Distribution network fault positioning method and device based on power quality monitoring data

Abstract

The invention provides a distribution network fault positioning method and device based on electric energy quality monitoring data, and relates to the technical field of distribution network fault detection and localization. Electric energy quality data before and after a fault of a distribution network is collected and preprocessed, and then voltage drop detection is carried out on each monitoring area; the method comprises the following steps: marking an area exceeding a threshold value as a fault area, monitoring the current of a distribution network line in the fault area, detecting the sudden increase of the current by using a moving average method so as to identify fault lines, carrying out frequency change and harmonic change detection on nodes in each fault line, and obtaining the frequency deviation degree and the total harmonic distortion rate as main fault indexes. A grade value is distributed for each node, a fault correlation value of each node is calculated, finally a comprehensive scoring mechanism is established, and the node with a high score and a large correlation value is determined as a main fault node. According to the invention, the power quality data of the distribution network is collected and analyzed, so that the fault nodes of the distribution network are efficiently positioned and identified.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault detection and location, and specifically provides a distribution network fault location method and device based on power quality monitoring data. Background Art

[0002] With the rapid development of the power system, the complexity and load volatility of the distribution network are increasing continuously, resulting in increasingly serious power quality problems. In the operation of the distribution network, rapid fault location is an important link to ensure power supply and equipment safety. The quality of power directly affects the operation efficiency, stability and safety of power equipment, and further affects the reliability of the entire power system. Therefore, timely and accurate detection and location of distribution network faults have become the key to ensuring the safe and stable operation of the power system.

[0003] Power quality monitoring mainly includes the monitoring of parameters such as voltage, current, frequency and harmonics. By real-time monitoring of these parameters, abnormal conditions in the power system can be detected in time. Especially when a fault occurs, the change of power quality can often provide important fault indications. For example, a sharp drop in voltage, a sudden increase in current, a fluctuation in frequency and an abnormal harmonic will all reflect potential fault types and locations.

[0004] In the prior art, traditional distribution network fault detection methods mainly rely on calculating the impedance at both ends of the distribution network line and using the dichotomy method to locate the specific fault location. They cannot consider the complexity of the distribution network line and fail to take into account the transient characteristics of voltage and current when a fault occurs. In addition, the implementation of the dichotomy method requires multiple measurements and judgments. Especially in the case of a long line and many nodes, the operation process is too cumbersome, increasing the time and labor costs of fault repair. Especially during emergency response, the operational complexity may lead to delays. Moreover, traditional methods often rely on post-event analysis after a fault occurs and lack a real-time monitoring and alarm mechanism. This means that when a fault occurs, the distribution network cannot obtain relevant data in time, resulting in an extended response time and affecting the safety and reliability of the system.

[0005] Therefore, it is necessary to provide a distribution network fault location method and device based on power quality monitoring data to solve the above problems.

[0006] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a distribution network fault location method and device based on power quality monitoring data to solve the problems raised in the above background art.

[0008] To achieve the above object, the present invention provides the following technical solutions: A distribution network fault location method and device based on power quality monitoring data, and the specific steps include: Step 1: Define a time detection window before and after the fault occurs, collect the power quality data of each monitoring area in the distribution network within the time detection window, and preprocess the obtained power quality data. The power quality data includes voltage data, current data, frequency data, and harmonic data. The preprocessing includes data cleaning and normalization processing of the power quality monitoring data; Step 2: Analyze the voltage data based on the voltage dip threshold to determine the distribution network fault area. Within the determined distribution network fault area, obtain the current sudden increase amplitude according to the moving average method, and determine the distribution network fault line within the distribution network fault area based on the current sudden increase amplitude; Step 3: Analyze the frequency data based on the fast Fourier transform in the distribution network fault line to determine the frequency deviation degree before and after the fault occurs at each distribution network node, and use the frequency deviation degree as the first fault index; Step 4: Analyze the harmonic data based on the fast Fourier transform in the distribution network fault line, extract the fundamental wave amplitude and the amplitudes of each harmonic from the transformation result, obtain the total harmonic distortion rate of each distribution network node based on the fundamental wave amplitude and the amplitudes of each harmonic, and use the total harmonic distortion rate as the second fault index; Step 5: Assign a grade value to the fault index of each distribution network node in the distribution network fault line, obtain the fault correlation value of each distribution network node according to the calculation method of the Spearman rank correlation coefficient, establish a comprehensive score mechanism based on the first and second fault indexes of each distribution network node and combined with the fault correlation value of the distribution network node, sort the comprehensive scores of each distribution network node from large to small, set a score threshold, form a set of distribution network nodes that exceed the score threshold, and select the distribution network node with the smallest fault correlation value in the set as the fault node.

[0009] Further, the method for preprocessing the obtained power quality data is as follows: Use the statistical method to identify the outliers and duplicate data in the power quality data, delete the outliers and duplicate data in the power quality data, and use the mean, median or mode of the power quality data to fill the missing values in the power quality data; The normalization processing of the power quality data is min-max normalization, and the data is scaled to range, so that all power quality data has the same scale. The formula is: ; where, is the power quality data after normalization processing, is the original power quality data, is the minimum value among the same type of power quality data in the dataset, is the maximum value among the same type of power quality data in the dataset.

