Open AccessReliable/cost‐effective maintenance schedules for a composite power system using fuzzy supported teaching learning algorithm
Author(s) -
Subramanian S.,
Abirami M.,
Ganesan S.
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.0718
Subject(s) - computer science , composite number , electric power system , algorithm , fuzzy logic , reliability engineering , machine learning , power (physics) , artificial intelligence , engineering , physics , quantum mechanics
With the increasing electricity power consumption and time‐varying load demands, maintaining system reliability and economic operation has become a challenging task for power system operators during the scheduled outages. Nevertheless, very few publications in the current literature tackle this maintenance schedule problem by means of evolutionary algorithms, and, when they do, they focus only on generators. In the proposed model, the authors have considered coordinated maintenance scheduling (CMS) with cost minimisation and reliability maximisation as a multi‐objective criterion, and fuzzy‐based teaching learning‐based optimisation is used as an optimisation tool for solving the formulated maintenance model. The proposed model solves the CMS along with security‐constrained unit commitment (SCUC) over 1‐year planning horizon and the final SCUC solution prescribes an economic and secure operation for composite power systems. The method is demonstrated on the IEEE 30‐bus, IEEE reliability test system (single and three areas) and practical Indian utility 19‐unit power systems, and the results are presented.