CN116720324A - Traction substation key equipment fault early warning method and system based on prediction model - Google Patents
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Abstract
The application provides a traction substation key equipment fault early warning method and system based on a prediction model, and relates to the field of power supply and transformation. The method comprises the following steps: acquiring information data of key equipment information, fault types, fault modes, fault reasons and fault influence factors of power supply and transformation through an information acquisition technology; classifying information data obtained through an information acquisition technology by a clustering method, performing correlation analysis, and mining potential association relations; and identifying unconventional data and/or finding data of unconventional discrete points for statistical analysis through data influence characteristics of the information data in the association process. In order to effectively improve operation and inspection efficiency, save operation and maintenance cost and reduce total workload of maintenance staff, research and development of a power supply and transformation critical equipment fault early warning prediction method are carried out, intelligent diagnosis and autonomous comprehensive state evaluation of traction power supply and transformation critical equipment are realized, and maintenance decisions are made according to equipment states.
Description
Technical Field
The application relates to the field of power supply and transformation, in particular to a traction substation key equipment fault early warning method and system based on a prediction model.
Background
With the development of the motor and electric appliance manufacturing industry, the electronic industry and the electric industry, the electrified railway transportation is widely valued by all countries around the world with huge economic benefits, so that the electric energy is rapidly developed, and the traction power supply refers to a power supply mode of electric energy required by dragging vehicles for transportation; the traction power supply system is an infrastructure and an important component of the electrified railway and provides power energy for the train, and the running reliability of the traction power supply system is directly related to the reliability of the electrified railway. Due to the complexity of the operation environment and the operation conditions, faults of traction power supply transformation equipment, contact networks and the like are unavoidable, the traction power supply system refers to a whole power supply system which is used for introducing 220 (110) KV power supply from a place to a railway and is used for reducing the voltage to 27.5KV through the traction power substation and sending the power supply to an electric locomotive, but the existing railway traction power supply is large in equipment and span, once the problems occur, the faults are difficult to detect, the problems occur in railway transportation, and the detection efficiency is improved.
Therefore, the fault early warning of the railway traction power supply equipment is an important problem in the railway traffic field. The complex and redundant workload of large quantity of on-site operation and tests and the like causes the lag in the production efficiency and the low economic benefit of the monitoring of the distribution network faults.
Disclosure of Invention
The application aims to provide a traction substation key equipment fault early warning method based on a prediction model, which can realize intelligent diagnosis and autonomous comprehensive state evaluation of traction power supply and transformation key equipment and make maintenance decisions according to equipment states.
The application further aims to provide a traction substation key equipment fault early warning system based on the prediction model, which can operate a traction substation key equipment fault early warning method based on the prediction model.
Embodiments of the present application are implemented as follows:
in a first aspect, an embodiment of the present application provides a traction substation key device fault early warning method based on a prediction model, which includes acquiring information data of power supply substation key device information, fault category, fault mode, fault cause and fault influence factor through an information acquisition technology; classifying information data obtained through an information acquisition technology by a clustering method, performing correlation analysis, and mining potential association relations; and identifying unconventional data and/or finding data of unconventional discrete points for statistical analysis through data influence characteristics of the information data in the association process.
In some embodiments of the application, the foregoing further comprises: and predicting the possible failure reasons and positions by testing the state parameters of the equipment, collecting and analyzing failure symptom data, and predicting the possible failure reasons and positions before failure occurs, wherein the failure prediction is performed by data driving, and/or the life model of the existing product is predicted by acquiring data through simulation and/or experiments.
In some embodiments of the present application, the performing fault prediction through data driving further includes: the fault prediction based on data driving is a fault recognition process through data acquisition, feature extraction and trend prediction, namely, the preparation of prediction sample data is completed through data acquisition, the training of a prediction model is completed through feature extraction of the prediction sample data, and finally, the fault trend prediction is carried out.
In some embodiments of the application, the foregoing further comprises: the reliability level of the whole system and each device is analyzed and evaluated, the residual life of the device is predicted, and various risk factors possibly brought by the external operation environment are combined to take differential protection measures in a targeted manner, wherein the method for analyzing and evaluating the reliability level of the whole system and each device comprises an analysis method, a Monte Carlo method and a mixing method.
In some embodiments of the application, the parsing method includes: and constructing a reliability model according to the functional relation among the system elements, synthesizing all fault states, analyzing the fault states, and finally obtaining the reliability index.
