Transmission conductor fault diagnosis method and system based on mechanism-data fusion, medium and processor

Abstract

The invention provides a transmission conductor fault diagnosis method based on mechanism-data fusion, and the method comprises the steps: measuring the axial tension, the span between a tower A, a tower B and a tower O, the included angle between the tower A, the tower B and the tower O, and the original length of a conductor at two sides of the tower O, and calculating basic parameters; measuring the deviation angle, the axial tension and the inclination angle under the conditions of wind and ice, and calculating the icing condition according to the basic parameters; a short-circuit fault and an open-circuit fault are judged based on the three-phase current, the zero-sequence current, the theoretical current and the fault phase current; based on a data driving method, mountain fire and lightning stroke faults of the power transmission line are diagnosed; and fusing the mechanism analysis result and the data driving diagnosis result to obtain the fault type of the power transmission conductor. By performing decision-making level fusion on a mechanism analysis method and a data driving method based on a decision-making tree, the comprehensiveness and accuracy of fault diagnosis are improved, and effective identification of ice coating, short circuit, open circuit, forest fire and lightning stroke faults of the power transmission conductor is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid line fault diagnosis, and in particular to a transmission line fault diagnosis method, system, medium and processor based on mechanism-data fusion. Background Art

[0002] With the rapid development of my country's economy, the power system is becoming increasingly complex. Transmission lines are the most prone to failure among all components of the power system. If a transmission line failure is not promptly addressed, it can lead to a wider range of failures. Therefore, to reduce the occurrence of failures, it is crucial to monitor the operating status of transmission lines, conduct fault diagnosis in advance, and accurately identify the fault type.

[0003] Currently, mechanism analysis and data-driven methods are commonly used to diagnose faults in power equipment. For transmission line fault diagnosis:

[0004] Patent [Li Yuhua, Wang Chaolong, Wang Shun, Fang Ying, Zhang Zhimin. A transmission line fault diagnosis method based on edge computing [P]. Chongqing: CN117706270B, 2024-09-06.] proposes a transmission line fault diagnosis method based on edge computing, which improves the accuracy and efficiency of the transmission line diagnosis through efficient collection of the transmission line signal and precise calculation of the edge computing model.

[0005] Patent [Cai Jianfeng, Guo Jinzhi, Lu Jie, Song Xinli, Yang Xinyu, Hou Lifeng, Ji Ning, Dong Yanyan, Xu Shuo, Li Wei, Yu Ze. A method, system and architecture for diagnosing transmission line faults based on an expert system [P]. Hebei Province: CN113281616A, 2021-08-20.] The designed expert system infers and analyzes the acquired fault information, environmental information near the fault location and fault-related data to obtain the cause of the fault, thereby overcoming the disadvantage that the cause of the fault can only be finally determined after a large-scale manual search for the fault point, thereby reducing labor costs.

[0006] Patent [Zheng Jian, Jin Yao, Wu Kai, Yang Qing, Wang Sining, Zhu Wenjun, Zhang Yiying, Ma Caixia, Qi Bochao, Sun Jian, Liang Zhiyuan. A transmission line fault diagnosis method based on graph convolutional neural network [P]. Tianjin: CN114137358B, 2023-04-28.] uses multi-dimensional information acquisition technology and information fusion technology to realize transmission line fault diagnosis, achieve more accurate fault classification, and improve the efficiency of transmission line fault diagnosis.

[0007] Patent [Li Ting, Zhang Xiaojun, Pan Hua, Le Jian, Mao Wenqi, Zhu Weijun, Xie Yaoheng, Liao Xiaobing, Liu Haoliang, Mao Tao, Chen Ming, Peng Tao. Rapid diagnosis method, device, equipment and medium for transmission line faults based on multi-source information fusion [P]. Hunan Province: CN113960417A, 2022-01-21.] A fault feature vector is constructed through fault characteristics, and the fault feature vector is input into a fault diagnosis model to obtain the fault type of the transmission line.

[0008] The above methods have achieved good results in practical applications and provide a feasible solution for transmission line fault diagnosis.

[0009] Due to the frequent occurrence of mixed transmission line faults and their complex causal mechanisms, mechanistic models struggle to accurately reflect actual fault conditions. Data-driven approaches rely on large amounts of data to train models. However, transmission line fault data lacks objective physical constraints, and models trained with data-driven approaches may struggle to distinguish complex mixed fault conditions, resulting in reduced diagnostic accuracy. Therefore, relying on a single approach is incapable of addressing the current challenges of transmission line faults with diverse operating conditions and multiple fault types, making comprehensive, efficient, and accurate fault diagnosis difficult.

