Direct-current power distribution network fault positioning method, system and device based on traveling wave analysis and medium

Abstract

The invention relates to the technical field of data center energy management and power dispatching, and discloses a traveling wave analysis-based DC power distribution network fault positioning method, system and device, and a medium. The method comprises the following steps: acquiring a transient traveling wave signal, and preprocessing; when the extracted fault feature reaches a starting condition, recording the arrival time and polarity of the traveling wave; establishing an identification model according to the traveling wave data, and judging a fault type; carrying out positioning calculation according to the fault type, and combining a power grid structure and traveling wave correction to obtain a fault position; and generating a protection instruction according to the fault type and the position information, and performing optimization operation. According to the method, millisecond-level rapid detection can be realized, accurate positioning in a complex network is realized, the anti-interference capability is high, and the reliability, the accuracy and the self-adaptive level of fault processing of the direct current power distribution network are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of data center energy management and power dispatching technology, specifically to a method, system, equipment, and medium for fault location in DC distribution networks based on traveling wave analysis. Background Technology

[0002] With the rapid construction and promotion of DC distribution networks in my country's data centers, electric vehicle charging stations, and high-proportion distributed renewable energy access, the demand for rapid fault isolation and accurate fault location is becoming increasingly prominent. However, current fault detection and location methods for DC distribution networks struggle to cope with multiple challenges, including extremely fast fault transients, complex network topologies, and strong electromagnetic interference. While existing methods can achieve a certain degree of fault identification, their slow response speed based on steady-state quantities, large errors in traditional impedance-based location methods in complex networks, and weak anti-interference capabilities in signal processing stages mean that a collaborative solution mechanism that balances speed, accuracy, and reliability has not yet been formed. The timeliness of system protection actions, location accuracy, and resistance to maloperation are all significantly insufficient. Currently, the field of fault detection and location for DC distribution networks still lacks a systematic technical solution, exhibiting significant deficiencies in millisecond-level rapid detection, accurate location in complex networks, and reliable operation under high-interference environments. Summary of the Invention

[0003] In view of the above-mentioned existing problems, the present invention provides a method, system, equipment and medium for fault location in DC distribution networks based on traveling wave analysis.

[0004] Therefore, the technical problem solved by this invention is: how to organically combine rapid fault detection, accurate location and anti-interference capability, and achieve multi-target collaborative protection with millisecond-level rapid response, high-precision location of complex networks and high-reliability action by constructing a fault handling framework and adaptive anti-interference mechanism based on multi-terminal traveling wave collaborative analysis and intelligent feature recognition.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a fault location method for DC distribution networks based on traveling wave analysis, comprising, Acquire transient traveling wave signals in a DC distribution network and perform preprocessing operations on the transient traveling wave signals to obtain fault characteristics; In response to the fault characteristics meeting the first start-up condition, the arrival of the fault traveling wave is determined and the data is recorded. Based on the fault traveling wave data, a first identification model is constructed, which is used to determine the fault type. In response to the fault type location requirement, the first location calculation is performed to obtain the fault location information; Based on the fault type and fault information, a first protection action command is generated and an optimized operation is performed.

[0006] As a preferred embodiment of the DC distribution network fault location method based on traveling wave analysis described in this invention, the step of determining the arrival of the fault traveling wave and recording data in response to the fault characteristics satisfying the first initiation condition includes: In response to the fulfillment of the first initiation condition, the arrival of the fault traveling wave is determined and its arrival time and polarity are recorded. The first start condition is that the fault characteristics exceed the corresponding preset threshold.

[0007] As a preferred embodiment of the DC distribution network fault location method based on traveling wave analysis described in this invention, wherein: the construction of a first identification model, the first identification model being used to determine the fault type, includes, The extracted fault features are combined into a first feature vector, which is then input into the first recognition model, and the recognition result of the fault type is output.

