MIMO Fuzzy to Improve Search Speed and Reduce Oscillation in Maximum Efficiency Point Tracking of Small-Scale Compressed Air Energy Storage

Abstract

This paper presents the research results on a small scale compressed air energy storage (SS-CAES) operated at its maximum efficiency value in the discharged phase. This study used two methods to find the maximum efficiency; Standard Perturb and Observe (Standard P&O), and Multiple Input Multiple Output Fuzzy Perturb and Observe (MIMO Fuzzy P&O). The essential ideas of this study are to operate SS-CAES in its maximum efficiency in specific loading power and to improve the performance of standard P&O method in achieving maximum efficiency and reduce the oscillation amplitude of the efficiency value as well. To do this, in this study, we use two devices, namely a motorized valve to ensure sufficient power required for the load and a boost converter whose duty cycle will be adjusted so that the system can operate at its maximum efficiency. The study was conducted using a simulation model and compared these two methods performance for the same loading case. The experimental results reveal that the addition of the MIMO Fuzzy algorithm can improve the performance of the Standard P&O method in accelerating maximum efficiency achievement and reducing oscillations when the system has reached the maximum operating points. The performance improvement on the maximum efficiency point tracking (MEPT) in SS-CAES operations, evidenced by the increase in the average efficiency value in the given loading scenario of about 1.22%. Besides, the application of MIMO Fuzzy P&O also impacts reducing the amplitude of speed oscillations in the steady-state area, which is about half of the amplitude of speed oscillations that occur with the application of the standard P&O method. This proposed method also shows better performance than the MEPT method used in previous studies in terms of accelerating maximum efficiency achievement.

An oscillatory search method for relay protection logic diagrams based on sequential traversal

“…Increasing the iterative process will increase the complexity of the algorithm operation. When the dimension is greater than 8, the effect of finding the optimal value will increase significantly [8].…”