GLMS control strategy based DSTATCOM for PQ enhancement: modeling and comparative analysis

The power quality (PQ) improvement employing appropriate intelligent controllers is identified as a suitable solution for the dependable, safe, and stable operation in the electric utility systems (EUS). Hence, this paper proposes new types of neural network (NN) based Greedy Least Mean Square (GLMS) controllers for distributed static compensators (DSTATCOM). First, the EUS is developed by considering current source-based non linear load using the MATLAB/Simulink environment. Next, both controllers are implemented by their own learning principle using embedded systems toolboxes. The proposed scheme combines different weighting factors like step size, learning rate and convergence factor to achieve the approximate tuned weight. Then, the final weighting components are achieved by adding DC and AC PI controller weights corresponding to the mean value of active/reactive weight of load currents for the triggering pattern. The proposed control algorithm provides a precise control for the system by maintaining reduced voltage across the self-braced capacitor as compared to the Adaptive Least Mean Square (ALMS) controller. The other PQ merits like better voltage regulation, load balancing, total harmonic distortion (THD) suppression, and power factor (P.F) improvement are attained. Finally, experimental results obtained from the proposed controller fulfill all the shunt-related PQ requirements as per IEEE benchmark for the different case studies.