CN110378492A - A method of reinforcing the control of distribution net equipment O&M - Google Patents
A method of reinforcing the control of distribution net equipment O&M Download PDFInfo
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Abstract
Has the characteristics that randomness and paroxysmal for grid equipment failure, thus it is difficult to this problem of Accurate Prediction, a kind of method of reinforcement distribution net equipment O&M control is provided, belong to grid equipment O&M technical field, by applying clustering, the big datas technology such as machine learning is to power information acquisition system, PMS2.0, the system datas such as D5000 are effectively integrated and are excavated, the data silo between each system is broken, according to neighbouring propagation effect analysis theories, establish equipment fault prediction model, equipment fault probability of happening can be prejudged in advance, schedule ahead overhaul of the equipments, reduce frequency of power cut and time, improve power supply quality, significant increase distribution O&M is horizontal.The present invention issues alarm signal before equipment fault, equipment fault is prejudged in advance and countermeasure is provided, the possibility that innovation and application big data technology will break down according to the failure logging having occurred and that in history pre- measurement equipment future, the operating mode that realizationization is passively repaired are the operating mode of actively maintenance.
Description
Technical field
The invention belongs to grid equipment O&M technical field, especially relates to a kind of maintenance data analytical technology reinforcement and match
Net equipment O&M management-control method.
Background technique
At present since distribution automation is still in promoting construction, Distribution Network Equipment is unable to get comprehensive monitoring, operations staff
Equipment running status information can not effectively be obtained；In addition grid equipment failure has the characteristics that randomness and paroxysmal, conventional pre-
Survey method is difficult to Accurate Prediction.In this context, there are problems that following pain spot in power distribution network operation and maintenance, first is that equipment event
Barrier is difficult to Accurate Prediction, and previous working method is mainly passive finding failure problems, and distribution net equipment is more, wiring is complicated, therefore
Barrier point investigation is handled by rule of thumb entirely, is needed to take a significant amount of time, is influenced first-aid repair efficiency；Second is that equipment state overhauling lack science according to
According to, current state maintenance mainly formulates maintenance plan according to equipment operation information by equipment expert, and it is too strong to the dependence of people,
Repair based on condition of component needs to combine a large amount of historical datas and as-is data simultaneously, and manpower can not be accomplished to divide the real-time of grid equipment comprehensively
Analysis, there are still the possibility for having analysis blind spot.
The mass data of grid company operation accumulation embodies bigger under the development of big data rapid technological improvement
Value, make it possible grid equipment failure predication using big data technology.Therefore, a kind of new skill is needed in the prior art
Art scheme solves the problems, such as this.
Summary of the invention
The technical problems to be solved by the present invention are: a kind of method of reinforcement distribution net equipment O&M control is provided, to solve
Equipment fault is difficult to Accurate Prediction in certainly existing power distribution network operation and maintenance, equipment state overhauling lacks scientific basis, distribution
Net O&M can only be the technical problems such as the operating mode passively repaired.
A method of reinforcing the control of distribution net equipment O&M, it is characterized in that: include the following steps, and following steps sequentially into
Row,
Step 1: case database is established
By acquiring geography information, technical parameter, power equipment O&M information and the historical data of power equipment, application
Data cleansing and clustering screening failure, defect and abnormal data, establish case database；
Step 2: establishing electricity consumption condition monitoring figure
Using Distribution GIS technology and BIM spatial modeling technology, by Distribution Network Equipment geographical location information and
Power equipment real-time running state information marks on the electronic map, establishes electricity consumption condition monitoring figure；
Step 3: establishing equipment fault prediction model
In conjunction with closing on propagation effect, each case in the case database obtained by association analysis method to the step 1
Multi dimensional analysis is carried out, pests occurrence rule between correlation and case between data is obtained, establishes equipment fault prediction model；
Step 4: failure studies and judges navigation
The equipment fault prediction model established using the step 3 predicts future malfunction, carries out defect to power equipment
Tracking, failure anticipation and failure initiative alarming；
Step 5: intelligence distributes work order
It is studied and judged by the failure that step 4 obtains as a result, obtains inspection electric power apparatus examination scheme and emergency plan, it is logical
It crosses step 2 and obtains electronic map, check man is singly sent to operation maintenance personnel and carries out electric power apparatus examination.
