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Tania, P. (2009) Composite Load Model Decomposition: Induction Motor Contribution. Dissertation for the Doctoral Degree, Faculty of Built Environment and Engineering, School of Engineering Systems, Queensland University of Technology, Brisbane.

has been cited by the following article:

• TITLE: Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

  AUTHORS: Nitin Kumar Saxena, Ashwani Kumar

  KEYWORDS: Reactive Power Control, Hybrid Power Systems, Genetic Algorithms, Load, Artificial Neural Network, Adaptive Neuro Fuzzy Interface System

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.3 No.4, November 10, 2015

  ABSTRACT: This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.