CN109726200B - Power grid information system fault positioning system and method based on bidirectional deep neural network - Google Patents
Power grid information system fault positioning system and method based on bidirectional deep neural network Download PDFInfo
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
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Abstract
The invention discloses a system and a method for positioning faults of a power grid information system based on a bidirectional deep neural network, wherein the system comprises a fault monitoring module, an inference engine, a database and a deep learning module; and performing forward and reverse training on the fault tree analysis result to obtain a corresponding relation between the fault characteristics and the fault positions, storing the corresponding relation in an expert knowledge base, performing fault monitoring on the power grid information system, writing the fault characteristics obtained according to the monitoring data into a characteristic database, and reasoning to obtain a fault positioning result according to the expert knowledge base and the fault characteristics. The invention can effectively solve the problems of difficult positioning, slow positioning and inaccurate positioning of the power grid information system, so that the system can give an alarm at the first time when the fault occurs, and the fault self-processing system is combined to realize the intelligent positioning and intelligent processing functions of the fault of the information system, thereby further improving the operation and maintenance efficiency of the power grid information system and ensuring the safe and stable operation of the information system.
Description
Technical Field
The invention belongs to the technical field of information system fault diagnosis, and particularly relates to a system and a method for positioning power grid information system faults based on a bidirectional deep neural network.
Background
With the development of science and technology, information systems are increasingly widely used in enterprises. The primary deployment application systems such as the coordination office, the unified authority, the ERP and the like of the mesh network company reach 76 sets, and the number of core systems such as secondary deployment marketing, finance, production and the like is increased year by year. The information systems provide guarantees for production, operation and management of the power grid, and once the information systems fail, the information systems can have disastrous effects on the business of the power grid.
The power grid information system has the characteristics of high complexity, strong real-time performance, high dynamic performance, high safety requirement and the like, and meanwhile, the system machine is old, the performance is gradually reduced, so that the failure rate of the information system is improved year by year, the failure discovery time is late, the failure recovery period is long, and the reliability problem of the system is increasingly prominent. Although fault diagnosis is already in an automatic stage at present, the fault diagnosis still depends on the experience of fortune dimension experts and lacks intelligent brain management. Therefore, how to build intelligent fault diagnosis of the power grid information system by using the novel technology is particularly important, and fault positioning is the basis of fault diagnosis.
Fault location technology has evolved to today with many approaches to solving the location problem. The method is characterized by comprising a software fault positioning method based on a data chain, a fault positioning method combining a configuration management database with a fault tree, a fault positioning technology based on an immune algorithm and a fault positioning method based on a BP neural network and the fault tree.
The fault positioning method based on the BP neural network and the fault tree is provided for transformer faults, firstly, the method collects and sorts the information quantity of the transformer faults as training and identifying samples, establishes a transformer fault diagnosis model based on the BP neural network, and then utilizes a fault tree analysis method to divide the level, severity and the like of the transformer faults. However, compared with a transformer, the power grid information system is more complex, the possible positions of faults are changeable and difficult to remove, so that the new method is required to accurately position the faults according to the fault characteristics of the power grid information system.
The prior technical proposal is not specially designed for the fault location of the power grid information system, and is mainly aimed at a specific machine or a simple system. The existing fault positioning guarantee of the power grid information system mainly takes manpower and lacks an automatic means. The existing operation guarantee system takes no problem as a guide, mainly relies on people to conduct overall control, needs to spend a great deal of manpower to follow the fault, then conducts post-processing, and lacks an effective means for automatically conducting fault positioning through an information system. As the "nerve" of the national network company, the grid information system plays an increasingly important role, and the reliability requirement of the information system is also increasing. By using the existing technical scheme, the requirement of the power grid information system on reliability cannot be met, and the intelligent requirement of fault positioning processing of the information system cannot be met.
Meanwhile, the traditional BP neural network is an optimization method for local search, and has the problems of local minimization, slow convergence speed, inconsistent BP neural network structure selection, contradiction between an application instance and a network scale, contradiction between BP neural network prediction capability and training capability and BP neural network sample dependence.
Disclosure of Invention
The invention aims to: aiming at the problems, the invention provides a system and a method for positioning the faults of the power grid information system based on a bidirectional deep neural network, which are used for implementing monitoring analysis on the power grid information system so as to realize accurate positioning of the faults of the whole process of the power grid information system.