In some embodiments of the application, the monte carlo method described above includes: the random operation state of each element of the system is endowed by random sampling, and the required reliability index is calculated by using a probability statistical method.
In some embodiments of the application, the above mixing method comprises: the random running state of each element of the system is simulated by using the Monte Carlo method, and then the average duration of each state is calculated by using the analytic method, so that the sampling of the state time originally required is replaced, and the iterative convergence time of the Monte Carlo method is reduced while the operand of the analytic method is reduced.
In some embodiments of the application, the foregoing further comprises: and evaluating the technical risk of the project by using a triangular whitening weight function in the gray theory, and whitening the gray system with definite or whole insufficient information so as to obtain a correct risk evaluation result according to less information.
In a second aspect, an embodiment of the present application provides a traction substation key equipment fault early warning system based on a prediction model, which includes a main statistics sub-module, configured to obtain information data of power supply substation key equipment information, fault category, fault mode, fault cause, and fault influence factor through an information acquisition technology;
the correlation analysis module is used for classifying the information data acquired by the information acquisition technology through a clustering method, carrying out correlation analysis and mining potential association relations;
the time regression analysis module is used for identifying unconventional data and/or finding data of unconventional discrete points for statistical analysis through data influence characteristics of the information data in the association process;
the fault prediction module is used for predicting the possible fault reasons and positions by testing the state parameters of the equipment, collecting and analyzing fault symptom data and predicting the possible fault reasons and positions before the fault occurs, wherein the fault prediction is performed by data driving, and/or the life model of the existing product is built by acquiring data through simulation and/or experiments;
the reliability analysis module is used for analyzing and evaluating the reliability level of the whole system and each device, predicting the residual life of the device, and taking differential protection measures in a targeted manner by combining various risk factors possibly brought by the external operation environment, wherein the method for analyzing and evaluating the reliability level of the whole system and each device comprises an analysis method, a Monte Carlo method and a mixing method;
and the risk assessment module is used for assessing the technical risk of the project by utilizing the triangular whitening weight function in the gray theory and carrying out whitening treatment on the gray system with definite or whole insufficient information so as to obtain a correct risk assessment result according to less information.
In a third aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as any one of the traction substation critical equipment failure pre-warning methods based on a predictive model.
Compared with the prior art, the embodiment of the application has at least the following advantages or beneficial effects:
the method can convert the passive operation and maintenance of the current railway power supply into the active operation and maintenance, and realize intelligent diagnosis and autonomous comprehensive state evaluation of the traction power supply and transformation key equipment by researching the fault early warning prediction method of the traction power substation key equipment, and make maintenance decisions according to the equipment state.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of steps of a method for early warning faults of key equipment of a traction substation based on a prediction model according to an embodiment of the present application;
fig. 2 is a schematic diagram of a traction substation key equipment fault early warning system based on a prediction model according to an embodiment of the present application;
fig. 3 is an electronic device provided in an embodiment of the present application.
Icon: 101-memory; 102-a processor; 103-communication interface.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
It should be noted that the term "comprises," "comprising," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The various embodiments and features of the embodiments described below may be combined with one another without conflict.
Example 1
An important theoretical basis of the machine learning algorithm is classical statistics, and the main research content of the machine learning algorithm is progressive theory when the number of samples tends to infinity. However, the number of samples in a practical problem is often limited, and the inherent relevance of these samples is not known, and the cost of obtaining additional samples is typically high. Therefore, machine learning algorithms designed based on progressive theory when the number of samples tends to be infinite are used, and are not attractive when solving the practical problem of limited number of samples. In particular, when the number of samples is limited and the dimension of the sample space is extremely high (e.g., several thousand dimensions, hundreds of thousands of dimensions), the classical statistical based machine learning method is even more challenging.
The AT & TBell laboratory group of Vapnik and his leadership in the mid 90 th century has proposed a Support Vector Machine (SVM) learning algorithm based on statistical learning theory, kernel function mapping theory, which is a typical small sample machine learning algorithm that has been proposed to draw widespread attention of researchers in the field of machine learning with its excellent theoretical performance, good generalization performance.
