CN107992959A - A kind of power failure Forecasting Methodology based on electric power big data visualization Neural Network Data digging technology - Google Patents

A kind of power failure Forecasting Methodology based on electric power big data visualization Neural Network Data digging technology Download PDF

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CN107992959A
CN107992959A CN201710283241.5A CN201710283241A CN107992959A CN 107992959 A CN107992959 A CN 107992959A CN 201710283241 A CN201710283241 A CN 201710283241A CN 107992959 A CN107992959 A CN 107992959A
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electric power
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洪建光
孔晓昀
陈立跃
汪自翔
上官琳琳
刘周斌
张彩友
黄海潮
王志强
刘鸿宁
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Zhejiang Hua Yun Electric Industry Group Co Ltd
State Grid Zhejiang Electric Power Co Ltd
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Zhejiang Hua Yun Electric Industry Group Co Ltd
State Grid Zhejiang Electric Power Co Ltd
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

A kind of power failure Forecasting Methodology based on electric power big data visualization Neural Network Data digging technology, including an electric power large database concept, a data mining pretreatment and visualization processing module, a visualization BP neural network data-mining module, result output module composition, it is this that failure predication is realized with graphical Neural Network Data digging technology, reduce electric power big data and use difficulty, improve service efficiency.

Description

A kind of electric power event based on electric power big data visualization Neural Network Data digging technology Hinder Forecasting Methodology
Technical field
It is especially a kind of to be based on the big number of electric power the present invention relates to a kind of power failure Forecasting Methodology based on electric power big data According to the power failure Forecasting Methodology of visualization BP neural network data mining technology.
Background technology
During power generation, power transmission and distribution, mass data can be produced, becomes electric power big data, these data packets contain The various information of electric system, it is all particularly significant to Operation of Electric Systems, scheduling, maintenance, fault diagnosis, prediction.At present, electric power Collection, analysis and the use of big data are extremely limited, the stage analyzed and processed greatly all in artificial treatment or with simple software, consumption Take a large amount of manpower and materials, use is cumbersome, and efficiency is very low, effect unobvious.Meanwhile the harm that power failure is brought is very huge Greatly, power failure Predicting Technique very imperfection.Electric power big data be conveniently used for power failure prediction to theory analysis with Practical application is all of great significance.
The content of the invention
The present invention seeks to build a kind of electric power based on electric power big data visualization BP neural network data mining technology Failure prediction method, solves the problems, such as that electric power big data utilization ratio is low, power failure prediction is difficult.
Technical solution is used by the present invention solves the technical problem:
A kind of power failure Forecasting Methodology based on electric power big data visualization Neural Network Data digging technology, including one Electric power large database concept, a data mining pretreatment and visualization processing module, a visualization BP neural network data-mining module, One result output module forms.
It is preferred that an electric power large database concept, is used for:Electric power data is provided, is the basis for carrying out data mining analysis.Specific bag Two aspect data are included, are on the one hand electrical nature parameters, including project, equipment, assets, goods and materials, personnel, customer information;It is another Aspect is operation power data, including operating parameter over the years, including power, voltage, electric current, power angle etc. change over time number According to, and break down in operational process and fault type data.
It is preferred that a data mining pretreatment and visualization processing module, are used for:
Data type conversion is carried out, electrical nature parameter is into line label, operation power data conversion into floating number;
Carry out data cleansing, default value processing and data integration, hence it is evident that mistakes and omissions data are weighted average completion;
Carry out correlation significance parameter setting;
The correlation analysis of historical data and different types of n kinds failure is carried out, calculates correlation significance;
According to down time distance, filter out the electrical nature parameter more than significance (implying for 0.05) And operating parameter m, obtain the parameter that n kinds fault type exceedes correlation significance, composition of vector X:(x1,x2…, xm).The nearlyer data dependence of distance is stronger, and the correlation time maximum for taking this group of parameter is boundary, takes out P groups (X1,T1,S1;X2, T2,S2;…;Xp,Tp,Sp) it is used as sample data, Xi, i=1,2 ... p are the parameter group filtered out, Ti, i=1,2 ... p, for correspondence Rate of breakdown, take the value between 0 to 1, Si, i=1,2 ... p are fault type, take 1 to the value between n;
The data of electrical nature parameter and operating parameter report based on 3D visualization techniques are carried out to show;
