0069] (2) Ground fault simulation includes direct grounding and indirect grounding. The relevant parameter settings are as follows:

[0070] ① Direct grounding

[0071] Current setting: Simulates the current value of direct grounding.

[0072] Voltage changes: Monitor voltage changes and simulate the impact of ground faults on system voltage.

[0073] Fault Area Identification: Records ground current and voltage data to help identify the fault area.

[0074] ②Indirect grounding

[0075] Current and voltage settings: Adjust the current source and voltage source of the simulator to set different ground current and voltage distributions.

[0076] Analysis Tools: Built-in tools analyze current distribution and voltage changes and evaluate the impact of indirect grounding.

[0077] (3) Leakage simulation includes current amplitude adjustment and current amplitude adjustment, and the relevant parameters are set as follows:

[0078] ①Current amplitude adjustment

[0079] Current setting: adjust the amplitude of leakage current to simulate different leakage scenarios.

[0080] Frequency Adjustment: Provides frequency adjustment options for leakage current to simulate the changing trend of leakage current.

[0081] ②Current amplitude adjustment

[0082] Frequency setting: Set the frequency of leakage current to simulate the impact of different frequencies on the system, DC leakage current or AC leakage current.

[0083] Dynamic test: Dynamically adjust the frequency and observe the system's response under leakage at different frequencies.

[0084] Furthermore, multiple fault types are simulated simultaneously to observe their combined impact on the power grid. Figure 2 , the steps are as follows:

[0085] Configure the settings for multiple fault combinations to occur simultaneously in the simulator. For example, set the simulator to start a single-phase short circuit and a two-phase grounding fault at 0 seconds, and introduce a leakage fault at 4 seconds.

[0086] Set the activation sequence and duration of faults to ensure simultaneous activation at the predetermined time and duration.

[0087] Start the simulator and introduce all configured fault types simultaneously according to the set fault combination.

[0088] Compare the data before and after the simulated failure to locate the affected nodes.

[0089] The fault area is divided according to the electrical changes between the affected nodes to determine the preliminary fault area. For example, for a short circuit fault, it can be determined by the node where the current increases sharply; for grounding and leakage faults, it is necessary to analyze the abnormal current path and voltage fluctuation.

[0090] S3: Conduct electromagnetic, acoustic and critical fluctuation detection on the preliminary fault area to obtain electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data.

[0091] The electromagnetic wave detection device transmits electromagnetic waves to penetrate the fault object, and penetrates the electromagnetic waves in the preliminary fault area through the quantum tunneling effect, and then performs signal amplification processing to generate an electromagnetic wave reflection signal; wherein, the electromagnetic wave detection device includes an adaptive frequency electromagnetic wave transmitter, a quantum tunneling detection circuit, and a signal amplification and filtering circuit; the adaptive frequency electromagnetic wave transmitter automatically adjusts the frequency and power of the electromagnetic wave according to the material properties and thickness of different fault areas to ensure the penetration and accuracy of the detection signal. For example, in the transformer winding, the parts with different thicknesses of the insulating material can automatically switch to the appropriate frequency, thereby avoiding detection errors caused by frequency mismatch. The quantum tunneling detection circuit can capture smaller tunneling effects by using more sensitive high-frequency tunneling transistors. In the case of weak signals, the sensor sensitivity is adjusted in real time through the feedback circuit to enhance the weak signal detection capability, which is particularly suitable for monitoring subtle insulation faults in complex structures. Finally, the signal amplification and filtering circuit are used to achieve high dynamic range amplification and effectively eliminate environmental noise, retaining useful signals.

[0092] Preferably, when electromagnetic waves encounter metal or insulating obstacles, the quantum tunneling effect allows part of the wave to pass through the obstacle, thereby detecting the deeper structure inside the material. At the same time, by automatically adjusting the frequency, especially in complex material layer structures, the most suitable frequency band is selected to achieve the tunneling effect.

[0093] Furthermore, by controlling the vibration of the piezoelectric crystal to generate sound waves, the sound waves penetrate the faulty object to produce an acoustic-electric coupling effect, and generate secondary electromagnetic wave signals. In addition, the acoustic wave detection device is equipped with a nonlinear acoustic-electric effect sensor to monitor the coupling signal of weaker sound waves and the internal microstructure of the material, so as to detect more subtle physical changes and reduce the interference of environmental noise on electromagnetic signals. Finally, through dynamic filtering technology, the filter bandwidth is automatically adjusted for different acoustic wave reflection signals.

