CN117289085A - Multi-line fault analysis and diagnosis method and system - Google Patents
Multi-line fault analysis and diagnosis method and system Download PDFInfo
- Publication number
- CN117289085A CN117289085A CN202311565423.3A CN202311565423A CN117289085A CN 117289085 A CN117289085 A CN 117289085A CN 202311565423 A CN202311565423 A CN 202311565423A CN 117289085 A CN117289085 A CN 117289085A
- Authority
- CN
- China
- Prior art keywords
- line
- coordinate
- fault
- diagnosis
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 144
- 238000000034 method Methods 0.000 title claims abstract description 58
- 239000011159 matrix material Substances 0.000 claims abstract description 46
- 239000013598 vector Substances 0.000 claims abstract description 42
- 238000012544 monitoring process Methods 0.000 claims abstract description 15
- 238000012545 processing Methods 0.000 claims description 19
- 238000012423 maintenance Methods 0.000 claims description 18
- 238000012549 training Methods 0.000 claims description 15
- 230000015654 memory Effects 0.000 claims description 14
- 238000010801 machine learning Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 238000000605 extraction Methods 0.000 claims description 7
- 230000008439 repair process Effects 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 2
- 239000000306 component Substances 0.000 description 21
- 238000010586 diagram Methods 0.000 description 9
- 238000001514 detection method Methods 0.000 description 8
- 238000006243 chemical reaction Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 5
- 238000011835 investigation Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/085—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of power fault diagnosis, in particular to a multi-line fault analysis and diagnosis method and a system, which can realize real-time monitoring and fault accurate identification of the running state of a power multi-line system; the method is applied to fault diagnosis of the power multi-line system, and comprises the following steps: collecting basic information of the electric power multi-line system, wherein the basic information comprises an electric power line topological structure and electric parameters; establishing a multi-line data acquisition coordinate set according to basic information of the electric power multi-line system; the multi-line data acquisition coordinate set consists of coordinate nodes of all components in the electric power multi-line system; acquiring a coordinate set based on multi-line data, and acquiring operation parameters of the electric power multi-line system in real time to obtain an operation parameter heterogeneous vector of each coordinate node; and arranging and converting the heterogeneous vectors of the operation parameters with the same acquisition time and different coordinate nodes according to the position relation of the coordinate nodes to obtain a system operation state feature matrix.
Description
Technical Field
The invention relates to the technical field of power fault diagnosis, in particular to a multi-line fault analysis and diagnosis method and system.
Background
The power multi-line system refers to a power system which is provided with a plurality of power lines running in parallel and is used for generating, transmitting and distributing electric energy; a multi-line system may increase the reliability of the system and if one line fails or requires maintenance, the other lines may continue to provide power, thereby reducing the likelihood of system interruption. The power multi-line system is designed to improve the robustness and flexibility of the power system, which ensures that the power system can maintain stable operation in the face of faults, maintenance or different load demands.
When the electric power multi-line system fails, the fault detection becomes relatively difficult due to the complexity of the lines; the existing diagnosis method needs to analyze and check line by line, even if part of the circuit system adopts a sensor to carry out shunt monitoring, only which line is faulty can be identified, and manual check is still needed for the fault type. Therefore, a multi-line fault analysis and diagnosis method capable of accurately identifying a fault location and a fault type is needed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-line fault analysis and diagnosis method capable of realizing real-time monitoring and fault accurate identification of the running state of an electric power multi-line system.
In a first aspect, the present invention provides a multi-line fault analysis and diagnosis method, which is applied to fault diagnosis of an electric power multi-line system, the method comprising:
collecting basic information of an electric power multi-line system, wherein the basic information comprises an electric power line topological structure and electric parameters;
establishing a multi-line data acquisition coordinate set according to basic information of the electric power multi-line system; the multi-line data acquisition coordinate set consists of coordinate nodes of all components in the electric power multi-line system;
acquiring a coordinate set based on multi-line data, and acquiring operation parameters of the electric power multi-line system in real time to obtain an operation parameter heterogeneous vector of each coordinate node;
arranging and converting heterogeneous vectors of operation parameters with the same acquisition time and different coordinate nodes according to the position relation of the coordinate nodes to obtain a system operation state feature matrix;
inputting the characteristic matrix of the system running state into a pre-constructed multi-line fault diagnosis model to obtain a system diagnosis information set; the input of the multi-line fault diagnosis model is a system running state feature matrix, the output of the multi-line fault diagnosis model is a system diagnosis information set, and the system diagnosis information set comprises a diagnosis result of each coordinate node;
Traversing the system diagnosis information set, extracting coordinate nodes with faults and corresponding fault types in the system diagnosis information set, and sending prompts to operation and maintenance personnel.
Further, the method for collecting the operation parameters of the electric power multi-line system in real time comprises the following steps:
determining the composition of an operating parameter isomerous vector, wherein the operating parameter isomerous vector consists of current, voltage, frequency and temperature;
installing measuring equipment on the power component corresponding to each coordinate node so as to acquire the operation parameters of the power component corresponding to each coordinate node of the power multi-line system in real time;
determining a time interval of data acquisition;
for each coordinate node, current, voltage, frequency and temperature parameters acquired in real time are assembled into heterogeneous vectors.
Further, the operating parameter heterogeneous vector is:
；
wherein I is nm Representing the current at the coordinate node m at acquisition time n; u (U) nm Representing the voltage at coordinate node m at acquisition time n; f (F) nm Representing the frequency of the coordinate node m at the acquisition time n; t (T) nm The temperature at the coordinate node m at acquisition time n is indicated.
Further, the method for establishing the multi-line data acquisition coordinate set according to the basic information of the electric power multi-line system comprises the following steps:
Analyzing a circuit topology structure of the electric power multi-circuit system, and determining connection relations among all circuits, wherein the connection relations comprise layout and connection modes of a generator, a cable, a transformer and a switch equipment component;
abstracting each component in the power system into coordinate nodes, wherein each coordinate node represents a specific power device;
integrating the determined coordinate nodes into a multi-line data acquisition coordinate set; the multi-line data acquisition coordinate set comprises all power components in the system, so that the running state of the system can be comprehensively monitored;
and (4) carrying out document recording on the established multi-line data acquisition coordinate set for future reference and system maintenance.
