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Adaptive Neurofuzzy Inference System Least-Mean-Square-Based Control Algorithm for DSTATCOM
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DOI：10.1109/TII.2016.2516823.png)
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Abstract
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This paper proposes the real-time implementation of a three-phase distribution static compensator (DSTATCOM) using adaptive neurofuzzy inference system least-mean-square (ANFIS-LMS)-based control algorithm for compensation of current-related power quality problems. This algorithm is verified for various functions of DSTATCOM, such as harmonics compensation, power factor correction, load balancing, and voltage regulation. The ANFIS-LMS-based control algorithm is used for the extraction of fundamental active and reactive power components from nonsinusoidal load currents to estimate reference supply currents. Real-time validation of the proposed control algorithm is performed on a developed laboratory prototype of a shunt compensator. The real-time performance of shunt compensator with ANFIS-LMS-based control algorithm is found satisfactory under steady-state and dynamic load conditions. The performance of the proposed control algorithm is also compared with fixed-step LMS and variable-step LMS (VSLMS) to demonstrate its improved performance.
Keywords:
Adaptive filtering
adaptive neurofuzzy inference system (ANFIS)
harmonics compensation
power quality
unity power factor
voltage regulation
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