[0010] Furthermore, by combining the voltage data of the time detection window with the moving average method, the voltage sag values of each monitoring area before and after the fault in the distribution network system are obtained. The calculation formula is: ; ; ; Among them, represents the average voltage within the time detection window before the fault in the monitoring area, represents the average voltage within the time detection window after the fault in the monitoring area, represents the voltage sag value before and after the fault, represents the time length of the time detection window, represents at the moment in the monitoring area the voltage value, is the time variable within the time detection window, is the moment when the fault occurs; The voltage sag values of each monitoring area in the distribution network system are compared with the preset voltage sag threshold . If , then it is determined that this monitoring area is a distribution network fault area.

[0011] Furthermore, based on the current sudden increase amplitude, the distribution network fault line is determined in the marked distribution network fault area. The method is as follows: According to the current data of the time detection window combined with the moving average method, the current sudden increase values of each line in the distribution network fault area are obtained. The formula is: ; ; ; Among them, represents the average current within the time detection window before the fault of the distribution network line in the monitoring area, represents the average current within the time detection window after the fault of the distribution network line in the monitoring area, represents the current sudden increase value before and after the fault; Set the current sudden increase threshold , and the current sudden increase values of each line in the determined distribution network fault area Compare with If then this line in the distribution network fault area is determined as a distribution network fault line.

[0012] Furthermore, analyze the frequency data of each distribution network node in the distribution network fault line to determine the frequency deviation degree of each distribution network node in the distribution network fault line. The method is as follows: Use the fast Fourier transform technology to convert the frequency data within the time detection window before and after the fault occurs, perform windowing processing on the collected time-domain frequency signal, take the preset time detection window size as the signal segment with a fixed length after windowing processing, use the fast Fourier transform technology to transform this signal segment to obtain a complex number array, representing the amplitude and phase of each frequency component, then calculate the modulus of this complex number array to obtain the amplitude value of each frequency component, determine the frequency value corresponding to each frequency component through the calculation of frequency resolution, and finally select the frequency component with the largest amplitude as the fundamental frequency according to the magnitude of the amplitude, calculate the difference between the fundamental frequency values of the two time detection windows before and after the fault occurs at the distribution network node, and take the ratio of the difference between the fundamental frequency values of the two time detection windows to the fundamental frequency value of the time detection window before the fault occurs as the frequency deviation degree of each distribution network node .

[0013] Furthermore, analyze the harmonic data of each distribution network node in the distribution network fault line, and determine the total harmonic distortion rate of each distribution network node in the distribution network fault line according to the amplitude of the frequency-domain signal. The method is as follows: Use the fast Fourier transform to analyze the signal frequency domain, convert the time-domain signal to the frequency-domain signal, identify the fundamental wave and its harmonics, and obtain the total harmonic distortion rate of each distribution network node. The formula is as follows: ; where represents the total harmonic distortion rate of the distribution network node, represents the fundamental wave amplitude, , , represent the amplitudes of each harmonic, represents the total order of the harmonics.

[0014] Furthermore, establish a comprehensive score mechanism to determine the fault abnormal nodes. The method is as follows: Take the frequency deviation degree and the total harmonic distortion rate of each distribution network node as the first fault index and the second fault index respectively, assign a grade value to the fault index of each distribution network node in the distribution network fault line, calculate the grade difference between the first and second fault indexes, and obtain the fault correlation value of the two fault indexes of each distribution network node based on the grade difference. The formula is as follows: ; ; Among them, represents the grade difference between the first and second fault indicators in the th power distribution network node, , are respectively the grade values assigned to the first and second fault indicators in the th power distribution network node, represents the fault correlation value between the first and second fault indicators in the th power distribution network node, represents the total number of power distribution network nodes; Establish a comprehensive score mechanism, set the score range of each fault indicator to 0-100, and assign proportional weights , to the first and second fault indicators respectively. Then the formula for determining the comprehensive score of each power distribution network node is: ; Among them, represents the comprehensive score of the th power distribution network node, represents the degree of frequency deviation of the th power distribution network node, represents the total harmonic distortion rate of the th power distribution network node, and =1, ; Sort the fault correlation values and comprehensive scores of the first and second fault indicators of each power distribution network node from small to large, and select the power distribution network node with the largest fault correlation value and the highest comprehensive score as the power distribution network fault node.

