Traveling wave matrix algorithm-based current collection line fault positioning method

Abstract

The invention discloses a current collection line fault positioning method based on a traveling wave matrix algorithm, and relates to the technical field of electrical variable measurement. The electrical parameters of each line monitoring node are firstly monitored and obtained, the state of the current collection line can be analyzed more accurately, the environmental data of each line monitoring node are retrieved at the same time, and primary monitoring configuration is performed on each line monitoring node, so that the health condition of the current collection line can be evaluated more comprehensively, and the accuracy of fault position node analysis is improved; the method comprises the following steps: firstly, configuring a plurality of traveling wave signals, then collecting signal parameters of each configured node, performing signal processing configuration on each configured node, filtering noise and interference of the traveling wave signals, improving the quality of the traveling wave signals, thereby improving the accuracy of traveling wave signal analysis, and finally analyzing and positioning the traveling wave signals of each configured node to obtain a fault position node of a current collection line. And fault position nodes can be timely positioned, and measures can be rapidly taken to reduce the influence of the current collection line fault on the operation of the wind power plant.

Description

Technical Field

[0001] This invention relates to the field of electrical variable measurement technology, specifically to a fault location method for power line based on the traveling wave matrix algorithm. Background Technology

[0002] In the field of fault location technology for power collection lines, with the increasing complexity of power systems, rapid and accurate fault location has become one of the key technologies for improving power grid operating efficiency and ensuring power supply security. As a crucial transmission part of the power system, power collection lines typically involve long lines and complex load conditions, with diverse fault types and wide-ranging impacts. Combining traveling wave signals for power collection line fault location can form a more refined monitoring network in the power system, improve the automation and intelligence level of the wind farm's power grid, and enhance the operating efficiency and power supply stability of the wind farm.

[0003] The existing technology, such as the invention patent announcement CN104698338B, discloses a method for locating traveling wave faults in overhead power lines. The method involves: (1) When the line is in an open state, traveling wave acquisition devices are installed at the beginning and end of the line; (2) When the line is closed, a traveling wave signal is generated. The traveling wave acquisition device measures the time of the traveling wave signal at the beginning of the line and the time of arrival at the end of the line to be tested, respectively, and calculates the time difference ΔT1, i.e., the traveling wave transmission time is: ΔT=|ΔT1|; (3) The arrival time of the traveling wave signal at the beginning and end of the line during normal operation is monitored in real time, and the time difference ΔT2 between the arrival time of the traveling wave signal at the beginning and end of the line is calculated; (4) The line's relay protection device trips, indicating a fault in the line; (5) The proportion of the distance between the fault point and the beginning of the line to the total length of the line to be tested is calculated; (6) The proportion of the distance between each tower position and the beginning of the line to the total length of the line is calculated, which is the relative position of the tower; (7) The relative position of the tower closest to Y is determined, thus indicating that the fault point is located near that tower's relative position.

[0004] Existing technology, such as the invention patent announcement CN109001594B, discloses a fault traveling wave localization method, the steps of which include: S1. When a fault occurs in a transmission line, the fault traveling wave signal of the transmission line is collected; S2. The collected fault traveling wave signal is decoupled and transformed to obtain the component signals of the fault traveling wave; S3. The component signals of the fault traveling wave are decomposed using VMD to obtain the decomposed modal components; S4. The instantaneous frequency of the modal components is extracted using Hilbert transform, and the arrival time of the component signal of the initial traveling wave of the fault is determined based on the extracted instantaneous frequency; S5. The location of the fault point is determined using the arrival time of the component signal of the initial traveling wave of the fault.

[0005] Based on the above solutions, it was found that current technologies in the field of power collection line fault location typically only monitor and analyze traveling wave signals. However, analyzing only traveling wave signals cannot fully assess the actual situation of power collection line faults. This not only affects the accuracy of power collection line fault location but may also lead to untimely fault handling, posing a risk of missed or misdiagnosis. This affects the operation and maintenance efficiency and safe operation of wind farms, and may ultimately lead to problems such as power supply interruption and equipment damage. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a fault location method for power collection lines based on a traveling wave matrix algorithm, which can effectively solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: a fault location method for power collection lines based on traveling wave matrix algorithm, comprising: statistically analyzing each monitoring node of the power collection lines of an offshore wind farm, denoted as each line monitoring node; monitoring and acquiring the electrical parameters of each line monitoring node; synchronously retrieving the environmental data of each line monitoring node; analyzing and obtaining the comprehensive evaluation value of the electrical anomalies of each line monitoring node; and then performing initial monitoring configuration for each line monitoring node.

[0008] Each monitoring node of the line after the initial monitoring configuration is recorded as a configured node, and the signal parameters of each configured node are collected. The signal anomaly evaluation value of each configured node is analyzed, and the signal processing configuration of each configured node is performed based on the signal anomaly evaluation value of each configured node.

[0009] The traveling wave signals of each configured node are collected, processed, and analyzed to locate the fault location node of the power collection line.

[0010] Furthermore, the electrical parameters of each line monitoring node include the current, voltage, power, inductance, and capacitance of each line monitoring node.

[0011] The environmental data for each line monitoring node includes humidity, temperature, wind speed, and electric field strength.

[0012] Furthermore, the analysis yields a comprehensive evaluation value of electrical anomalies for each line monitoring node. The specific analysis process is as follows: based on the electrical parameters of each line monitoring node, the electrical anomaly index value of each line monitoring node is obtained.

[0013] A comprehensive analysis of the environmental data of each line monitoring node is conducted to obtain the environmental anomaly index values ​​of each line monitoring node. The environmental anomaly index values ​​of each line monitoring node are matched with the electrical monitoring interference factors corresponding to the environmental anomaly index value intervals stored in the line fault location database. The electrical monitoring interference factors corresponding to the intervals in which the environmental anomaly index values ​​of each line monitoring node are located are statistically analyzed and recorded as the electrical assessment impact factors of each line monitoring node.