[0010] In view of this, a transmission line fault diagnosis method, system, medium and processor based on mechanism-data fusion are needed. Summary of the Invention

[0011] To address the difficulty in achieving comprehensive, efficient, and accurate fault diagnosis in existing technologies, the present invention provides a method, system, medium, and processor for diagnosing power line faults based on mechanism-data fusion, which can achieve comprehensive, efficient, and accurate fault diagnosis. The specific technical solution is as follows:

[0012] A transmission line fault diagnosis method based on mechanism-data fusion, comprising:

[0013] S1: Under windless and ice-free conditions, measure the axial tension of the insulator string, the spacing between towers A, B and tower O, the angle between towers A, B and tower O, and the original length of the conductors on both sides of tower O, and calculate the basic parameters under windless and ice-free conditions;

[0014] S2: Measure the overall geometric plane deviation angle of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction under windy and icy conditions, and calculate the icing situation based on the measured parameters and the basic parameters obtained under windless and ice-free conditions;

[0015] S3: Determine the short-circuit fault and open-circuit fault of the transmission line based on the three-phase current, zero-sequence current, theoretical current and fault phase current;

[0016] S4: Diagnose transmission line wildfire and lightning faults based on data-driven methods;

[0017] S5: Fusion the mechanism analysis results with the data-driven diagnosis results to obtain the fault type of the transmission line.

[0018] Furthermore, the calculation of basic parameters under windless and ice-free conditions includes the following steps:

[0019] Calculate the horizontal stress σ of conductors S1 and S2 in the vertical plane A , σ B , the formula is as follows:

[0020]

[0021] Calculate the distance from the lowest point of the conductor to O under windless and ice-free conditions as l a and l b , the formula is as follows:

[0022]

[0023] Calculate the conductor length S from the lowest point of the conductor on the tower A and B sides to the tower O under windless and ice-free conditions a and S b , the formula is as follows:

[0024]

[0025] In the above formula, L a and L b are the spacings of towers A, B and O under no-wind conditions; β a , β b are the angles between points A and B and point O under no-wind conditions; γ is the specific load of the tower conductor's deadweight; S1 and S2 are the original lengths of the conductors on both sides.

[0026] Furthermore, in step S2, the overall offset angle of the geometric plane formed by the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction are measured under windy and icy conditions, and the icing condition is calculated based on the measured parameters and the calculated basic parameters under windless and icy conditions. The calculation formula is as follows:

[0027] Measure the overall deviation angle of the geometric plane composed of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction in windy conditions;

[0028] Calculate the distance l' from the lowest point of the conductor to O in the wind deflection plane under wind conditions a and l′ b , the formula is as follows:

[0029]

[0030] Calculate the height difference angle β′ in windy conditions a and β′ b , the formula is as follows:

[0031]

[0032] Calculate the horizontal stress σ′ of conductors S1 and S2 in windy conditions A and σ′ B , the formula is as follows:

[0033]

[0034] Calculate the self-weight specific load γ′ in windy conditions using the following formula:

[0035] γ ′ =γ / cosη;

[0036] Calculate the conductor length S' from the lowest point of the conductor on the A and B sides of the tower to the tower O in windy conditions a and S′ b , the formula is as follows:

[0037]

[0038] The formula for calculating the insulator string skew angle α′ in the windage plane is as follows:

[0039]

[0040] The vertical upward tension F measured by the sensor after ice cover v Satisfy the following calculation formula:

[0041]

[0042] Calculate the average ice load per unit length of each split conductor;

[0043]

[0044] Assuming that the line is a uniform cylinder after ice covering, the calculation formula of the equivalent ice thickness D is as follows:

[0045]

[0046] In the above formula, ρ is the ice density of the line; is the wire diameter; q c is the average ice load per unit length of each split conductor; F L is the axial tension of the insulator string; F J is the weight of the insulator string and its metal accessories; F Dis the weight of the transmission line; n is the number of conductor splits; Fv is the vertical upward tension; α is the inclination angle of the insulator string along the line direction; η is the overall offset angle of the geometric plane composed of the conductor and its insulator string.

[0047] Furthermore, in step S4, the data-driven method is used to diagnose transmission line wildfire and lightning faults, including the following steps:

[0048] Obtain the historical operation records of the transmission line and collect data including discrete and continuous features during the operation of the transmission line, while recording the corresponding fault conditions, and summarize them to obtain the environmental feature database and the elements it contains;

[0049] Encode discrete feature data in the environmental feature database and normalize continuous features;

[0050] Divide the processed environmental feature database into a training set and a test set;

[0051] Construct a decision tree model, and use the training set and test set to train and test the decision tree model to obtain a fault diagnosis model;

[0052] The fault diagnosis model is used to diagnose forest fire and lightning faults in power lines.

[0053] Furthermore, the discrete characteristic data include voltage level, line, terrain, wind direction and weather.

[0054] Furthermore, the continuous characteristic data includes average temperature, relative humidity, sunshine time, daily precipitation, average wind speed, lightning strike density and line current.

