An artificial intelligent approach for the optimization of organic rankine cycle power generation systems

Jian Ding Tan, Chin Wai Lim, Johnny Siaw Paw Koh, Sieh Kiong Tiong, Ying Ying Koay

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The study on Organic Rankine Cycle (ORC) power generation system has gained significant popularity among researchers over the past decade, mainly due to the financial and environmental benefits that the system provides. A good maximum power point tracking (MPPT) mechanism can push the efficiency of an ORC to a higher rate. In this research, a Self-Adjusted Peak Search algorithm (SAPS) is proposed as the MPPT scheme of an ORC system. The SAPS has the ability to perform a relatively detailed search when the convergence reaches the near-optima peak without jeopardizing the speed of the overall convergence process. The SAPS is tested in a simulation to track for a moving maximum power pint (MPP) of an ORC system. Experiment results show that the SAPS outperformed several other well-established optimization algorithm in tracking the moving MPP, especially in term of the solution accuracies. It can thus be concluded that the proposed SAPS performs well as a mean of an MPPT scheme in an ORC system.

Original languageEnglish
Pages (from-to)340-345
Number of pages6
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume14
Issue number1
DOIs
Publication statusPublished - 01 Apr 2019

Fingerprint

Rankine cycle
Search Algorithm
Power generation
Cycle System
Cycle
Optimization
Optimization Algorithm
Term
Experiment
Simulation

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

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