[0014] Based on the electrical anomaly index values ​​of each line monitoring node and the electrical assessment influencing factors of each line monitoring node, a comprehensive analysis is conducted to obtain the comprehensive electrical anomaly assessment value of each line monitoring node.

[0015] Furthermore, the initial monitoring configuration for each line monitoring node is specifically performed as follows: based on the comprehensive electrical anomaly assessment value of each line monitoring node, the comprehensive electrical anomaly assessment result of each line monitoring node is analyzed.

[0016] The comprehensive assessment results of electrical anomalies at each line monitoring node include electrical normal, electrical anomaly, and electrical fault.

[0017] Based on the comprehensive assessment results of electrical anomalies at each line monitoring node, the initial monitoring configuration for each line monitoring node is carried out.

[0018] Furthermore, the initial monitoring configuration of each line monitoring node based on the comprehensive assessment results of electrical anomalies of each line monitoring node is specifically as follows: extract the comprehensive assessment results of electrical anomalies of each line monitoring node; if the comprehensive assessment result of electrical anomalies of a certain line monitoring node is that it is electrically normal, then continue to configure the integrated sensing device of that line monitoring node at the current sensing monitoring frequency.

[0019] If the comprehensive assessment result of electrical anomalies of a certain line monitoring node is an electrical anomaly, then the line monitoring node is recorded as an abnormal node. The difference between the comprehensive assessment value of electrical anomalies of each abnormal node and the set second threshold for comprehensive assessment of electrical anomalies is extracted and recorded as the sampling adjustment reference value of each abnormal node. This value is then matched with the sensor sampling execution frequency corresponding to each sampling adjustment reference value interval stored in the line fault location database. The sensor sampling execution frequency corresponding to the interval in which the sampling adjustment reference value of each abnormal node is located is calculated and recorded as the target sensor sampling execution frequency of each abnormal node. The integrated sensing device is then configured for each abnormal node using the corresponding target sensor sampling execution frequency.

[0020] If the comprehensive assessment result of the electrical anomaly of a certain line monitoring node is an electrical fault, then the line monitoring node is recorded as a fault node, and the initial monitoring configuration is performed for each fault node.

[0021] Furthermore, the initial monitoring configuration for each fault node is specifically performed as follows: extract the comprehensive electrical anomaly assessment values ​​of the two neighboring nodes of each fault node, and take the average value to obtain the average comprehensive electrical anomaly assessment value of the two neighboring nodes of each fault node.

[0022] The average comprehensive electrical anomaly assessment value of the two neighboring nodes of each fault node is compared with the set first threshold for comprehensive electrical anomaly assessment. If the average comprehensive electrical anomaly assessment value of the two neighboring nodes of a fault node is higher than the set first threshold for comprehensive electrical anomaly assessment, a configuration replacement reminder is issued for the integrated sensing device of the fault node.

[0023] If the average comprehensive electrical anomaly assessment value of two neighboring nodes of a faulty node is lower than or equal to the set first threshold for comprehensive electrical anomaly assessment, then the faulty node is initialized and updated, and the integrated sensing device is configured for the faulty node at the set highest sensing sampling execution frequency.

[0024] Furthermore, the analysis yields signal anomaly assessment values ​​for each configured node. Specifically, the signal parameters of each configured node include the signal strength, signal-to-noise ratio, number of time-domain waveform distortions, and signal attenuation rate of each configured node.

[0025] Based on the signal parameters of each configured node, and combined with the comprehensive evaluation value of electrical anomalies of each configured node after the initial monitoring configuration, the signal anomaly evaluation value of each configured node is obtained through comprehensive analysis.

[0026] Furthermore, the specific process of configuring signal processing for each configured node based on the signal anomaly evaluation value of each configured node is as follows: the corresponding signal processing parameters are obtained by matching the signal anomaly evaluation value of each configured node, and are recorded as the signal processing parameters corresponding to each configured node. The signal of the configured node is then processed according to the signal processing parameters of each configured node.

[0027] The signal processing parameters corresponding to each configured node include the filter cutoff frequency, the filter order, and the filter sampling frequency.

[0028] Furthermore, the specific process of locating the fault location node of the collector line is as follows: the traveling wave signal of each configured node is collected and processed, the traveling wave matrix algorithm is used to analyze the traveling wave signal of each configured node, the fault node of each collector line is located, and thus the fault location node of the collector line is obtained.

[0029] Furthermore, the comprehensive evaluation results of electrical anomalies at each line monitoring node are analyzed in the following manner:

[0030] The comprehensive assessment value of electrical anomalies at each line monitoring node is compared with the set first threshold for comprehensive assessment of electrical anomalies. If the comprehensive assessment value of electrical anomalies at a certain line monitoring node is higher than the set first threshold for comprehensive assessment of electrical anomalies, the comprehensive assessment result of electrical anomalies at that line monitoring node is marked as an electrical fault.

[0031] If the comprehensive assessment value of electrical anomalies of a certain line monitoring node is lower than or equal to the set first threshold for comprehensive assessment of electrical anomalies, then the comprehensive assessment value of electrical anomalies of the line monitoring node is compared with the set second threshold for comprehensive assessment of electrical anomalies. If the comprehensive assessment value of electrical anomalies of the line monitoring node is higher than the set second threshold for comprehensive assessment of electrical anomalies, then the comprehensive assessment result of electrical anomalies of the line monitoring node is marked as electrical anomaly.

[0032] If the comprehensive assessment value of electrical anomalies of the monitoring node of the line is lower than or equal to the set second threshold for comprehensive assessment of electrical anomalies, then the comprehensive assessment result of electrical anomalies of the monitoring node of the line is marked as electrically normal, and so on, the comprehensive assessment results of electrical anomalies of each monitoring node of the line are obtained.

[0033] The present invention has the following beneficial effects:

[0034] (1) This invention provides a fault location method for a collector line based on the traveling wave matrix algorithm. First, the electrical parameters are analyzed to more accurately identify various electrical parameters that affect the traveling wave signal. Simultaneously, environmental data is retrieved and analyzed to help understand the influence of external conditions on electrical parameters. Then, the signal parameters of each configured node are analyzed to help detect signal anomalies in a timely manner. Finally, the fault location node of the collector line is obtained through analysis, which can more accurately locate the fault location node.