Further, the method for constructing the multi-line fault diagnosis model comprises the following steps:
collecting operation data of the electric power multi-line system in different states, wherein the operation data comprise normal operation data and fault data;
constructing a machine learning model, wherein the input of the machine learning model is a system running state feature matrix, and the output of the machine learning model is a system diagnosis information set;
performing data processing on the collected operation data to obtain a training data set;
training a machine learning model using the partial training data set, by adjusting parameters of the model to optimize performance of the model; the training data set which does not participate in training is used for verifying and testing the model so as to ensure the accuracy and generalization capability of the model;
Deploying the trained model into an electric power multi-line system, and monitoring the running state of the system in real time; for each new run state feature matrix, the model output is compared to the actual results to evaluate the performance of the model.
Further, the method for extracting the coordinate node with the fault and the corresponding fault type in the system diagnosis information set comprises the following steps:
traversing the system diagnosis information set, and extracting the corresponding coordinate node when the diagnosis result of a certain coordinate node is found to contain fault information;
identifying a fault type according to the extracted fault information;
once the fault type and the corresponding coordinate node are identified, a prompt is sent to operation and maintenance personnel; the prompt comprises a specific position of the fault, a fault type, a fault reason and suggested solving measures;
fault information and corresponding processing measures are recorded.
Further, the set of system diagnostic information includes: diagnostic results of the coordinate nodes, probability of failure, description of failure type, time stamp, and repair measure suggestion.
In another aspect, the present application further provides a multi-line fault analysis and diagnosis system, the system including:
The information collection module is used for collecting basic information of the electric power multi-line system, wherein the basic information comprises an electric power line topological structure and electric parameters;
the system comprises a coordinate set establishing module, a power multi-line system and a power multi-line system, wherein the coordinate set establishing module is used for establishing a multi-line data acquisition coordinate set according to basic information of the power multi-line system, and the multi-line data acquisition coordinate set consists of coordinate nodes of all components in the power multi-line system;
the real-time data acquisition module is used for acquiring the coordinate set according to the multi-line data, acquiring the operation parameters of the electric power multi-line system in real time, and obtaining the operation parameter heterogeneous vector of each coordinate node, wherein the operation parameter heterogeneous vector consists of current, voltage, frequency and temperature;
the state characteristic matrix generation module is used for arranging and converting the heterogeneous vectors of the operation parameters with the same acquisition time and different coordinate nodes according to the position relation of the coordinate nodes to obtain a system operation state characteristic matrix;
the system comprises a fault diagnosis module, a fault diagnosis module and a coordinate node, wherein the fault diagnosis module is used for storing a multi-line fault diagnosis model, inputting a system operation state feature matrix into the multi-line fault diagnosis model to obtain a system diagnosis information set, wherein the input of the multi-line fault diagnosis model is the system operation state feature matrix, the output of the multi-line fault diagnosis model is the system diagnosis information set, and the system diagnosis information set comprises a diagnosis result of each coordinate node;
The fault information extraction module is used for traversing the system diagnosis information set, extracting coordinate nodes with faults in the system diagnosis information set and corresponding fault types, and sending the coordinate nodes and the corresponding fault types;
and the alarm module is used for receiving the coordinate node with the fault and the corresponding fault type, generating a corresponding alarm signal according to the coordinate node with the fault and the corresponding fault type and transmitting the alarm signal to alarm equipment.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program implementing the steps of any of the methods described above when executed by the processor.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
Compared with the prior art, the invention has the beneficial effects that:
by collecting basic information of the electric power multi-line system, including topological structures and electric parameters, comprehensive understanding of all aspects of the system is ensured, and the method is helpful for establishing a comprehensive and accurate system running state feature matrix; the operation parameter heterogeneous vector of each coordinate node is acquired in real time based on the multi-line data acquisition coordinate set, parameters of multiple aspects of current, voltage, frequency and temperature are considered, and the system state is more comprehensively represented;
The heterogeneous vectors of the operation parameters with the same acquisition time and different coordinate nodes are arranged and converted according to the position relation of the coordinate nodes, and the processing mode considers the topological structure of the system, so that the operation state of the system can be accurately captured; the pre-built multi-line fault diagnosis model is used, so that the accuracy of the system for identifying various faults can be improved; such models can learn the normal and fault conditions of the system through training the data set, thereby making better diagnoses;
the system operation state feature matrix is input into a fault diagnosis model, so that automatic extraction of system diagnosis information is realized; this helps to reduce the workload of manual investigation and to improve the efficiency of fault diagnosis; through traversing the system diagnosis information set, extracting fault nodes and fault types in the system diagnosis information, and sending prompts to operation and maintenance personnel, the system can respond rapidly and take necessary actions;
in conclusion, the method can realize real-time monitoring and fault accurate identification of the running state of the power multi-line system, improves the efficiency and accuracy of fault investigation, and provides powerful guarantee for the stable running of the power system.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart for establishing a multi-line data acquisition coordinate set;
FIG. 3 is a flow chart for modeling a multi-line fault diagnosis;
fig. 4 is a block diagram of a multi-line fault analysis and diagnosis system.
Detailed Description
In the description of the present application, those skilled in the art will appreciate that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Accordingly, the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media, which contain computer program code.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The technical scheme of the application is that the acquisition, storage, use, processing and the like of the data meet the relevant regulations of national laws.
The present application describes methods, apparatus, and electronic devices provided by the flowchart and/or block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings in the present application.