[0094] A self-organized criticality detection device is deployed in the preliminary fault area to monitor the critical fluctuations of the object under test in the stress concentration area. Such fluctuations usually occur in the edge area where catastrophic failure is about to occur. Therefore, after the preliminary fault area is determined, the device is started to deeply monitor potential critical changes. The self-organized criticality detection device includes a stress sensor network circuit, a vibrator detection circuit and a signal processing circuit; the stress sensor network circuit includes a plurality of stress sensors based on the piezoresistive effect, which are used to detect stress signals in the preliminary fault area, and the stress signals are transmitted to the signal processing circuit through the front-end amplifier; the vibrator detection circuit is used to capture the vibration signal in the preliminary fault area, and convert the vibration signal into an electrical signal and transmit it to the signal processing circuit, and the stress signal in the preliminary fault area and the vibration signal in the preliminary fault area are amplified and filtered by the signal processing circuit to generate critical effect fluctuation data.

[0095] S4: Fusion of electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data.

[0096] Reference Figure 3 , the electromagnetic wave reflection signal, secondary electromagnetic signal and critical effect fluctuation data are integrated:

[0097] (1) Time synchronization and spatial correction

[0098] The electromagnetic wave reflection signal, secondary electromagnetic signal and critical effect fluctuation data are time synchronized to ensure that all signals are captured at the same time point.

[0099] Furthermore, based on their respective detection ranges and locations, various types of signals are spatially corrected so that signals from different sensors correspond to the same fault area or node.

[0100] (2) Feature extraction

[0101] The time delay, amplitude, and spectrum characteristics of the electromagnetic wave reflection signal are extracted, and the frequency domain characteristics of the reflection signal are analyzed using Fourier transform to identify abnormal frequency components that are different from the normal reflection pattern.

[0102] Wavelet transform technology is used to extract high-frequency components in secondary electromagnetic signals, identify abnormal fluctuation patterns in electromagnetic fields, and extract transient characteristics of current and voltage.

[0103] Extract the spectrum and time domain characteristics of critical effect fluctuation data, analyze the duration and frequency range of stress fluctuations, and identify potential fatigue, crack and other characteristics.

[0104] (3) Data Fusion

[0105] The extracted signal features are fused using weighted average, and the confidence of each feature is combined to generate a fused feature vector.

[0106] For example, the abnormal frequency of the electromagnetic wave reflection signal, the high-frequency component of the secondary electromagnetic signal, the stress peak value of the critical effect fluctuation, etc. are integrated together to construct a multi-dimensional feature vector.

[0107] S5: Perform multimodal analysis on the fused signal and generate a fault report.

[0108] The fault report includes the fault location, fault level and fault type.

[0109] The fused feature vector is input into the trained fault diagnosis model for multimodal analysis, and the fault location and fault level are output.

[0110] The fault diagnosis model includes an input layer, a feature extraction layer, a self-supervised learning pre-training unit, a task sharing and multi-task learning layer, anomaly detection layer and an output layer; the feature extraction layer uses a convolutional neural network to extract local features of the input, and at the same time extracts multi-scale features through void convolution to enhance the model's ability to capture long-term and short-term dependencies. Furthermore, before the model is formally trained, the feature extraction layer will be pre-trained through a self-supervised learning unit, and the parameters of the feature extraction layer will be optimized through self-supervised tasks. The pre-trained model weights provided by the self-supervised learning unit are loaded into the feature extraction layer as initial parameters, so that the feature extraction layer can better extract features in subsequent tasks. Furthermore, through the task sharing and multi-task learning layer, the extracted features are simultaneously subjected to the fault level classification task and the fault location regression task, and then optimized through a joint loss function, which includes cross entropy loss and mean square error loss. Furthermore,

[0111] The anomaly detection layer is parallel to the task sharing and multi-task learning layer. It can directly obtain feature representations from the feature extraction layer, and determine the fault type of the current input data by building an autoencoder. The anomaly detection layer will pass the anomaly score to the task sharing and multi-task learning layer as additional information to assist decision-making. For example, when the anomaly score is high, a warning will be sent to the task sharing and multi-task learning layer through a feedback mechanism to reduce its confidence.

[0112] Finally, the output layer receives the results from the task sharing and multi-task learning layer and the anomaly detection layer, and integrates the scores of fault level, fault location, and fault type into the final fault report.

[0113] The output layer is also connected to the online learning unit, which can feed the prediction results of the fault diagnosis model, user feedback or annotation data back to the feature extraction layer, fine-tune the parameters of the feature extraction layer, and further improve the accuracy of the fault diagnosis model.