[0008] As a preferred embodiment of the DC distribution network fault location method based on traveling wave analysis described in this invention, wherein: in response to the location requirements of the fault type, a first location calculation is performed to obtain fault location information, including, Based on the data of the fault traveling wave, the initial fault location is calculated and obtained in response to the arrival of the fault traveling wave at the preset observation point; The traveling wave is corrected based on the traveling wave round-trip data of known calibration points, and the fault location information is obtained by combining the power grid topology.

[0009] As a preferred embodiment of the DC distribution network fault location method based on traveling wave analysis described in this invention, the step of generating a first protection action command based on the fault type and fault information includes: When the fault type is the first fault type, the control circuit breaker will trip immediately; When the fault type is the second fault type, an alarm signal is generated and it is determined whether to control the circuit breaker to trip.

[0010] As a preferred embodiment of the DC distribution network fault location method based on traveling wave analysis described in this invention, the step of performing optimization operations includes: Based on the feature vectors and corresponding fault types in historical fault data, the parameters of the first identification model are updated, the traveling wave parameters of the first positioning calculation are corrected, and the threshold parameters of the first start-up condition are adjusted.

[0011] As a preferred embodiment of the DC distribution network fault location method based on traveling wave analysis described in this invention, the step of acquiring transient traveling wave signals in the DC distribution network and preprocessing the transient traveling wave signals to obtain fault characteristics includes: The transient traveling wave signal is preprocessed to extract the transient components of the transient traveling wave signal within a preset frequency band, and the fault characteristics are calculated.

[0012] This invention detects the characteristics of traveling waves during a fault, records their arrival time and polarity, constructs a model to identify the fault type, and calculates and corrects the fault location based on the traveling wave data. This enables rapid fault location, accurate identification, and precise fault positioning, achieving efficient and reliable fault location in DC distribution networks.

[0013] This invention provides a DC distribution network fault location system based on traveling wave analysis, comprising: The signal acquisition and preprocessing module is responsible for acquiring transient traveling wave signals in real time and performing feature extraction preprocessing to obtain fault characteristics; The judgment and recording module is activated. By judging whether the fault characteristics exceed the preset threshold, the recording mechanism is triggered to save the arrival time and polarity information of the fault traveling wave. The fault type identification module uses the first identification model to classify the extracted fault features and identify the fault type. The fault location calculation module calculates the specific location of the fault point based on the time difference of arrival or waveform characteristics of the traveling wave, combined with power grid topology information. The protection action execution module generates corresponding protection commands based on the fault type and location, and controls the circuit breaker to perform the operation. The optimization and update module utilizes historical fault data to optimize the parameters of the first identification model, the traveling wave parameters of the first positioning calculation, and the threshold parameters of the first start-up condition.

[0014] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a DC distribution network fault location method based on traveling wave analysis.

[0015] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a DC distribution network fault location method based on traveling wave analysis.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a fault location method for DC distribution networks based on traveling wave analysis. It combines traveling wave analysis technology with intelligent algorithms to form a complete solution, improving system protection effectiveness. First, signal acquisition devices are installed in critical locations to capture instantaneous wave signals as the basis for fault location. Then, these signals are processed using methods such as wavelet transform to extract fault features and quickly identify the problem. Next, an intelligent classification model is used to determine the type of fault, and the fault location is calculated by combining the signal arrival time difference and power grid structure information. Based on this information, protective measures are formulated, controlling circuit breakers to isolate the problematic area, and secondary confirmation is performed to prevent misoperation. Furthermore, a fault case library and model optimization mechanism are established to regularly update model parameters and enhance system adaptability. This method can shorten fault location time, improve accuracy, and is reliable even in complex environments. It benefits power grid safety and operation and maintenance efficiency, is highly practical, and can be expanded. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used 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 those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an implementation diagram of a DC distribution network fault location method based on traveling wave analysis provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Example 1, referring to Figure 1 The first embodiment of the present invention provides a fault location method for DC distribution networks based on traveling wave analysis, comprising: S1: Acquire transient traveling wave signals in the DC distribution network and perform preprocessing operations on the transient traveling wave signals to obtain fault characteristics.