Referring to fig. 1, fig. 1 is a schematic diagram of steps of a method for early warning a fault of a key device of a traction substation based on a prediction model according to an embodiment of the present application, which is as follows:
step S100, obtaining information data of power supply and transformation key equipment information, fault types, fault modes, fault reasons and fault influence factors through an information acquisition technology;
in some embodiments, the primary statistics include a number of faults, a number of parameter anomalies, a number of system reduced/disabled states, a fault rate, a first time to fault, an inter-fault time, a time to power interruption, a repair rate, a time to repair fault, and the like.
Taking a motor train unit as an example, the running state, fault information and maintenance conditions of the motor train unit in the whole life cycle can be recorded timely and completely by means of an information acquisition technology. The information provides basis for the diagnosis of the motor train unit faults, and the statistical analysis of the fault information provides assistance for the optimization of the repair process and repair of the motor train unit, the optimization of the types and the number of after-sales parts and the normal operation of motor train unit equipment. Through macroscopic statistical analysis of fault information, a worker can acquire time of multiple faults, multiple lines and multiple devices, and can also acquire main fault modes, fault reasons, fault influence factors and the like. The deep statistical analysis of the fault information can optimize the maintenance interval period of the equipment, adjust the maintenance content of each level of maintenance program and reasonably allocate maintenance resources.
In some embodiments, the power supply and transformation key equipment (traction transformer, high-voltage circuit breaker, gas insulated metal enclosed switchgear (GIS), lightning arrester, high-voltage cable, transformer and the like) and fault types can be definitely provided, and necessary acquisition data can be acquired through an operation and maintenance management unit or related departments.
Step S110, classifying information data obtained through an information acquisition technology by a clustering method, performing correlation analysis, and mining potential association relations;
in some embodiments, the correlation analysis includes various types of monitoring detection data, fault data, maintenance data, correlation analysis functions of the monitoring data in different time periods and different sections, and the like, for example, a clustering method is adopted to classify the data, and potential association relations are mined.
Step S120, identifying unconventional data and/or finding data of unconventional discrete points for statistical analysis through data influence characteristics of information data in the association process;
in some embodiments, the temporal regression analysis refers to: in the specified railway line, under different working conditions and environmental parameter conditions, a time regression function is provided, the change trend of the data can be judged, a user can clearly and correspondingly represent the evolution condition of the parameter or the equipment failure frequency according to the trend, and a basis is provided for failure early warning.
In some embodiments, historical data is obtained while at the same time, parameters affecting the data are sought, specialized data software such as SPSS is used, correlation analysis is employed, data affecting features of the correlation process of data parameters are utilized, unusual data is identified, or data finding unusual discrete points is calculated using a formula. When the acquired data volume is insufficient, an SVM algorithm can be adopted to compensate the influence that the data is insufficient and the conventional machine learning cannot process; an improved ash correlation analysis model may also be constructed.
The fault early warning is to automatically give early warning information to key components according to a fault prediction model and an early warning threshold value, and remind operators on duty in the modes of popup text windows, audible and visual warning and the like. When the equipment is in early stage of failure or in the latent period of failure, the hidden trouble of the failure is found in time, the future development trend of the failure is accurately predicted, and early warning and failure elimination are carried out in time before the failure result is expressed. The actual working mode of fault early warning, the current fault prediction model and how the early warning threshold value is determined need to be researched and known.
Step S130, carrying out fault prediction on possible fault reasons and parts before faults occur by testing state parameters of equipment, collecting and analyzing fault symptom data, wherein the fault prediction is carried out through data driving, and/or the life model of the existing product is built and predicted by acquiring data through simulation and/or experiments;
in some embodiments, different fault symptoms may be generated by different fault locations or causes, with some linear or nonlinear mapping between the fault symptoms and the fault causes or locations. The mapping relation is described by using an inference method or an applicable model, and the reasons and the parts of possible faults are predicted before the faults occur by testing the state parameters of the equipment, collecting and analyzing fault symptom data, so that a fault prediction and health management technology is formed.
In some embodiments, fault prediction may be achieved by two approaches: one is based on existing product life models, and one is based on historical data modeling. The former can acquire data through simulation or experiments, and the latter can be realized through data driving with wider application, namely the processes of data acquisition, feature extraction, trend prediction and fault identification:
in some embodiments, the actual application of the product is difficult to obtain the characteristic data of the whole life cycle, so that if a model with a complete cycle is needed to be obtained, 2 methods of simulation and experiment are available to obtain the data. The simulation cost is low, and verification of various fault injection can be realized through simulation, so that different response characteristic data of the product are obtained.
