CN113917285A - Fault intelligent positioning method and system based on power distribution network power flow distribution identification - Google Patents
Fault intelligent positioning method and system based on power distribution network power flow distribution identification Download PDFInfo
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- CN113917285A CN113917285A CN202111225254.XA CN202111225254A CN113917285A CN 113917285 A CN113917285 A CN 113917285A CN 202111225254 A CN202111225254 A CN 202111225254A CN 113917285 A CN113917285 A CN 113917285A
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 5
- 238000004088 simulation Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000002940 Newton-Raphson method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
A fault intelligent positioning method and system based on power distribution network tide distribution identification relates to the technical field of power distribution network fault positioning, and the method comprises the following steps: a. monitoring current loads flowing through all elements or equipment in real time by taking the elements or the equipment as nodes, and establishing a power flow distribution dynamic graph in the power distribution network; b. coding the current load flowing through any element or equipment according to a color system and color depth, and according to the principle that the color is darker when the load is larger; c. simulating the known power failure condition to obtain a known failure power tide distribution map set, and storing the known failure power tide distribution map set in a database of a computer; d. the computer compares the real-time monitored power flow distribution dynamic graph with the fault power flow distribution graph set in the database, and if the same pattern is found, an alarm signal is sent.
Description
Technical Field
The invention relates to the technical field of power distribution network fault positioning, in particular to a fault intelligent positioning method and system based on power distribution network power flow distribution identification.
Background
When an electric power system is in operation, under the excitation of the potential of the power supply, current or power flows from the power supply to the load through the various elements of the system and is distributed throughout the power grid, referred to as power flow, i.e. distribution grid flow.
The load flow is the steady distribution of voltage (each node) and power (active power and reactive power) (each branch) in the power system, and the load flow calculation means that the accurate value of the load flow distribution is finally obtained by repeatedly iterating and giving initial values assumed in some parameters, known values and unknown values in the power grid, and common methods include a Newton-Raphson method and a PQ decomposition method.
The distribution network generally is the radiation net, and especially low voltage distribution system operating mode is complicated and complicated, and when electric power system received the disturbance, the operation personnel received a large amount of warning messages through data acquisition and supervisory control system, can't distinguish the accuracy of warning message and the accurate position of trouble fast, has aggravated operation personnel's work burden for operation personnel are difficult to in time carry out the fault maintenance.
Disclosure of Invention
The invention provides a fault intelligent positioning method and system based on power distribution network power flow distribution identification, aiming at solving the problem that operators can visually and quickly identify fault information and fault positions, wherein a method of establishing a fault power flow distribution map set and comparing the fault power flow distribution map set with a real-time power flow distribution dynamic map is adopted to realize visual fault monitoring and positioning, the fault power flow distribution map set is a power flow distribution map when a fault actually occurs, the accuracy of the fault information is ensured, the workload of the operators is reduced, the operators do not need to identify the accuracy of the fault information, the fault maintenance can be quickly carried out according to the fault information, and the fault can be timely eliminated.
The invention provides a fault intelligent positioning method based on power distribution network power flow distribution identification, which is realized by means of a computer and matched software, and comprises the following steps:
a. monitoring current loads flowing through all elements or equipment in real time by taking the elements or the equipment as nodes, and establishing a power flow distribution dynamic graph in the power distribution network;
b. coding the current load flowing through any element or equipment according to a color system and color depth, and according to the principle that the color is darker when the load is larger;
c. simulating the known power failure condition to obtain a known failure power tide distribution map set, and storing the known failure power tide distribution map set in a database of a computer;
d. and the computer compares the power flow distribution dynamic graph monitored in real time with the fault power flow distribution graph set in the database, and if the same patterns are found, an alarm signal is sent out.
In the step d, when the power flow distribution dynamic graph monitored in real time is not in the fault power flow distribution graph set but is confirmed to be an unknown power fault, the monitored power flow distribution dynamic graph is updated to the fault power flow distribution graph set.
The known power fault conditions include fault conditions in the n-1 power safety guidelines and all fault conditions simulated in software.
In the step a, the elements or devices are marked by different shapes or symbols in the power flow distribution dynamic diagram.
The system comprises a fault simulation module, a power tide flow distribution image generation module and a fault identification module,
the known power failure condition of the failure simulation module is simulated to obtain a known failure power tide distribution map set,
the power flow distribution map generation module generates the corresponding relation between the power grid section characteristics under different power grid flows and topologies and the known fault power flow distribution map set,
and the fault identification module compares the power flow distribution dynamic graph monitored in real time with a fault power flow distribution graph set in the database.
The system also comprises a power tide current distribution image training module, wherein the power tide current distribution image training module trains the generated distribution network tide current distribution image by adopting a deep neural network, establishes a fault intelligent positioning model, and maps the power grid characteristics to the distribution network tide current distribution image diagnosis model when the distribution network has faults so as to identify corresponding faults.