[0021] S2: In response to the fault characteristics meeting the first start-up condition, determine the arrival of the fault traveling wave and record the data.

[0022] S3: Based on the data of the fault traveling wave, a first identification model is constructed, which is used to determine the fault type.

[0023] S4: In response to the location requirements of the fault type, perform the first location calculation to obtain the fault location information.

[0024] S5: Based on the fault type and fault information, generate a first protection action command and execute the optimized operation.

[0025] It should be noted that this embodiment designs a fault location method for DC distribution networks based on traveling wave analysis. This method does not rely on traditional steady-state quantity detection and impedance location methods, and can quickly detect and accurately locate faults within milliseconds, with strong anti-interference capabilities. It acquires fault transient traveling waves through high-speed sampling, then intelligently identifies the fault type using wavelet denoising, multi-feature fusion, and support vector machine classification algorithms. Finally, it uses traveling wave time difference ranging and topology analysis to accurately locate the fault point. Furthermore, a closed-loop optimization mechanism continuously improves detection reliability based on historical data, providing reliable technical support for the safe and stable operation of the power grid.

[0026] This embodiment also addresses the problems of slow detection response, large positioning error, and weak anti-interference capability caused by fast transient processes, complex network topology, and strong on-site interference in DC distribution network fault detection and location. To overcome these shortcomings, a fault location method based on traveling wave analysis is proposed. Transient traveling wave signals are acquired through a high-speed synchronous acquisition device, and wavelet denoising, multi-criteria fusion, and support vector machine are used to achieve rapid extraction and type identification of fault features. Furthermore, the method combines the arrival time difference of multi-terminal traveling waves with grid topology information for precise positioning, and introduces an online traveling wave velocity calibration and closed-loop self-optimization mechanism to continuously correct model parameters and thresholds. This enables fault detection and high-precision positioning to be completed within milliseconds, significantly improving the reliability and adaptability of the system in complex interference environments.

[0027] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a fault location method for DC distribution networks based on traveling wave analysis is provided.

[0028] In this embodiment of the application, step S1 involves acquiring a transient traveling wave signal in the DC distribution network and performing preprocessing operations on the transient traveling wave signal to obtain fault characteristics. Specifically, this includes the following steps A1-A3: A1: Acquire transient traveling wave signals in DC distribution networks.

[0029] Understandably, high-speed voltage and current sensors are installed at critical locations in the DC distribution network, such as converter outputs, bus nodes, and line branch points. Current measurement uses Hall effect sensors with a response time of less than 1 microsecond, accurately capturing high-frequency transient signals. Voltage measurement employs a resistor divider, using a high-precision resistor network to reduce the DC bus voltage at a ratio of 1000:1, ensuring both measurement accuracy and electrical safety isolation.

[0030] Each measurement point has a synchronous sampling module with a sampling frequency of 1 MHz, used to completely record the high-frequency components of the traveling wave signal. To ensure time synchronization, the IEEE 1588 Precision Time Protocol (PTP) is used, controlling the synchronization error between measurement points to within 1 microsecond. The acquired raw data is transmitted to the central processing unit via optical fiber, with a total delay not exceeding 10 microseconds.

[0031] A2: Preprocess the transient traveling wave signal.

[0032] It should be noted that the original acquired signal contains measurement noise and high-frequency interference, requiring preprocessing to extract effective fault features. The system first performs wavelet decomposition on the signal, allocating it to different frequency bands; then, it selects the Daubechies wavelet (db4) for a 5-level decomposition to obtain high-frequency detail coefficients and low-frequency approximation coefficients. The main energy of the fault traveling wave is concentrated in the 1kHz to 100kHz frequency band, corresponding to the 3rd and 4th level detail coefficients in the wavelet decomposition. After reconstructing these two levels of coefficients, the denoised transient signal can be obtained.