[0015] The present invention also provides a power distribution network fault location device based on power quality monitoring data. The power distribution network fault location device is used to execute the above-mentioned power distribution network fault location method based on power quality monitoring data, including: A data acquisition and preprocessing module, which is used to define a time detection window before and after the fault occurs, collect power quality data of each monitoring area in the power distribution network during the time detection window, and preprocess the acquired power quality data. The power quality data includes voltage data, current data, frequency data, and harmonic data. The preprocessing includes data cleaning and normalization processing of the power quality monitoring data; Voltage sag and current surge detection module, which is used to analyze voltage data based on the sag voltage threshold to determine the distribution network fault area. Within the determined distribution network fault area, the current surge amplitude is obtained according to the moving average method, and the distribution network fault line within the distribution network fault area is determined based on the current surge amplitude; Frequency change detection module, which is used to analyze frequency data based on the fast Fourier transform in the distribution network fault line to determine the frequency deviation degree before and after the fault occurs at each distribution network node, and use the frequency deviation degree as the first fault index; Harmonic change detection module, which is used to analyze harmonic data based on the fast Fourier transform in the distribution network fault line, extract the fundamental wave amplitude and the amplitudes of each harmonic from the transformation result, obtain the total harmonic distortion rate of each distribution network node based on the fundamental wave amplitude and the amplitudes of each harmonic, and use the total harmonic distortion rate as the second fault index; Fault correlation and comprehensive score module, which assigns a grade value to the fault index of each distribution network node in the distribution network fault line, obtains the fault correlation value of each distribution network node according to the calculation method of the Spearman rank correlation coefficient, establishes a comprehensive score mechanism based on the first and second fault indexes of each distribution network node and combines the fault correlation value of the distribution network node, sorts the comprehensive scores of each distribution network node from large to small, sets a score threshold, forms a set of distribution network nodes that exceed the score threshold, and selects the distribution network node with the smallest fault correlation value in the set as the fault node.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: Aiming at the deficiencies of the traditional distribution network fault detection method, the present invention comprehensively designs a fault location scheme, significantly improving the efficiency and accuracy of fault detection. Different from the limitations of the traditional method that relies on impedance calculation and dichotomy to locate faults, the present invention comprehensively considers the complexity of the distribution network line, based on real-time monitoring and analysis of voltage, current, frequency and harmonic data, from the determination of the distribution network fault area to the fault line and then to the fault node, adopts a time detection window design to achieve real-time monitoring and rapid response; Secondly, the established comprehensive score mechanism further refines the dimension of fault judgment by introducing two indexes of frequency deviation degree and total harmonic distortion rate. This method not only enhances the ability to identify different types of faults, but also effectively evaluates the fault correlation of each node through the Spearman rank correlation coefficient, simplifies the operation process, and makes fault location more efficient and intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0018] Figure 2 Schematic diagram of the contribution of the first fault index comprehensive score of the present invention

[0019] Figure 3 Schematic diagram of the contribution of the second fault index comprehensive score of the present invention

[0020] Figure 4 Schematic diagram of the system module process of the present invention Detailed implementation manners

[0021] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments

[0022] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative position relationships, and when the absolute position of the object being described changes, the relative position relationship may also change accordingly

[0023] Embodiment Please refer to Figure 1 , a distribution network fault location method based on power quality monitoring data, and the specific steps include Step 1: Define a time detection window before and after the fault occurs, collect the power quality data of each monitoring area in the distribution network within the time detection window, and preprocess the obtained power quality data. The power quality data includes voltage data, current data, frequency data and harmonic data. The preprocessing includes data cleaning and normalization processing of the power quality monitoring data Step 2: Analyze the voltage data based on the voltage sag threshold to determine the distribution network fault area. Within the determined distribution network fault area, obtain the current sudden increase amplitude according to the moving average method, and determine the distribution network fault line within the distribution network fault area based on the current sudden increase amplitude Step 3: Analyze the frequency data based on the fast Fourier transform in the distribution network fault line to determine the frequency deviation degree before and after the fault occurs at each distribution network node, and use the frequency deviation degree as the first fault index Step 4: Analyze the harmonic data in the distribution network fault line based on the fast Fourier transform, extract the fundamental wave amplitude and the amplitudes of each harmonic from the transformation result, obtain the total harmonic distortion rate of each distribution network node based on the fundamental wave amplitude and the amplitudes of each harmonic, and use the total harmonic distortion rate as the second fault index; Step 5: Assign a grade value to the fault index of each distribution network node in the distribution network fault line, obtain the fault correlation value of each distribution network node according to the calculation method of the Spearman rank correlation coefficient, establish a comprehensive scoring mechanism based on the first and second fault indices of each distribution network node and combined with the fault correlation value of the distribution network node, sort the comprehensive scores of each distribution network node from large to small, set a score threshold, form a set of distribution network nodes that exceed the score threshold, and select the distribution network node with the smallest fault correlation value in the set as the fault node.

[0024] It should be noted that by detecting and deleting outliers and duplicate data from the power quality data, the noise and errors in the data can be effectively removed, which is crucial for power grid fault analysis. Outliers may result from equipment failures or external interferences. If not processed, they will lead to misjudgment and incorrect fault location, affecting the safety of the system. Using the mean, median, or mode to fill in the missing values can maximize the retention of the data's effectiveness, avoid data deviation caused by missing values, and thus ensure a reliable analysis of power quality changes. Performing min-max normalization processing scales all power quality data to a unified scale, enabling different types of data to be compared and analyzed within the same framework.