[0114] Example 2, reference Figure 4 , which is an embodiment of the present invention, provides a system for a low-voltage substation fault location method, characterized in that it includes a topology construction module 100, a low-voltage fault simulation module 200, a signal detection module 300, a signal fusion module 400 and a fault location module 500.

[0115] The topology building module 100 is used to build a grid topology in a low voltage area, and the grid topology includes a plurality of nodes.

[0116] The low voltage fault simulation module 200 is used to perform low voltage fault simulation at multiple nodes to determine a preliminary fault area.

[0117] The signal detection module 300 is used to perform electromagnetic, acoustic and critical fluctuation detection on the preliminary fault area to obtain electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data.

[0118] The signal fusion module 400 is used to fuse the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data.

[0119] The fault location module 500 is used to perform multi-modal analysis on the fused signal and generate a fault report including the fault location, fault level and fault type.

[0120] The low voltage fault simulation module 200 is specifically used to collect electrical parameters of each node before and after simulation, and the electrical parameters include voltage, current and power factor.

[0121] The electrical parameters before and after the simulation are compared and analyzed, and the affected nodes are located based on the simulated fault type.

[0122] The simulated fault types include short circuit simulation, ground fault simulation, and leakage simulation.

[0123] The fault area is divided according to the electrical changes between the affected nodes.

[0124] The signal detection module 300 is specifically used to transmit electromagnetic waves through the electromagnetic wave detection device to penetrate the fault object, and penetrate the electromagnetic waves in the preliminary fault area through the quantum tunneling effect, and then perform signal amplification processing to generate an electromagnetic wave reflection signal.

[0125] By controlling the vibration of the piezoelectric crystal, sound waves are generated. The sound waves penetrate the faulty object to produce an acoustic-electric coupling effect and generate secondary electromagnetic wave signals.

[0126] The signal detection module 300 is specifically used to deploy a self-organized criticality detection device in the preliminary fault area. The self-organized criticality detection device includes a stress sensor network circuit, a vibrator detection circuit and a signal processing circuit.

[0127] The stress sensor network circuit includes a plurality of stress sensors based on the piezoresistive effect, which are used to detect stress signals in the preliminary fault area. The stress signals are transmitted to the signal processing circuit through the front-end amplifier.

[0128] The vibrator detection circuit is used to capture the vibration signal in the preliminary fault area, and convert the vibration signal into an electrical signal and transmit it to the signal processing circuit. The signal processing circuit amplifies and filters the stress signal in the preliminary fault area and the vibration signal in the preliminary fault area to generate critical effect fluctuation data.

[0129] The fault location module 500 is specifically used to extract features from the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data, and to generate a fused feature vector by integrating the confidence of each feature.

[0130] The fused feature vector is input into the trained fault diagnosis model for multimodal analysis, and the fault location, fault level and fault type are output.

[0131] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0132] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0133] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0134] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0135] Example 3: In this example, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This example conducts experiments on the existing traditional method and the method of this example, as shown in Table 1.

[0136] In the field of low-voltage area fault location, traditional methods usually rely on manual inspection and empirical judgment, which is not only time-consuming and labor-intensive, but also due to the uncertainty of subjective judgment, the accuracy of fault location is often not high. In order to overcome these shortcomings, the present invention proposes a low-voltage area fault location system based on intelligent data analysis and remote monitoring technology.

[0137] Table 1 Experimental effect comparison chart

[0138]

[0139] Due to the adoption of intelligent data analysis, the present invention can automatically identify the fault point in a short time, thus greatly shortening the fault location time.

[0140] Artificial intelligence algorithms can analyze data more accurately, reducing the possibility of human misjudgment.

[0141] The system response time of the method of the present invention is faster, the fault can be handled in time, and the power outage time is reduced.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for locating a low voltage station fault, characterized in that: include: Construct grid topology in low voltage area; Conduct low voltage fault simulation to determine the preliminary fault area; Conduct electromagnetic, acoustic and critical fluctuation detection on the preliminary fault area to obtain electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data; Fusing electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data; Perform multimodal analysis on the fused signal and generate a fault report.

2. A low voltage station fault location method as claimed in claim 1, characterized in that: The low voltage fault simulation includes collecting electrical parameters of each node before and after the simulation, wherein the electrical parameters include voltage, current and power factor; Comparing and analyzing the electrical parameters before and after the simulation, and locating the affected nodes in combination with the simulated fault types, wherein the simulated fault types include short circuit simulation, ground fault simulation, and leakage simulation; The fault area is divided according to the electrical changes between the affected nodes.