[0033] A3: Perform preprocessing operations on the transient traveling wave signal to obtain fault characteristics.

[0034] It should be noted that, based on the denoised signal, the rate of change of voltage and current is calculated as fault characteristic parameters. The rate of change of voltage is defined as: , in, This is the rate of change of voltage, measured in volts per microsecond. The voltage after noise reduction at the current moment; Sampling time; The voltage after noise reduction at the previous moment; The sampling interval is 1 microsecond.

[0035] Furthermore, the rate of change of current is calculated: , in, The current change rate is expressed in amperes per microsecond. The noise-reduced current at the current moment; Sampling time; The current after noise reduction at the previous moment; The sampling interval is 1 microsecond.

[0036] Furthermore, the traveling wave features are extracted to calculate the transient energy jump: , , in, This refers to the transient energy fluctuation, measured in joules. For time indexing; Sampling time; for Voltage after noise reduction at any time; for Current after noise reduction at all times; The sliding window width is set to 50 microseconds, corresponding to 50 sampling points; it is determined by the characteristic impedance of the line. This is the current energy weighting coefficient; The line impedance is approximately 30 ohms, therefore The value is set to 900. The transient energy mutation can sensitively reflect the arrival of fault traveling waves.

[0037] Furthermore, the preprocessing module will extract the feature parameters. , and The signal is passed to the traveling wave detection module for fault traveling wave detection.

[0038] Based on step S1, this embodiment addresses the problem of excessive noise interference and unclear features in the original signal by employing a high-speed synchronous sensor to effectively capture high-frequency signals, and then using wavelet decomposition technology to eliminate the influence of noise. Voltage change rate, current change rate, and transient energy fluctuation are extracted as key features, enabling more accurate and sensitive detection of fault traveling wave signals.

[0039] In this embodiment of the invention, step S2, in response to the fault characteristics satisfying the first start-up condition, determines the arrival of the fault traveling wave and records the data, specifically includes the following steps B1-B3: B1: In response to the first activation condition being met, determine the arrival of the fault traveling wave and record the arrival time and polarity; the first activation condition is that the fault characteristic exceeds the corresponding preset threshold.

[0040] It should be noted that the traveling wave detection module continuously monitors the extracted feature parameters. When these feature parameters exceed the preset threshold, it determines that the fault traveling wave has arrived and initiates the fault identification process.

[0041] B2: The first start-up condition may include: voltage change rate start-up criterion, current change rate start-up criterion, and energy change start-up criterion.

[0042] Furthermore, the voltage change rate trigger criterion is as follows: , in, The voltage change rate starting coefficient is determined by analyzing the maximum voltage change rate during normal operation, and is set to 0.05, which is 5% of the rated voltage per millisecond. This is the rated voltage, which is 750 volts for a 750V DC system. This serves as a preset threshold for the rate of voltage change.

[0043] The current change rate start-up criterion is: , in, The current change rate starting coefficient is set according to the current change during normal load switching of the line, and is set to 0.1, which is 10% of the rated current per millisecond. This refers to the rated current of the line. This serves as a preset threshold for the rate of voltage change.

[0044] The criterion for initiating an energy mutation is: , in, The preset threshold for energy fluctuations is set at 5 times the standard deviation of normal fluctuations by statistically analyzing the energy fluctuation range under normal operating conditions. For a typical DC distribution network, the value is approximately 500 joules.

[0045] B3: When any two of the above three criteria are met simultaneously, the system determines that the fault traveling wave has arrived and records the arrival time of the traveling wave. At the same time, the system records the initial polarity of the traveling wave.

[0046] Furthermore, the direction of the voltage change can be used to determine this: A sudden voltage drop indicates a positive polarity fault. A sudden voltage spike indicates a negative polarity fault or a reflected wave.

[0047] The traveling wave detection module transmits the arrival time and polarity information to the fault identification module.