[0025] Therefore, it is necessary to preprocess the obtained power quality data, and the methods are as follows: Use the statistical method to identify outliers and duplicate data in the power quality data, delete the outliers and duplicate data in the power quality data, and use the mean, median, or mode of the power quality data to fill in the missing values in the power quality data; The normalization processing of the power quality data is min-max normalization, which scales the data to the range, so that all power quality data have the same scale. The formula is as follows: ; where is the power quality data after normalization processing, is the original power quality data, is the minimum value of the same type of power quality data in the dataset, is the maximum value of the same type of power quality data in the dataset.

[0026] It should be noted that by analyzing the voltage data in the time detection window before and after the fault based on the voltage dip threshold, and using the moving average method to obtain the average voltage before and after the fault, the dynamic characteristics of voltage changes can be effectively captured, and the fault area of the distribution network can be identified in a timely manner. In addition, with the design of the time detection window, the system can flexibly adjust the length of the time detection window to adapt to the operating conditions and fault characteristics of different distribution networks, further enhancing the universality and adaptability of fault location.

[0027] Therefore, it is necessary to combine the voltage data in the time detection window with the moving average method to obtain the voltage dip values in each monitoring area before and after the fault in the distribution network system. The calculation formula is as follows: ; ; ; where represents the average voltage in the time detection window before the fault in the monitoring area, represents the average voltage in the time detection window after the fault in the monitoring area, represents the voltage dip value before and after the fault, represents the time length of the time detection window, represents at the moment in the monitoring area the voltage value, is the time variable within the time detection window, is the fault occurrence moment; Compare the voltage dip value of each monitoring area in the distribution network system with the preset voltage dip threshold . If , then determine that this monitoring area is the fault area of the distribution network.

[0028] It should be noted that by using the moving average method to analyze the collected current data, the average current before and after the fault is calculated in real time to identify the sudden increase value of the current, and by setting the sudden increase threshold of the current, the effective discrimination of the fault line is realized, significantly improving the sensitivity and accuracy of the distribution network fault detection.

[0029] Therefore, it is necessary to determine the distribution network fault line based on the sudden increase amplitude of the current in the marked distribution network fault area. The method is as follows: According to the current data in the time detection window combined with the moving average method, obtain the sudden increase value of the current of each line in the distribution network fault area. The formula is as follows: ; ; ; Among them, represents the average current within the time detection window before a fault occurs in the distribution network line in the monitoring area, represents the average current within the time detection window after a fault occurs in the distribution network line in the monitoring area, represents the sudden increase value of the current before and after the fault occurs; Set the current sudden increase threshold , and compare the sudden increase value of the current of each line in the determined distribution network fault area with . If , then determine that this line in the distribution network fault area is a distribution network fault line.

[0030] It should be noted that by using the fast Fourier transform technology, the characteristics of the frequency signals before and after the fault can be accurately extracted, the change of the fundamental frequency can be identified, and the acquisition of the frequency deviation degree provides a quantitative basis for fault diagnosis, enabling the operation and maintenance personnel to quickly locate the problem nodes and take effective measures for treatment.

[0031] Therefore, it is necessary to analyze the frequency data of each distribution network node in the distribution network fault line to determine the frequency deviation degree of each distribution network node in the distribution network fault line. The method is as follows: Use the fast Fourier transform technology to convert the frequency data within the time detection window before and after the fault occurs, perform windowing processing on the collected time-domain frequency signals, use the preset time detection window size as the signal segment with a fixed length after windowing processing, use the fast Fourier transform technology to transform this signal segment to obtain a complex number array, which represents the amplitude and phase of each frequency component, and then calculate the modulus of this complex number array to obtain the amplitude of each frequency component. By calculating the frequency resolution, determine the frequency value corresponding to each frequency component. Finally, according to the magnitude of the amplitude, select the frequency component with the largest amplitude as the fundamental frequency, calculate the difference between the fundamental frequency values of the two time detection windows before and after the fault occurs at the distribution network node, and use the ratio of the difference between the fundamental frequency values of the two time detection windows to the fundamental frequency value of the time detection window before the fault occurs as the frequency deviation degree of each distribution network node .

[0032] It should be noted that by converting the time-domain signal to the frequency-domain signal through the fast Fourier transform, the fundamental wave and its harmonic components can be effectively identified, thereby quantifying the harmonic distortion degree. In the formula for obtaining the total harmonic distortion rate , the higher the value, the more serious the harmonic distortion and the worse the power quality. is the fundamental wave amplitude, which is the amplitude of the lowest frequency component in the signal. The fundamental wave is the main component of the signal and usually refers to the AC power frequency of 50Hz or 60Hz in the power system. The magnitude of the fundamental wave amplitude directly affects the effective power of the power system. Harmonics are integer multiples of the fundamental wave frequency components and usually originate from non-linear loads. is the second harmonic. is the third harmonic, and so on until the nth harmonic. Calculate the ratio of the harmonic effective value to the fundamental wave amplitude to indicate the intensity of the harmonic component relative to the fundamental wave. The higher this ratio, the greater the impact of the harmonics on the signal and the worse the power quality.

