Sparse LMS algorithm for two-level DSTATCOM

Abstract

Sparse least mean square algorithm is proposed for the DSTATCOM as an optimal current harmonic extractor to cope with the intermittent nature of loadings. Sparse least mean square is the improved version of adaptive least mean square learning mechanism with regards to sparsity. This innovative approach is utilized for better parameter estimation due to its algorithmic simplicity and parallel computing nature. Hence, sparse least mean square is expected to reduce the computation and storage requirements significantly. This suggested controller consists of six subnets. Three subnets for active and another three for reactive weight component are used to extract the fundamental component of the load current. Several factors like previous weight, normalizing weight and learning rate are involved in the sparse least mean square based weight-updating rule to have better dynamic performance, reduced computational burden and better estimation speed etc. The detailed control algorithm is formulated using MATLAB/Simulink, and validated using experimental analysis. Among these two algorithms, the sparse least mean square offers better voltage regulation, voltage balancing, source current harmonic reduction and power factor correction under various loading scenarios.