[0035] (2) This invention obtains the comprehensive evaluation value of electrical anomalies of each line monitoring node through analysis, and then performs initial monitoring configuration for each line monitoring node. This allows for targeted configuration of monitoring resources, improves the monitoring accuracy of each line monitoring node, enhances the quality of basic data for traveling wave signal analysis, reduces uncertainty and error in signal analysis, and enhances the stability and accuracy of traveling wave signal analysis under different environments.

[0036] (3) By configuring the signal processing of each configured node according to the signal anomaly evaluation value of each configured node, the present invention can adjust the signal processing method for the specific situation of each configured node, improve the accuracy of traveling wave signal analysis, and the signal processing configuration can make adjustments for the abnormal situation of each configured node, enhance signal stability, improve the quality of traveling wave signal, and thus improve the accuracy of traveling wave signal analysis in fault detection and diagnosis.

[0037] (4) This invention analyzes the traveling wave signals of each configured node to locate the fault location node of the collector line, thereby improving the ability to analyze traveling wave signal data. By analyzing and processing the configured traveling wave signals, the fault location of the collector line can be located more accurately, reducing the error in the location of the collector line fault, thereby improving the efficiency of the collector line fault handling, and improving the operation and maintenance efficiency of the wind farm and the reliability of the system.

[0038] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1 As shown, this embodiment of the invention provides a technical solution: a fault location method for power collection lines based on traveling wave matrix algorithm, which includes counting each monitoring node of the power collection lines of an offshore wind farm, denoted as each line monitoring node, monitoring and acquiring the electrical parameters of each line monitoring node, synchronously retrieving the environmental data of each line monitoring node, analyzing and obtaining the comprehensive evaluation value of the electrical anomalies of each line monitoring node, and then performing initial monitoring configuration for each line monitoring node.

[0042] Each monitoring node of the line after the initial monitoring configuration is recorded as a configured node, and the signal parameters of each configured node are collected. The signal anomaly evaluation value of each configured node is analyzed, and the signal processing configuration of each configured node is performed based on the signal anomaly evaluation value of each configured node.

[0043] The traveling wave signals of each configured node are collected, processed, and analyzed to locate the fault location node of the power collection line.

[0044] It should be noted that traveling wave signals refer to electromagnetic waves generated by faults (such as short circuits, grounding faults, etc.) in equipment such as collector lines or transformers in offshore wind farms.

[0045] Specifically, the electrical parameters of each line monitoring node include the current, voltage, power, inductance, and capacitance of each line monitoring node.

[0046] It should be added that the current at each line monitoring node refers to the magnitude of the current flowing through the conductor at that node, which can be measured using a current transformer. The voltage at each line monitoring node reflects the potential difference between the monitoring node and the reference node (usually the ground), which can be measured directly using a voltmeter. The power at each line monitoring node refers to the rate of electrical energy transmission, which can be measured using a power meter (such as a digital or analog power meter). The inductance at each line monitoring node is the self-reaction force generated by the conductor in the magnetic field. The capacitance at each line monitoring node refers to the ability of the electric field to store charge. Both the inductance and capacitance at each line monitoring node can be measured using an LCR meter.

[0047] The environmental data for each line monitoring node includes humidity, temperature, wind speed, and electric field strength.

[0048] It should be added that the humidity of each monitoring node refers to the water vapor content in the air, which is usually measured directly by a humidity sensor (such as a capacitive or resistive humidity sensor). The temperature of each monitoring node refers to the degree of hotness or coldness of an object, which can be measured directly by a temperature sensor. The wind speed of each monitoring node refers to the speed of air flow, which can be measured by an anemometer. The electric field strength of each monitoring node refers to the strength of the electric and magnetic fields in the propagation of electromagnetic waves, which can be measured by a magnetometer.

[0049] Specifically, the comprehensive evaluation value of electrical anomalies of each line monitoring node is obtained through analysis. The specific analysis process is as follows: based on the electrical parameters of each line monitoring node, the electrical anomaly index value of each line monitoring node is obtained.

[0050] In this embodiment, the electrical anomaly index values ​​of each line monitoring node represent the numerical quantification results of the line's electrical health status obtained by analyzing the electrical parameters of each line monitoring node. These values ​​are used to comprehensively quantify the impact of electrical parameters on the traveling wave signal. The values ​​can be obtained through the following analysis method, with the specific analysis conditions as follows:

[0051] ;

[0052] In the formula, This represents the electrical anomaly index value of the j-th line monitoring node. This represents the current at the j-th line monitoring node. This indicates the reference current for each line monitoring node. This represents the weighting factor corresponding to the set current. This represents the voltage of the j-th line monitoring node. This indicates the reference voltage for each line monitoring node. This represents the weighting factor corresponding to the set voltage. This represents the power of the j-th line monitoring node. This indicates the reference power set for each line monitoring node. This represents the weighting factor corresponding to the set power. This represents the inductance of the j-th line monitoring node. This indicates the reference inductance for each line monitoring node. This indicates the weighting factor corresponding to the set inductance. This represents the capacitance of the j-th line monitoring node. This indicates the reference capacitance for each line monitoring node. This represents the weighting factor corresponding to the set capacitor, and j represents the number of each line monitoring node. , where m represents the total number of line monitoring nodes.

[0053] It should be added that, in this embodiment, preset weighting factors for current, voltage, power, inductance, and capacitance are obtained from the line fault location database.