[0055] Furthermore, in step S5, the mechanism analysis result is integrated with the data-driven diagnosis result to obtain the fault type of the transmission line, including the following steps:

[0056] Let H i Represents the i-th fault state, and determines the probability P(H i );

[0057] The information is independently input into the data-driven model and the mechanism model to generate preliminary diagnostic results E1 and E2 respectively;

[0058] According to the preliminary diagnosis result E1, we can know that the i-th fault state H is obtained based on the data-driven model. i The probability is P(E1|H i ), according to the preliminary diagnosis result E2, it can be known that the i-th fault state H is obtained based on the mechanism model i The probability P(E2|H i );

[0059] According to Bayes' theorem, combined with the preliminary diagnosis result E1 of the data-driven model, the fault state probability is updated. The update formula is:

[0060]

[0061] Through the information carried by E1, the system is in different fault states H i reassess and adjust the possibilities;

[0062] Continuing to combine the preliminary diagnosis result E2 of the mechanism model, the fault state probability is further updated. The update formula is as follows:

[0063]

[0064] After the above two probability updates, the final posterior probability of the combined diagnosis results of the two models is obtained; among all possible fault states, the fault state with the highest final posterior probability is determined as the optimal diagnosis result of the fault state, and the formula is as follows:

[0065] P(H k |E1,E2)=max{P(H1|E1,E2),P(H2|E1,E2)…P(H n |E1,E2)}.

[0066] A transmission line fault diagnosis system based on mechanism-data fusion, applied to the above-mentioned transmission line fault diagnosis method based on mechanism-data fusion, comprises:

[0067] The basic calculation module is used to measure the axial tension of the insulator string, the spacing between towers A, B and tower O, the angle between towers A, B and tower O, and the original length of the conductors on both sides of tower O under windless and ice-free conditions, and calculate basic parameters under windless and ice-free conditions;

[0068] The first mechanism calculation module is used to measure the overall offset angle of the geometric plane composed of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction under windy and icy conditions, and calculate the icing situation based on the measured parameters and the basic parameters calculated under windless and ice-free conditions;

[0069] The second mechanism calculation module is used to judge the short circuit fault and open circuit fault of the transmission line based on the three-phase current, zero-sequence current, theoretical current and fault phase current;

[0070] A data-driven computing module, which is used to diagnose transmission line wildfires and lightning faults based on a data-driven approach;

[0071] The fusion module is used to fuse the mechanism analysis results with the data-driven diagnosis results to obtain the fault type of the transmission line.

[0072] A computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned transmission line fault diagnosis method based on mechanism-data fusion.

[0073] A processor is used to run a program, wherein the program executes the above-mentioned transmission line fault diagnosis method based on mechanism-data fusion when running.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] 1. A transmission line fault diagnosis method based on mechanism-data fusion is proposed. The purpose is to integrate the mechanism analysis method with the data-driven method based on decision tree at the decision level, improve the comprehensiveness and accuracy of fault diagnosis, and realize the effective identification of transmission line faults such as icing, short circuit, open circuit, forest fire, and lightning strike. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0077] Figure 1 The figure is a flow chart of a transmission line fault diagnosis method based on mechanism-data fusion;

[0078] Figure 2 This is a schematic diagram of the transmission line model under windless and ice-free conditions;

[0079] Figure 3 Schematic diagram of the transmission line model under windy conditions;

[0080] Figure 4 It is a flow chart for the diagnosis of intelligent and open circuit faults of transmission lines based on mechanism analysis;

[0081] Figure 5 The schematic diagram of the structure of a transmission line fault diagnosis system based on mechanism-data fusion. DETAILED DESCRIPTION

[0082] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0083] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0084] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0085] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0086] Example 1

[0087] like Figure 1 The figure shows a flow chart of a transmission line fault diagnosis method based on mechanism-data fusion, which includes the following steps:

[0088] When high-voltage overhead transmission lines are covered with ice, the increased gravity on the lines causes stress on the insulator strings, and the angle of the transmission lines changes accordingly. By measuring the tension in the insulator strings, the tilt angle of the transmission lines, the ambient temperature and humidity, wind direction, wind speed, air pressure, and light intensity, the real-time icing status of the overhead transmission lines can be analyzed and the severity of the icing determined.

[0089] Therefore, tension sensors are installed on transmission line insulator strings to measure the axial tension. Tilt sensors measure the insulator string's tilt angle along the line direction and the windage angle perpendicular to the line direction. Micro-meteorological sensors measure temperature, humidity, wind speed, wind direction, air pressure, sunlight, and other influencing factors. Calculations and analysis are performed based on data collected under windless and ice-free conditions at the installation locations to determine the parameters necessary to determine whether overhead lines are covered with ice.

[0090] Figure 2This is a simulated plan view of the overhead transmission line under windless and ice-free conditions. An ice monitoring substation is installed at tower O. The spacing between towers A, B, and O is L. a and L b , the height differences between A and B and O are H a and H b , the angles are β a and β b , the original lengths of the wires on both sides are S1 and S2.

[0091] S1: Under windless and ice-free conditions, measure the axial tension of the insulator string, the spacing between towers A, B and tower O, the angle between towers A, B and tower O, and the original length of the conductors on both sides of tower O. Calculate the basic parameters under windless and ice-free conditions.