Example 1
As shown in fig. 1 to 3, the method for analyzing and diagnosing a multi-line fault of the present invention is applied to fault diagnosis of a power multi-line system, and specifically includes the following steps:
s1, collecting basic information of an electric power multi-line system, wherein the basic information comprises an electric power line topological structure and electric parameters;
in the power system, the line topology structure refers to the connection relation and the power flow direction between the power lines, and the electrical parameters include current, voltage, frequency, temperature and the like; s1, the step is the basis of an electric power multi-line fault analysis and diagnosis method, and by collecting and analyzing basic information of an electric power multi-line system, an important reference basis can be provided for subsequent fault diagnosis, and key information such as a topological structure and electrical parameters of the system can be accurately obtained; the method specifically comprises the following steps:
a. Collecting a topological structure of a power line: obtaining a circuit layout diagram of a power system, wherein the diagram comprises the connection mode, node distribution, positions of key elements such as a power supply, a load and the like of each circuit; this may be obtained by field investigation, system design drawings or computer aided design software; establishing a topological relation table among the lines, and defining the connection mode, the parallel relation and the electrical relation among the nodes of each line; this helps to build up a subsequent data acquisition coordinate set;
b. and (3) electric parameter collection: collecting parameters of electrical equipment involved in each line, including, but not limited to, parameters of rated capacity, resistance, reactance, etc. of a transformer, parameters of rated power, current, voltage, etc. of a generator, and electrical characteristics of length, cross-sectional area, etc. of the line; if sensors are used in the system for real-time data acquisition, collecting data of the sensors, including parameters such as current, voltage, frequency and temperature; this helps to achieve real-time monitoring of the system operating state;
c. system documents and history data: collecting historical operation logs of the system, and knowing the behavior of the system in normal operation and fault occurrence; this helps to take into account typical operating conditions of the system when building a fault diagnosis model; acquiring detailed technical parameters of the equipment by referring to manuals and specifications provided by equipment manufacturers; this is critical for establishing accurate tables of electrical device parameters.
In general, the key of the S1 stage is to ensure that the acquired basic information is accurate and complete, so that the subsequent steps can perform reliable multi-line fault analysis and diagnosis based on real data; in addition, the updating and maintenance of information are also important links in the continuous operation of the system so as to ensure the accuracy and the practicability of the fault diagnosis model.
S2, establishing a multi-line data acquisition coordinate set according to basic information of the electric power multi-line system; the multi-line data acquisition coordinate set consists of coordinate nodes of all components in the electric power multi-line system;
s2, establishing a multi-line data acquisition coordinate set, specifically determining coordinate nodes of all components in the electric power multi-line system, and providing a basis for subsequent real-time acquisition and fault diagnosis; the purpose of establishing a multi-line data acquisition coordinate set is to integrate the information in the form of coordinate nodes so as to acquire the operation parameter heterogeneous vectors of each coordinate node in real time when the system operates; the method specifically comprises the following steps:
s21, analyzing a line topological structure: the connection relation among all the lines is determined by analyzing the line topology structure of the electric power multi-line system, and the connection relation comprises the layout and the connection modes of components such as a generator, a transformer, a switch device and the like;
S22, defining a coordinate node: abstracting each component in the power system into coordinate nodes, wherein each node represents a specific power equipment or key point; these nodes may be defined by physical location of the components, electrical properties, etc.;
s23, establishing a multi-line data acquisition coordinate set: integrating the determined coordinate nodes into a multi-line data acquisition coordinate set; this coordinate set should include all important power components in the system, ensuring that the operating state of the system can be monitored comprehensively; the establishment of the coordinate set should be system specific and needs to be flexibly designed according to actual conditions;
s24, considering real-time performance and precision: when a coordinate set is established, the balance of instantaneity and precision is required to be considered; the number and choice of coordinate nodes should be able to provide enough information to support fault diagnosis, but not be too bulky to increase overhead;
s25, document recording: carrying out document recording on the established multi-line data acquisition coordinate set so as to prepare future reference and system maintenance; this helps ensure that teams understand the system architecture and update it if necessary.
Through line topology analysis (S21) and coordinate node definition (S22), comprehensive understanding and accurate abstraction of the power multi-line system are ensured, which is helpful for establishing a multi-line data acquisition coordinate set containing all important power components, so as to comprehensively monitor the running state of the system; by establishing a multi-line data acquisition coordinate set (S23), the system can acquire the operation parameter heterogeneous vectors of each coordinate node in real time during operation, which provides support for real-time monitoring and provides a basis for subsequent fault diagnosis, thereby helping to quickly identify and solve problems;
The definition (S22) of the coordinate nodes can be performed based on the physical position, the electrical property and the like of the components, so that the establishment of the multi-line data acquisition coordinate set has certain flexibility and adaptability; such a design allows for personalized customization according to the characteristics of the particular system, ensuring adequate consideration of the system architecture; step S24 emphasizes the balance of real-time and precision to be considered when the coordinate set is established, and by reasonably selecting the number and attribute of the coordinate nodes, the information is ensured to be enough to support fault diagnosis, and meanwhile, the system overhead possibly caused by too large coordinate set is avoided;
in general, step S2 establishes a multi-line data acquisition coordinate set by a systematic method, which provides a solid foundation for real-time monitoring and fault diagnosis of the electric power multi-line system, while taking into account the balance of flexibility, instantaneity and accuracy.
S3, acquiring a coordinate set based on multi-line data, and acquiring operation parameters of the electric power multi-line system in real time to obtain operation parameter heterogeneous vectors of each coordinate node; the operation parameter heterogeneous vector consists of current, voltage, frequency and temperature;
s3, acquiring operation parameters of the electric power multi-line system in real time based on a multi-line data acquisition coordinate set so as to obtain an operation parameter heterogeneous vector of each coordinate node; the method is one of key steps in the whole method, and involves accurately acquiring the real-time running state of the power system so as to provide necessary data support for subsequent fault analysis;
S31, defining a multi-line data acquisition coordinate set: defining a multi-line data acquisition coordinate set, namely determining coordinate nodes of all components in the electric power multi-line system, wherein each device comprises a unique identifier in the whole system, and comprises a generator, a transformer, a switching device and the like;
s32, definition of operation parameter heterogeneous vectors: determining components of the operating parameter isomerism vector; in an electrical power system, typical operating parameters include current, voltage, frequency, and temperature; for each coordinate node, it is necessary to determine which specific parameters to collect to construct an operating parameter heterogeneous vector;
s33, a data acquisition method: selecting proper sensors and measuring equipment to acquire the operation parameters of each coordinate node of the power system in real time, wherein the sensors are installed on key equipment, and a remote monitoring system is used for data acquisition and the like;
s34, time sequence of data acquisition: determining the time interval of data acquisition to ensure frequent enough data update, and simultaneously avoiding data redundancy, wherein the time-series data acquisition is beneficial to capturing the dynamic change of the system;
s35, assembling a heterogeneous vector: for each coordinate node, parameters such as current, voltage, frequency and temperature acquired in real time are assembled into heterogeneous vectors, units of different parameters are unified, and data consistency is ensured.