3. A low voltage station fault location method as claimed in claim 2, characterized in that: The electromagnetic and acoustic wave detection in the electromagnetic, acoustic and critical fluctuation detection includes transmitting electromagnetic waves through the fault object through an electromagnetic wave detection device, penetrating the electromagnetic waves in the preliminary fault area through the quantum tunneling effect, performing signal amplification processing, and generating an electromagnetic wave reflection signal; By controlling the vibration of the piezoelectric crystal, sound waves are generated. The sound waves penetrate the faulty object to produce an acoustic-electric coupling effect and generate secondary electromagnetic wave signals.

4. A low voltage station fault location method as claimed in claim 3, characterized in that: The critical fluctuation detection in the electromagnetic, acoustic and critical fluctuation detection includes deploying a self-organized criticality detection device in the preliminary fault area, wherein the self-organized criticality detection device includes a stress sensor network circuit, a vibrator detection circuit and a signal processing circuit; The stress sensor network circuit includes a plurality of stress sensors based on the piezoresistive effect, which are used to detect stress signals in the preliminary fault area, and the stress signals are transmitted to the signal processing circuit through a front-end amplifier; The vibrator detection circuit is used to capture the vibration signal in the preliminary fault area, and convert the vibration signal into an electrical signal and transmit it to the signal processing circuit. The signal processing circuit amplifies and filters the stress signal in the preliminary fault area and the vibration signal in the preliminary fault area to generate the critical effect fluctuation data.

5. A low voltage station fault location method as claimed in claim 4, characterized in that: The multimodal analysis includes extracting features from the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data, synthesizing the confidence of each feature, and generating a fusion feature vector; The fused feature vector is input into a trained fault diagnosis model for multimodal analysis, and the fault location, fault level and fault type are output.

6. A system using a low voltage station fault location method as claimed in any one of claims 1 to 5, characterized in that: It comprises a topology construction module (100), a low voltage fault simulation module (200), a signal detection module (300), a signal fusion module (400) and a fault location module (500); The topology building module (100) is used to build a grid topology in a low-voltage area, and the grid topology includes a plurality of nodes; The low voltage fault simulation module (200) is used to perform low voltage fault simulation at the multiple nodes to determine a preliminary fault area; The signal detection module (300) is used to perform electromagnetic, acoustic and critical fluctuation detection on the preliminary fault area to obtain electromagnetic wave reflection signals, secondary electromagnetic signals and critical effect fluctuation data; The signal fusion module (400) is used to fuse the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data; The fault location module (500) is used to perform multi-modal analysis on the fused signal and generate a fault report including the fault location, fault level and fault type.

7. A low voltage station fault location system as claimed in claim 6, characterized in that: The low voltage fault simulation module (200) is specifically used to collect electrical parameters of each node before and after simulation, wherein the electrical parameters include voltage, current and power factor; Compare and analyze the electrical parameters before and after the simulation, and locate the affected nodes based on the simulated fault type; The simulated fault types include short circuit simulation, ground fault simulation, and leakage simulation; Divide the fault area according to the electrical changes between the affected nodes; The signal detection module (300) is specifically used to transmit electromagnetic waves through the electromagnetic wave detection device to penetrate the fault object, and penetrate the electromagnetic waves in the preliminary fault area through the quantum tunneling effect, and then perform signal amplification processing to generate an electromagnetic wave reflection signal; By controlling the vibration of the piezoelectric crystal, sound waves are generated. The sound waves penetrate the faulty object to produce an acoustic-electric coupling effect and generate secondary electromagnetic wave signals.

8. A low voltage area fault location system as claimed in claim 7, characterized in that: The signal detection module (300) is specifically used to deploy a self-organized criticality detection device in the preliminary fault area, wherein the self-organized criticality detection device comprises a stress sensor network circuit, a vibrator detection circuit and a signal processing circuit; The stress sensor network circuit includes a plurality of stress sensors based on the piezoresistive effect, which are used to detect stress signals in the preliminary fault area, and the stress signals are transmitted to the signal processing circuit through a front-end amplifier; The vibrator detection circuit is used to capture the vibration signal in the preliminary fault area, and convert the vibration signal into an electrical signal and transmit it to the signal processing circuit, and the stress signal in the preliminary fault area and the vibration signal in the preliminary fault area are amplified and filtered by the signal processing circuit to generate the critical effect fluctuation data; The fault location module (500) is specifically used to extract features from the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data, and to synthesize the confidence of each feature to generate a fusion feature vector; The fused feature vector is input into a trained fault diagnosis model for multimodal analysis, and the fault location, fault level and fault type are output.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a low-voltage substation fault location method described in any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a low-voltage substation fault location method described in any one of claims 1 to 5 are implemented.

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