[0092] In a specific implementation, the calculation of basic parameters under windless and ice-free conditions includes the following steps:

[0093] S11: Calculate the horizontal stress σ of conductors S1 and S2 in the vertical plane A , σ B , the formula is as follows:

[0094]

[0095] S12: Calculate the distance from the lowest point of the conductor to O under windless and ice-free conditions as l a and l b , the formula is as follows:

[0096]

[0097] S13: Calculate the conductor length S from the lowest point of the conductor on the tower A and B sides to the tower O under windless and ice-free conditions a and S b , the formula is as follows:

[0098]

[0099] In the above formula, L a and L b are the spacings of towers A, B and O under no-wind conditions; β a , β b are the angles between points A and B and point O under no-wind conditions (i.e., the inclination angles of the insulator strings along the line direction); γ is the tower conductor deadweight load ratio; S1 and S2 are the original lengths of the conductors on both sides.

[0100] Substituting the sensor data under windless and ice-free conditions into the above formula, the basic parameters required to determine whether there is ice cover can be obtained: the distance l from the lowest point of the wire on both sides to O a and l b, horizontal stress of conductors on both sides σ A and σ B And the length S from the lowest point of the conductor on both sides to the tower O a and S b .

[0101] Furthermore, the conductor deadweight load of a transmission line refers to the weight of the conductor per unit length and unit cross-sectional area. It is an important parameter in conductor mechanics calculations and is usually represented by the symbol γ, with the unit being N / (m·mm 2 ). For overhead transmission lines, the formula for calculating their deadweight load ratio is:

[0102]

[0103] Where g is the acceleration due to gravity, generally taken as g = 9.81m / s 2 ;S p is the mass of the wire per meter (kg / m), which can be found in the wire specification parameter table; A is the calculated cross-sectional area of the wire (mm 2 ), which can also be obtained from the wire specification table; where gS p It can also be obtained by measuring the axial tension of the insulator string under windless and snowless conditions and dividing it by the length, or by using the theoretical calculation method in Gong Gong (2).

[0104] S2: Measure the overall offset angle of the geometric plane composed of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction under windy and icy conditions. Calculate the icing situation based on the measured parameters and the basic parameters calculated under windless and ice-free conditions.

[0105] In a specific implementation, in step S2, the overall offset angle of the geometric plane formed by the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction are measured under windy and icy conditions, and the icing condition is calculated based on the measured parameters and the calculated basic parameters under windless and icy conditions. The calculation formula is as follows:

[0106] S21: If Figure 3 As shown, the overall offset angle of the geometric plane composed of the conductor and its insulator string, the axial tension of the insulator string, and the tilt angle of the insulator string along the line direction are measured in the case of wind;

[0107] S22: Calculate the distance l' from the lowest point of the conductor to O in the windward plane under windy conditions a and l′ b , the formula is as follows:

[0108]

[0109] S23: Calculate the height difference angle β′ in windy conditionsa and β′ b , the formula is as follows:

[0110]

[0111] S24: Calculate the horizontal stress σ′ of conductors S1 and S2 in windy conditions A and σ′ B , the formula is as follows:

[0112]

[0113] S25: Calculate the specific load γ′ under wind conditions using the following formula:

[0114] γ′=γ / cosη; (8)

[0115] S26: Calculate the conductor length S′ from the lowest point of the conductor on the tower A and B sides to the tower O in windy conditions a and S′ b , the formula is as follows:

[0116]

[0117] S27: Calculate the insulator string skew angle α′ in the windage plane using the following formula:

[0118]

[0119] S28: The vertical upward tension F measured by the sensor after ice cover v Satisfy the following calculation formula:

[0120]

[0121] S29: Calculate the average ice load per unit length of each split conductor;

[0122]

[0123] S210: Assuming that the line is a uniform cylinder after ice coating, the calculation formula for the equivalent ice coating thickness D is as follows:

[0124]

[0125] In the above formula, ρ is the ice density of the line, which is 900 kg / m 3 ; is the wire diameter; q c is the average ice load per unit length of each split conductor; F L is the axial tension of the insulator string; F J is the weight of the insulator string and its metal accessories; F Dis the weight of the transmission line; n is the number of conductor splits; Fv is the vertical upward tension; α is the inclination angle of the insulator string along the line direction; η is the overall offset angle of the geometric plane composed of the conductor and its insulator string.

[0126] By calculating the equivalent ice thickness of the line, the ice condition of the line surface can be judged. When the ice thickness D<3mm, the line is normal; when 3mm≤D<10mm, the line is lightly iced; when 10mm≤D≤20mm, the line is moderately iced; when D>20mm, the line is heavily iced.

[0127] S3: Determine the short-circuit fault and open-circuit fault of the transmission line based on the three-phase current, zero-sequence current, theoretical current and fault phase current.

[0128] Design of a specific judgment process for short-circuit faults in transmission lines Figure 4 Assume that the effective value of the three-phase current of the conductor is I A , I B , I C , the effective value of zero-sequence current is I0, the theoretical calculated current is I, and the fault phase current is I f Based on the above data, it is determined whether a short circuit fault or an open circuit fault occurs in the transmission line.

