CN110503211A - Failure prediction method based on machine learning - Google Patents
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Abstract
Failure prediction method based on machine learning is related to power grid power supply and sets field, especially a kind of by data monitoring, adjustment, comparison, realizes that monitoring device voluntarily learns to adjust, the failure prediction method based on machine learning predicted line defct.The present invention includes data monitoring, comparing, failure prediction and prediction four modules of adjustment, the operation data of data monitoring module monitors supply network, it is analysed and compared by data comparing module, in comparison process, prediction data is corrected by prediction adjustment module, prediction data is analyzed, the exception that will be generated is sounded an alarm by failure prediction module.The present invention is by training supply network operation data to it, evolution algorithm is introduced simultaneously, realize analysis result being mutually authenticated with real-time running data, the error between analysis result and real time data for analyzing supply network data reaches tolerance interval, to use analysis modified result abnormal data or amended record missing data, the influence of data exception is solved.
Description
Technical field
The present invention relates to power grid power supplies to set field, especially a kind of by data monitoring, adjustment, comparison, realizes that monitoring is set
Standby voluntarily study adjustment, the failure prediction method based on machine learning that line defct is predicted.
Background technique
Currently, in a power supply network, including power supply unit and power supply line, it is all made of traditional maintenance mode, is such as carried out
Periodic maintenance, detection, it is when a failure occurs, then interim to carry out to the components of equipment or route by the way of periodic replacement
Maintenance replacement, but often there is certain equipment or route, because components are overhauled and replaced to failure, within a short period of time, again
Because periodic maintenance is overhauled, components are replaced again, so cause the waste of lot of materials, also result in the maintenance of power supply system
Cost is high, is not only unfavorable for energy conservation and environmental protection, and also cause the waste of manpower, when an unexpected situation occurs, causes not
It can repair in time, generate bigger loss.
Meanwhile in the power system, due to by conditions such as unit creep speed, power grid static security, whole society's total loads
Constraint, operation of power networks parameter can change in a certain range.When cataclysm occurs for operation of power networks parameter, illustrate that operation of power networks is deposited
It is often exactly overvoltage when the yielding of its voltage is raised and lowered if main transformer voltage is relative constant in substation in some problems
Or the generation of substation power loss event.
Summary of the invention
To be solved by this invention is exactly in existing supply network, using conventional maintenance mode, so that maintenance cost is high, no
Conducive to energy conservation and environmental protection, the problem of manpower wastes also will cause, provide one kind by data monitoring, adjustment, comparison, realize that monitoring is set
Standby voluntarily study adjustment, the failure prediction method based on machine learning that line defct is predicted.
Failure prediction method based on machine learning of the invention, it is characterised in that the prediction technique include data monitoring,
Comparing, failure prediction and prediction four modules of adjustment, the operation data of data monitoring module monitors supply network pass through
Data comparing module is analysed and compared, and in comparison process, is corrected prediction data by prediction adjustment module, is analyzed prediction data,
The exception that will be generated is sounded an alarm by failure prediction module, in which:
1) data monitoring module includes voltage monitoring and current monitoring, the real time data of monitoring power-supply networks network in the process of running,
Including current value and voltage value；
2) data comparing module records the real time data of the voltage and current of data monitoring module monitors, analyzed and is compared
Right, when recording data, using the period as parameter, when normal operating condition, the numerical value in daily each period of record takes it
Average value, as the reference point in the period
In above formula,For the sampled data in the daily same period, average value is calculated using week or the moon as the period；
Meanwhile the fluctuation numerical value in the period a cycle is recorded, which is gone out in a cycle by mean value calculation
Interior deviation and median, and maximum value and minimum value in the period are recorded, while calculating the frequency distribution of deviation；
3) by technical staff's setting electric current and the standard value of voltage, threshold value, the difference of standard value and threshold value in prediction adjustment module
Three parameters of range are modified standard value by being compared with the average value that data comparing module calculates；And by inclined
The difference of the range of difference and frequency correction threshold, revised standard value and threshold value is no more than the standard value and threshold value set
Difference；
4) failure prediction module is according to the real time data of data monitoring module, compares the modified standard value of prediction adjustment module, partially
Difference and threshold value, pre-process real time data, when real time data exceeds deviation, issue primary alarm, prompt prison
Survey personnel issue second-level alarm, monitoring personnel are notified to handle when real time data exceeds threshold value.