Fault prediction model can be set by the method progress electric power that big data analysis technology machinery learns in the step 3
Standby fault pre-alarming self study, real-time update case database information carry out equipment fault prediction model Automatic Optimal.
Data source in the step 1 be power grid PMS system, SG186 system, power consumer electricity consumption acquisition system,
D5000 system and 95598 customer service systems, data-interface use servlet interface.
Clustering uses K-means algorithm in the step 1, and the case database of acquisition includes facility information data
Library, electricity consumption user behavior data library, typical fault database, operational monitoring database, failure occurrence condition history library.
Through the above design, the present invention can be brought the following benefits: a kind of reinforcement distribution net equipment O&M control
Method, alarm signal can be issued before equipment fault, is prejudged and provided countermeasure to equipment fault in advance, create
The possibility that new opplication big data technology will break down according to the failure logging having occurred and that in history pre- measurement equipment future, realizationization
The operating mode passively repaired is the operating mode of actively maintenance.
Further, the present invention utilizes big data technology, is collected, analyzes, handles from data source header, has broken power grid
Using the data silo between each system.Equipment fault is reached to prejudge, safeguard in advance in advance, has reported business for repairment and handle rapidly, rapidly
It finishes, changes passive repairing actively to overhaul, increase the covering surface of the unified early warning of grid equipment failure, improve power distribution network and set
The standby general level of the health changes power distribution network overall work from " empirical " to " intelligent ".
The present invention reduces frequency of power cut and power off time in supply district, while equipment being avoided seriously to damage, and reduces overhaul
Technological transformation number；Meaning in terms of society is, power supply reliability is improved, promotes residential electricity consumption satisfaction.
Detailed description of the invention
Below in conjunction with the drawings and specific embodiments, the present invention is further illustrated:
Fig. 1 is a kind of system block diagram for the method for reinforcing the control of distribution net equipment O&M of the present invention.
Fig. 2 is a kind of method equipment fault prediction model techniqueflow frame for reinforcing the control of distribution net equipment O&M of the present invention
Figure.
Specific embodiment
A method of reinforcing the control of distribution net equipment O&M, as shown in Figure 1, including electric network data layer, database layer, core
Application module layer, System Functional Layer four levels.From top to bottom sequence indicates that system function implementation process, solid box indicate in figure
Application and building subsystem, dotted line frame indicate the integration module of application existing logical relation building.Concrete operation step
To be as follows,
Step 1: case database is established
By acquiring geography information, technical parameter, power equipment O&M information and the historical data of power equipment, application
Data cleansing and clustering screening failure, defect and abnormal data, establish case database；
Step 2: establishing electricity consumption condition monitoring figure
Using Distribution GIS technology and BIM spatial modeling technology, by Distribution Network Equipment geographical location information and
Power equipment real-time running state information marks on the electronic map, establishes electricity consumption condition monitoring figure；
Step 3: establishing equipment fault prediction model
In conjunction with closing on propagation effect, each case in the case database obtained by association analysis method to the step 1
Multi dimensional analysis is carried out, pests occurrence rule between correlation and case between data is obtained, establishes equipment fault prediction model；
Step 4: failure studies and judges navigation
The equipment fault prediction model established using the step 3 predicts the following event according to occurrence of equipment fault message
Barrier, provides the secondary distributions net O&Ms such as equipment deficiency tracking, failure anticipation, failure initiative alarming, maintenance or modification scheme suggestion
Person works' information；
Step 5: intelligence distributes work order
It is studied and judged by the failure that step 4 obtains as a result, obtains inspection electric power apparatus examination scheme and emergency plan, it is logical
It crosses step 2 and obtains electronic map, maintenance work order is precisely sent to operation maintenance personnel, while providing device location, appearance information,
Operation maintenance personnel is assisted quick and precisely to carry out overhaul of the equipments.
