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.
|Journal||IOP Conference Series: Earth and Environmental Science|
|Publication status||Published - 01 Jan 2013|
|Event||26th IAHR Symposium on Hydraulic Machinery and Systems - Beijing, China|
Duration: 19 Aug 2012 → 23 Aug 2012
All Science Journal Classification (ASJC) codes
- Environmental Science(all)
- Earth and Planetary Sciences(all)