[0033] Therefore, it is necessary to detect the harmonic changes of each distribution network node in the faulty distribution network line, and determine the total harmonic distortion rate of each distribution network node in the faulty distribution network line according to the amplitude of the frequency domain signal. The method is as follows: Use the fast Fourier transform to analyze the signal frequency domain, convert the time domain signal into the frequency domain signal, identify the fundamental wave and its harmonics, and obtain the total harmonic distortion rate of each distribution network node. The formula is as follows: ; where, represents the total harmonic distortion rate of the distribution network node, represents the fundamental wave amplitude, 、 、 represent the amplitudes of each harmonic, represents the total order of the harmonics.

[0034] It should be noted that by taking the frequency deviation degree and the total harmonic distortion rate as key fault indicators, and calculating the grade difference and fault correlation value between the two, the fault risk of each distribution network node can be effectively evaluated. The setting of the comprehensive score quantifies different fault indicators, facilitates the ranking of nodes, and thus accurately locates the fault nodes. This mechanism not only improves the effectiveness of fault diagnosis, but also provides a scientific basis for subsequent fault handling and maintenance, and helps to improve the reliability and stability of the distribution network; The frequency deviation degree and the total harmonic distortion rate are divided into four grades, namely grade 1, grade 2, grade 3 and grade 4. When the frequency deviation is within it is defined as grade 1, that is, the grade value of the first fault indicator ; when the frequency deviation is within it is defined as grade 2, that is, the grade value of the first fault indicator ; when the frequency deviation is within it is grade 3, that is, the grade value of the first fault indicator ; when the frequency deviation exceeds When it is, it is defined as level 4, that is, the level value of the first fault index . When the total harmonic distortion rate is in the range of it is defined as level 1, that is, the level value of the second fault index ; when the total harmonic distortion rate is in this interval it is defined as level 2, that is, the level value of the second fault index ; when the total harmonic distortion rate is in the range of it is defined as level 3, that is, the level value of the second fault index ; when the total harmonic distortion rate exceeds it is defined as level 4, that is, the level value of the second fault index .

[0035] It should be noted that when the first fault index and the second fault level index are both at level 1, it is determined that this node is not a distribution network fault node; when the first fault index and the second fault level index are both at level 4, it is directly determined that this node is a distribution network fault node; when the first fault index and the second fault level index are not both at level 1 and level 4, it is necessary to judge in combination with the comprehensive score formula.

[0036] Therefore, it is necessary to establish a comprehensive scoring mechanism to determine the fault abnormal nodes. The method is as follows: Take the frequency deviation degree and the total harmonic distortion rate of each distribution network node as the first fault index and the second fault index respectively, assign level values to the fault indexes of each distribution network node in the distribution network fault line, calculate the level difference between the first and second fault indexes, and obtain the fault correlation value of the two fault indexes of each distribution network node based on the level difference. The formula is as follows: ; ; Among them, represents the level difference between the first and second fault indexes in the th distribution network node, , are the level values assigned to the first and second fault indexes in the th distribution network node respectively, represents the fault correlation value of the first and second fault indexes in the th distribution network node, represents the total number of distribution network nodes; in the above formula for calculating the fault correlation value, when the level difference of the first and second fault indexes is closer, is smaller, the fault correlation value is smaller, which means the higher the authenticity of the fault generated by this distribution network node; the It is a normalization factor calculated using the number of nodes, which makes remain within a reasonable range, facilitating the comparison of fault correlation values between different nodes. The numerator uses the square form of the grade difference to prevent the situation where the fault correlation value is negative.

[0037] As shown in the following table, in the fault correlation value ranking table, we select 16 distribution network nodes from the distribution network fault nodes, assign grade values to their fault indicators, and calculate the grade difference and fault correlation value. It can be seen from the chart that the fault correlation value ranges from 0.998786 to 1, indicating that the fault correlation values of most nodes are very close to 1, meaning that the fault indicators of these nodes have relatively high stability or low fault risk. The fault correlation values of nodes 1, 6, 11, and 16 are the largest, indicating that their grade difference is 0, meaning that their frequency deviation degree and total harmonic distortion rate grade are exactly the same. At this time, judge the frequency deviation degree grade and total harmonic distortion rate grade, and select the node with the largest grade value of both the frequency deviation degree and total harmonic distortion rate as the distribution network fault abnormal node.