The simulation has the limitation that the research result depends on the accuracy degree of a model and the understanding depth of a failure mechanism of a product to a great extent, the current application is that a reliability experiment technology is adopted to develop a product accelerated life experiment with a certain sample size, the product performance degradation characteristic of the whole experiment process is monitored in real time, the fault prediction is carried out by using a similarity modeling-based method, and a common accelerated life experiment model comprises a temperature acceleration model and a temperature and humidity acceleration model.
In some embodiments, the prediction based on data driving is based on understanding and grasping the running state and state change rule of the system, and estimating the propagation and development of faults and the performance degradation trend of the system according to a certain prediction method by predicting the state change trend, which is an important means for accident prevention, maintenance and health management. The data-driven fault prediction method mainly utilizes historical working data, fault injection data, simulation experiment data and the like of equipment, performs trend prediction through various data analysis processing algorithms, and is a prediction method which is widely applied at present.
The fault prediction based on data driving is a process of data acquisition, feature extraction, trend prediction and fault identification, and mainly comprises the following 3 steps: prediction sample data preparation and prediction model training, trend prediction and trend prediction.
Step S140, analyzing and evaluating the reliability level of the whole system and each device, predicting the residual life of the device, and taking differential protection measures in a targeted manner by combining various risk factors possibly brought by the external operation environment, wherein the method for analyzing and evaluating the reliability level of the whole system and each device comprises an analysis method, a Monte Carlo method and a mixing method;
in some embodiments, from the perspective of long-term operation, the reliability level of the whole system and each device is analyzed and evaluated, the residual life of the device is predicted, and various risk factors possibly brought by the external operation environment are combined, so that differentiated protection measures are taken in a targeted manner. Probability theory and numerical statistics are frequently applied in reliability analysis, so that the derived evaluation methods mainly comprise methods such as an analysis method, a simulation method, a mixing method and the like:
in some embodiments, the analysis method mainly builds a reliability model according to the functional relationship among the system elements, synthesizes all fault states, and analyzes the fault states to finally obtain relevant reliability indexes. Its advantages are clear concept and high model precision. The disadvantage is that when applied to larger systems, implementation is more complex and difficult due to the need for one-to-one enumeration analysis of all fault conditions. In practical application, the original analysis method can be correspondingly improved according to different practical conditions, so that the aim of reducing the workload is fulfilled. The formed new method mainly comprises a minimum cut-set method, a minimum way set method, a state space cut-off method, a network equivalence method and the like.
In some embodiments, the simulation method may also be called a Monte Carlo method, and the principle is that the random operation states of each element of the system are given through random sampling, and the required reliability index is calculated by using a probability statistics method and the like. The method has the advantages of being easier to realize than an analytic method, and can be applied to a more complex system. The disadvantage is the slow convergence rate of the calculation and the large error.
In some embodiments, the central idea of the hybrid approach is to first simulate the random operating states of the various elements of the system using the monte carlo method, and then calculate the average duration of each state using the analytical method, instead of sampling the state time that was originally required. The method can reduce the operation amount of the analysis method and simultaneously reduce the iteration convergence time of the Monte Carlo method. (4) Artificial intelligence method.
In some embodiments, studies of fault data for a large number of different types of components have shown that the component fault rate curve is bathtub-shaped, so that an appropriate probability distribution (e.g., exponential, normal, and Weibull) distribution) can be selected, such as the fault rate curve; and one of an analysis method, an analog method or a mixed method is adopted on the basis of the components or the equipment, so that the reliability of the system level is established.
And step S150, evaluating the technical risk of the project by utilizing a triangular whitening weight function in the gray theory, and performing whitening treatment on the gray system with definite or integral information shortage so as to obtain a correct risk evaluation result according to less information.
In some embodiments, in order to improve the degree of intelligence in fault diagnosis, residual life prediction and maintenance decisions, many new techniques and methods are adopted, and since the new techniques lack a large amount of historical data as the basis for evaluation, most of them are based on human experience and knowledge. Thus, there is a certain "ash" in the assessment of technical risk.
In some implementations, the content is evaluated explicitly; the technical risk of the project is evaluated by utilizing a triangular whitening weight function in the gray theory, so that a gray system with less definite and insufficient overall information is whitened as much as possible, and a correct evaluation result is obtained according to less information.