[0129] First, if the maximum effective value of the three-phase current (I A , I B or I C ) is greater than 1.5 times the theoretical calculated current I, the fault type can be determined to be a short circuit fault. At this time, if the zero sequence current effective value I0=0, it is an ungrounded short circuit, and if I0≠0, it is a grounded short circuit. In an ungrounded short circuit fault, when I A =I B =I C If I0=I f , it is a single-phase ground short circuit, if it is not equal, it is a two-phase ground short circuit.

[0130] Secondly, if the maximum effective value of the three-phase current (I A , I B or I C ) is less than or equal to 1.5 times the theoretical calculated current I, and the effective value of the three-phase current I A , I B or I C If the three-phase current is equal to 0, the transmission line is broken; if the effective values of the three-phase currents are not 0, the transmission line is in normal operation.

[0131] S4: Diagnose transmission line wildfire and lightning faults based on data-driven methods.

[0132] In a specific implementation, in step S4, the data-driven method is used to diagnose wildfire and lightning faults in power transmission lines, including the following steps:

[0133] S41: Obtain historical operation records of the transmission line, and collect data including discrete features and continuous features during the operation of the transmission line, while recording corresponding fault conditions, and summarize to obtain an environmental feature database and the elements contained therein.

[0134] In a specific implementation, the discrete characteristic data includes voltage level, line, terrain, wind direction and weather.

[0135] In a specific implementation, the continuous characteristic data includes average temperature, relative humidity, sunshine time, daily precipitation, average wind speed, lightning strike density and line current.

[0136] The historical operation records of the transmission lines are obtained, and relevant data during the operation of the transmission lines are collected, covering discrete features (voltage level, line, terrain, wind direction, weather) and continuous features (average temperature, relative humidity, sunshine hours, daily precipitation, average wind speed, lightning density, line current). At the same time, the corresponding fault conditions (wildfire faults, lightning faults) are recorded. The environmental feature input database and its elements are summarized as shown in Table 1:

[0137] Table 1 Summary of selected environmental characteristics and elements

[0138]

[0139] According to the input data in Table 1, the diagnosis method based on the decision tree is used to diagnose the wildfire and lightning faults of the transmission line.

[0140] S42: Encode the discrete feature data in the environmental feature database and normalize the continuous features.

[0141] (1) Discrete feature encoding: Discrete features are encoded using one-hot encoding to convert voltage levels, lines, terrain, wind direction, weather, and other features into numerical vectors. Taking terrain as an example, there are six types of terrain: mountain tops, plains, ridges, hills, forests, and wetlands. These can be encoded as [1,0,0,0,0,0], [0,1,0,0,0,0], [0,0,1,0,0,0], [0,0,0,1,0,0], [0,0,0,0,1,0], and [0,0,0,0,0,1], respectively.

[0142] (2) Normalization of continuous features: To make continuous features of different magnitudes comparable, the continuous features are normalized using Min-Max Scaling, as shown in Equation (15):

[0143]

[0144] Where x is the original value, x min and x max are the minimum and maximum values of the feature, respectively.

[0145] S43: Divide the processed environmental feature database into a training set and a test set.

[0146] The preprocessed data is divided into a training set and a test set in a ratio of 8:2. The training set is used to build the decision tree model, and the test set is used to evaluate the performance of the model.

[0147] S44: Construct a decision tree model, and use a training set and a test set to train and test the decision tree model to obtain a fault diagnosis model.

[0148] (4) Constructing a decision tree model: The CART algorithm (i.e., the Classification and Regression Tree algorithm) is a decision tree algorithm widely used in data mining and machine learning, and can be used for classification and regression tasks. It is suitable for classification and regression problems and can handle both continuous and discrete features. Therefore, the CART algorithm is selected. The CART algorithm uses Gini Impurity as the partitioning criterion. Gini Impurity measures the impurity of the data set, and the calculation formula is shown in Equation (16):

[0149]

[0150] Where D is the dataset (i.e., the environmental feature database), k is the number of categories, and p i is the proportion of samples of the i-th category in the data set. When constructing a decision tree, select the features and partition points that minimize the Gini impurity of the child nodes after partitioning.

[0151] (5) Recursive partitioning: Starting from the root node, traverse all features and their possible partitioning points, calculate the Gini impurity after partitioning, select the optimal partitioning method, and partition the dataset into two child nodes. Then repeat the above process for each child node until the stopping condition is met, such as the number of samples in the node is less than a certain threshold, the Gini impurity of the node is less than a certain threshold, or the depth of the tree reaches a preset value.

[0152] (6) Use the training set data to train the decision tree model, that is, according to the above-mentioned decision tree construction method, recursively divide the training data according to discrete features and continuous features, and finally obtain a complete decision tree model, that is, the fault diagnosis model.

[0153] A decision tree consists of nodes and edges. Each internal node represents a test on an attribute, branches are the test outputs, and leaf nodes represent categories or values. It analyzes the various characteristic attributes in the training data and divides the samples according to the values of different attributes, constructing a tree-like structure that can classify or predict unknown data. For example, in power transmission line fault diagnosis, an internal node might be "Is the wind speed greater than 10m / s?" If so, the system enters one branch; otherwise, it enters another. Ultimately, based on a series of such judgments, it reaches a leaf node, concluding whether the fault is a wildfire or a lightning strike.