By defining a multi-line data acquisition coordinate set, the system can accurately acquire the real-time running state of each component in the power system; the method provides accurate and timely data support for subsequent fault analysis, and is beneficial to early discovery and problem solving; by determining unique identification for each power system component (such as a generator, a transformer, a switching device, etc.), the uniqueness of each device in the system is ensured, thereby avoiding information confusion and erroneous collection; by defining the component parts of the heterogeneous vector of the operation parameters, the system can flexibly select the parameters to be acquired according to actual requirements; the flexibility enables the system to acquire different operation parameters according to specific scenes, and improves the applicability of the method;
by selecting proper sensors and measuring equipment in the data acquisition method, the accuracy and the reliability of the data are ensured; this helps the system obtain true, trusted operating parameter data; by determining the time intervals for data acquisition, the system can update the data on a frequent enough basis to capture dynamic changes in the system; meanwhile, the time sequence of data acquisition is reasonably controlled, so that data redundancy is avoided, and the effective utilization rate of the data is improved;
The parameters such as current, voltage, frequency and temperature acquired in real time are assembled into the heterogeneous vector, units of different parameters are unified, and data consistency is guaranteed; such consistency helps to simplify the complexity of data processing and analysis; the system can effectively capture the dynamic change of the power system through time sequence data acquisition and heterogeneous vector assembly; the method has important significance in the aspects of real-time monitoring, fault prediction and the like;
in general, the design of step S3 takes into account a number of key factors, including the accuracy, flexibility, reliability and sensitivity to dynamic changes of the system, making it an indispensable key step in the overall process.
S4, arranging and converting the heterogeneous vectors of the operation parameters with the same acquisition time and different coordinate nodes according to the position relation of the coordinate nodes to obtain a system operation state feature matrix;
s4, arranging and converting heterogeneous vectors of operation parameters with the same acquisition time and different coordinate nodes according to the position relation of the coordinate nodes to obtain a system operation state feature matrix; in the multi-line fault analysis and diagnosis of the power system, the step is to prepare data for processing by a subsequent multi-line fault diagnosis model; specifically, this step is achieved by the following sub-steps:
S41, data preprocessing: firstly, preprocessing collected operation parameter data; because the data may have noise, missing and other problems due to the factors of acquisition equipment, environment and the like, operations such as denoising, filling missing values and the like are needed; in addition, for some abnormal data, correction or elimination is also needed to ensure the accuracy and reliability of the data;
s42, feature extraction: after data preprocessing, extracting features reflecting the running state of the system from the running parameter heterogeneous vector of each coordinate node; these characteristics may include statistics (e.g., mean, variance, maximum, minimum, etc.) of parameters such as current, voltage, frequency, and temperature, and correlations between these parameters; this step may be implemented by various machine learning and data mining algorithms, such as principal component analysis, random forests, etc.;
s43, constructing a feature matrix: arranging the extracted features according to the position relation of the coordinate nodes to form a feature matrix; the matrix not only contains the operation parameter information of each coordinate node, but also reflects the relevance among different coordinate nodes;
S44, data conversion: in order to better reflect the operation state of the power multi-line system, the feature matrix may need to be converted; the conversion can be normalization and standardization of data, and can also be adjustment and optimization of a matrix structure; for example, the feature matrix can be subjected to dimension reduction processing by singular value decomposition and other methods so as to reduce the complexity of data and keep main information;
s45, abnormality detection: after feature extraction and data conversion, the data need to be subjected to anomaly detection; the anomaly detection herein may include anomaly detection of single point data (e.g., anomaly point detection based on 3 sigma principle) and anomaly detection of distribution of the whole data (e.g., using a hypothesis testing method); for abnormal data points, further processing and correction are required to ensure the accuracy and reliability of the data.
The accuracy and the reliability of the data can be improved by denoising the acquired data and processing the abnormal value, and the interference of the factors on the subsequent analysis is avoided; by using machine learning and data mining algorithms, features reflecting the running state of the system are extracted from the running parameters, so that the performance and state of the power multi-line system can be more comprehensively known;
The extracted features are arranged according to the position relation of the coordinate nodes to form a feature matrix, and the matrix not only contains the operation parameter information of each coordinate node, but also reflects the relevance among different coordinate nodes; the matrix can provide comprehensive data support for a subsequent multi-line fault diagnosis model; the operation state of the electric power multi-line system can be better reflected by carrying out data conversion on the feature matrix, such as normalization, standardization or dimension reduction processing, etc., the structure and the quality of the data are optimized, and the accuracy and the generalization capability of the model are improved; the operation state of the electric power multi-line system can be better reflected by carrying out data conversion on the feature matrix, such as normalization, standardization or dimension reduction processing, etc., the structure and the quality of the data are optimized, and the accuracy and the generalization capability of the model are improved;
in summary, the step S4 provides high-quality data support for the subsequent multi-line fault diagnosis model by performing comprehensive and accurate data preprocessing, feature extraction, feature matrix construction, data conversion and anomaly detection on the collected data, which is helpful for improving the accuracy and reliability of fault diagnosis.