In the step 4), after failure prediction module issues primary alarm, monitoring personnel determines according to the actual situation should
Exceeded numerical value belongs to normal fluctuation or improper fluctuation；Judgement belong to normal fluctuation, then data comparing module by the numerical value the most
Maximum value or minimum value are recorded, and update average value, deviation level-one median；When being judged as improper fluctuation, then by
Monitoring personnel carries out emergent management, such as reports, maintenance is even shut down.
The failure prediction method based on machine learning, including training stage and detection-phase, training stage utilize
Data object constructs data model；Data exception situation is judged according to data model in detection-phase.
Failure prediction method based on machine learning of the invention, by being trained to supply network operation data to it, together
When introduce evolution algorithm, realize analysis result being mutually authenticated with real-time running data, and continue through real time data and promote prison
Measurement equipment is learnt, and the error between analysis result and real time data for analyzing supply network data reaches acceptable model
It encloses, to solve the influence that data exception runs supply network with analysis modified result abnormal data or amended record missing data.
Detailed description of the invention
Fig. 1 is module connection diagram of the present invention.
Fig. 2 is work step schematic diagram of the present invention.
Specific embodiment
Embodiment 1: a kind of failure prediction method based on machine learning, the prediction technique include data monitoring, data ratio
Data ratio is passed through to, failure prediction and prediction four modules of adjustment, the operation data of data monitoring module monitors supply network
Module analysis is compared, in comparison process, prediction data is corrected by prediction adjustment module, prediction data is analyzed, by lacking
Prediction module is fallen into sound an alarm the exception that will be generated, in which:
1) data monitoring module includes voltage monitoring and current monitoring, the real time data of monitoring power-supply networks network in the process of running,
Including current value and voltage value；
2) data comparing module records the real time data of the voltage and current of data monitoring module monitors, analyzed and is compared
Right, when recording data, using the period as parameter, when normal operating condition, the numerical value in daily each period of record takes it
Average value, as the reference point in the period
In above formula,For the sampled data in the daily same period, average value is calculated using week or the moon as the period；
Meanwhile the fluctuation numerical value in the period a cycle is recorded, which is gone out in a cycle by mean value calculation
Interior deviation and median, and maximum value and minimum value in the period are recorded, while calculating the frequency distribution of deviation；
3) by technical staff's setting electric current and the standard value of voltage, threshold value, the difference of standard value and threshold value in prediction adjustment module
Three parameters of range are modified standard value by being compared with the average value that data comparing module calculates；And by inclined
The difference of the range of difference and frequency correction threshold, revised standard value and threshold value is no more than the standard value and threshold value set
Difference；
4) failure prediction module is according to the real time data of data monitoring module, compares the modified standard value of prediction adjustment module, partially
Difference and threshold value, pre-process real time data, when real time data exceeds deviation, issue primary alarm, prompt prison
Survey personnel issue second-level alarm, monitoring personnel are notified to handle when real time data exceeds threshold value.
After failure prediction module issues primary alarm, monitoring personnel determines that the exceeded numerical value belongs to normally according to the actual situation
Fluctuation or improper fluctuation；Judgement belong to normal fluctuation, then data comparing module by the numerical value maximum value or minimum value the most into
Row record, and update average value, deviation level-one median；When being judged as improper fluctuation, then carried out by monitoring personnel urgent
Processing is such as reported, maintenance is even shut down.
Above-mentioned failure prediction method, including training stage and detection-phase, training stage utilize data object, construct number
According to model；Data exception situation is judged according to data model in detection-phase.
