Distributed Adaptive Control Framework for Enhanced Voltage and Frequency Regulation in Inverter Interfaced Autonomous Distribution Network

This article presents a unique design method for an adaptive neural network based backstepping-like control (ANNBC) scheme. The technique is employed for synthesizing the primary controller for inverter interfaced distributed generators (IIDGs) integrated to an autonomous distribution network. Further, an optimal distributed secondary control framework is developed for a multiple IIDGs-based autonomous distribution network. The secondary controller facilitates optimal gain selection to regulate the frequency of the system and voltage of the critical bus to their desired set points. The framework also achieves accurate real and reactive power sharing among the IIDGs according to their power ratings. The novel design procedure of the proposed control framework takes into account the entire system dynamics of the IIDG including the uncertain terms (viz., load current and network dynamics) and is completely independent of the system parameters information. Suitable update laws are designed for estimating the unknown weights of the neural network and the uncertain system parameters. Lyapunov analysis is used to show that the tracking errors and parameter estimation errors are uniformly ultimately bounded. Finally, case studies are conducted on a typical autonomous distribution network having a single and multiple IIDGs modeled in MATLAB/Simulink platform.