The method has the advantages that the fault power flow distribution map set is generated by the fault power flow distribution map, the fault power flow distribution map set comprises all fault power flow distribution maps which are preset and have faults, the power flow distribution dynamic map is established and monitored in real time and is compared with the fault power flow distribution maps contained in the fault power flow distribution map set, when the fault power flow distribution maps are the same, alarm information is sent out, the accuracy of the alarm information is guaranteed, operators do not need to screen the alarm information, and the operators can quickly position the fault positions and timely eliminate the faults through the alarm information and the power flow distribution dynamic map.
Detailed Description
The invention provides a fault intelligent positioning method based on power distribution network load flow distribution identification, which is realized by means of a computer and matched software, and comprises the following steps:
a. monitoring current loads flowing through all elements or equipment in real time by taking the elements or the equipment as nodes, and establishing a power flow distribution dynamic graph in the power distribution network;
b. coding the current load flowing through any element or equipment according to a color system and color depth, and according to the principle that the color is darker when the load is larger;
c. simulating the known power failure condition to obtain a known failure power tide distribution map set, and storing the known failure power tide distribution map set in a database of a computer;
d. and the computer compares the power flow distribution dynamic graph monitored in real time with the fault power flow distribution graph set in the database, and if the same patterns are found, an alarm signal is sent out.
In the step d, when the power flow distribution dynamic graph monitored in real time is not in the fault power flow distribution graph set but is confirmed to be an unknown power fault, the monitored power flow distribution dynamic graph is updated to the fault power flow distribution graph set.
The known power fault conditions include fault conditions in the n-1 power safety guidelines and all fault conditions simulated in software.
In the step a, the elements or devices are marked by different shapes or symbols in the power flow distribution dynamic diagram.
The system comprises a fault simulation module, a power tide flow distribution image generation module and a fault identification module,
the known power failure condition of the failure simulation module is simulated to obtain a known failure power tide distribution map set,
the power flow distribution map generation module generates the corresponding relation between the power grid section characteristics under different power grid flows and topologies and the known fault power flow distribution map set,
and the fault identification module compares the power flow distribution dynamic graph monitored in real time with a fault power flow distribution graph set in the database.
The system also comprises a power tide current distribution image training module, wherein the power tide current distribution image training module trains the generated distribution network tide current distribution image by adopting a deep neural network, establishes a fault intelligent positioning model, and maps the power grid characteristics to the distribution network tide current distribution image diagnosis model when the distribution network has faults so as to identify corresponding faults.
The power tide current distribution map set is generated through power distribution network fault simulation, the power distribution network fault simulation generates corresponding relations between power grid section characteristics and fault sets under different power grid tides and topologies through N-1, N-2, expected fault set formulation and typical distribution network fault case analysis, the N-1, N-2 and expected fault sets are obtained through online analysis and calculation software (DSA), the typical distribution network fault cases comprise faults in a plan and faults under an extreme mode, and the simulation is carried out through an operation mode and the power grid tides in a simulation case.
The method comprises the steps of mapping different power grid flow characteristics and operation mode characteristics of simulation to an image, representing equipment flow changes through different colors, normalizing equipment flow, displaying the image according to a normalized result, wherein the image has the advantages that the color of large flow data is darker, the color of small flow data is lighter, and different types of equipment such as lines, main transformers, buses and the like are represented through different shapes.
The method comprises the steps of establishing a fault power tide current distribution map set by using a fault power tide current distribution image, mapping power grid characteristics to the power tide current distribution map set for comparison when a distribution network is in fault, identifying corresponding faults, sending alarm information, enabling an operator to quickly identify fault positions according to a power tide distribution dynamic image and the alarm information, and effectively avoiding wrong alarm information.
Claims (6)
1. A fault intelligent positioning method based on power distribution network load flow distribution identification is realized by means of a computer and matched software, and is characterized in that: the method comprises the following steps:
a. monitoring current loads flowing through all elements or equipment in real time by taking the elements or the equipment as nodes, and establishing a power flow distribution dynamic graph in the power distribution network;
b. coding the current load flowing through any element or equipment according to a color system and color depth, and according to the principle that the color is darker when the load is larger;
c. simulating the known power failure condition to obtain a known failure power tide distribution map set, and storing the known failure power tide distribution map set in a database of a computer;
d. and the computer compares the power flow distribution dynamic graph monitored in real time with the fault power flow distribution graph set in the database, and if the same patterns are found, an alarm signal is sent out.
2. The fault intelligent positioning method based on power distribution network power flow distribution identification as claimed in claim 1, wherein: in the step d, when the power flow distribution dynamic graph monitored in real time is not in the fault power flow distribution graph set but is confirmed to be an unknown power fault, the monitored power flow distribution dynamic graph is updated to the fault power flow distribution graph set.