Claims (3)
1. a kind of failure prediction method based on machine learning, it is characterised in that the prediction technique includes data monitoring, data ratio
Data ratio is passed through to, failure prediction and prediction four modules of adjustment, the operation data of data monitoring module monitors supply network
Module analysis is compared, in comparison process, prediction data is corrected by prediction adjustment module, prediction data is analyzed, by lacking
Prediction module is fallen into sound an alarm the exception that will be generated, in which:
1) data monitoring module includes voltage monitoring and current monitoring, the real time data of monitoring power-supply networks network in the process of running,
Including current value and voltage value；
2) data comparing module records the real time data of the voltage and current of data monitoring module monitors, analyzed and is compared
Right, when recording data, using the period as parameter, when normal operating condition, the numerical value in daily each period of record takes it
Average value, as the reference point in the period
In above formula,For the sampled data in the daily same period, average value is calculated using week or the moon as the period；
Meanwhile the fluctuation numerical value in the period a cycle is recorded, which is gone out in a cycle by mean value calculation
Interior deviation and median, and maximum value and minimum value in the period are recorded, while calculating the frequency distribution of deviation；
3) by technical staff's setting electric current and the standard value of voltage, threshold value, the difference of standard value and threshold value in prediction adjustment module
Three parameters of range are modified standard value by being compared with the average value that data comparing module calculates；And by inclined
The difference of the range of difference and frequency correction threshold, revised standard value and threshold value is no more than the standard value and threshold value set
Difference；
4) failure prediction module is according to the real time data of data monitoring module, compares the modified standard value of prediction adjustment module, partially
Difference and threshold value, pre-process real time data, when real time data exceeds deviation, issue primary alarm, prompt prison
Survey personnel issue second-level alarm, monitoring personnel are notified to handle when real time data exceeds threshold value.
2. the failure prediction method based on machine learning as described in claim 1, it is characterised in that in the step 4), lack
Fall into prediction module issue primary alarm after, monitoring personnel determine according to the actual situation the exceeded numerical value belong to normal fluctuation or it is non-just
Ordinary wave is dynamic；Judgement belongs to normal fluctuation, then data comparing module records numerical value maximum value or minimum value the most, and more
New average value, deviation level-one median；When being judged as improper fluctuation, then emergent management is carried out by monitoring personnel, such as report,
Maintenance is even shut down.
3. the failure prediction method based on machine learning as described in claim 1, it is characterised in that described based on engineering
The failure prediction method of habit, including training stage and detection-phase, training stage utilize data object, construct data model；In
Detection-phase judges data exception situation according to data model.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860936A (en) * | 2020-05-25 | 2020-10-30 | 北京致远互联软件股份有限公司 | Method for predicting defects of office business process |
| CN113465777A (en) * | 2021-06-03 | 2021-10-01 | 广州番禺电缆集团有限公司 | Cable temperature monitoring platform and method |
| CN113945823A (en) * | 2021-09-26 | 2022-01-18 | 成都嘉纳海威科技有限责任公司 | Method for detecting potential defects of chip |
| CN117349778A (en) * | 2023-12-04 | 2024-01-05 | 湖南蓝绿光电科技有限公司 | Online real-time monitoring system of consumer based on thing networking |
-
2019
- 2019-08-22 CN CN201910779470.5A patent/CN110503211A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860936A (en) * | 2020-05-25 | 2020-10-30 | 北京致远互联软件股份有限公司 | Method for predicting defects of office business process |
| CN113465777A (en) * | 2021-06-03 | 2021-10-01 | 广州番禺电缆集团有限公司 | Cable temperature monitoring platform and method |
| CN113945823A (en) * | 2021-09-26 | 2022-01-18 | 成都嘉纳海威科技有限责任公司 | Method for detecting potential defects of chip |
| CN113945823B (en) * | 2021-09-26 | 2024-04-09 | 成都嘉纳海威科技有限责任公司 | Method for detecting potential defects of chip |
| CN117349778A (en) * | 2023-12-04 | 2024-01-05 | 湖南蓝绿光电科技有限公司 | Online real-time monitoring system of consumer based on thing networking |
| CN117349778B (en) * | 2023-12-04 | 2024-02-20 | 湖南蓝绿光电科技有限公司 | Online real-time monitoring system of consumer based on thing networking |
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