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.
Highlights
Over 400 million people in India, including 47.5% of those living in India’s rural areas, still had no access to electricity [1]
It has been concluded that power system voltage stability behaviour and choice of compensation techniques significantly depends on selection of the load model and its parameters [21] [22]
This paper presents a design methodology based on artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) for an adaptive proportional integral (PI) voltage reactive powers control
Summary
Over 400 million people in India, including 47.5% of those living in India’s rural areas, still had no access to electricity [1]. (2015) Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System. The reactive power is required in system for stabilizing the voltage response due to the changes in load and input power demand. This demand is fulfilled by fast acting dynamic device so that the system may attain its steady state in least time. STATCOM operation as dynamic reactive power compensator is most popular in such studies. This paper contribution may be summarized as: i) effect on the performance of dynamic compensation using wide range of probabilistic pattern in composite load model and input wind power; ii) estimation of tuning parameters for STATCOM using GA, ANN and ANFIS based soft-tuning methods
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