[0048] Based on step S2, this embodiment uses the combined judgment of multiple factors such as voltage, current change rate and energy change to avoid the problem of misjudgment or missed detection caused by interference from a single characteristic, thus ensuring the accuracy and reliability of fault traveling wave detection.

[0049] In this embodiment of the application, step S3 involves constructing a first identification model based on the fault traveling wave data. The first identification model is used to determine the fault type and specifically includes the following steps C1-C3: C1: The extracted fault features are combined into a first feature vector, which is then input into the first recognition model, and the recognition result of the fault type is output.

[0050] It should be noted that the main types of faults in DC distribution networks include: positive-to-ground short circuit, negative-to-ground short circuit, and short circuit between positive and negative poles. The traveling wave characteristics generated by different fault types differ significantly, and the fault identification module classifies faults based on waveform characteristics and a first identification model.

[0051] C2: Extract fault feature vectors.

[0052] It should be noted that, in addition to the aforementioned voltage and current rate of change and energy mutation, the steady-state offset and spectral characteristics of voltage and current after the fault are also calculated. The steady-state voltage offset is defined as the difference between the average voltage within 100 microseconds after the fault and the steady-state voltage before the fault. Spectral characteristics are extracted using Fast Fourier Transform, and the amplitudes at three characteristic frequency points (1 kHz, 10 kHz, and 50 kHz) are calculated. These characteristics are then combined into a feature vector. It includes seven characteristic components: voltage change rate, current change rate, energy jump, voltage offset, and amplitude at three frequency points.

[0053] C3: The first identification model classifies faults.

[0054] It should be noted that the first identification model can classify faults using a support vector machine (SVM). A training sample set is constructed based on offline simulation and historical fault data. The sample set contains feature vectors and corresponding labels for each fault type at different locations and under different transition resistances.

[0055] During online execution, the feature vectors extracted in real time are fed into a support vector machine (SVM) to output fault type labels. The values ​​1, 2, and 3 represent positive grounding, negative grounding, and short circuit between electrodes, respectively. The confidence level is determined by the decision function value of the Support Vector Machine (SVM). Classify, when A value greater than 0.85 indicates a reliable classification result; the fault identification module will label the fault type. and confidence level It is then transmitted to the positioning calculation module.

[0056] Based on step S3, this embodiment extracts information from multiple aspects such as voltage changes and current changes from the data, and then uses support vector machine (SVM) to classify this information. This allows for a more accurate determination of different types of faults in the DC distribution network, such as positive grounding, negative grounding, or inter-pole short circuit.

[0057] In this embodiment of the application, step S4, in response to the location requirement of the fault type, performs a first location calculation to obtain fault location information, specifically including the following steps D1-D2: D1: Based on the fault traveling wave data, the initial fault location is calculated and obtained in response to the fault traveling wave reaching the preset observation point.

[0058] It should be noted that the location calculation module determines the location of the fault point based on the time difference of the fault traveling wave arriving at each measuring point, taking into account both the network topology and the traveling wave propagation speed. For double-ended measurements on a single line, the fault distance calculation formula is: , in, This is the distance from the fault point to end A, in meters; The traveling wave propagation speed is approximately 150 meters per microsecond in a DC cable; The time it takes for the traveling wave to reach point A is expressed in microseconds. The time it takes for the traveling wave to reach point B is measured in microseconds. This represents the total length of the line, in meters.

[0059] Furthermore, for multiple branches in complex topologies, a multi-terminal positioning method is adopted. First, the faulty section is determined based on the arrival time and polarity of the traveling waves at each measuring point. The measuring point where the traveling wave arrives first is closest to the faulty point, and the faulty branch is determined by comparing the arrival times. Then, on the identified faulty branch, the precise location of the fault is calculated using a two-terminal ranging formula.

[0060] D2: Correct the traveling wave based on the traveling wave round-trip data of the known calibration point, and obtain the fault location information by combining the power grid topology.