Also included are maintenance aid decisions: by combining the functions, a reasonable maintenance strategy is comprehensively established, and the optimal maintenance period, the optimal time and mode of fault rush-repair and the selection of various state thresholds are determined.
In some embodiments, an optimized maintenance decision reference is provided from the point of view of reliability and maintenance costs of the system, depending on the state of health of the current system. And the optimal maintenance scheme is generated by optimizing parameters such as comprehensive maintenance period, maintenance mode, personnel allocation, planned maintenance times and the like by taking the lowest overall maintenance cost and the highest overall reliability of the system as optimization targets.
Specifically, on the basis of the previous research content, considering various dynamic and random properties in the maintenance activities, examining the multi-scale effects of the maintenance activities in time (such as urgent emergency maintenance, real-time performance of state maintenance, periodic maintenance and the like) and the multi-scale effects in space (such as component level, equipment level, subsystem level, system level maintenance and the like), establishing a multi-factor and multi-scale space-time joint decision model, and focusing on the decision problems in aspects of maintenance time, maintenance period, maintenance degree, maintenance mode and the like; the problems of optimization and optimal combination of research and maintenance decision variables such as a sensitivity analysis method, a nonlinear mathematical programming method and a multi-objective intelligent optimization algorithm are solved; analyzing various attributes of the traction power supply system in maintenance activities, reasonably distributing weights of the traction power supply system to form a maintenance influence factor set, establishing a maintenance library by combining spare parts, tools, work types and the like required by the maintenance activities, and finally providing a whole set of customized maintenance solutions for each traction power supply system with different structures and performances. By assisting the optimization decision section, the project team will provide generic algorithm programs and typical case verification results.
Example 2
Referring to fig. 2, fig. 2 is a schematic diagram of a traction substation key equipment fault early warning system based on a prediction model according to an embodiment of the present application, which is as follows:
the main statistics sub-module is used for acquiring information data of power supply and transformation key equipment information, fault types, fault modes, fault reasons and fault influence factors through an information acquisition technology;
the correlation analysis module is used for classifying the information data acquired by the information acquisition technology through a clustering method, carrying out correlation analysis and mining potential association relations;
the time regression analysis module is used for identifying unconventional data and/or finding data of unconventional discrete points for statistical analysis through data influence characteristics of the information data in the association process;
the fault prediction module is used for predicting the possible fault reasons and positions by testing the state parameters of the equipment, collecting and analyzing fault symptom data and predicting the possible fault reasons and positions before the fault occurs, wherein the fault prediction is performed by data driving, and/or the life model of the existing product is built by acquiring data through simulation and/or experiments;
the reliability analysis module is used for analyzing and evaluating the reliability level of the whole system and each device, predicting the residual life of the device, and taking differential protection measures in a targeted manner by combining various risk factors possibly brought by the external operation environment, wherein the method for analyzing and evaluating the reliability level of the whole system and each device comprises an analysis method, a Monte Carlo method and a mixing method;
and the risk assessment module is used for assessing the technical risk of the project by utilizing the triangular whitening weight function in the gray theory and carrying out whitening treatment on the gray system with definite or whole insufficient information so as to obtain a correct risk assessment result according to less information.
As shown in fig. 3, an embodiment of the present application provides an electronic device including a memory 101 for storing one or more programs; a processor 102. The method of any of the first aspects described above is implemented when one or more programs are executed by the processor 102.
And a communication interface 103, where the memory 101, the processor 102 and the communication interface 103 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules that are stored within the memory 101 for execution by the processor 102 to perform various functional applications and data processing. The communication interface 103 may be used for communication of signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 102 may be an integrated circuit chip with signal processing capabilities. The processor 102 may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method and system may be implemented in other manners. The above-described method and system embodiments are merely illustrative, for example, flow charts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
In another aspect, an embodiment of the application provides a computer readable storage medium having stored thereon a computer program which, when executed by the processor 102, implements a method as in any of the first aspects described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In summary, the method and the system for early warning faults of the traction substation key equipment based on the prediction model provided by the embodiment of the application can convert the current passive operation and maintenance of railway power supply into active operation and maintenance, realize intelligent diagnosis and autonomous comprehensive state evaluation of the traction power supply and transformation key equipment by researching the method for early warning faults of the traction substation key equipment, and make maintenance decisions according to equipment states.
