A Multiagent Design for Power Distribution Systems Automation
A new multiagent system (MAS) design for fault location, isolation, and restoration in power distribution systems (PDSs) is presented. When there is a fault in the PDS, MAS quickly isolates the fault and restores the service to the fault-free zones. Hierarchical coordination strategy is introduced t...
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| Veröffentlicht in: | IEEE transactions on smart grid 2016-01, Vol.7 (1), p.329-339 |
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| creator | Ghorbani, Mohammad Jawad Choudhry, Muhammad Akram Feliachi, Ali |
| description | A new multiagent system (MAS) design for fault location, isolation, and restoration in power distribution systems (PDSs) is presented. When there is a fault in the PDS, MAS quickly isolates the fault and restores the service to the fault-free zones. Hierarchical coordination strategy is introduced to manage the agents, which integrate the advantages of both centralized and decentralized coordination strategies. Proposed MAS is composed of zone agents (ZAs), feeder agents (FAs), and substation agents. In this framework, ZA locate and isolate the fault based on the locally available information, and assist the FA for reconfiguration and restoration. FA can solve the restoration problem using the existing algorithms for the 0-1 knapsack problem. A novel {Q} -learning mechanism is also introduced to support the FA in decision making for restoration. The design is illustrated by the use of case studies of fault location, isolation, and restoration on West Virginia super circuit. The results from the case studies indicate the performance of proposed MAS design. |
| doi_str_mv | 10.1109/TSG.2015.2453884 |
| format | Article |
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When there is a fault in the PDS, MAS quickly isolates the fault and restores the service to the fault-free zones. Hierarchical coordination strategy is introduced to manage the agents, which integrate the advantages of both centralized and decentralized coordination strategies. Proposed MAS is composed of zone agents (ZAs), feeder agents (FAs), and substation agents. In this framework, ZA locate and isolate the fault based on the locally available information, and assist the FA for reconfiguration and restoration. FA can solve the restoration problem using the existing algorithms for the 0-1 knapsack problem. A novel {Q} -learning mechanism is also introduced to support the FA in decision making for restoration. The design is illustrated by the use of case studies of fault location, isolation, and restoration on West Virginia super circuit. 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When there is a fault in the PDS, MAS quickly isolates the fault and restores the service to the fault-free zones. Hierarchical coordination strategy is introduced to manage the agents, which integrate the advantages of both centralized and decentralized coordination strategies. Proposed MAS is composed of zone agents (ZAs), feeder agents (FAs), and substation agents. In this framework, ZA locate and isolate the fault based on the locally available information, and assist the FA for reconfiguration and restoration. FA can solve the restoration problem using the existing algorithms for the 0-1 knapsack problem. A novel {Q} -learning mechanism is also introduced to support the FA in decision making for restoration. The design is illustrated by the use of case studies of fault location, isolation, and restoration on West Virginia super circuit. The results from the case studies indicate the performance of proposed MAS design.</description><subject>Algorithms</subject><subject>Circuit faults</subject><subject>Design engineering</subject><subject>Fault location</subject><subject>Fault location and isolation (FLI)</subject><subject>Faults</subject><subject>Indexes</subject><subject>Monitoring</subject><subject>multiagent system (MAS)</subject><subject>Multiagent systems</subject><subject>Optimization</subject><subject>Power distribution</subject><subject>Q-learning</subject><subject>Restoration</subject><subject>self-healing</subject><subject>Strategy</subject><subject>Substations</subject><issn>1949-3053</issn><issn>1949-3061</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1LAzEQxYMoWGrvgpccvWzNxya7OXgorVahotB6DtndSYnsR02ySP97d2npXGbm8d47_BC6p2ROKVFPu-16zggVc5YKnufpFZpQlaqEE0mvL7fgt2gWwg8ZhnMumZqg5wX-6OvozB7aiFcQ3L7FtvP4q_sDj1cuRO-KPrquxdtjiNAEvOhj15hRukM31tQBZuc9Rd-vL7vlW7L5XL8vF5uk5FLGBCpqqtJUBVcGOEszlilLbUmYIrkopBQiI5Vh1aDmw6PKgtiMSVIVwK0kfIoeT70H3_32EKJuXCihrk0LXR80zZkQJJNcDVZyspa-C8GD1QfvGuOPmhI9wtIDLD3C0mdYQ-ThFHEAcLFnNBuQCf4Pm8Rk2A</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Ghorbani, Mohammad Jawad</creator><creator>Choudhry, Muhammad Akram</creator><creator>Feliachi, Ali</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20160101</creationdate><title>A Multiagent Design for Power Distribution Systems Automation</title><author>Ghorbani, Mohammad Jawad ; Choudhry, Muhammad Akram ; Feliachi, Ali</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c366t-ed1adcadb39ae3247279f1fc029085b665570da2d9f186559cb0f7260dbe3f603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Circuit faults</topic><topic>Design engineering</topic><topic>Fault location</topic><topic>Fault location and isolation (FLI)</topic><topic>Faults</topic><topic>Indexes</topic><topic>Monitoring</topic><topic>multiagent system (MAS)</topic><topic>Multiagent systems</topic><topic>Optimization</topic><topic>Power distribution</topic><topic>Q-learning</topic><topic>Restoration</topic><topic>self-healing</topic><topic>Strategy</topic><topic>Substations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghorbani, Mohammad Jawad</creatorcontrib><creatorcontrib>Choudhry, Muhammad Akram</creatorcontrib><creatorcontrib>Feliachi, Ali</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on smart grid</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ghorbani, Mohammad Jawad</au><au>Choudhry, Muhammad Akram</au><au>Feliachi, Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Multiagent Design for Power Distribution Systems Automation</atitle><jtitle>IEEE transactions on smart grid</jtitle><stitle>TSG</stitle><date>2016-01-01</date><risdate>2016</risdate><volume>7</volume><issue>1</issue><spage>329</spage><epage>339</epage><pages>329-339</pages><issn>1949-3053</issn><eissn>1949-3061</eissn><coden>ITSGBQ</coden><abstract>A new multiagent system (MAS) design for fault location, isolation, and restoration in power distribution systems (PDSs) is presented. When there is a fault in the PDS, MAS quickly isolates the fault and restores the service to the fault-free zones. Hierarchical coordination strategy is introduced to manage the agents, which integrate the advantages of both centralized and decentralized coordination strategies. Proposed MAS is composed of zone agents (ZAs), feeder agents (FAs), and substation agents. In this framework, ZA locate and isolate the fault based on the locally available information, and assist the FA for reconfiguration and restoration. FA can solve the restoration problem using the existing algorithms for the 0-1 knapsack problem. A novel {Q} -learning mechanism is also introduced to support the FA in decision making for restoration. The design is illustrated by the use of case studies of fault location, isolation, and restoration on West Virginia super circuit. 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| subjects | Algorithms Circuit faults Design engineering Fault location Fault location and isolation (FLI) Faults Indexes Monitoring multiagent system (MAS) Multiagent systems Optimization Power distribution Q-learning Restoration self-healing Strategy Substations |
| title | A Multiagent Design for Power Distribution Systems Automation |
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