Abstract

A methodology for designing and assessing an optimal controller to regulate frequency in a two-area interconnected power system with Renewable Energy Sources (RES) is demonstrated in this paper. The Load Frequency Control (LFC) system makes it possible to restore both system frequency and scheduled tie-line power to their nominal values in a deregulated environment. The introduction of an advanced controller has the potential to improve the LFC mechanism's performance. The present article employs the Inertia Emulation Technique (IET) to demonstrate the potential influence of the novel High Voltage Direct Current (HVDC) tie-line model and converter capacitors. The Integral Time-weighted Absolute Error (ITAE) has been taken as an objective function for the proposed controller. This investigation proposes a novel adaptive control strategy i.e., ANN-based (PIλf + PIλDN) controller for the anticipated LFC mechanism. The modified Quasi-Opposition-learning-based Volleyball Premier League (QOVPL) method is utilized to assess the optimal control parameters with the highest efficacy. The effectiveness of the proposed LFC framework has been evaluated through the implementation of established methodologies for managing step and random load perturbations. The supremacy and effectiveness of the proposed control scheme are validated over recently published work.

Key words: High voltage direct current; Load frequency control; Artificial neural network; Inertia emulation technique; Quasi-opposition-learning-based volleyball premier league.