Open AccessThe Implementation of K-Means Clustering in Coal-fired Power Plant Performance
Author(s) -
Dian Wahyuningtyas,
Muhammad Asrol
Publication year - 2022
Publication title -
international journal emerging technology and advanced engineering
Language(s) - English
Resource type - Journals
ISSN - 2250-2459
DOI - 10.46338/ijetae0322_01
Subject(s) - cluster analysis , power station , coal fired power plant , k means clustering , computer science , index (typography) , power (physics) , reliability engineering , data mining , engineering , artificial intelligence , physics , quantum mechanics , world wide web , electrical engineering
Performance monitoring for power plant industry is important to know current condition and determined the prior way for improvement strategy. This study aims to classified performance index which are Equivalent Availability Factor (EAF) and Equivalent Forced Outage Rate (EFOR) in various coal-fired power plant in Java Bali, Indonesia. The results of this plants grouping possible to be applied as one of the supports systems in real-time operational decisions to determine plant maintenance priorities. The proposed clustering model is possible to be applied for power plant’s control centre unit in Indonesia in grouping power plants based on performance indexes. The result show that there were 4 clusters of the power plant performance. The clustering drives to identify which plant that must be prioritized for improvement. The clustering method has a model accuracy rate of 87% which indicates that model was valid and applicable. For further research, a decision support system design was required for operational validation. Keywords—Decision-making, Decision support system, Kmeans clustering, Performance, Power plant.