[0061] It should be noted that, to improve positioning accuracy, the traveling wave velocity is corrected. Factors such as cable dielectric and temperature can cause deviations in the traveling wave propagation velocity. The system uses a known calibration point (e.g., the end of the line) to calculate the actual propagation velocity by measuring the round-trip time of the traveling wave, as shown in the following formula: , in, This represents the actual traveling wave propagation speed; The known distance between the calibration point and the measuring point; This is the round-trip time of the traveling wave. Use the corrected velocity. Substitute the corrected traveling wave propagation velocity into the fault distance calculation formula. Then, perform positioning calculations.

[0062] The location calculation module outputs the fault location and location reliability, and transmits the location results to the protection linkage module. The location reliability depends on the time synchronization accuracy and signal quality of each measuring point.

[0063] Based on step S4, this embodiment suffers from low fault location accuracy and poor reliability due to inaccurate traveling wave propagation speed, complex power grid structure, and measurement errors. Therefore, by calculating the initial fault location and combining it with the traveling wave round-trip data from known calibration points, online correction of the traveling wave velocity can be performed, thereby improving the location accuracy and reliability of the results.

[0064] In this embodiment of the application, step S5 generates a first protection action command and performs an optimization operation based on the fault type and fault information, specifically including the following steps E1-E4: E1: When the fault type is the first fault type, the control circuit breaker will trip immediately; An alarm signal is generated when the fault type is the second fault type.

[0065] It should be noted that the protection linkage module generates action protection commands based on the fault location and type information, controlling the circuit breaker to quickly isolate the fault. Corresponding isolation strategies are formulated for different fault types.

[0066] The first type of fault is inter-pole short circuit. For inter-pole short circuit faults, due to their large fault current and significant hazards, the circuit breaker closest to the fault point should be tripped immediately.

[0067] The second type of fault is single-pole grounding. For single-pole grounding faults, if the system is ungrounded or grounded via high resistance, the system can be allowed to operate for a short period of time, issuing only an alarm signal without immediately clearing the fault.

[0068] E2: Determine whether to control the circuit breaker to trip.

[0069] It should be noted that, to avoid accidental operation, the linkage module introduces a secondary confirmation mechanism. This mechanism is used when the confidence level of fault identification is... If the location result deviates significantly from the expectation, the system will pause briefly (2 milliseconds) before issuing the trip command, and at the same time perform real-time monitoring of the fault characteristics.

[0070] If the fault characteristics still exist and meet the criteria, the circuit breaker will be tripped. If the fault characteristics disappear or the conditions are not met, it is determined to be an interference signal, the trip command is canceled, and only the event log is saved.

[0071] E3: The system establishes a fault feature database to store the feature vectors, type labels, and location results of historical faults.

[0072] It should be noted that after taking protective measures, the system records all fault-related information in detail, including the time, type, location, waveform data, and the circuit breaker that operated. This data is then stored in the fault characteristic database. As this data accumulates, the system periodically analyzes it and updates and improves the model based on this information.

[0073] E4: Based on the feature vectors and corresponding fault types in historical fault data, update the parameters of the first identification model, correct the traveling wave parameters of the first positioning calculation, and adjust the threshold parameters of the first start-up condition.

[0074] It should be noted that by calculating the recognition accuracy and positioning error statistics, the weaknesses of the first recognition model are identified. If the recognition accuracy for a certain type of fault is less than 95% or the positioning error is higher than 3%, the model retraining process is triggered. All samples of that type of fault are extracted from the database, and the support vector machine (SVM) is retrained by incorporating the newly added cases, updating the model parameters. For the positioning algorithm, the deviation of the traveling wave propagation speed is deduced from the actual positioning error, and the speed parameter is adjusted accordingly. Make corrections.

[0075] Furthermore, the system analyzes cases of malfunctions and failures to operate. For malfunctions, it extracts the signal features at the time of triggering and adds these features to the training set as negative examples, allowing the model to better identify normal operating states. For failures to operate, it analyzes the characteristics of the fault signal and then adjusts the threshold parameters for triggering the system. , and This closed-loop optimization mechanism can improve the accuracy and reliability of system detection, enabling it to adapt to changes in the operating conditions of the distribution network.

