Open AccessUnified reliability centric preventive generator MS measure using fuzzy decision mechanism‐based ALO
Author(s) -
Umamaheswari Elango,
Ganesan Sivarajan,
Abirami Manoharan,
Subramanian Srikrishna
Publication year - 2017
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0090
Subject(s) - preventive maintenance , reliability engineering , computer science , probabilistic logic , fuzzy logic , economic dispatch , reliability (semiconductor) , scheduling (production processes) , operations research , mathematical optimization , engineering , electric power system , mathematics , artificial intelligence , power (physics) , physics , quantum mechanics
Regular preventive maintenance scheduling (PMS) of generating units is required in order to prolong their life expectancy so as to ensure safe operating conditions, and most importantly to reduce the risk of unplanned outages due to failures. The decision making of PMS is related to the measures of economic dispatch and other planning activities that necessitate an exact mathematical PMS model. Traditionally, generator PMS problem is based on levelling the spinning reserve. However, in practical, levelling the reserve is not alone good enough. Including a probabilistic model along with the deterministic model is a welcome perspective. In this study, a new multi‐objective model combining deterministic/probabilistic models is proposed. Furthermore, the generating unit failures have been incorporated in PMS to realise their impacts on reliability objectives. These aspects will increase further the complexity of the solution procedure. The advantages of modern bio‐inspired algorithm, namely ant lion optimiser (ALO) attracts to use it as a main optimisation tool. The fuzzy decision mechanism is incorporated in the ALO to extract the best compromise solution in the multi‐objective solution space. The standard test systems are used for implementation. Numerical simulation results indicate that the intended method has the capability of obtaining best compromised solution.