The technical scheme is as follows: in order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows: a power grid information system fault positioning system based on a bidirectional deep neural network comprises a fault monitoring module, an inference engine, a database and a deep learning module; the database comprises an expert knowledge base, a characteristic database and a position database, wherein the expert knowledge base stores the corresponding relation between fault characteristics and fault positions, the position database stores fault positions obtained by fault tree analysis of historical faults, and the characteristic database stores fault characteristics obtained by fault tree analysis of historical faults; the deep learning module performs forward and reverse training on the fault position and the fault characteristic obtained by analyzing the fault tree of the historical fault through a deep neural network to obtain a corresponding relation between the fault characteristic and the fault position, and stores the corresponding relation in an expert knowledge base; the fault monitoring module is used for carrying out real-time fault monitoring on the power grid information system to obtain fault characteristics of the information system faults; and the inference engine infers and obtains a fault positioning result according to the corresponding relation between the fault characteristics and the fault positions stored in the expert knowledge base and the fault characteristics of the information system fault.
Further, the fault positioning system also comprises a man-machine interface module, and the fault positioning result is fed back to operation and maintenance personnel in time and a fault alarm signal is sent out.
A power grid information system fault positioning method based on a bidirectional deep neural network comprises the following steps:
(1) Performing fault tree analysis of historical accidents of the power grid information system, storing fault positions obtained by the fault tree analysis in a position database, and storing fault characteristics in a characteristic database;
(2) Performing forward and reverse training on the fault tree analysis result by using a deep neural network to obtain a corresponding relation between fault characteristics and fault positions, and storing the corresponding relation in an expert knowledge base;
(3) Performing power grid information system fault monitoring to obtain fault characteristics of the information system faults;
(4) And deducing to obtain a fault positioning result according to the corresponding relation between the fault characteristics and the fault positions stored in the expert knowledge base and the fault characteristics of the information system faults.
Further, step 5 is included, through the constructed system man-machine interface of the fault positioning result, the fault positioning result is fed back to operation and maintenance personnel in time, and a fault alarm signal is sent out.
Further, the step 1 includes:
(1.1) providing a system architecture diagram according to the composition of the power grid information system and the relation among all parts;
(1.2) investigating system history faults and reasons;
(1.3) finding out accidents which have serious consequences and are more likely to occur from the historical faults as overhead events;
(1.4) from the top event, progressively finding out the event of the direct cause, and drawing out a fault tree according to the logic relation;
and (1.5) constructing a database based on historical data according to the analysis result of the fault tree, storing the fault position obtained by the analysis of the fault tree in a position database, and storing the fault characteristics in a characteristic database.
Further, the step 2 includes:
(2.1) extracting fault characteristics and position information according to a fault tree analysis result;
(2.2) performing forward propagation training of the deep neural network from the fault feature to the fault location;
(2.3) performing deep neural network back propagation training from the fault location to the fault feature;
and (2.4) storing the training results, the corresponding relation between the fault characteristics and the fault positions into an expert database.
The beneficial effects are that: the invention can effectively solve the problems of difficult positioning, slow positioning and inaccurate positioning of the power grid information system, so that the system can give an alarm at the first time when the fault occurs, and the fault self-processing system is combined to realize the intelligent positioning and intelligent processing functions of the fault of the information system, thereby further improving the operation and maintenance efficiency of the power grid information system and ensuring the safe and stable operation of the information system.
Drawings
FIG. 1 is a schematic diagram of a grid information system fault location system based on a bi-directional deep neural network.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, the system for positioning the fault of the power grid information system based on the bidirectional Deep Neural Network (DNN) and the fault tree comprises a fault monitoring module, an inference engine, a database, a deep learning module and a man-machine interface module.
The fault monitoring module is used for monitoring the real-time state of each component of the power grid information system, and the system monitoring software deployed at a hardware layer, a software layer, a network layer and an application layer is used for completing multi-granularity cross-layer joint sensing of the information system and providing real-time operation data of the system.
The inference engine is used for repeatedly carrying out inference according to the real-time monitoring data and the rules of the expert knowledge base to obtain a conclusion, which is a brain for realizing fault location, and the accuracy and the agility of fault location are determined. The reasoning is a process of analyzing and obtaining a result by collecting data according to a set rule, and a database and a reasoning machine are two cores of fault positioning.
The database comprises an expert knowledge base, a feature database and a position database, wherein the expert database is used for storing fault positioning rules of operation and maintenance experience and DNN training, namely the corresponding relation between fault features and fault positions; the feature database is used for storing fault features obtained by the power grid information system according to fault tree analysis and the information system fault features obtained by the fault monitoring module, and the position database is used for storing fault position points obtained by the power grid information system according to fault tree analysis.
The deep learning module comprises forward propagation training and backward propagation training for fault characteristics and position data of the information system, applies fault tree analysis and bidirectional Deep Neural Network (DNN), analyzes and self-learns the characteristic data given by the monitoring system, gives out fault positions and determines the fault source.
The man-machine interface module comprises a positioning result and a fault alarm and is used for informing operation and maintenance personnel of the fault positioning result and timely sending out a fault alarm signal. And feeding back a conclusion obtained by combining the result of DNN positive direction training by the inference engine to an operation and maintenance personnel through a man-machine interface, and determining the fault position through fault positioning and fault alarming.