S5, inputting the characteristic matrix of the system running state into a pre-constructed multi-line fault diagnosis model to obtain a system diagnosis information set; the input of the multi-line fault diagnosis model is a system running state feature matrix, the output of the multi-line fault diagnosis model is a system diagnosis information set, and the system diagnosis information set comprises a diagnosis result of each coordinate node;
in step S5, the establishment of a multi-line fault diagnosis model is a key part, which is a core component for analyzing an operation state feature matrix of the electric power multi-line system and providing fault diagnosis information; how to build the multi-line fault diagnosis model specifically comprises the following steps:
s51, preparing data: firstly, collecting operation data of the electric power multi-line system in different states, including normal operation data and fault data; these data may be obtained by an on-line monitoring system or by historical data records; for each state, enough samples should be collected to build up a representative dataset;
s52, constructing a model: using the collected data, constructing a machine learning model or a deep learning model; models that may be selected include, but are not limited to, neural networks, support vector machines, decision trees, and the like; the input of the model is a system running state feature matrix, and the output of the model is a system diagnosis information set;
S53, training a model: training the selected model using the prepared dataset; in this process, the parameters of the model need to be adjusted to optimize the performance of the model;
s54, verification and test: verifying and testing the model by using data which does not participate in training so as to ensure the accuracy and generalization capability of the model;
s55, deployment and monitoring: deploying the trained model into an electric power multi-line system, and monitoring the running state of the system in real time; for each new run state feature matrix, the model output is compared to the actual results to evaluate the performance of the model.
In addition, the system diagnostic information set is used as the output of a multi-line fault diagnosis model, and provides detailed information about the current state of the electric multi-line system; specifically, the system diagnostic information set generally includes the following:
diagnosis result of coordinate node: for each coordinate node, the system diagnosis information set should contain the diagnosis result of the node; this may be a classification tag indicating the status of the node, such as "normal" or a specific type of failure;
probability of failure or confidence: in addition to simple classification labels, the set of system diagnostic information may also include a probability or confidence assessment of each node state; this is helpful in assessing how confident the model is in diagnosing each node;
Fault type and description: for a failed node, the system diagnostic information set should provide detailed information about the type of failure; this may include a description of the specific type of fault (e.g., short circuit, open circuit, overload, etc.) and the fault;
timestamp: to track changes in system status, the system diagnostic information set typically contains a timestamp for each diagnostic result; this helps to analyze the state of the system at different points in time;
other relevant features: other relevant characteristics that lead to failure, such as information on current, voltage, temperature, etc.; this helps to more fully understand the operating conditions of the system;
suggested repair measures: in some cases, the set of system diagnostic information may also include suggested repair measures for each fault; such information is critical for the operation and maintenance personnel to take action quickly and solve the problem.
In the step, by a machine learning or deep learning method, the model can automatically learn and extract fault characteristics from a large amount of data without manually carrying out complex characteristic engineering; through sufficient training and verification, the model can accurately distinguish normal states and fault states, and faults of different types; the model can receive the running state characteristic matrix of the electric power multi-line system in real time as input and output a diagnosis result, so that fault monitoring and early warning can be performed in real time;
The model can adapt to different environments and conditions, and new conditions can be adapted by retraining or adjusting model parameters even when the running state of the system changes; the results of the model may provide diagnostic results for each coordinate node, which allows the results to be easily understood and interpreted, and the operator can clearly know at which coordinate node the fault occurred and the type of fault.
S6, traversing the system diagnosis information set, extracting coordinate nodes with faults and corresponding fault types in the system diagnosis information set, and sending prompts to operation and maintenance personnel;
s6, traversing the system diagnosis information set, extracting coordinate nodes with faults and corresponding fault types in the system diagnosis information set, and sending prompts to operation and maintenance personnel; the key of the design of the stage is to efficiently and accurately locate faults so as to take repairing measures in time, and the method is realized by the following steps:
s61, extracting fault coordinate nodes: traversing the system diagnosis information set, and extracting a certain coordinate node when the diagnosis result of the coordinate node is found to contain fault information; such fault information may include fault type, time of occurrence of the fault, severity of the fault, etc.;
S62, fault type identification: identifying a fault type according to the extracted fault information; this may require extensive knowledge and research of the characteristics of the various fault types, combined with the output of the diagnostic model and specific system operating conditions for comprehensive judgment;
s63, sending out a prompt: once the fault type and the corresponding coordinate node are identified, a prompt can be sent to an operation and maintenance personnel; these cues include the specific location of the fault, the type of fault, the possible cause of the fault, suggested solutions, etc.; the prompt may be communicated to the operation and maintenance personnel by email, text message, telephone, or other means;
s64, recording and reporting: meanwhile, fault information and corresponding processing measures are required to be recorded to form a detailed report; the report can be used as a basis for subsequent problem analysis and improvement, and also is beneficial to long-term tracking and analysis of the operation condition of the system.
In the step, through traversing the system diagnosis information, the coordinate node with faults can be rapidly and accurately positioned; this is critical to quick response and resolution of system faults, helping to shorten system downtime and improve system availability; through deep knowledge of the characteristics of various fault types and combination of the output of a diagnosis model and the actual system running condition, the fault types can be more accurately identified, and effective repair measures can be formulated;
Once the fault is located and identified, the system can immediately send a prompt to the operation and maintenance personnel; thus, the relevant responsible person can be notified rapidly, so that the responsible person can take appropriate measures, and the influence of faults on the system is reduced to the greatest extent; the prompt information can be transmitted to operation staff in various modes, such as e-mail, short message, telephone, etc.; the design considers the working habits of different operation and maintenance personnel and the way of receiving information, and improves the opportunity of timely response; recording fault information and processing measures to form a detailed report, which is helpful for analysis and improvement of post-matters; this provides a basis for long-term tracking and analysis of system operating conditions, helping to continuously optimize system stability and performance.
