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Self-healing Mechanism Of Active Distribution System With DGs Based On Multi-agent System

Posted on:2020-05-06Degree:MasterType:Thesis
Country:ChinaCandidate:Z H DongFull Text:PDF
GTID:2392330590960978Subject:Engineering
Abstract/Summary:PDF Full Text Request
As the terminal link between the power system and the users,the distribution network's stable,reliable operation and economic are closely related to the all users.In recent years,a large number of distributed generators and energy storage devices have emerged in the level of medium and low voltage distribution network,which has changed the traditional single-ended active radiation operation mode of distribution network,and posed great challenges to power flow control,fault diagnosis and multipower supply recovery of distribution network.However,the existing centralized decision-making self-healing strategy of distribution network is no longer suitable for the operation requirements after a large number of DG access,so it is necessary to study the self-healing mechanism of active distribution network with distributed generation.To give full play to the initiative of active distribution network,this paper proposes a reliable,flexible and perfect self-healing mechanisms based on the available power supply and load characteristics of non-fault after the fault is isolated.The specific research work includes the following aspects:(1)A framework of self-healing control system based on multi-agent technology is proposed.In order to realize the self-healing function of distribution network after fault,this paper establishes a two-tier cooperative multi-agent system control framework,which sets up intelligent agents on the feeder layer and node area layer respectively.The judgment and decision-making that require rapid response and can be based on local information are autonomously performed by lower-level Region Agent(RA).For the optimization task that requires a large range of operation information(including power distribution of multiple feedbacks),the upper-level feeder agent(FA)completes the centralized global optimization decision.Thus,the primary system monitoring,protection and self-healing mechanism is realized.(2)A fast power restoration strategy under active distribution network is proposed.After the fault isolation,the outage feeder will be decomposed firstly.By defining the power balance and the transfer capacity margin,the restoration strategy not only initiating microgrid expansion to restore load,but also utilizes inter-connection tie to initiate load transfer,which can achieve the objective of comprehensive restoration.Also,it guarantees the security and stabilization of distribution network after restoration by limiting the magnitude of voltage and current.By building the four-feeder distribution system on DIGSILENT,the feasibility and effectiveness of the proposed strategy are verified.(3)Dynamic reconfiguration strategy based on partition of time intervals with improved fuzzy C-means clustering is proposed.The fluctuation of output of intermittent power sources such as photovoltaic and wind power,and the time-varying characteristics of distribution network load is fully considered.Firstly,the equivalent load forecasting curve is established based on the time variability of DG and load.The improved fuzzy mean clustering algorithm is applied for partition of time intervals,and the combined loss function is used to determine the optimal partition number and plan.Secondly,the interval valued is adopted to describe the uncertainty of DG and load and establish the dynamic reconfiguration model based on minimum network operating cost.Then the reconfiguration model is solved by the power flow calculation based on affine Taylor expansion and the decimal particle swarm optimization(PSO)algorithm based on loop search.Finally,it is proved effective by four-feeder distribution system that the proposed model and strategy.
Keywords/Search Tags:Distributed generation, Multi-agent system, Power restoration, Network reconfiguration, Active distribution system
PDF Full Text Request
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