[0038] Table 1 - Fault Correlation Value Ranking Table

[0039] Establish a comprehensive score mechanism, set the score range of each fault indicator to 0 - 100, and assign proportional weights to the first and second fault indicators respectively 、 , then the formula for determining the comprehensive score of each distribution network node is: ; Among them, represents the comprehensive score of the th distribution network node, represents the frequency deviation degree of the th distribution network node, represents the total harmonic distortion rate of the th distribution network node, and = 1, ; In the formula for calculating the comprehensive score of the distribution network node above, the reason for setting the weight ratio to is that the frequency deviation degree has a more direct and significant impact on the stability and security of the distribution network. The deviation of frequency will cause the imbalance of the operation of the power system, which may lead to equipment damage or system failure. Therefore, giving a higher weight to the frequency deviation degree can better reflect its impact on the overall security of the system; while the total harmonic distortion rate will also affect the normal operation of power equipment, but its impact is usually gradual, potential, and may take a long time to show serious consequences. Therefore, a lower weight is given In the calculation of the comprehensive score; Set a score threshold , and form a set of distribution network nodes that exceed the score threshold, and select the distribution network node with the smallest fault correlation value in the set as the distribution network fault node.

[0040] As shown in the following table, in the comprehensive score ranking table of distribution network nodes, we calculate the comprehensive scores of sixteen randomly selected distribution network nodes. It can be clearly seen that once the grade values of the first fault index and the second fault index exceed grade 4, the comprehensive score will increase significantly, and there is an obvious threshold effect on the comprehensive score of distribution network nodes after the fault index exceeds grade 4. That is to say, the closer to or exceeding a certain threshold, such as 40%, the higher the sensitivity of the comprehensive score to the index change. For the improvement of frequency deviation and total harmonic distortion rate, monitoring and management should be strengthened. Especially when the index is close to or exceeds grade 4, corresponding preventive measures should be taken to prevent potential faults from occurring.

[0041] Table 2 - Comprehensive Score Ranking Table of Distribution Network Nodes

[0042] Please refer to Figure 2 , as shown in the schematic diagram of the comprehensive score contribution of the first fault index, the horizontal axis represents the degree of frequency deviation, and its range is from 0 to 1, which reflects the frequency deviation of the distribution network node. The higher the value, the greater the degree of frequency deviation; the vertical axis represents the comprehensive score, and its range is from 0 to 100, which represents the overall fault risk and power quality level of the node. The red curve represents the trend relationship between the comprehensive score and the degree of frequency deviation, and the black square represents the actual data point; it can be seen that as the degree of frequency deviation increases, the comprehensive score also shows an obvious upward trend. When is relatively low, close to 0, the comprehensive score is also relatively low, indicating that the state of the distribution network node is relatively normal at this time. When increases to a certain extent, the comprehensive score rises rapidly, especially when is close to 0.4 and above, the increase in the comprehensive score is more obvious, which is consistent with the aforementioned threshold effect.

[0043] Please refer to Figure 3 , as shown in the schematic diagram of the comprehensive score contribution of the second fault index, the horizontal axis represents the total harmonic distortion rate, which reflects the harmonic distortion of the distribution network node. The higher the value, the more serious the harmonic influence. The red curve represents the trend relationship between the comprehensive score and the total harmonic distortion rate, and the black square represents the actual data point. As the total harmonic distortion rate increases, the comprehensive score also shows an obvious upward trend, indicating that the increase in the degree of harmonic distortion will lead to the increase of the comprehensive score. Combining Figure 3The fitting conclusion is obtained: the values of Reduced Chi-Sqr and COD are 0.9978 and 0.9924 respectively, indicating a good fit. The relationship between the comprehensive score and the degree of frequency deviation and the total harmonic distortion rate is well described, which means that most data points can be reasonably predicted. The adjusted R-squared value is 0.9983, further verifying the accuracy of the formula and indicating that the fitting result of this formula is suitable for predicting and analyzing the relationship between power quality and fault indicators.

[0044] Please refer to Figure 4 , the present invention further provides a distribution network fault location device based on power quality monitoring data. The distribution network fault location device is used to execute the above-mentioned distribution network fault location method based on power quality monitoring data, including: A data acquisition and preprocessing module, which is used to define a time detection window before and after the occurrence of a fault, collect power quality data of each monitoring area in the distribution network during the time detection window, and preprocess the obtained power quality data. The power quality data includes voltage data, current data, frequency data, and harmonic data. The preprocessing includes data cleaning and normalization processing of the power quality monitoring data; A voltage sag and current surge detection module, which is used to analyze voltage data based on a voltage sag threshold to determine the distribution network fault area. In the determined distribution network fault area, the current surge amplitude is obtained according to the moving average method, and the distribution network fault line in the distribution network fault area is determined based on the current surge amplitude; A frequency change detection module, which is used to analyze frequency data in the distribution network fault line based on the fast Fourier transform to determine the degree of frequency deviation before and after a fault occurs at each distribution network node, and use the degree of frequency deviation as the first fault index; A harmonic change detection module, which is used to analyze harmonic data in the distribution network fault line based on the fast Fourier transform, extract the fundamental wave amplitude and the amplitudes of each harmonic from the transformation result, obtain the total harmonic distortion rate of each distribution network node based on the fundamental wave amplitude and the amplitudes of each harmonic, and use the total harmonic distortion rate as the second fault index; A fault association and comprehensive score module, which assigns a grade value to the fault indicators of each distribution network node in the distribution network fault line, obtains the fault association value of each distribution network node according to the calculation method of the Spearman rank correlation coefficient, establishes a comprehensive score mechanism based on the first and second fault indicators of each distribution network node and combines the fault association value of the distribution network node, sorts the comprehensive scores of each distribution network node from large to small, sets a score threshold, forms a set of distribution network nodes that exceed the score threshold, and selects the distribution network node with the smallest fault association value in the set as the fault node.