[0154] Leaf nodes are nodes in a decision tree that have no child nodes, also known as terminal nodes. Starting from the root node, data is continuously passed down the branches of the decision tree according to the division rules of each node until it reaches a leaf node. This process completes a data classification or prediction.

[0155] S45: Use the fault diagnosis model to diagnose wildfire and lightning faults of the line.

[0156] The discrete and continuous features of the transmission line to be diagnosed are preprocessed (encoded and normalized) and then fed into a trained decision tree model. The decision tree model, starting from the root node and following the partitioning rules, eventually reaches a leaf node. The category corresponding to this leaf node is the diagnosis result, determining whether the transmission line has experienced a wildfire or lightning strike fault.

[0157] S5: Fusion the mechanism analysis results with the data-driven diagnosis results to obtain the fault type of the transmission line.

[0158] Integrate data-driven models and mechanism models at the decision-making level to build a more efficient and accurate fault diagnosis system.

[0159] In a specific implementation, in step S5, the mechanism analysis result is integrated with the data-driven diagnosis result to obtain the fault type of the transmission line, including the following steps:

[0160] Let H i Represents the i-th fault state, and determines the probability P(H i );

[0161] Independently input information into the data-driven model and the mechanism model (the mechanism model is the above-mentioned ice condition, short circuit fault and open circuit fault diagnosis algorithm) to generate preliminary diagnosis results E1 and E2 respectively;

[0162] According to the preliminary diagnosis result E1, we can know that the i-th fault state H is obtained based on the data-driven model. iThe probability is P(E1|H i ), according to the preliminary diagnosis result E2, it can be known that the i-th fault state H is obtained based on the mechanism model i The probability P(E2|H i );

[0163] According to Bayes' theorem, combined with the preliminary diagnosis result E1 of the data-driven model, the fault state probability is updated. The update formula is:

[0164]

[0165] Through the information carried by E1, the system is in different fault states H i reassess and adjust the possibilities;

[0166] Continuing to combine the preliminary diagnosis result E2 of the mechanism model, the fault state probability is further updated. The update formula is as follows:

[0167]

[0168] After the above two probability updates, the final posterior probability of the combined diagnosis results of the two models is obtained; among all possible fault states, the fault state with the highest final posterior probability is determined as the optimal diagnosis result of the fault state, and the formula is as follows:

[0169] P(H k ∣E1,E2)=max{P(H1∣E1,E2),P(H2∣E1,E2)…P(H n |E1,E2)}; (18)

[0170] In the above formula, E1 represents the preliminary diagnosis result of the data-driven model; E2 represents the preliminary diagnosis result of the mechanism model; H i Indicates different fault status types (e.g. H1 for wildfire, H2 for lightning, etc.); P(H i ) indicates that the transmission line is in various fault states H i The probability of P(E1|H i ) indicates that the transmission line is in a fault state H i When the data-driven model obtains the probability of the corresponding diagnosis result E1; P(E2|H i ) indicates that the transmission line is in a fault state H i When , the mechanism model obtains the probability of the corresponding diagnosis result E2.

[0171] The fusion method of this application reduces the uncertainty of the output results of a single model, improving the accuracy while enhancing the system's anti-interference ability and interpretability.

[0172] Bayes' Theorem

[0173]

[0174] P(H|E): posterior probability (the probability that hypothesis H is true after observing evidence E);

[0175] P(H): prior probability (initial probability based on historical data or domain knowledge);

[0176] P(E|H): likelihood probability (the probability of observing evidence E when hypothesis H is true);

[0177] P(E): The marginal probability of the evidence (the total probability of E occurring under all possible hypotheses).

[0178] Implementation effect analysis

[0179] The fault types that the monitoring system can identify are numbered and recorded in the table below. Multiple simulations of each fault type are conducted, and the identification results and the number of occurrences are counted. Repeated testing of multiple fault types is then repeated, and the diagnostic results for each type are combined to calculate the fault diagnosis accuracy of the detection system. Testing simulated data for several typical fault types yielded an overall fault diagnosis accuracy exceeding 92%, demonstrating the excellent diagnostic effectiveness of this method.

[0180] Table 2 Test data of equipment typical fault diagnosis accuracy

[0181]

[0182] This method effectively integrates the mechanism analysis method with the data-driven method based on decision trees at the decision-making level. It can comprehensively diagnose transmission line faults such as icing, short circuit, open circuit, wildfire, and lightning strike, thereby improving the diagnosis speed and accuracy of transmission line faults under various types, making it easier for staff to take effective measures in a timely manner.

[0183] Compared with existing methods and technologies, this method integrates the decision-making layer by introducing a mechanism analysis method and a data-driven method based on a decision tree to screen and verify the data. It can improve the diagnosis speed and accuracy of transmission line faults such as icing, short circuit, open circuit, wildfire, and lightning strike, thereby realizing comprehensive, efficient and accurate fault diagnosis of transmission line faults under various complex working conditions and various fault types.