[0054] It needs to be explained that the weighting factors corresponding to current, voltage, power, inductance, and capacitance represent the numerical values ​​of the influence of each line monitoring node's current, voltage, power, inductance, and capacitance on the electrical anomaly index. These correspondences are pre-defined mapping relationships. For example, by inputting the real-time current of each line monitoring node into the mapping set of current and corresponding weighting factors of each line monitoring node obtained from the line fault location database, the weighting factors corresponding to the current of each line monitoring node are obtained. Similarly, by inputting the real-time voltage of each line monitoring node into the mapping set of voltage and corresponding weighting factors of each line monitoring node obtained from the line fault location database, the voltage of each line monitoring node is obtained. The corresponding weighting factors are obtained by inputting the real-time power of each line monitoring node into the mapping set of power and corresponding weighting factors of each line monitoring node obtained from the line fault location database, and obtaining the weighting factors corresponding to the power of each line monitoring node. Similarly, the real-time inductance of each line monitoring node is input into the mapping set of inductance and corresponding weighting factors of each line monitoring node obtained from the line fault location database, and obtaining the weighting factors corresponding to the inductance of each line monitoring node. Finally, the real-time capacitance of each line monitoring node is input into the mapping set of capacitance and corresponding weighting factors of each line monitoring node obtained from the line fault location database, and obtaining the weighting factors corresponding to the capacitance of each line monitoring node.

[0055] It should be added that the current, voltage, power, inductance, and capacitance of each line monitoring node are related and do not exist independently. For example, the presence of inductance causes a phase difference between current and voltage. The magnitude of this phase difference determines the magnitude of power. When the combination of inductance and capacitance causes the system to reach resonance, the current may increase abnormally, leading to system overload or fault. There is a positive correlation between current and voltage.

[0056] It should be noted that analyzing the current at each line monitoring node helps to accurately identify abnormal patterns in current fluctuations, improve the timing synchronization of traveling wave signals, and enhance the accuracy of fault location in the collector lines. Analyzing the voltage at each line monitoring node helps to reduce noise interference and improve the reliability of the traveling wave signal. Analyzing the power at each line monitoring node can effectively detect anomalies at each line monitoring node. Analyzing the inductance at each line monitoring node helps to understand the electromagnetic response signal of the line, thereby improving the stability of signal transmission. Analyzing the capacitance at each line monitoring node can help to accurately calculate the response time of the line, thereby improving the timeliness and accuracy of the traveling wave signal.

[0057] In this implementation plan, the current, voltage, power, inductance, and capacitance of each line monitoring node are comprehensively analyzed to obtain the electrical anomaly index values ​​of each line monitoring node, which can assess the degree of influence of the electrical parameters of each line monitoring node on the traveling wave signal.

[0058] A comprehensive analysis of the environmental data of each line monitoring node is conducted to obtain the environmental anomaly index values ​​of each line monitoring node. The environmental anomaly index values ​​of each line monitoring node are matched with the electrical monitoring interference factors corresponding to the environmental anomaly index value intervals stored in the line fault location database. The electrical monitoring interference factors corresponding to the intervals in which the environmental anomaly index values ​​of each line monitoring node are located are statistically analyzed and recorded as the electrical assessment impact factors of each line monitoring node.

[0059] In this embodiment, the environmental anomaly index value of each line monitoring node represents the numerical quantification result of environmental anomalies obtained by analyzing the environmental data of each line monitoring node. It is used to comprehensively quantify the impact of environmental data on the traveling wave signal and can be obtained through the following analysis method, with the specific analysis conditions as follows:

[0060] ;

[0061] In the formula, This represents the environmental anomaly index value of the j-th line monitoring node. This represents the humidity of the j-th line monitoring node. This indicates the reference humidity for each monitoring node on the designated line. This indicates the compensation factor corresponding to the set humidity level. This represents the temperature of the j-th line monitoring node. This indicates the reference temperature set for each monitoring node on the line. This indicates the compensation factor corresponding to the set temperature. This represents the wind speed at the j-th line monitoring node. This indicates the reference wind speed for each monitoring node along the designated line. This represents the compensation factor corresponding to the set wind speed. This represents the electric field strength at the j-th line monitoring node. This indicates the reference electric field strength for each monitoring node on the line. This represents the compensation factor corresponding to the set electric field strength, and j represents the number of each line monitoring node. , where m represents the total number of line monitoring nodes.

[0062] This embodiment provides the degree of influence of humidity, temperature, wind speed, and electric field strength of different line monitoring nodes on the environmental anomaly index values. The environmental anomaly index values ​​comprehensively consider the humidity, temperature, wind speed, and electric field strength of each line monitoring node, providing data support for the analysis of the environmental anomaly index values ​​of each line monitoring node.

[0063] It should be added that, in this embodiment, preset compensation factors corresponding to humidity, temperature, wind speed, and electric field strength are obtained from the line fault location database.

[0064] It should be explained that the compensation factors corresponding to humidity, temperature, wind speed, and electric field strength represent the numerical values ​​of the influence of humidity, temperature, wind speed, and electric field strength of each line monitoring node on the environmental anomaly index value. These correspondences are pre-defined mapping relationships. For example, the real-time humidity of each line monitoring node is input into the mapping set of humidity and corresponding compensation factors of each line monitoring node obtained from the line fault location database to obtain the compensation factor corresponding to humidity of each line monitoring node. Similarly, the real-time temperature of each line monitoring node is input into the mapping set of temperature and corresponding compensation factors of each line monitoring node obtained from the line fault location database to obtain the compensation factor corresponding to temperature of each line monitoring node. The real-time wind speed of each line monitoring node is input into the mapping set of wind speed and corresponding compensation factors of each line monitoring node obtained from the line fault location database to obtain the compensation factor corresponding to wind speed of each line monitoring node. Finally, the real-time electric field strength of each line monitoring node is input into the mapping set of electric field strength and corresponding compensation factors of each line monitoring node obtained from the line fault location database to obtain the compensation factor corresponding to electric field strength of each line monitoring node.

[0065] It should be added that the humidity, temperature, wind speed and electric field strength of each monitoring node are related and do not exist independently. For example, temperature and humidity often interact. High temperature environment may lead to increased air humidity, and increased wind speed may lead to a more uniform distribution of temperature and humidity, affecting air flow and thus affecting electric field strength. In particular, when the wind speed is high, it may interfere with the propagation of electromagnetic signals.

[0066] It should be noted that by analyzing the humidity of each monitoring node, the errors that may occur during signal transmission can be assessed more accurately. Analyzing the temperature and wind speed of each monitoring node can improve the accuracy of traveling wave signal analysis. Analyzing the electric field strength of each monitoring node can effectively identify the source and intensity of electromagnetic interference, thereby assessing the impact of electric field strength on traveling wave signals.

