A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

• Main Authors: Aziz N.L.A.A., Yap K.S., Bunyamin M.A.
• Other Authors: 55812399400
• Format: Conference paper
• Published: Institute of Physics Publishing 2023
• Subjects: Fault detection; Fuzzy logic; Learning systems; Waterworks; Circulating water system; Extreme learning machine; Fuzzy logic system; Improving efficiency; Mathematical tools; Natural languages; Overall efficiency; Power generation plants; algorithm; electricity generation; energy efficiency; fuzzy mathematics; natural resource; numerical model; pollution monitoring; uncertainty analysis; Knowledge acquisition

This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of �computing the word�. The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions. � Published under licence by IOP Publishing Ltd.