C
C.S. Chang
Researcher at National University of Singapore
Publications - 70
Citations - 2445
C.S. Chang is an academic researcher from National University of Singapore. The author has contributed to research in topics: Electric power system & Genetic algorithm. The author has an hindex of 26, co-authored 62 publications receiving 2235 citations.
Papers
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Journal ArticleDOI
Optimising train movements through coast control using genetic algorithms
C.S. Chang,S.S. Sim +1 more
TL;DR: A genetic algorithm (GA) is proposed to optimise train movements using appropriate coast control that can be integrated within automatic train operation (ATO) systems and the results, although preliminary, suggest that the method is promising.
Journal ArticleDOI
Area load frequency control using fuzzy gain scheduling of PI controllers
C.S. Chang,Weihui Fu +1 more
TL;DR: In this paper, a fuzzy gain scheduling of proportional-integral (PI) controllers is proposed for area load frequency control (LFC) problem using fuzzy-gain scheduling of PI controllers.
Journal ArticleDOI
An Intelligent Tuned Harmony Search algorithm for optimisation
TL;DR: A new variant of the HS algorithm is proposed that maintains a proper balance between diversification and intensification throughout the search process by automatically selecting the proper pitch adjustment strategy based on its Harmony Memory.
Journal ArticleDOI
Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting
TL;DR: The implementation and forecasting results of a hybrid fuzzy neural technique, which combines neural network modeling, and techniques from fuzzy logic and fuzzy set theory for electric load forecasting, and is simple to implement on a personal computer are described.
Journal Article
Parallel Neural Network-Fuzzy Expert System Strategy for Short-term Load Forecasting System Implementation and Performance Evaluation
TL;DR: The online implementation and results from a hybrid short-term electrical load forecaster that is being evaluated by a power utility are documented in this paper, whereby Kohonen's self-organizing feature map with unsupervised learning is used to classify daily load patterns.