The above is only a preferred embodiment of the present application, and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A traction substation key equipment fault early warning method based on a prediction model is characterized by comprising the following steps:
acquiring information data of key equipment information, fault types, fault modes, fault reasons and fault influence factors of power supply and transformation through an information acquisition technology;
classifying information data obtained through an information acquisition technology by a clustering method, performing correlation analysis, and mining potential association relations;
and identifying unconventional data and/or finding data of unconventional discrete points for statistical analysis through data influence characteristics of the information data in the association process.
2. The traction substation key equipment fault early-warning method based on the prediction model as claimed in claim 1, further comprising:
and predicting the possible failure reasons and positions by testing the state parameters of the equipment, collecting and analyzing failure symptom data, and predicting the possible failure reasons and positions before failure occurs, wherein the failure prediction is performed by data driving, and/or the life model of the existing product is predicted by acquiring data through simulation and/or experiments.
3. The method for pre-warning faults of key equipment of a traction substation based on a prediction model as claimed in claim 2, wherein the step of performing fault prediction through data driving further comprises the steps of:
the fault prediction based on data driving is a fault recognition process through data acquisition, feature extraction and trend prediction, namely, the preparation of prediction sample data is completed through data acquisition, the training of a prediction model is completed through feature extraction of the prediction sample data, and finally, the fault trend prediction is carried out.
4. The traction substation key equipment fault early-warning method based on the prediction model as claimed in claim 2, further comprising:
the reliability level of the whole system and each device is analyzed and evaluated, the residual life of the device is predicted, and various risk factors possibly brought by the external operation environment are combined to take differential protection measures in a targeted manner, wherein the method for analyzing and evaluating the reliability level of the whole system and each device comprises an analysis method, a Monte Carlo method and a mixing method.
5. The method for early warning of faults of key equipment of a traction substation based on a prediction model as claimed in claim 4, wherein the analysis method comprises the following steps:
and constructing a reliability model according to the functional relation among the system elements, synthesizing all fault states, analyzing the fault states, and finally obtaining the reliability index.
6. The traction substation key equipment fault early-warning method based on a prediction model as claimed in claim 4, wherein the monte carlo method comprises:
the random operation state of each element of the system is endowed by random sampling, and the required reliability index is calculated by using a probability statistical method.
7. The traction substation key equipment fault early-warning method based on a prediction model as claimed in claim 4, wherein the mixing method comprises:
the random running state of each element of the system is simulated by using the Monte Carlo method, and then the average duration of each state is calculated by using the analytic method, so that the sampling of the state time originally required is replaced, and the iterative convergence time of the Monte Carlo method is reduced while the operand of the analytic method is reduced.
8. The traction substation key equipment fault early-warning method based on the prediction model as claimed in claim 2, further comprising:
and evaluating the technical risk of the project by using a triangular whitening weight function in the gray theory, and whitening the gray system with definite or whole insufficient information so as to obtain a correct risk evaluation result according to less information.
9. A traction substation key equipment fault early warning system based on a prediction model is characterized by comprising:
the main statistics sub-module is used for acquiring information data of power supply and transformation key equipment information, fault types, fault modes, fault reasons and fault influence factors through an information acquisition technology;
the correlation analysis module is used for classifying the information data acquired by the information acquisition technology through a clustering method, carrying out correlation analysis and mining potential association relations;
the time regression analysis module is used for identifying unconventional data and/or finding data of unconventional discrete points for statistical analysis through data influence characteristics of the information data in the association process;
the fault prediction module is used for predicting the possible fault reasons and positions by testing the state parameters of the equipment, collecting and analyzing fault symptom data and predicting the possible fault reasons and positions before the fault occurs, wherein the fault prediction is performed by data driving, and/or the life model of the existing product is built by acquiring data through simulation and/or experiments;
the reliability analysis module is used for analyzing and evaluating the reliability level of the whole system and each device, predicting the residual life of the device, and taking differential protection measures in a targeted manner by combining various risk factors possibly brought by the external operation environment, wherein the method for analyzing and evaluating the reliability level of the whole system and each device comprises an analysis method, a Monte Carlo method and a mixing method;
and the risk assessment module is used for assessing the technical risk of the project by utilizing the triangular whitening weight function in the gray theory and carrying out whitening treatment on the gray system with definite or whole insufficient information so as to obtain a correct risk assessment result according to less information.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-8.
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