As shown in fig. 1, the fault positioning method of the power grid information system based on the bidirectional deep neural network and the fault tree comprises the following steps:
step one, performing fault tree analysis of historical accidents of a power grid information system, storing fault positions obtained by the fault tree analysis in a position database, and storing fault characteristics in a characteristic database; according to the analysis result of the fault tree, constructing an expert knowledge base based on historical data;
s11, familiarizing with a power grid information system, and providing a system architecture diagram according to the system composition and the relation among the parts;
s12, investigating historical faults and reasons of the system, collecting accident cases, carrying out accident statistics, and assuming possible accidents of a given system, investigating all cause events and various factors related to the accidents;
s13, determining a top event: the object to be analyzed is the overhead event, the investigated accidents are comprehensively analyzed, and the accidents with serious consequences and easy occurrence are found out from the accidents to be used as the overhead event;
s14, drawing a fault tree: from the top event, gradually finding out the event of the direct cause until the depth to be analyzed, and drawing out a fault tree according to the logic relation;
s15, constructing a database based on historical data according to the analysis result of the fault tree.
Step two, monitoring faults of the power grid information system, and writing monitoring data into a characteristic database;
s21, the monitoring system is responsible for collecting various probe data and generating an alarm according to a monitoring rule;
s22, writing the monitoring data into a characteristic database;
and S23, summarizing the alarm information according to the rule and formatting the alarm information.
Step three, forward and reverse training of the deep neural network of fault characteristics and position information is carried out, and a training result is output to a fault inference engine;
s31, extracting characteristics and position information from a fault characteristic database and a fault position database of the power grid information system;
s32, performing forward propagation training of the deep neural network from the fault characteristics to the fault position, and calculating the output of the next layer by using the output of the previous layer, namely, a forward propagation algorithm of the deep neural network;
s33, performing deep neural network back propagation training from the fault position to the fault feature, and selecting a loss function to measure the loss between the output calculated by the training sample and the real training sample output before performing a deep neural network back propagation algorithm. In DNN, the most common process of optimizing extremum solving of the loss function is to carry out iterative solving through a gradient descent method, and the process of carrying out iterative optimizing to solve the minimum value on the loss function of DNN through the gradient descent method is a back propagation algorithm.
S34, outputting the training result to a fault inference engine.
Step four, a fault inference engine is realized, and fault characteristics and fault positions of the power grid information system are analyzed;
s41, obtaining a fault positioning reasoning rule according to historical data of the power grid information system and fault tree analysis;
s42, combining the expert knowledge base and fault characteristics, and obtaining a fault positioning result through reasoning.
And fifthly, constructing a system man-machine interface of fault locating features and results.
The man-machine interface is constructed so as to assist in visualizing the fault positioning result, the fault positioning result can be directly displayed on the client terminal, and related responsible persons can be timely notified through short messages or other modes.
The invention can effectively solve the problems of difficult positioning, slow positioning and inaccurate positioning of the power grid information system, so that the system can give an alarm at the first time when the fault occurs, and the fault self-processing system is combined to realize the intelligent positioning and intelligent processing functions of the fault of the information system, thereby further improving the operation and maintenance efficiency of the power grid information system and ensuring the safe and stable operation of the information system. The quality and efficiency of fault diagnosis of the information system can be improved by one level on the basis of manual fault removal, the workload of operation and maintenance personnel can be reduced, and the intelligent fault positioning diagnosis of the power grid information system is realized. Meanwhile, the application of the method accelerates the pace of technological innovation of national network companies, improves the technological level and the management level of informationized construction of power grid enterprises, improves the information service level of the power grid enterprises, and further improves the enterprise image of the power grid enterprises.
Claims (4)
1. The power grid information system fault positioning system based on the bidirectional deep neural network is characterized by comprising a fault monitoring module, an inference engine, a database and a deep learning module;
the database comprises an expert knowledge base, a characteristic database and a position database, wherein the expert knowledge base stores the corresponding relation between fault characteristics and fault positions, the position database stores fault positions obtained by fault tree analysis of historical faults, and the characteristic database stores fault characteristics obtained by fault tree analysis of historical faults;
the deep learning module performs forward and reverse training of the deep neural network on the fault position and the fault characteristic obtained by analyzing the fault tree of the historical fault to obtain a corresponding relation between the fault characteristic and the fault position;
the fault monitoring module is used for carrying out real-time fault monitoring on the power grid information system to obtain fault characteristics of the information system faults;
the inference engine infers and obtains a fault positioning result according to the corresponding relation between the fault characteristics and the fault positions and the fault characteristics of the information system faults;
the database comprises:
(1.1) providing a system architecture diagram according to the composition of the power grid information system and the relation among all parts;
(1.2) investigating system history faults and reasons;
(1.3) finding out accidents which have serious consequences and are more likely to occur from the historical faults as overhead events;
(1.4) from the top event, progressively finding out the event of the direct cause, and drawing out a fault tree according to the logic relation;
(1.5) constructing a database based on historical data according to the analysis result of the fault tree, storing the fault position obtained by the analysis of the fault tree in a position database, and storing the fault characteristics in a characteristic database;
the deep learning module includes:
(2.1) extracting fault characteristics and position information according to a fault tree analysis result;
(2.2) performing forward propagation training of the deep neural network from the fault feature to the fault location;
(2.3) performing deep neural network back propagation training from the fault location to the fault feature;
and (2.4) training to obtain the corresponding relation between the fault characteristics and the fault positions.