[0067] In this implementation plan, the humidity, temperature, wind speed and electric field strength of each line monitoring node are comprehensively analyzed to obtain the environmental anomaly index values ​​of each line monitoring node, which can assess the degree of influence of the environmental data of each line monitoring node on the traveling wave signal.

[0068] Based on the electrical anomaly index values ​​of each line monitoring node and the electrical assessment influencing factors of each line monitoring node, a comprehensive analysis is conducted to obtain the comprehensive electrical anomaly assessment value of each line monitoring node.

[0069] In this embodiment, the comprehensive evaluation value of electrical anomalies at each line monitoring node represents the numerical quantification result of electrical anomalies obtained by analyzing the electrical anomaly index values ​​and electrical evaluation influencing factors of each line monitoring node. This value is used to comprehensively quantify the impact of electrical anomalies on traveling wave signals. It can be obtained through the following analysis method, with the specific analysis conditions as follows:

[0070] ;

[0071] In the formula, This represents the comprehensive assessment value of electrical anomalies at the j-th line monitoring node. This represents the electrical anomaly index value of the j-th line monitoring node. Let represent the electrical assessment impact factor of the j-th line monitoring node, where j represents the number of each line monitoring node. , where m represents the total number of line monitoring nodes.

[0072] In this implementation plan, the electrical anomaly index values ​​of each line monitoring node and the electrical assessment influencing factors of each line monitoring node are comprehensively analyzed to obtain the comprehensive electrical anomaly assessment value of each line monitoring node, which can assess the degree of influence of environmental data and electrical parameters on traveling wave signals.

[0073] Specifically, the initial monitoring configuration for each line monitoring node is carried out. The specific process is as follows: based on the comprehensive evaluation value of electrical anomalies of each line monitoring node, the comprehensive evaluation result of electrical anomalies of each line monitoring node is analyzed.

[0074] The comprehensive assessment results of electrical anomalies at each line monitoring node include electrical normal, electrical anomaly, and electrical fault.

[0075] The comprehensive assessment value of electrical anomalies at each line monitoring node is compared with the set first threshold for comprehensive assessment of electrical anomalies. If the comprehensive assessment value of electrical anomalies at a certain line monitoring node is higher than the set first threshold for comprehensive assessment of electrical anomalies, the comprehensive assessment result of electrical anomalies at that line monitoring node is marked as an electrical fault.

[0076] If the comprehensive assessment value of electrical anomalies of a certain line monitoring node is lower than or equal to the set first threshold for comprehensive assessment of electrical anomalies, then the comprehensive assessment value of electrical anomalies of the line monitoring node is compared with the set second threshold for comprehensive assessment of electrical anomalies. If the comprehensive assessment value of electrical anomalies of the line monitoring node is higher than the set second threshold for comprehensive assessment of electrical anomalies, then the comprehensive assessment result of electrical anomalies of the line monitoring node is marked as electrical anomaly.

[0077] If the comprehensive electrical anomaly assessment value of the line monitoring node is lower than or equal to the set second threshold for comprehensive electrical anomaly assessment, then the comprehensive electrical anomaly assessment result of the line monitoring node is marked as electrically normal. In this way, the comprehensive electrical anomaly assessment results of each line monitoring node are obtained through iteration, and the initial monitoring configuration of each line monitoring node is performed based on the comprehensive electrical anomaly assessment results of each line monitoring node.

[0078] It should be added that the first threshold for comprehensive assessment of electrical anomalies is higher than the second threshold for comprehensive assessment of electrical anomalies.

[0079] Specifically, based on the comprehensive assessment results of electrical anomalies at each line monitoring node, the initial monitoring configuration for each line monitoring node is carried out. The specific process is as follows: extract the comprehensive assessment results of electrical anomalies at each line monitoring node. If the comprehensive assessment result of electrical anomalies at a certain line monitoring node is that it is electrically normal, then continue to configure the integrated sensing device for that line monitoring node at the current sensing monitoring frequency.

[0080] If the comprehensive assessment result of electrical anomalies of a certain line monitoring node is an electrical anomaly, then the line monitoring node is recorded as an abnormal node. The difference between the comprehensive assessment value of electrical anomalies of each abnormal node and the set second threshold for comprehensive assessment of electrical anomalies is extracted and recorded as the sampling adjustment reference value of each abnormal node. This value is then matched with the sensor sampling execution frequency corresponding to each sampling adjustment reference value interval stored in the line fault location database. The sensor sampling execution frequency corresponding to the interval in which the sampling adjustment reference value of each abnormal node is located is calculated and recorded as the target sensor sampling execution frequency of each abnormal node. The integrated sensing device is then configured for each abnormal node using the corresponding target sensor sampling execution frequency.

[0081] If the comprehensive assessment result of the electrical anomaly of a certain line monitoring node is an electrical fault, then the line monitoring node is recorded as a fault node, and the initial monitoring configuration is performed for each fault node.

[0082] Specifically, the initial monitoring configuration for each fault node is performed. The specific process is as follows: extract the comprehensive electrical anomaly assessment values ​​of the two neighboring nodes of each fault node, and take the average value to obtain the average comprehensive electrical anomaly assessment value of the two neighboring nodes of each fault node.

[0083] The average comprehensive electrical anomaly assessment value of the two neighboring nodes of each fault node is compared with the set first threshold for comprehensive electrical anomaly assessment. If the average comprehensive electrical anomaly assessment value of the two neighboring nodes of a fault node is higher than the set first threshold for comprehensive electrical anomaly assessment, a configuration replacement reminder is issued for the integrated sensing device of the fault node.

[0084] If the average comprehensive electrical anomaly assessment value of two neighboring nodes of a faulty node is lower than or equal to the set first threshold for comprehensive electrical anomaly assessment, then the faulty node is initialized and updated, and the integrated sensing device is configured for the faulty node at the set highest sensing sampling execution frequency.