[0076] Based on step S5, this embodiment uses different protection methods, adds a secondary confirmation mechanism to prevent misoperation, and uses historical data for continuous optimization, solving the problems of unstable power supply due to insufficient protection action, easy interference of traditional protection, and inability of fixed parameter models to adapt to changes in the power grid.

[0077] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides a DC distribution network fault location system based on traveling wave analysis, including: The signal acquisition and preprocessing module is responsible for acquiring transient traveling wave signals in real time and performing feature extraction preprocessing to obtain fault characteristics; The judgment and recording module is activated. By judging whether the fault characteristics exceed the preset threshold, the recording mechanism is triggered to save the arrival time and polarity information of the fault traveling wave. The fault type identification module uses the first identification model to classify the extracted fault features and identify the fault type. The fault location calculation module calculates the specific location of the fault point based on the time difference of arrival or waveform characteristics of the traveling wave, combined with power grid topology information. The protection action execution module generates corresponding protection commands based on the fault type and location, and controls the circuit breaker to perform the operation. The optimization and update module utilizes historical fault data to optimize the parameters of the first identification model, the traveling wave parameters of the first positioning calculation, and the threshold parameters of the first start-up condition.

[0078] This embodiment also provides an electronic device suitable for fault location in DC distribution networks based on traveling wave analysis, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the fault location method for DC distribution networks based on traveling wave analysis proposed in the above embodiment.

[0079] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the DC distribution network fault location method based on traveling wave analysis as proposed in the above embodiments.

[0080] The storage medium proposed in this embodiment and the method for locating DC distribution network faults based on traveling wave analysis proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0081] Example 4, the fourth embodiment of the present invention, provides a case study analysis and simulation verification of a fault location method for DC distribution networks based on traveling wave analysis, including: This simulation example is based on an actual ±750V DC distribution network in an industrial park. The distribution network consists of a 5km main line and two branch lines of 2km and 3km respectively, using copper-core cross-linked polyethylene cables with a line impedance of 30 ohms. The network is connected to three bidirectional converters with a total capacity of 2MW, and the loads are mainly data center servers and electric vehicle charging stations. A distribution network model was built on the MATLAB platform to simulate various fault scenarios, including different locations, different transition resistances, and different fault types. The simulation sampling frequency was set to 1MHz, consistent with the actual system. By setting faults in the model and recording the voltage and current waveforms at each monitoring point, the detection and location algorithm proposed in this invention was run, and the detection time, identification accuracy, and location error were statistically analyzed.

[0082] Table 1 Comparison of Fault Detection Speed

[0083] Among 50 different fault scenarios, compared with the traditional steady-state detection method of 16.7 milliseconds, the average detection time of this invention is 1.8 milliseconds, which improves the detection speed by 89.2%, and the shortest detection time is 1.2 milliseconds, which meets the requirements of DC distribution network for millisecond-level protection.

[0084] Table 2 Comparison of Fault Location Accuracy

[0085] In location tests for different fault distances, the average location error of the traditional impedance method reached 336 meters, with an error rate of 12.4%; the average location error of the present invention was only 53 meters, with an error rate as low as 2.0%, and the location accuracy was improved by 83.9%; the location error of all test cases was within 3% of the line length, which was effectively controlled and the design goal was achieved.

[0086] Table 3. Recognition accuracy under different interference conditions

[0087] In 100 test scenarios involving various interference factors, the traditional method achieved an average recognition accuracy of 77.3% with 12.2 false triggers. The method employed in this invention, using wavelet denoising technology and a support vector machine (SVM) intelligent recognition algorithm, achieved an average accuracy of 96.4% with only 2 false triggers, significantly improving its anti-interference capability. Even under extreme working conditions with multiple interference factors, the recognition accuracy remains above 94.6%.