3. The fault intelligent positioning method based on power distribution network power flow distribution identification as claimed in claim 1, wherein: the known power fault conditions include fault conditions in the n-1 power safety guidelines and all fault conditions simulated in software.
4. The fault intelligent positioning method based on power distribution network power flow distribution identification as claimed in claim 1, wherein: in the step a, the elements or devices are marked by different shapes or symbols in the power flow distribution dynamic diagram.
5. A system matched with the intelligent fault positioning method of claim 1, wherein: the system comprises a fault simulation module, a power tide flow distribution image generation module and a fault identification module,
the known power failure condition of the failure simulation module is simulated to obtain a known failure power tide distribution map set,
the power flow distribution map generation module generates the corresponding relation between the power grid section characteristics under different power grid flows and topologies and the known fault power flow distribution map set,
and the fault identification module compares the power flow distribution dynamic graph monitored in real time with a fault power flow distribution graph set in the database.
6. The intelligent fault location system of claim 5, wherein: the system also comprises a power tide current distribution image training module, wherein the power tide current distribution image training module trains the generated distribution network tide current distribution image by adopting a deep neural network, establishes a fault intelligent positioning model, and maps the power grid characteristics to the distribution network tide current distribution image diagnosis model when the distribution network has faults so as to identify corresponding faults.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103077484A (en) * | 2013-02-20 | 2013-05-01 | 华东电网有限公司 | Multi-dimensional power flow evaluation index method based on statistic analysis of historical information of power grid |
| CN103632311A (en) * | 2013-11-28 | 2014-03-12 | 国家电网公司 | Fault examining system and method for power grid operation |
| CN103632223A (en) * | 2013-11-26 | 2014-03-12 | 国家电网公司 | Regional power grid intelligent scheduling early warning system |
| CN103701212A (en) * | 2013-12-23 | 2014-04-02 | 国家电网公司 | Method and system for monitoring and analyzing power grid safety and stability control device |
| CN104091014A (en) * | 2014-07-02 | 2014-10-08 | 国家电网公司 | Power flow simulation control method based on time series |
| CN104914837A (en) * | 2015-05-26 | 2015-09-16 | 国家电网公司 | Intelligent dispatching visualization early warning system |
| CN109061391A (en) * | 2018-09-07 | 2018-12-21 | 中国电力科学研究院有限公司 | A kind of electric network failure diagnosis method and system based on computer vision tidal current chart |
| CN111027161A (en) * | 2019-11-20 | 2020-04-17 | 积成电子股份有限公司 | Automatic generation method for large-scale power transmission network tidal current diagram |
| CN112000923A (en) * | 2020-07-14 | 2020-11-27 | 中国电力科学研究院有限公司 | Power grid fault diagnosis method, system and equipment |
-
2021
- 2021-10-21 CN CN202111225254.XA patent/CN113917285A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103077484A (en) * | 2013-02-20 | 2013-05-01 | 华东电网有限公司 | Multi-dimensional power flow evaluation index method based on statistic analysis of historical information of power grid |
| CN103632223A (en) * | 2013-11-26 | 2014-03-12 | 国家电网公司 | Regional power grid intelligent scheduling early warning system |
| CN103632311A (en) * | 2013-11-28 | 2014-03-12 | 国家电网公司 | Fault examining system and method for power grid operation |
| CN103701212A (en) * | 2013-12-23 | 2014-04-02 | 国家电网公司 | Method and system for monitoring and analyzing power grid safety and stability control device |
| CN104091014A (en) * | 2014-07-02 | 2014-10-08 | 国家电网公司 | Power flow simulation control method based on time series |
| CN104914837A (en) * | 2015-05-26 | 2015-09-16 | 国家电网公司 | Intelligent dispatching visualization early warning system |
| CN109061391A (en) * | 2018-09-07 | 2018-12-21 | 中国电力科学研究院有限公司 | A kind of electric network failure diagnosis method and system based on computer vision tidal current chart |
| CN111027161A (en) * | 2019-11-20 | 2020-04-17 | 积成电子股份有限公司 | Automatic generation method for large-scale power transmission network tidal current diagram |
| CN112000923A (en) * | 2020-07-14 | 2020-11-27 | 中国电力科学研究院有限公司 | Power grid fault diagnosis method, system and equipment |
Non-Patent Citations (3)
| Title |
|---|
| BOWEN HAO: "Fault Diagnosis Using Deep Convolutional NeuralNetwork with Dynamic Computer-visualised Power Flow", 《PREPRINTS OF THE 3RD IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION》, 10 November 2019 (2019-11-10) * |
| 王英英: "采用图论的电网连锁故障模式搜索方法", 《高电压技术》, 30 December 2010 (2010-12-30) * |
| 谭凯: "可视化技术在电力调度自动化主站系统中的应用", 《中国优秀硕士学位论文全文数据库（信息科技辑）》, 15 October 2020 (2020-10-15) * |
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