It is preferred that a visualization BP neural network data-mining module, is used for:Neural Network Data is provided with form of icons to dig Module is dug, including input parameter number, that is, first layer neuron number is set, the neutral net number of plies (implying for 2 layers) is set, in Interbed neuron number (implicit with input neuron number equal), output layer are two outputs, respectively fault rate, Failure occurrence type;
The characteristic function of each layer neuron is set;
It is X the strong parameter composition input of failure dependency, selects the P groups of its different time to input and exported with corresponding failure (X1,T1,S1;X2,T2,S2;…;Xp,Tp,Sp) it is used as sample, desired output T1,S1;T2,S2;…;Tp,Sp, to neutral net Decline learning algorithm according to BP gradients to be trained.
It is preferred that a result output module, is used for:Neural Network Data excavate intermediate result and end product report form or Person's graphic form exports;With storage, setting of printing and export;When difference is small between neutral net reality output and desired output (implied in step-up error for 0.1%), neural metwork training terminates, the strong parameter X of input fault correlation:(x1,x2... x) Current live value inputs neutral net, obtains probability and the type that may be broken down.
The present invention beneficial outcomes be:
A kind of power failure Forecasting Methodology based on electric power big data visualization Neural Network Data digging technology is provided, is adopted With can correlation screening technique, realize with the strong parameter of failure dependency, by easily building visualization Neural Network Data Digging system, passes through the strong P group supplemental characteristics (X of conventional historical failure correlation1,T1,S1;X2,T2,S2;…;Xp,Tp,Sp) make For sample input and desired output, study is trained to neutral net.After the completion of study, event is realized by input of current data Hinder the prediction of type and incidence.
It is an advantage of the invention that:Failure predication is realized with graphical Neural Network Data digging technology, reduces the big number of electric power According to using difficulty, service efficiency is improved.
Brief description of the drawings
Fig. 1 is data screening and correlation analysis schematic diagram
Fig. 2 is that visualization Neural Network Data excavates training process schematic diagram
Fig. 3 is prediction process schematic
Embodiment
Referring to the drawings 1, Fig. 2, Fig. 3:
It is clearer to discuss the present invention, with reference to the data screening and correlation analysis schematic diagram 1 and tool of the present invention Body embodiment Fig. 2, Fig. 3 is further described, it is necessary to illustrate, instantiation described herein is only used for explaining this hair It is bright, do not limit the present invention.
In Fig. 1, using the data mining pretreatment and visualization processing module 3 to the electric power big data 1 with it is described Conventional down time, type 2, such as it is 1,2,3 that line fault, circuit breaker failure, transformer fault are numbered respectively, is consulted Such fault time occurred in the past, carries out a period of time progress data dependence inspection from the near to the remote in time of failure, It is 0.05 to set correlation significance, wherein being there is failure numbering 1 of the time point more than 0.05 to have a related ginseng Number, failure numbering 2 have b relevant parameter, and failure numbering 3 has c relevant parameter, and all a+b+c=m parameters are denoted as Xm,;This Parameter carries out visualization output by the correlation significance output module 4, and output can be form or figure shape Formula;
In Fig. 2, in Xm, taking the relevant maximum duration parameter of all parameters to be limited for the time, take and temporally take P groups, with institute Corresponding fault type 1,2,3 and corresponding probability of malfunction, corresponding probability of malfunction are 1, other fault types are zero, form institute Desired output 6 is stated, as the sample input data 5, inputs the graphical function module 7 of the BP neural network data mining, The input number is carried out to the graphical function module 7 of the BP neural network data mining sets the 8, number of plies to set 9, BP nerves E-learning rule setting 10, inputs the sample input data 5, obtains output and is compared with the desired output 6, is pressed The connection weight between neutral net is changed according to BP neural network learning rules;By repetition learning, until reality output and institute The difference of desired output 6 is stated less than untill step-up error such as 0.1%;
In Fig. 3, using the real time data of selected parameter as the real time input data 12, input the training and complete god Through network 11, the prediction output result 13 is obtained, by obtaining the prediction output analysis of result 13 by the class that breaks down Type and probability of happening.
It can be seen from the above that the present invention can efficiently use electric power big data, and pass through Neural Network Data by visual pattern Digging technology is predicted typical fault.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, protection of the invention The concrete form for being not construed as being only limitted to embodiment and being stated of scope, protection scope of the present invention is also and in this area skill Art personnel conceive according to the present invention it is conceivable that equivalent technologies mean.