[0085] Specifically, the signal anomaly assessment values ​​of each configured node are obtained through analysis. The specific process is as follows: the signal parameters of each configured node include the signal strength, signal-to-noise ratio, number of time-domain waveform distortions, and signal attenuation rate of each configured node.

[0086] It should be added that the signal strength of each configured node usually refers to the power of the received signal, which can be measured using a signal analyzer or spectrum analyzer. The signal-to-noise ratio (SNR) of each configured node is the ratio of signal strength to noise strength, which can be measured using a spectrum analyzer or network analyzer. The ratio of signal to noise power is used as the SNR of each configured node. The number of time-domain waveform distortions of each configured node refers to the number of times the waveform is distorted during transmission due to various factors (such as signal attenuation, noise interference, frequency shift, etc.). The number of time-domain waveform distortions is recorded using an oscilloscope, digital storage oscilloscope, or dedicated signal analyzer. The signal attenuation rate of each configured node refers to the rate at which the signal strength is weakened during transmission due to distance, medium, or other factors. The signal attenuation rate of each configured node is measured and analyzed using a spectrum analyzer or signal analyzer.

[0087] Based on the signal parameters of each configured node, and combined with the comprehensive evaluation value of electrical anomalies of each configured node after the initial monitoring configuration, the signal anomaly evaluation value of each configured node is obtained through comprehensive analysis.

[0088] In this embodiment, the signal anomaly evaluation value of each configured node represents the numerical quantification result of the signal anomaly obtained by analyzing the signal parameters of each configured node in conjunction with the comprehensive evaluation value of the electrical anomalies of each configured node after the initial monitoring configuration. This value is used to comprehensively quantify the degree of influence of the signal anomaly on the traveling wave signal.

[0089] Specifically, the signal anomaly assessment values ​​for each configured node are analyzed under the following conditions:

[0090] ;

[0091] In the formula, This represents the signal anomaly assessment value of the i-th configured node. This represents the comprehensive assessment value of electrical anomalies for the i-th configured node. This represents the correction factor corresponding to the set comprehensive assessment value of electrical anomalies. This represents the signal strength of the i-th configured node. This represents the reference signal strength of the i-th configured node. This represents the correction factor corresponding to the set signal strength. This represents the signal-to-noise ratio of the i-th configured node. This represents the correction factor corresponding to the set signal-to-noise ratio. This represents the number of time-domain waveform distortions for the i-th configured node. This represents the correction factor corresponding to the set number of time-domain waveform distortions. This represents the signal attenuation rate of the i-th configured node. This represents the correction factor corresponding to the set signal attenuation rate, where i represents the number of each configured node. , where n represents the total number of configured nodes.

[0092] It should be noted that the larger the absolute value of the difference between the signal strength and the reference signal strength, the larger the signal anomaly assessment value. A small signal-to-noise ratio indicates a high noise level and poor signal quality, leading to an increase in the signal anomaly assessment value. An increase in the number of time-domain waveform distortions indicates that the signal has been subjected to more interference or damage during transmission, which will lead to an increase in the signal anomaly assessment value. An increase in the signal attenuation rate indicates that the signal has been lost more during transmission, which will lead to an increase in the signal anomaly assessment value. The larger the comprehensive electrical anomaly assessment value, the greater the influence of electrical parameters on the signal, and the larger the signal anomaly assessment value.

[0093] It should be added that, in this embodiment, the correction factors corresponding to the preset comprehensive evaluation value of electrical anomalies, the correction factors corresponding to the signal strength, the correction factors corresponding to the signal-to-noise ratio, the correction factors corresponding to the number of time-domain waveform distortions, and the correction factors corresponding to the signal attenuation rate are obtained from the line fault location database.

[0094] It needs to be explained that the correction factors corresponding to the comprehensive electrical anomaly assessment value, signal strength, signal-to-noise ratio, number of time-domain waveform distortions, and signal attenuation rate of each configured node represent the numerical values ​​of the influence of these factors on the signal anomaly assessment value. These correspondences are pre-defined mapping relationships. For example, by inputting the real-time comprehensive electrical anomaly assessment values ​​of each configured node into the mapping set of the comprehensive electrical anomaly assessment values ​​and corresponding correction factors of each configured node obtained from the line fault location database, the correction factors corresponding to the comprehensive electrical anomaly assessment values ​​of each configured node are obtained. Similarly, by inputting the real-time signal strength of each configured node into the mapping set of the signal strength and corresponding correction factors of each configured node obtained from the line fault location database, the correction factors corresponding to the comprehensive electrical anomaly assessment values ​​of each configured node are obtained. The correction factor corresponding to the signal strength of each configured node is obtained from the mapping set. The real-time signal-to-noise ratio (SNR) of each configured node is input into the mapping set of SNR and corresponding correction factors of each configured node obtained from the line fault location database to obtain the correction factor corresponding to the SNR of each configured node. The real-time time-domain waveform distortion count of each configured node is input into the mapping set of time-domain waveform distortion count and corresponding correction factors of each configured node obtained from the line fault location database to obtain the correction factor corresponding to the time-domain waveform distortion count of each configured node. The real-time signal attenuation rate of each configured node is input into the mapping set of signal attenuation rate and corresponding correction factors of each configured node obtained from the line fault location database to obtain the correction factor corresponding to the signal attenuation rate of each configured node.

[0095] It should be added that there is a correlation between the signal strength, signal-to-noise ratio, number of time-domain waveform distortions, and signal attenuation rate of each configured node, and they do not exist independently. For example, signal strength and signal-to-noise ratio are positively correlated. The greater the signal strength, the less noise is received and the higher the signal-to-noise ratio. When the signal strength is high, the signal is less affected by noise and attenuation during transmission, and the number of time-domain waveform distortions is also less. When the signal-to-noise ratio is low, there is more noise, and the time-domain waveform of the signal may be more significantly interfered with, increasing the number of time-domain waveform distortions.