Example two
As shown in fig. 4, the system for analyzing and diagnosing a multi-line fault of the present invention specifically includes the following modules:
the information collection module is used for collecting basic information of the electric power multi-line system, wherein the basic information comprises an electric power line topological structure and electric parameters;
the system comprises a coordinate set establishing module, a power multi-line system and a power multi-line system, wherein the coordinate set establishing module is used for establishing a multi-line data acquisition coordinate set according to basic information of the power multi-line system, and the multi-line data acquisition coordinate set consists of coordinate nodes of all components in the power multi-line system;
The real-time data acquisition module is used for acquiring the coordinate set according to the multi-line data, acquiring the operation parameters of the electric power multi-line system in real time, and obtaining the operation parameter heterogeneous vector of each coordinate node, wherein the operation parameter heterogeneous vector consists of current, voltage, frequency and temperature;
the state characteristic matrix generation module is used for arranging and converting the heterogeneous vectors of the operation parameters with the same acquisition time and different coordinate nodes according to the position relation of the coordinate nodes to obtain a system operation state characteristic matrix;
the system comprises a fault diagnosis module, a fault diagnosis module and a coordinate node, wherein the fault diagnosis module is used for storing a multi-line fault diagnosis model, inputting a system operation state feature matrix into the multi-line fault diagnosis model to obtain a system diagnosis information set, wherein the input of the multi-line fault diagnosis model is the system operation state feature matrix, the output of the multi-line fault diagnosis model is the system diagnosis information set, and the system diagnosis information set comprises a diagnosis result of each coordinate node;
the fault information extraction module is used for traversing the system diagnosis information set, extracting coordinate nodes with faults in the system diagnosis information set and corresponding fault types, and sending the coordinate nodes and the corresponding fault types;
And the alarm module is used for receiving the coordinate node with the fault and the corresponding fault type, generating a corresponding alarm signal according to the coordinate node with the fault and the corresponding fault type and transmitting the alarm signal to alarm equipment.
In the embodiment, the system can acquire the operation parameters of the electric power multi-line system in real time, and then analyze the operation parameters through a multi-line fault diagnosis model to accurately identify the fault position and type; the burden of manual investigation can be reduced, and the accuracy and the speed of fault diagnosis are improved; due to the existence of the real-time data acquisition module, the system can timely capture the change of the system state, so that an operator can quickly respond to the fault condition, the possibility of system interruption is reduced, and the reliability of the system is improved;
the system can arrange and convert the collected operation parameter heterogeneous vectors according to the position relation of the coordinate nodes, so as to generate a system operation state feature matrix; this facilitates comprehensive analysis of the relationships between the different coordinate nodes, helping to more fully understand the state of the system; through the automatic alarm module, the system can timely inform operators of the coordinate node and the fault type of faults, so that the operators can take proper measures to maintain the system; this helps to improve the safety and reliability of the power system;
In general, the multi-line fault analysis and diagnosis system can improve the robustness and flexibility of the power system, reduce the risk of system interruption, improve the reliability and safety of the power system, and reduce the workload of manual investigation and diagnosis; these advantages are of great importance in ensuring stable operation of the power system.
The various modifications and embodiments of the multi-line fault analysis and diagnosis method in the first embodiment are equally applicable to the multi-line fault analysis and diagnosis system of the present embodiment, and those skilled in the art will be aware of the implementation method of the multi-line fault analysis and diagnosis system of the present embodiment through the foregoing detailed description of the multi-line fault analysis and diagnosis method, so that the detailed description thereof will not be repeated for brevity.
In addition, the application further provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for controlling output data are realized, and the same technical effects can be achieved, so that repetition is avoided and redundant description is omitted.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that it will be apparent to those skilled in the art that modifications and variations can be made without departing from the technical principles of the present invention, and these modifications and variations should also be regarded as the scope of the invention.
Claims (10)
1. A multi-line fault analysis and diagnosis method, wherein the method is applied to fault diagnosis of an electric power multi-line system, the method comprising:
collecting basic information of an electric power multi-line system, wherein the basic information comprises an electric power line topological structure and electric parameters;
establishing a multi-line data acquisition coordinate set according to basic information of the electric power multi-line system; the multi-line data acquisition coordinate set consists of coordinate nodes of all components in the electric power multi-line system;
acquiring a coordinate set based on multi-line data, and acquiring operation parameters of the electric power multi-line system in real time to obtain an operation parameter heterogeneous vector of each coordinate node;
arranging and converting heterogeneous vectors of operation parameters with the same acquisition time and different coordinate nodes according to the position relation of the coordinate nodes to obtain a system operation state feature matrix;
inputting the characteristic matrix of the system running state into a pre-constructed multi-line fault diagnosis model to obtain a system diagnosis information set; the input of the multi-line fault diagnosis model is a system running state feature matrix, the output of the multi-line fault diagnosis model is a system diagnosis information set, and the system diagnosis information set comprises a diagnosis result of each coordinate node;
Traversing the system diagnosis information set, extracting coordinate nodes with faults and corresponding fault types in the system diagnosis information set, and sending prompts to operation and maintenance personnel.
2. The multi-line fault analysis and diagnosis method according to claim 1, wherein the method for collecting the operation parameters of the power multi-line system in real time comprises the following steps:
determining the composition of an operating parameter isomerous vector, wherein the operating parameter isomerous vector consists of current, voltage, frequency and temperature;
installing measuring equipment on the power component corresponding to each coordinate node so as to acquire the operation parameters of the power component corresponding to each coordinate node of the power multi-line system in real time;
determining a time interval of data acquisition;
for each coordinate node, current, voltage, frequency and temperature parameters acquired in real time are assembled into heterogeneous vectors.
3. The multi-line fault analysis and diagnosis method according to claim 2, wherein the operation parameter heterogeneous vector is:
；
wherein I is nm Representing the current at the coordinate node m at acquisition time n; u (U) nm Representing the voltage at coordinate node m at acquisition time n; f (F) nm Representing the frequency of the coordinate node m at the acquisition time n; t (T) nm The temperature at the coordinate node m at acquisition time n is indicated.