Wherein, data source is that it is pre- for Distribution Network Equipment failure to collect integration by doing data-interface using servlet
The electric network data of analysis is surveyed, PMS system that data source is applied in State Grid Corporation of China, SG186 system, power consumer electricity consumption are adopted
The historical summaries such as the overhaul technological transformation accumulated under collecting system, D5000 system, 95598 customer service systems and line.
The foundation of case database passes through to the power equipment geography information of acquisition, technical parameter, O&M data, overhaul skill
Change, defect record, load record and the mass datas such as historical events and Changes in weather, the input that will be present by data cleansing
The dirty datas such as mistake are rejected, and are clustered using K-means algorithm to data, and set of metadata of similar data extraction process is constructed for setting
The case database of standby Fault Forecast Analysis, comprising: equipment information database, electricity consumption user behavior data library, typical fault
Database, operational monitoring database, failure occurrence condition history library.
Electricity consumption condition monitoring figure and equipment fault prediction model are core application module of the present invention, wherein electricity consumption status monitoring
Module utilizes Distribution GIS technology and BIM spatial modeling technology, by equipment information database, user power utilization behavior number
On the electronic map and each region electro dynamic of real-time update according to data exhibitings such as libraries, equipment operation shape is provided for operation maintenance personnel
State, general level of the health real time information assist operation maintenance personnel to grasp the real-time dynamic of power grid；Equipment fault prediction model module application pattra leaves
This analysis etc. association analysis methods in database case carry out multi dimensional analysis, in conjunction with propagation effect is closed on, deep-cut data it
Between correlation and each case between potential rule, companion signal rule when finding out device fails, in conjunction with what is occurred
Equipment fault data, the companion signal of failure and equipment running status monitoring data three-dimensional information, predict the time of equipment fault
The place and.Using the machine learning method of big data analysis technology, the active forewarning self study of electrical equipment fault is realized, such as scheme
Shown in 2.By continuous expanding data library, check in conjunction with operation maintenance personnel scene as a result, constantly to equipment fault prediction model mould
Block is iterated training, further promotes predictablity rate.
The present invention successfully prejudges at defect elimination 334, failure accuracy rate is up between Changchun Power Supply Company's test operation 1 year
82%, power supply reliability is promoted to 99.97%, and the averagely arrival fault in-situ time shortens 11 minutes, mean failure rate handling duration
Shorten 15 minutes, reduces and make an inspection tour 60% or more maintenance workload.
The present invention it is a kind of reinforce distribution net equipment O&M control method, by power grid power information acquisition system, PMS2.0,
The equipment fault data of the records such as D5000 are effectively integrated and are excavated, and in conjunction with neighbouring propagation effect analysis theories, are established and are set
Standby fault prediction model, the probability that full forecast equipment breaks down within a certain period of time.Scientific basis is provided for repair based on condition of component,
Reasonable arrangement makes an inspection tour service work, by work by repairing Mode change passively as active elimination of equipment defect mode, solves power distribution network
The pain spot of maintenance work reduces frequency of power cut and power off time, promotes power supply reliability, creates good economy and society economy
Benefit.
Claims (4)
1. a kind of method for reinforcing the control of distribution net equipment O&M, it is characterized in that: include the following steps, and following steps sequentially into
Row,
Step 1: case database is established
By acquiring geography information, technical parameter, power equipment O&M information and the historical data of power equipment, using data
Cleaning and clustering screening failure, defect and abnormal data, establish case database；
Step 2: establishing electricity consumption condition monitoring figure
Using Distribution GIS technology and BIM spatial modeling technology, by Distribution Network Equipment geographical location information and electric power
Equipment real-time running state information marks on the electronic map, establishes electricity consumption condition monitoring figure；
Step 3: establishing equipment fault prediction model
In conjunction with propagation effect is closed on, each case is carried out in the case database obtained by association analysis method to the step 1
Multi dimensional analysis obtains pests occurrence rule between correlation and case between data, establishes equipment fault prediction model；
Step 4: failure studies and judges navigation
Using the step 3 establish equipment fault prediction model, predict future malfunction, to power equipment carry out Bug Tracking,
Failure anticipation and failure initiative alarming；
Step 5: intelligence distributes work order
It is studied and judged by the failure that step 4 obtains as a result, obtains patrol electric power apparatus examination scheme and emergency plan, passes through step
Rapid two obtain electronic map, and check man is singly sent to operation maintenance personnel and carries out electric power apparatus examination.