[0045] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0046] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0047] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0048] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application.

Claims

1. A distribution network fault location method based on power quality monitoring data, characterized in that The specific steps include: Step 1: Define a time detection window before and after the fault occurs, collect the power quality data of each monitoring area in the distribution network within the time detection window, and preprocess the obtained power quality data. The power quality data includes voltage data, current data, frequency data, and harmonic data. The preprocessing includes data cleaning and normalization processing of the power quality monitoring data; Step 2: Analyze the voltage data based on the voltage sag threshold to determine the fault area of the distribution network. Within the determined fault area of the distribution network, obtain the sudden increase amplitude of the current according to the moving average method, and determine the faulty line in the distribution network within the fault area of the distribution network based on the sudden increase amplitude of the current; Step 3: Analyze the frequency data in the faulty line of the distribution network based on the fast Fourier transform to determine the degree of frequency deviation before and after the fault occurs at each distribution network node, and use the degree of frequency deviation as the first fault index; Step 4: Analyze the harmonic data in the faulty line of the distribution network based on the fast Fourier transform, extract the fundamental wave amplitude and the amplitudes of each harmonic from the transformation result, obtain the total harmonic distortion rate of each distribution network node based on the fundamental wave amplitude and the amplitudes of each harmonic, and use the total harmonic distortion rate as the second fault index; Step 5: Assign a grade value to the fault index of each distribution network node in the faulty line of the distribution network, obtain the fault correlation value of each distribution network node according to the calculation method of the Spearman rank correlation coefficient, establish a comprehensive scoring mechanism based on the first and second fault indices of each distribution network node and combined with the fault correlation value of the distribution network node, sort the comprehensive scores of each distribution network node from large to small, set a score threshold, form a set of distribution network nodes that exceed the score threshold, and select the distribution network node with the smallest fault correlation value in the set as the fault node.

2. The power distribution network fault location method based on power quality monitoring data according to claim 1, wherein The method for preprocessing the obtained power quality data is as follows: Use the statistical method to identify the outliers and duplicate data in the power quality data, delete the outliers and duplicate data in the power quality data, and fill the missing values in the power quality data with the mean, median, or mode of the power quality data; The normalization process of power quality data is min-max normalization, which scales the data to range, so that all power quality data have the same scale. The formula is as follows: ； Among them, is the power quality data after normalization processing, is the original power quality data, is the minimum value among the power quality data of the same type in the dataset, is the maximum value among the power quality data of the same type in the dataset.

3. A power distribution network fault location method based on power quality monitoring data according to claim 1, characterized in that, Based on the voltage sag threshold, analyze the voltage data in the time detection window before and after the fault occurs, and mark the fault area of the distribution network. The method is as follows: Combine the voltage data in the time detection window with the moving average method to obtain the voltage sag values of each monitoring area before and after the fault occurs in the distribution network system. The calculation formula is: ； ； ； Among them, represents the average voltage within the time detection window before the fault occurs in the monitoring area, represents the average voltage within the time detection window after the fault occurs in the monitoring area, represents the voltage drop value before and after the fault occurs, represents the time length of the time detection window, represents at the moment in the monitoring area the voltage value at the moment, is the time variable within the time detection window, is the fault occurrence moment; The drop voltage value of each monitoring area in the distribution network system is compared with a preset drop voltage threshold . If , it is determined that the monitoring area is a distribution network fault area.

4. A power distribution network fault location method based on power quality monitoring data according to claim 3, characterized in that, Based on the sudden increase amplitude of the current, determine the faulty line in the marked fault area of the distribution network. The method is as follows: According to the current data in the time detection window and combined with the moving average method, obtain the sudden increase value of the current of each line in the fault area of the distribution network. The formula is: ； ； ； Among them, represents the average current within the time detection window before a fault occurs in the distribution network line in the monitoring area, represents the average current within the time detection window after a fault occurs in the distribution network line in the monitoring area, represents the sudden increase value of the current before and after the fault occurs; Set the threshold for sudden increase in current , and compare the sudden increase value of the current of each line in the determined distribution network fault area with . If , then determine this line in the distribution network fault area as a distribution network fault line.