2. The system for positioning faults of a power grid information system based on a bidirectional deep neural network according to claim 1, further comprising a man-machine interface module, feeding back fault positioning results to operation and maintenance personnel in time and sending out fault alarm signals.
3. The fault positioning method of the power grid information system based on the bidirectional deep neural network is characterized by comprising the following steps of:
(1) Performing fault tree analysis of historical accidents of the power grid information system, storing fault positions obtained by the fault tree analysis in a position database, and storing fault characteristics in a characteristic database;
the step (1) comprises:
(1.1) providing a system architecture diagram according to the composition of the power grid information system and the relation among all parts;
(1.2) investigating system history faults and reasons;
(1.3) finding out accidents which have serious consequences and are more likely to occur from the historical faults as overhead events;
(1.4) from the top event, progressively finding out the event of the direct cause, and drawing out a fault tree according to the logic relation;
(1.5) constructing a database based on historical data according to the analysis result of the fault tree, storing the fault position obtained by the analysis of the fault tree in a position database, and storing the fault characteristics in a characteristic database;
(2) Training a bidirectional deep neural network on the analysis result of the fault tree to obtain a corresponding relation between fault characteristics and fault positions;
the step (2) comprises:
(2.1) extracting fault characteristics and position information according to a fault tree analysis result;
(2.2) performing forward propagation training of the deep neural network from the fault feature to the fault location;
(2.3) performing deep neural network back propagation training from the fault location to the fault feature;
training to obtain a corresponding relation between the fault characteristics and the fault positions;
(3) Performing power grid information system fault monitoring to obtain fault characteristics of the information system faults;
(4) According to the corresponding relation between the fault characteristics and the fault positions and the fault characteristics of the information system faults, a fault positioning result is obtained in a reasoning mode;
and (5) feeding back the fault positioning result to operation and maintenance personnel in time through a constructed system man-machine interface of the fault positioning result, and sending out a fault alarm signal.
4. A method for locating a fault in a grid information system based on a bi-directional deep neural network according to claim 3, wherein the correspondence between the fault signature and the fault location is stored in an expert knowledge base.
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| CN110766143A (en) * | 2019-10-31 | 2020-02-07 | 上海埃威航空电子有限公司 | Equipment fault intelligent diagnosis method based on artificial neural network |
| CN111060815B (en) * | 2019-12-17 | 2021-09-14 | 西安工程大学 | GA-Bi-RNN-based high-voltage circuit breaker fault diagnosis method |
| CN111259947A (en) * | 2020-01-13 | 2020-06-09 | 国网浙江省电力有限公司信息通信分公司 | Power system fault early warning method and system based on multi-mode learning |
| CN111291245A (en) * | 2020-02-17 | 2020-06-16 | 广东电网有限责任公司电力科学研究院 | Case online generation system and method applied to PAS and computer equipment |
| CN111666978B (en) * | 2020-05-11 | 2023-12-01 | 深圳供电局有限公司 | Intelligent fault early warning system for IT system operation and maintenance big data |
| CN112630595A (en) * | 2020-12-09 | 2021-04-09 | 国网冀北电力有限公司 | Power grid fault treatment knowledge base management method |
| CN112929977B (en) * | 2021-02-10 | 2022-05-31 | 山西大学 | Deep learning amplification forwarding cooperative network energy efficiency resource allocation method |
| CN113283462B (en) * | 2021-03-24 | 2022-09-20 | 国网四川省电力公司电力科学研究院 | Secondary system fault positioning method based on improved IDNN model |
| CN113222140B (en) * | 2021-05-10 | 2022-09-20 | 重庆邮电大学 | C4.5 algorithm and BP neuron-based power distribution network fault auxiliary decision-making method |
| CN113721579A (en) * | 2021-07-08 | 2021-11-30 | 河北工业大学 | Loom fault diagnosis method based on fusion of expert system and neural network algorithm |
| CN116827764B (en) * | 2023-08-23 | 2023-11-03 | 山西绿柳科技有限公司 | Internet of things fault detection control method and system based on neural network |
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