Claims (5)

1. a kind of power failure Forecasting Methodology based on electric power big data visualization Neural Network Data digging technology, including an electricity Power large database concept, a data mining pretreatment and visualization processing module, a visualization BP neural network data-mining module, one As a result output module forms.
A kind of 2. electric power event based on electric power big data visualization Neural Network Data digging technology according to claim 1 Hinder Forecasting Methodology, it is characterised in that the electric power large database concept, is used for:Electric power data is provided, is to carry out data mining analysis Basis.Two aspect data are specifically included, are on the one hand electrical nature parameters, including project, equipment, assets, goods and materials, personnel, visitor Family information;On the other hand be operation power data, including operating parameter over the years, including power, voltage, electric current, power angle etc. with Break down in time-variable data, and operational process and fault type data.
A kind of 3. electric power event based on electric power big data visualization Neural Network Data digging technology according to claim 1 Hinder Forecasting Methodology, it is characterised in that the data mining pretreatment and visualization processing module, are used for:
Data type conversion is carried out, electrical nature parameter is into line label, operation power data conversion into floating number;
Carry out data cleansing, default value processing and data integration, hence it is evident that mistakes and omissions data are weighted average completion;
Carry out correlation significance parameter setting;
The correlation analysis of historical data and different types of n kinds failure is carried out, calculates correlation significance;
According to down time distance, filter out electrical nature parameter and fortune more than significance (implying for 0.05) Row parameter m, obtains the parameter that n kinds fault type exceedes correlation significance, composition of vector X:(x1,x2…,xm).Away from Stronger from nearlyer data dependence, the correlation time maximum for taking this group of parameter is boundary, takes out P groups (X1,T1,S1;X2,T2, S2;…;Xp,Tp,Sp) it is used as sample data, Xi, i=1,2 ... p are the parameter group filtered out, Ti, i=1,2 ... p, are corresponding Rate of breakdown, takes the value between 0 to 1, Si, i=1,2 ... p are fault type, take 1 to the value between n;
The data of electrical nature parameter and operating parameter report based on 3D visualization techniques are carried out to show.
A kind of 4. electric power event based on electric power big data visualization Neural Network Data digging technology according to claim 1 Hinder Forecasting Methodology, it is characterised in that the visualization BP neural network data-mining module, is used for:Nerve is provided with form of icons Network data excavation module, including input parameter number, that is, first layer neuron number is set, set the neutral net number of plies (implicit For 2 layers), intermediate layer neuron number (implicit equal with input neuron number), output layer is two outputs, is respectively failure Probability of happening, failure occurrence type;
The characteristic function of each layer neuron is set;
It is X the strong parameter composition input of failure dependency, selects the P groups of its different time to input and export (X with corresponding failure1, T1,S1;X2,T2,S2;…;Xp,Tp,Sp) it is used as sample, desired output T1,S1;T2,S2;…;Tp,Sp, to neutral net according to BP gradients decline learning algorithm and are trained.
A kind of 5. electric power event based on electric power big data visualization Neural Network Data digging technology according to claim 1 Hinder Forecasting Methodology, it is characterised in that the result output module, is used for:
Neural Network Data excavates intermediate result and end product report form or graphic form output;
With storage, setting of printing and export;
When difference is less than step-up error (implying for 0.1%), neutral net instruction between neutral net reality output and desired output White silk terminates, the strong parameter X of input fault correlation:(x1,x2... current live value input neutral net x), obtains possibility The probability and type of failure.
CN201710283241.5A 2017-04-26 2017-04-26 A kind of power failure Forecasting Methodology based on electric power big data visualization Neural Network Data digging technology Pending CN107992959A (en)

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CN108898249A (en) * 2018-06-28 2018-11-27 鹿寨知航科技信息服务有限公司 A kind of electric network fault prediction technique
CN109768625A (en) * 2019-03-29 2019-05-17 国网山东省电力公司费县供电公司 A kind of electric system overhaul management terminal and method
CN110222452A (en) * 2019-06-14 2019-09-10 国网上海市电力公司 Oil-immersed transformer failure based on big data association mining deduces visualization system
CN110955746A (en) * 2019-10-22 2020-04-03 中国科学院信息工程研究所 Electromagnetic data collecting and processing device and method
CN112863007A (en) * 2021-03-01 2021-05-28 中车株洲电力机车有限公司 Fault early warning model of traction converter, modeling method, early warning method and early warning system
CN113159517A (en) * 2021-03-24 2021-07-23 国网浙江省电力有限公司宁波供电公司 Three-dimensional visual power grid operation data analysis system

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CN108898249A (en) * 2018-06-28 2018-11-27 鹿寨知航科技信息服务有限公司 A kind of electric network fault prediction technique
CN109768625A (en) * 2019-03-29 2019-05-17 国网山东省电力公司费县供电公司 A kind of electric system overhaul management terminal and method
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CN113159517A (en) * 2021-03-24 2021-07-23 国网浙江省电力有限公司宁波供电公司 Three-dimensional visual power grid operation data analysis system

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