2. a kind of method for reinforcing the control of distribution net equipment O&M according to claim 1, it is characterized in that: in the step 3
Fault prediction model can carry out electrical equipment fault early warning self study by the method that big data analysis technology machinery learns, in real time
Case database information is updated, equipment fault prediction model Automatic Optimal is carried out.
3. a kind of method for reinforcing the control of distribution net equipment O&M according to claim 1, it is characterized in that: in the step 1
Data source be power grid PMS system, SG186 system, power consumer electricity consumption acquisition system, D5000 system and 95598 customer services
System, data-interface use servlet interface.
4. a kind of method for reinforcing the control of distribution net equipment O&M according to claim 1, it is characterized in that: in the step 1
Clustering uses K-means algorithm, and the case database of acquisition includes equipment information database, electricity consumption user behavior data
Library, typical fault database, operational monitoring database, failure occurrence condition history library.
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Cited By (16)
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| CN110932150A (en) * | 2019-12-18 | 2020-03-27 | 深圳市康拓普信息技术有限公司 | Power equipment operation and maintenance system |
| CN111245097A (en) * | 2020-02-21 | 2020-06-05 | 国网山东省电力公司宁阳县供电公司 | Intelligent power grid management and control system and method |
| CN111242328A (en) * | 2020-02-27 | 2020-06-05 | 贵州智诚科技有限公司 | Method for improving operation and maintenance quality and efficiency of equipment |
| CN111274309A (en) * | 2020-02-27 | 2020-06-12 | 贵州智诚科技有限公司 | Global traffic management equipment operation monitoring method based on multi-dimensional data |
| CN111314137A (en) * | 2020-02-18 | 2020-06-19 | 国家电网有限公司 | Information communication network automation operation and maintenance method, device, storage medium and processor |
| CN111311133A (en) * | 2020-04-24 | 2020-06-19 | 广东卓维网络有限公司 | Monitoring system applied to power grid production equipment |
| CN111652313A (en) * | 2020-06-04 | 2020-09-11 | 重庆东电通信技术有限公司 | Multi-source heterogeneous data mining method based on cluster analysis |
| CN111709597A (en) * | 2020-04-24 | 2020-09-25 | 广东卓维网络有限公司 | Power grid production domain operation monitoring system |
| CN111983383A (en) * | 2020-08-17 | 2020-11-24 | 海南电网有限责任公司信息通信分公司 | Power system fault first-aid repair method and system |
| CN112115180A (en) * | 2020-09-11 | 2020-12-22 | 国网山东省电力公司枣庄供电公司 | Power grid accident prediction method based on big data |
| CN112163708A (en) * | 2020-09-30 | 2021-01-01 | 卖点国际展示(深圳)有限公司 | Monitoring management system and method based on intelligent display terminal |
| CN112488327A (en) * | 2020-11-10 | 2021-03-12 | 国网天津市电力公司电力科学研究院 | Self-learning power grid equipment fault defect early warning system and method thereof |
| CN113537783A (en) * | 2021-07-19 | 2021-10-22 | 国网吉林省电力有限公司吉林供电公司 | Intelligent management and control method for power grid maintenance safety risk |
| CN113765218A (en) * | 2021-08-04 | 2021-12-07 | 杭州丁卯智能科技有限公司 | GIS (geographic information System) and BIM (building information modeling + building information modeling) -based power distribution overhead line and power facility inspection system |
| CN115696532A (en) * | 2023-01-03 | 2023-02-03 | 博信通信股份有限公司 | Base station equipment management and control method and device, intelligent power controller and medium |
| CN116633018A (en) * | 2023-05-31 | 2023-08-22 | 国家电网有限公司 | Power industry fault power failure full-flow management and control platform |
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