[0096] It should be noted that by analyzing the signal strength of each configured node, the judgment of signal quality can be effectively improved, helping to identify areas with weak signals, thereby improving the accuracy of traveling wave signal analysis. Analyzing the signal-to-noise ratio of each configured node can more accurately identify the characteristics of the traveling wave, thereby reducing the masking effect of noise on the signal. Analyzing the number of time-domain waveform distortions of each configured node can promptly detect problems such as deformation and interference that occur during signal propagation. Analyzing the signal attenuation rate of each configured node helps to identify the degree of signal attenuation during propagation.

[0097] In this implementation plan, the signal strength, signal-to-noise ratio, number of time-domain waveform distortions, signal attenuation rate, and comprehensive evaluation value of electrical anomalies of each configured node are comprehensively analyzed to obtain the signal anomaly evaluation value of each configured node, which can assess the degree of influence of signal parameters on traveling wave signals.

[0098] Specifically, signal processing is configured for each configured node based on the signal anomaly evaluation value of each configured node. The specific process is as follows: the corresponding signal processing parameters are obtained by matching the signal anomaly evaluation value of each configured node, and are recorded as the signal processing parameters corresponding to each configured node. The signal of the configured node is processed according to the signal processing parameters of each configured node.

[0099] It should be added that the corresponding signal processing parameters are obtained by matching the signal anomaly evaluation values ​​of each configured node. Specifically, based on the mapping set between the signal anomaly evaluation value range and the signal processing parameters of each configured node pre-built in the line fault location database, the signal anomaly evaluation value of each configured node is input, the range in which the signal anomaly evaluation value of each configured node is located is obtained by matching, and the signal processing parameters corresponding to the signal anomaly evaluation value of each configured node are obtained through the mapping set, and are denoted as the signal processing parameters corresponding to each configured node.

[0100] The signal processing parameters corresponding to each configured node include the filter cutoff frequency, the filter order, and the filter sampling frequency.

[0101] Specifically, the process of locating the fault location node of the collector line is as follows: the traveling wave signals of each configured node are collected and processed, the traveling wave matrix algorithm is used to analyze the traveling wave signals of each configured node, the fault node of each collector line is located, and thus the fault location node of the collector line is obtained.

[0102] It should be added that the traveling wave matrix algorithm is used to analyze the traveling wave signals of each configured node to locate the fault nodes of each collector line, thereby determining the fault location nodes of the collector line. The specific process is as follows: the traveling wave signals of each configured node are collected and processed, and converted into a traveling wave matrix. The traveling wave matrix contains the spatiotemporal information of the signal between different configured nodes, which can reflect the propagation characteristics of the signal at each node. The instantaneous change characteristics of each configured node are identified through time domain analysis, and the propagation characteristics of the waveform of each configured node are determined. The propagation speed, amplitude and frequency of the traveling wave signal on the time axis are analyzed. Combined with the traveling wave signals of different configured nodes, the propagation time difference of the fault signal from the occurrence node to the measurement point is analyzed. By comparing the phase difference of the traveling wave signals between different configured nodes, the signal propagation path can be quickly identified after the fault occurs, thereby determining the fault location nodes of each collector line.

[0103] It should be noted that a fault location method for power line based on traveling wave matrix algorithm also includes a line fault location database, which stores the reference current, reference voltage, reference power, reference inductance, reference capacitance, weighting factors corresponding to current, voltage, power, inductance, and capacitance of each line monitoring node obtained by analyzing historical data, as well as the reference humidity, reference temperature, reference wind speed, reference electric field strength, compensation factors corresponding to humidity, temperature, wind speed, and electric field strength of each line monitoring node, the first threshold for comprehensive electrical anomaly assessment, the second threshold for comprehensive electrical anomaly assessment, the highest sensor sampling execution frequency, the reference signal strength of each configured node, the correction factor corresponding to the comprehensive electrical anomaly assessment value, the correction factor corresponding to signal strength, the correction factor corresponding to signal-to-noise ratio, the correction factor corresponding to the number of time-domain waveform distortions, the correction factor corresponding to signal attenuation rate, the electrical monitoring interference factor, the sensor sampling execution frequency, and signal processing parameters.

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0105] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A fault location method for a power line based on a traveling wave matrix algorithm, characterized in that, include: The monitoring nodes of the offshore wind farm's collection lines are statistically analyzed and designated as each line monitoring node. The electrical parameters of each line monitoring node are monitored and acquired, and the environmental data of each line monitoring node are retrieved simultaneously. The comprehensive evaluation value of the electrical anomalies of each line monitoring node is analyzed, and then the initial monitoring configuration of each line monitoring node is carried out. Each monitoring node of each line after the initial monitoring configuration is recorded as a configured node, and the signal parameters of each configured node are collected. The signal anomaly evaluation value of each configured node is analyzed, and the signal processing configuration of each configured node is performed based on the signal anomaly evaluation value of each configured node. The traveling wave signals of each configured node are collected, processed, and analyzed to locate the fault location node of the power collection line.

2. The method for locating faults in a power line based on a traveling wave matrix algorithm according to claim 1, characterized in that: The electrical parameters of each line monitoring node include the current, voltage, power, inductance, and capacitance of each line monitoring node. The environmental data for each line monitoring node includes humidity, temperature, wind speed, and electric field strength.

3. The fault location method for collector lines based on traveling wave matrix algorithm according to claim 2, characterized in that: The analysis yielded a comprehensive assessment value of electrical anomalies at each line monitoring node. The specific analysis process is as follows: Based on the electrical parameters of each line monitoring node, the electrical anomaly index values ​​of each line monitoring node are obtained. A comprehensive analysis of the environmental data of each line monitoring node is conducted to obtain the environmental anomaly index values ​​of each line monitoring node. The environmental anomaly index values ​​of each line monitoring node are matched with the electrical monitoring interference factors corresponding to the environmental anomaly index value intervals stored in the line fault location database. The electrical monitoring interference factors corresponding to the intervals of the environmental anomaly index values ​​of each line monitoring node are statistically analyzed and recorded as the electrical assessment impact factors of each line monitoring node. Based on the electrical anomaly index values ​​of each line monitoring node and the electrical assessment influencing factors of each line monitoring node, a comprehensive analysis is conducted to obtain the comprehensive electrical anomaly assessment value of each line monitoring node.