4. The multi-line fault analysis and diagnosis method according to claim 1, wherein the method for establishing the multi-line data acquisition coordinate set according to the basic information of the power multi-line system comprises the steps of:
analyzing a circuit topology structure of the electric power multi-circuit system, and determining connection relations among all circuits, wherein the connection relations comprise layout and connection modes of a generator, a cable, a transformer and a switch equipment component;
abstracting each component in the power system into coordinate nodes, wherein each coordinate node represents a specific power device;
integrating the determined coordinate nodes into a multi-line data acquisition coordinate set; the multi-line data acquisition coordinate set comprises all power components in the system, so that the running state of the system can be comprehensively monitored;
and (4) carrying out document recording on the established multi-line data acquisition coordinate set for future reference and system maintenance.
5. The multi-line fault analysis and diagnosis method as claimed in claim 1, wherein the multi-line fault diagnosis model construction method comprises:
collecting operation data of the electric power multi-line system in different states, wherein the operation data comprise normal operation data and fault data;
Constructing a machine learning model, wherein the input of the machine learning model is a system running state feature matrix, and the output of the machine learning model is a system diagnosis information set;
performing data processing on the collected operation data to obtain a training data set;
training a machine learning model using the partial training data set, by adjusting parameters of the model to optimize performance of the model; the training data set which does not participate in training is used for verifying and testing the model so as to ensure the accuracy and generalization capability of the model;
deploying the trained model into an electric power multi-line system, and monitoring the running state of the system in real time; for each new run state feature matrix, the model output is compared to the actual results to evaluate the performance of the model.
6. The method for diagnosing a multi-line fault analysis as claimed in claim 1, wherein the method for extracting the coordinate node of the fault and the corresponding fault type from the system diagnosis information set comprises:
traversing the system diagnosis information set, and extracting the corresponding coordinate node when the diagnosis result of a certain coordinate node is found to contain fault information;
identifying a fault type according to the extracted fault information;
Once the fault type and the corresponding coordinate node are identified, a prompt is sent to operation and maintenance personnel; the prompt comprises a specific position of the fault, a fault type, a fault reason and suggested solving measures;
fault information and corresponding processing measures are recorded.
7. The multi-line fault analysis and diagnosis method according to claim 1, wherein the system diagnosis information set includes: diagnostic results of the coordinate nodes, probability of failure, description of failure type, time stamp, and repair measure suggestion.
8. A multi-line fault analysis diagnostic system, the system comprising:
the information collection module is used for collecting basic information of the electric power multi-line system, wherein the basic information comprises an electric power line topological structure and electric parameters;
the system comprises a coordinate set establishing module, a power multi-line system and a power multi-line system, wherein the coordinate set establishing module is used for establishing a multi-line data acquisition coordinate set according to basic information of the power multi-line system, and the multi-line data acquisition coordinate set consists of coordinate nodes of all components in the power multi-line system;
the real-time data acquisition module is used for acquiring the coordinate set according to the multi-line data, acquiring the operation parameters of the electric power multi-line system in real time, and obtaining the operation parameter heterogeneous vector of each coordinate node, wherein the operation parameter heterogeneous vector consists of current, voltage, frequency and temperature;
The state characteristic matrix generation module is used for arranging and converting the heterogeneous vectors of the operation parameters with the same acquisition time and different coordinate nodes according to the position relation of the coordinate nodes to obtain a system operation state characteristic matrix;
the system comprises a fault diagnosis module, a fault diagnosis module and a coordinate node, wherein the fault diagnosis module is used for storing a multi-line fault diagnosis model, inputting a system operation state feature matrix into the multi-line fault diagnosis model to obtain a system diagnosis information set, wherein the input of the multi-line fault diagnosis model is the system operation state feature matrix, the output of the multi-line fault diagnosis model is the system diagnosis information set, and the system diagnosis information set comprises a diagnosis result of each coordinate node;
the fault information extraction module is used for traversing the system diagnosis information set, extracting coordinate nodes with faults in the system diagnosis information set and corresponding fault types, and sending the coordinate nodes and the corresponding fault types;
and the alarm module is used for receiving the coordinate node with the fault and the corresponding fault type, generating a corresponding alarm signal according to the coordinate node with the fault and the corresponding fault type and transmitting the alarm signal to alarm equipment.