[0184] Example 2

[0185] like Figure 5 FIG. 1 is a schematic diagram of a transmission line fault diagnosis system based on mechanism-data fusion, which is applied to the above-mentioned transmission line fault diagnosis method based on mechanism-data fusion, and includes:

[0186] The basic calculation module is used to measure the axial tension of the insulator string, the spacing between towers A, B and tower O, the angle between towers A, B and tower O, and the original length of the conductors on both sides of tower O under windless and ice-free conditions, and calculate basic parameters under windless and ice-free conditions;

[0187] The first mechanism calculation module is used to measure the overall offset angle of the geometric plane composed of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction under windy and icy conditions, and calculate the icing situation based on the measured parameters and the basic parameters calculated under windless and ice-free conditions;

[0188] The second mechanism calculation module is used to judge the short circuit fault and open circuit fault of the transmission line based on the three-phase current, zero-sequence current, theoretical current and fault phase current;

[0189] A data-driven computing module, which is used to diagnose transmission line wildfires and lightning faults based on a data-driven approach;

[0190] The fusion module is used to fuse the mechanism analysis results with the data-driven diagnosis results to obtain the fault type of the transmission line.

[0191] Example 3

[0192] A computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned transmission line fault diagnosis method based on mechanism-data fusion.

[0193] Example 4

[0194] A processor is used to run a program, wherein the program executes the above-mentioned transmission line fault diagnosis method based on mechanism-data fusion when running.

[0195] The present application proposes a transmission line fault diagnosis method based on mechanism-data fusion, including: S1: measuring the axial tension of the insulator string, the spacing between towers A, B and tower O, the angle between towers A, B and tower O, and the original length of the conductor on both sides of tower O under windless and ice-free conditions, and calculating the basic parameters under windless and ice-free conditions; S2: measuring the overall offset angle of the geometric plane composed of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction under windy and ice conditions, and calculating the icing situation based on the measured parameters under windless and ice-free conditions and the calculated basic parameters; S3: judging the short-circuit fault and open-circuit fault of the transmission line based on the three-phase current, zero-sequence current, theoretical current and fault phase current; S4: diagnosing forest fire and lightning faults of the transmission line based on a data-driven method; S5: fusing the mechanism analysis results with the data-driven diagnosis results to obtain the fault type of the transmission line. A transmission line fault diagnosis method based on mechanism-data fusion is proposed. The purpose is to integrate the mechanism analysis method with the data-driven method based on decision tree at the decision level, improve the comprehensiveness and accuracy of fault diagnosis, and realize the effective identification of transmission line faults such as icing, short circuit, open circuit, forest fire, and lightning strike.

[0196] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0197] In the embodiments provided by the present invention, it should be understood that the division of units is merely a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0198] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0199] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A transmission line fault diagnosis method based on mechanism-data fusion, characterized in that: include: S1: Under windless and ice-free conditions, measure the axial tension of the insulator string, the spacing between towers A, B and tower O, the angle between towers A, B and tower O, and the original length of the conductors on both sides of tower O, and calculate the basic parameters under windless and ice-free conditions; S2: Measure the overall geometric plane deviation angle of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction under windy and icy conditions, and calculate the icing situation based on the measured parameters and the basic parameters obtained under windless and ice-free conditions; S3: Determine the short-circuit fault and open-circuit fault of the transmission line based on the three-phase current, zero-sequence current, theoretical current and fault phase current; S4: Diagnose transmission line wildfire and lightning faults based on data-driven methods; S5: Fusion the mechanism analysis results with the data-driven diagnosis results to obtain the fault type of the transmission line.

2. The method for diagnosing transmission line faults based on mechanism-data fusion according to claim 1, characterized in that: The calculation of basic parameters under windless and ice-free conditions includes the following steps: Calculate the horizontal stress σ of conductors S1 and S2 in the vertical plane A , σ B , the formula is as follows: Calculate the distance from the lowest point of the conductor to O under windless and ice-free conditions as l a and l b , the formula is as follows: Calculate the conductor length S from the lowest point of the conductor on the tower A and B sides to the tower O under windless and ice-free conditions a and S b , the formula is as follows: In the above formula, L a and L b are the spacings of towers A, B and O under no-wind conditions; β a , β b are the angles between points A and B and point O under no-wind conditions; γ is the specific load of the tower conductor's deadweight; S1 and S2 are the original lengths of the conductors on both sides.