4. The method for fault location of a power line based on the traveling wave matrix algorithm according to claim 3, characterized in that: The initial monitoring configuration for each line monitoring node is as follows: Based on the comprehensive assessment values ​​of electrical anomalies at each line monitoring node, the comprehensive assessment results of electrical anomalies at each line monitoring node are analyzed. The comprehensive assessment results of electrical anomalies at each line monitoring node include electrical normal, electrical anomaly, and electrical fault. Based on the comprehensive assessment results of electrical anomalies at each line monitoring node, the initial monitoring configuration for each line monitoring node is carried out.

5. The method for locating faults in a power line based on a traveling wave matrix algorithm according to claim 4, characterized in that: The initial monitoring configuration for each line monitoring node is performed based on the comprehensive assessment results of electrical anomalies at each monitoring node. The specific process is as follows: Extract the comprehensive assessment results of electrical anomalies of each line monitoring node. If the comprehensive assessment result of electrical anomalies of a certain line monitoring node is that it is electrically normal, then continue to configure the integrated sensing device of the line monitoring node at the current sensing monitoring frequency. If the comprehensive assessment result of electrical anomaly of a certain line monitoring node is electrical anomaly, then the line monitoring node is recorded as an abnormal node. The difference between the comprehensive assessment value of electrical anomaly of each abnormal node and the set second threshold of comprehensive assessment of electrical anomaly is extracted and recorded as the sampling adjustment reference value of each abnormal node. This value is then matched with the sensor sampling execution frequency corresponding to each sampling adjustment reference value interval stored in the line fault location database. The sensor sampling execution frequency corresponding to the interval in which the sampling adjustment reference value of each abnormal node is located is statistically calculated and recorded as the target sensor sampling execution frequency of each abnormal node. The integrated sensing device is then configured for each abnormal node using the corresponding target sensor sampling execution frequency. If the comprehensive assessment result of the electrical anomaly of a certain line monitoring node is an electrical fault, then the line monitoring node is recorded as a fault node, and the initial monitoring configuration is performed for each fault node.

6. The fault location method for a collector line based on the traveling wave matrix algorithm according to claim 5, characterized in that: The initial monitoring configuration for each faulty node is performed as follows: Extract the comprehensive electrical anomaly assessment values ​​of the two neighboring nodes of each fault node, and take the average value to obtain the average comprehensive electrical anomaly assessment value of the two neighboring nodes of each fault node; The average comprehensive electrical anomaly assessment value of the two neighboring nodes of each fault node is compared with the set first threshold for comprehensive electrical anomaly assessment. If the average comprehensive electrical anomaly assessment value of the two neighboring nodes of a fault node is higher than the set first threshold for comprehensive electrical anomaly assessment, a configuration replacement reminder is issued for the integrated sensing device of the fault node. If the average comprehensive electrical anomaly assessment value of two neighboring nodes of a faulty node is lower than or equal to the set first threshold for comprehensive electrical anomaly assessment, then the faulty node is initialized and updated, and the integrated sensing device is configured for the faulty node at the set highest sensing sampling execution frequency.

7. The fault location method for collector lines based on traveling wave matrix algorithm according to claim 1, characterized in that: The analysis yields signal anomaly assessment values ​​for each configured node. The specific process is as follows: The signal parameters of each configured node include the signal strength, signal-to-noise ratio, number of time-domain waveform distortions, and signal attenuation rate of each configured node; Based on the signal parameters of each configured node, and combined with the comprehensive evaluation value of electrical anomalies of each configured node after the initial monitoring configuration, the signal anomaly evaluation value of each configured node is obtained through comprehensive analysis.

8. The fault location method for a collector line based on the traveling wave matrix algorithm according to claim 7, characterized in that: The specific process of configuring signal processing for each configured node based on the signal anomaly assessment value of each configured node is as follows: Based on the signal anomaly evaluation value of each configured node, the corresponding signal processing parameters are obtained and recorded as the signal processing parameters of each configured node. The signal of the configured node is then processed according to the signal processing parameters of each configured node. The signal processing parameters corresponding to each configured node include the filter cutoff frequency, the filter order, and the filter sampling frequency.

9. The method for locating faults in a power line based on a traveling wave matrix algorithm according to claim 1, characterized in that: The process of locating the fault location node of the power collection line is as follows: The traveling wave signals of each configured node are collected and processed. The traveling wave matrix algorithm is used to analyze the traveling wave signals of each configured node to locate the fault nodes of each collector line, thereby obtaining the fault location node of the collector line.

10. The fault location method for a collector line based on the traveling wave matrix algorithm according to claim 4, characterized in that: The comprehensive evaluation results of electrical anomalies at each line monitoring node are analyzed in the following manner: The comprehensive assessment value of electrical anomalies of each line monitoring node is compared with the set first threshold for comprehensive assessment of electrical anomalies. If the comprehensive assessment value of electrical anomalies of a certain line monitoring node is higher than the set first threshold for comprehensive assessment of electrical anomalies, the comprehensive assessment result of electrical anomalies of that line monitoring node is marked as an electrical fault. If the comprehensive assessment value of electrical anomalies of a certain line monitoring node is lower than or equal to the set first threshold for comprehensive assessment of electrical anomalies, then the comprehensive assessment value of electrical anomalies of the line monitoring node is compared with the set second threshold for comprehensive assessment of electrical anomalies. If the comprehensive assessment value of electrical anomalies of the line monitoring node is higher than the set second threshold for comprehensive assessment of electrical anomalies, then the comprehensive assessment result of electrical anomalies of the line monitoring node is marked as electrical anomaly. If the comprehensive assessment value of electrical anomalies of the monitoring node of the line is lower than or equal to the set second threshold for comprehensive assessment of electrical anomalies, then the comprehensive assessment result of electrical anomalies of the monitoring node of the line is marked as electrically normal, and so on, the comprehensive assessment results of electrical anomalies of each monitoring node of the line are obtained.

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