[0088] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A fault location method for DC distribution networks based on traveling wave analysis, characterized in that: include, Acquire transient traveling wave signals in a DC distribution network and perform preprocessing operations on the transient traveling wave signals to obtain fault characteristics; In response to the fault characteristics meeting the first start-up condition, the arrival of the fault traveling wave is determined and the data is recorded. Based on the fault traveling wave data, a first identification model is constructed, which is used to determine the fault type. In response to the fault type location requirement, the first location calculation is performed to obtain the fault location information; Based on the fault type and fault information, a first protection action command is generated and an optimized operation is performed.

2. The DC distribution network fault location method based on traveling wave analysis as described in claim 1, characterized in that: The response to the fault characteristics satisfying the first initiation condition, determining the arrival of the fault traveling wave and recording data, includes: In response to the fulfillment of the first initiation condition, the arrival of the fault traveling wave is determined and its arrival time and polarity are recorded. The first start condition is that the fault characteristics exceed the corresponding preset threshold.

3. The DC distribution network fault location method based on traveling wave analysis as described in claim 2, characterized in that: The construction of a first identification model, which is used to determine the fault type, includes, The extracted fault features are combined into a first feature vector, which is then input into the first recognition model, and the recognition result of the fault type is output.

4. The DC distribution network fault location method based on traveling wave analysis as described in claim 3, characterized in that: In response to the location requirement of the fault type, a first location calculation is performed to obtain the fault location information. include, Based on the data of the fault traveling wave, the initial fault location is calculated and obtained in response to the arrival of the fault traveling wave at the preset observation point; The traveling wave is corrected based on the traveling wave round-trip data of known calibration points, and the fault location information is obtained by combining the power grid topology.

5. The DC distribution network fault location method based on traveling wave analysis as described in claim 4, characterized in that: The first protection action command is generated based on the fault type and fault information, including: When the fault type is the first fault type, the control circuit breaker will trip immediately; When the fault type is the second fault type, an alarm signal is generated and it is determined whether to control the circuit breaker to trip.

6. The DC distribution network fault location method based on traveling wave analysis as described in claim 5, characterized in that: The optimization operation includes, Based on the feature vectors and corresponding fault types in historical fault data, the parameters of the first identification model are updated, the traveling wave parameters of the first positioning calculation are corrected, and the threshold parameters of the first start-up condition are adjusted.

7. The DC distribution network fault location method based on traveling wave analysis as described in claim 6, characterized in that: The process of acquiring transient traveling wave signals in the DC distribution network and performing preprocessing operations on the transient traveling wave signals to obtain fault characteristics includes, The transient traveling wave signal is preprocessed to extract the transient components of the transient traveling wave signal within a preset frequency band, and the fault characteristics are calculated.

8. A DC distribution network fault location system based on traveling wave analysis, employing the DC distribution network fault location method based on traveling wave analysis as described in any one of claims 1 to 7, characterized in that, include: The signal acquisition and preprocessing module is responsible for acquiring transient traveling wave signals in real time and performing feature extraction preprocessing to obtain fault characteristics; The judgment and recording module is activated. By judging whether the fault characteristics exceed the preset threshold, the recording mechanism is triggered to save the arrival time and polarity information of the fault traveling wave. The fault type identification module uses the first identification model to classify the extracted fault features and identify the fault type. The fault location calculation module calculates the specific location of the fault point based on the time difference of arrival or waveform characteristics of the traveling wave, combined with power grid topology information. The protection action execution module generates corresponding protection commands based on the fault type and location, and controls the circuit breaker to perform the operation. The optimization and update module utilizes historical fault data to optimize the parameters of the first identification model, the traveling wave parameters of the first positioning calculation, and the threshold parameters of the first start-up condition.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the DC distribution network fault location method based on traveling wave analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the DC distribution network fault location method based on traveling wave analysis as described in any one of claims 1 to 7.

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