3. The method for diagnosing transmission line faults based on mechanism-data fusion according to claim 2, characterized in that: In step S2, the overall offset angle of the geometric plane formed by the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction are measured under windy and icy conditions, and the icing condition is calculated based on the measured parameters and the calculated basic parameters under windless and icy conditions. The calculation formula is as follows: Measure the overall deviation angle of the geometric plane composed of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction in windy conditions; Calculate the distance l' from the lowest point of the conductor to O in the wind deflection plane under wind conditions a and l′ b , the formula is as follows: Calculate the height difference angle β′ in windy conditions a and β′ b , the formula is as follows: Calculate the horizontal stress σ′ of conductors S1 and S2 in windy conditions A and σ′ B , the formula is as follows: Calculate the self-weight specific load γ′ in windy conditions using the following formula: c ′ =γ / cosη; Calculate the conductor length S' from the lowest point of the conductor on the A and B sides of the tower to the tower O in windy conditions a and S′ b , the formula is as follows: The formula for calculating the insulator string skew angle α′ in the windage plane is as follows: The vertical upward tension F measured by the sensor after ice cover v Satisfy the following calculation formula: Calculate the average ice load per unit length of each split conductor; Assuming that the line is a uniform cylinder after ice covering, the calculation formula of the equivalent ice thickness D is as follows: In the above formula, ρ is the ice density of the line; is the wire diameter; q c is the average ice load per unit length of each split conductor; F L is the axial tension of the insulator string; F J is the weight of the insulator string and its metal accessories; F D is the weight of the transmission line; n is the number of conductor splits; Fv is the vertical upward tension; α is the inclination angle of the insulator string along the line direction; η is the overall offset angle of the geometric plane composed of the conductor and its insulator string.

4. The method for diagnosing transmission line faults based on mechanism-data fusion according to claim 2, characterized in that: In step S4, the data-driven method is used to diagnose transmission line wildfires and lightning faults, including the following steps: Obtain the historical operation records of the transmission line and collect data including discrete and continuous features during the operation of the transmission line, while recording the corresponding fault conditions, and summarize them to obtain the environmental feature database and the elements it contains; Encode discrete feature data in the environmental feature database and normalize continuous features; Divide the processed environmental feature database into a training set and a test set; Construct a decision tree model, and use the training set and test set to train and test the decision tree model to obtain a fault diagnosis model; The fault diagnosis model is used to diagnose forest fire and lightning faults in power lines.

5. The method for diagnosing transmission line faults based on mechanism-data fusion according to claim 4, characterized in that: The discrete characteristic data include voltage level, line, terrain, wind direction and weather.

6. The method for diagnosing transmission line faults based on mechanism-data fusion according to claim 4, characterized in that: The continuous characteristic data include average temperature, relative humidity, sunshine time, daily precipitation, average wind speed, lightning strike density and line current.

7. The method for diagnosing transmission line faults based on mechanism-data fusion according to claim 1, characterized in that: In step S5, the mechanism analysis result is integrated with the data-driven diagnosis result to obtain the fault type of the transmission line, including the following steps: Let H i Represents the i-th fault state, and determines the probability P(H i ); The information is independently input into the data-driven model and the mechanism model to generate preliminary diagnostic results E1 and E2 respectively; According to the preliminary diagnosis result E1, we can know that the i-th fault state H is obtained based on the data-driven model. i The probability is P(E1|H i ), according to the preliminary diagnosis result E2, it can be known that the i-th fault state H is obtained based on the mechanism model i The probability P(E2|H i ); According to Bayes' theorem, combined with the preliminary diagnosis result E1 of the data-driven model, the fault state probability is updated. The update formula is: Through the information carried by E1, the system is in different fault states H i reassess and adjust the possibilities; Continuing to combine the preliminary diagnosis result E2 of the mechanism model, the fault state probability is further updated. The update formula is as follows: After the above two probability updates, the final posterior probability of the combined diagnosis results of the two models is obtained; among all possible fault states, the fault state with the highest final posterior probability is determined as the optimal diagnosis result of the fault state, and the formula is as follows: P(H k |E1,E2)=max{P(H1|E1,E2),P(H2|E1,E2)…P(H n |E1,E2)}。 8. A transmission line fault diagnosis system based on mechanism-data fusion, characterized in that: The method for diagnosing transmission line faults based on mechanism-data fusion as claimed in any one of claims 1 to 7 comprises: The basic calculation module is used to measure the axial tension of the insulator string, the spacing between towers A, B and tower O, the angle between towers A, B and tower O, and the original length of the conductors on both sides of tower O under windless and ice-free conditions, and calculate basic parameters under windless and ice-free conditions; The first mechanism calculation module is used to measure the overall offset angle of the geometric plane composed of the conductor and its insulator string, the axial tension of the insulator string, and the inclination angle of the insulator string along the line direction under windy and icy conditions, and calculate the icing situation based on the measured parameters and the basic parameters calculated under windless and ice-free conditions; The second mechanism calculation module is used to judge the short circuit fault and open circuit fault of the transmission line based on the three-phase current, zero-sequence current, theoretical current and fault phase current; A data-driven computing module, which is used to diagnose transmission line wildfires and lightning faults based on a data-driven approach; The fusion module is used to fuse the mechanism analysis results with the data-driven diagnosis results to obtain the fault type of the transmission line.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the transmission line fault diagnosis method based on mechanism-data fusion according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the transmission line fault diagnosis method based on mechanism-data fusion according to any one of claims 1 to 7.

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