Multiagent based fault localisation and service restoration in Tanzanian secondary distribution network

Cogent Engineering

Nerey H. Mvungi¹  Rukia J. Mwifunyi²  Mussa M Kissaka³ 

[1] Department of Computer Systems and Engineering, University of Dar Es Salaam, Dar Es Salaam Tanzani;Department of Computer Systems and Engineering, University of Dar Es Salaam, Dar Es Salaam Tanzani;Department of Electronics and Telecommunication Engineering, University of Dodoma, Dodoma Tanzani;Department of Electronics and Telecommunication Engineering, University of Dar Es Salaam, Dar Es Salaam Tanzani;

关键词: Fault Location;    Service Restoration;    Multiagent System;    Distributed Control;    Load Shedding;   

DOI  :  10.1080/23311916.2021.1975900

来源: Taylor & Francis

【 摘 要 】

Fault Localisation and Service Restoration (FLSR) is a fundamental function during the distribution system’s fault management process for improved service reliability and resilience. In Tanzania, fault management in the Secondary Distribution Network (SDN) is accomplished manually through customer calls and manual-line inspections. Service restoration’s decision relies on prior experiences, rated capacity of transformers and peak hour demand, causing prolonged service restoration and load shedding. Approaches devised to solve the FLSR problem include centralised and distributed ones. The study aimed at designing and developing a distributed algorithm based on the Multiagent System (MAS) for FLSR in SDN. Data from the Tanzanian utility company, including load demand, distribution network topology, and emergency reports, enabled the development of the MAS for FLSR. An intensive literature review has also been conducted. Five agents, namely Control Agent, Grid Agent, Load Agent, Renewable Distributed Generation Agent and Switch Agent have been developed to support the FLSR process. The agents facilitated decision-making by locating faults and perform a restoration process through load transfer to the nearby transformer and shedding loads. Consideration of the stochastic nature of load demand through using the LSTM model maximised the restored load. Future work will focus on deploying and testing the developed algorithm on a real power system.

【 授权许可】

CC BY