5. A distribution network fault location method based on power quality monitoring data according to claim 4, characterized in that Analyze the frequency data of each distribution network node in the faulty line of the distribution network to determine the degree of frequency deviation of each distribution network node in the faulty line of the distribution network. The method is as follows: Using the fast Fourier transform technology to convert the frequency data within the time detection window before and after the fault occurs, windowing the collected time-domain frequency signal, taking the preset size of the time detection window as the signal segment with a fixed length after windowing, using the fast Fourier transform technology to transform this signal segment to obtain a complex number array representing the amplitude and phase of each frequency component, then calculating the modulus of this complex number array to obtain the amplitude value of each frequency component, determining the frequency value corresponding to each frequency component through the calculation of the frequency resolution, and finally, according to the magnitude of the amplitude, selecting the frequency component with the largest amplitude as the fundamental frequency, calculating the difference between the fundamental frequency values of the two time detection windows before and after the fault occurs at the distribution network node, and taking the ratio of the difference between the fundamental frequency values of the two time detection windows to the fundamental frequency value of the time detection window before the fault occurs as the frequency deviation degree of each distribution network node 。 6. A distribution network fault location method based on power quality monitoring data according to claim 5, characterized in that, Analyze the harmonic data of each distribution network node in the faulty line of the distribution network, and determine the total harmonic distortion rate of each distribution network node in the faulty line of the distribution network according to the amplitude of the frequency domain signal. The method is as follows: Analyze the signal frequency domain using the fast Fourier transform, convert the time-domain signal into a frequency-domain signal, identify the fundamental wave and its harmonics, and obtain the total harmonic distortion rate of each distribution network node. The formula is as follows: ； Among them, represents the total harmonic distortion rate of the distribution network node, represents the fundamental wave amplitude, , , represent the amplitudes of each harmonic, represents the total order of the harmonics.

7. A power distribution network fault location method based on power quality monitoring data according to claim 6, characterized in that, Establish a comprehensive score mechanism to determine fault and abnormal nodes. The method is as follows: Take the frequency deviation degree and total harmonic distortion rate of each distribution network node as the first and second fault indicators respectively. Assign a grade value to the fault indicators of each distribution network node in the distribution network fault line, calculate the grade difference between the first and second fault indicators, and obtain the fault correlation value of the two fault indicators of each distribution network node based on the grade difference. The formula is as follows: ； ； Among them, represents the level difference between the first and second fault indicators in the th distribution network node, , are respectively the level values assigned to the first and second fault indicators in the th distribution network node, represents the fault correlation value of the first and second fault indicators in the th distribution network node, represents the total number of distribution network nodes; Establish a comprehensive scoring mechanism, set the score range of each fault index to 0-100, and allocate proportional weights to the first and second fault indexes respectively 、 , then the formula for determining the comprehensive score of each distribution network node is as follows: ； Among them, represents the comprehensive score of the th power grid distribution node, represents the degree of frequency deviation of the th power grid distribution node, represents the total harmonic distortion rate of the th power grid distribution node, and = 1, ; Sort the fault correlation values and the comprehensive scores of the first and second fault indicators of each distribution network node in ascending order, and select the distribution network node with the largest fault correlation value and the highest comprehensive score as the distribution network fault node.

8. A distribution network fault location device based on power quality monitoring data, characterized in that, The distribution network fault location device is used to execute the distribution network fault location method based on power quality monitoring data according to any one of claims 1-7, including: A data acquisition and preprocessing module, which is used to define a time detection window before and after the fault occurs, collect the power quality data of each monitoring area in the distribution network during the time detection window, and preprocess the obtained power quality data. The power quality data includes voltage data, current data, frequency data, and harmonic data. The preprocessing includes data cleaning and normalization processing of the power quality monitoring data; A voltage sag and current surge detection module, which is used to analyze the voltage data based on the sag voltage threshold to determine the distribution network fault area. In the determined distribution network fault area, obtain the current surge amplitude according to the moving average method, and determine the distribution network fault line in the distribution network fault area based on the current surge amplitude; A frequency change detection module, which is used to analyze the frequency data based on the fast Fourier transform in the distribution network fault line to determine the frequency deviation degree before and after the fault occurs at each distribution network node, and take the frequency deviation degree as the first fault indicator; A harmonic change detection module, which is used to analyze the harmonic data based on the fast Fourier transform in the distribution network fault line, extract the fundamental wave amplitude and the amplitudes of each harmonic from the transformation result, obtain the total harmonic distortion rate of each distribution network node based on the fundamental wave amplitude and the amplitudes of each harmonic, and take the total harmonic distortion rate as the second fault indicator; A fault correlation and comprehensive score module, which is used to assign a grade value to the fault indicators of each distribution network node in the distribution network fault line, obtain the fault correlation value of each distribution network node according to the calculation method of the Spearman rank correlation coefficient, establish a comprehensive score mechanism based on the first and second fault indicators of each distribution network node and in combination with the fault correlation value of the distribution network node, sort the comprehensive scores of each distribution network node from large to small, set a score threshold, form a set of distribution network nodes that exceed the score threshold, and select the distribution network node with the smallest fault correlation value in the set as the fault node.

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