9. A multi-line fault analysis diagnostic electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps of the method according to any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311565423.3A CN117289085A (en) | 2023-11-22 | 2023-11-22 | Multi-line fault analysis and diagnosis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311565423.3A CN117289085A (en) | 2023-11-22 | 2023-11-22 | Multi-line fault analysis and diagnosis method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117289085A true CN117289085A (en) | 2023-12-26 |
Family
ID=89241115
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311565423.3A Pending CN117289085A (en) | 2023-11-22 | 2023-11-22 | Multi-line fault analysis and diagnosis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117289085A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572159A (en) * | 2024-01-17 | 2024-02-20 | 成都英华科技有限公司 | Power failure detection method and system based on big data analysis |
| CN117647706A (en) * | 2024-01-30 | 2024-03-05 | 山东昊能电力建设有限公司 | Intelligent power grid operation fault diagnosis system and method based on big data |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102735966A (en) * | 2012-06-12 | 2012-10-17 | 燕山大学 | Power transmission line evaluation and diagnosis system and power transmission line evaluation and diagnosis method |
| CN108614187A (en) * | 2018-04-10 | 2018-10-02 | 清华大学 | Power system oscillation source tracing method and system based on multi-modal synchronization phasor |
| CN111443259A (en) * | 2020-03-30 | 2020-07-24 | 国网山东省电力公司德州供电公司 | Active power distribution network fault diagnosis method and system based on local abnormal factor detection |
| CN111554074A (en) * | 2020-04-30 | 2020-08-18 | 安徽立卓智能电网科技有限公司 | Beidou data acquisition device with alarm function and repair reporting system |
| CN112284704A (en) * | 2020-10-19 | 2021-01-29 | 苏州容思恒辉智能科技有限公司 | Rotating equipment fault diagnosis method and system based on test matrix and readable storage medium |
| CN114355090A (en) * | 2022-01-13 | 2022-04-15 | 云南电网有限责任公司电力科学研究院 | Line loss analysis method, device and equipment based on power topology information acquisition system |
| CN115425760A (en) * | 2022-10-13 | 2022-12-02 | 国网山西省电力公司 | Digital power grid system based on digital twinning |
| CN115457591A (en) * | 2022-09-30 | 2022-12-09 | 国网天津市电力公司 | Identification method for topological relation of main wiring diagram of power grid station |
| WO2023071217A1 (en) * | 2021-10-27 | 2023-05-04 | 中国华能集团清洁能源技术研究院有限公司 | Multi-working-condition process industrial fault detection and diagnosis method based on deep transfer learning |
| CN116961215A (en) * | 2023-06-19 | 2023-10-27 | 蒋洁 | Rapid fault response processing method for power system |
| CN117060409A (en) * | 2023-10-13 | 2023-11-14 | 国网甘肃省电力公司白银供电公司 | Automatic detection and analysis method and system for power line running state |
-
2023
- 2023-11-22 CN CN202311565423.3A patent/CN117289085A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102735966A (en) * | 2012-06-12 | 2012-10-17 | 燕山大学 | Power transmission line evaluation and diagnosis system and power transmission line evaluation and diagnosis method |
| CN108614187A (en) * | 2018-04-10 | 2018-10-02 | 清华大学 | Power system oscillation source tracing method and system based on multi-modal synchronization phasor |
| CN111443259A (en) * | 2020-03-30 | 2020-07-24 | 国网山东省电力公司德州供电公司 | Active power distribution network fault diagnosis method and system based on local abnormal factor detection |
| CN111554074A (en) * | 2020-04-30 | 2020-08-18 | 安徽立卓智能电网科技有限公司 | Beidou data acquisition device with alarm function and repair reporting system |
| CN112284704A (en) * | 2020-10-19 | 2021-01-29 | 苏州容思恒辉智能科技有限公司 | Rotating equipment fault diagnosis method and system based on test matrix and readable storage medium |
| WO2023071217A1 (en) * | 2021-10-27 | 2023-05-04 | 中国华能集团清洁能源技术研究院有限公司 | Multi-working-condition process industrial fault detection and diagnosis method based on deep transfer learning |
| CN114355090A (en) * | 2022-01-13 | 2022-04-15 | 云南电网有限责任公司电力科学研究院 | Line loss analysis method, device and equipment based on power topology information acquisition system |
| CN115457591A (en) * | 2022-09-30 | 2022-12-09 | 国网天津市电力公司 | Identification method for topological relation of main wiring diagram of power grid station |
| CN115425760A (en) * | 2022-10-13 | 2022-12-02 | 国网山西省电力公司 | Digital power grid system based on digital twinning |
| CN116961215A (en) * | 2023-06-19 | 2023-10-27 | 蒋洁 | Rapid fault response processing method for power system |
| CN117060409A (en) * | 2023-10-13 | 2023-11-14 | 国网甘肃省电力公司白银供电公司 | Automatic detection and analysis method and system for power line running state |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572159A (en) * | 2024-01-17 | 2024-02-20 | 成都英华科技有限公司 | Power failure detection method and system based on big data analysis |
| CN117572159B (en) * | 2024-01-17 | 2024-03-26 | 成都英华科技有限公司 | Power failure detection method and system based on big data analysis |
| CN117647706A (en) * | 2024-01-30 | 2024-03-05 | 山东昊能电力建设有限公司 | Intelligent power grid operation fault diagnosis system and method based on big data |
| CN117647706B (en) * | 2024-01-30 | 2024-04-05 | 山东昊能电力建设有限公司 | Intelligent power grid operation fault diagnosis system and method based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zaher et al. | Online wind turbine fault detection through automated SCADA data analysis | |
| CN117289085A (en) | Multi-line fault analysis and diagnosis method and system | |
| US11348023B2 (en) | Identifying locations and causes of network faults | |
| CN107203816B (en) | Fault maintenance method and system for secondary equipment of power system | |
| US10444746B2 (en) | Method for managing subsystems of a process plant using a distributed control system | |
| CN108010305B (en) | Self-diagnosis method for data acquisition fault of integrated energy management platform | |
| CN112905371B (en) | Software change checking method and device based on heterogeneous multi-source data anomaly detection | |
| CN114255784A (en) | Substation equipment fault diagnosis method based on voiceprint recognition and related device | |
| EP3968479A1 (en) | Systems and methods for automatic power topology discovery | |
| CN110543658A (en) | Power plant equipment diagnosis method based on big data | |
| CN115877198A (en) | Primary and secondary fusion switch fault diagnosis early warning system based on edge calculation | |
| CN115081647A (en) | Industrial intelligent instrument fault pre-diagnosis method based on Bayesian network model | |
| CN117474357A (en) | Power distribution room operation and maintenance management method and system based on deep learning | |
| CN116595353A (en) | Remote fault diagnosis and intelligent decision system for camera | |
| CN112866839B (en) | Power failure warning device and method for ten-thousand-million passive optical network terminal equipment | |
| CN114726708A (en) | Network element equipment fault prediction method and system based on artificial intelligence | |
| CN113836203A (en) | Network data diagnosis detection analysis system | |
| Napier et al. | An automated fault analysis system for SP energy networks: Requirements, design and implementation | |
| McArthur et al. | Automated post-fault diagnosis of power system disturbances | |
| CN117907754B (en) | Power distribution network maintenance method and system based on fault analysis | |
| CN117272844B (en) | Method and system for predicting service life of distribution board | |
| CN116381419B (en) | Transmission line fault processing method, device, computer equipment and storage medium | |
| JP3858887B2 (en) | Failure judgment system and judgment method | |
| CN112104073B (en) | Real-time information calibration method for power system | |
| CN117541229A (en) | Fault operation and maintenance method and system for intelligent wind power |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |