Intelligent Transition Control between Grid-Connected and Standalone Modes of Three-Phase Grid-Integrated Distributed Generation Systems

Abstract

This paper proposes an intelligent seamless transition controller for smooth transition between grid-connected (GC) and standalone modes of distributed generation (DG) units in the grid. The development of this seamless controller contributes to two main processes in the transition modes: the synchronization process and an islanding process. For the synchronization process, the stationary reference frame phase-locked loop (SRF-PLL) associated with the voltage source inverter (VSI) is modified using the frequency, voltage deviation, and phase angle information. Furthermore, the islanding process is classified as intentional and unintentional islanding scenarios for achieving efficient transition control. Here, the intentional islanding process is achieved with the information that is available in the system due to the planned disconnection. For the unintentional islanding process, a fuzzy inference system (FIS) is used to modify the conventional droop control using the information of change in active power, voltage, and frequency. To identify the action of the proposed approach during the transition process, numerical simulations are conducted with the hardware-in-loop (HIL) simulator by developing a 10kWp three-phase grid-connected DG system. The results identified the efficient control of the VSI for both islanding and grid connection processes. In the islanding conditions, the proposed controller provides advantage with less detection and disconnection time, and during synchronization, it instantly minimizes the phase-angle deviation to achieve efficient control.

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

“…With the implementation of AI in transition techniques, the switching between the modes has become transition free. In [128,129], a fuzzy logic (FL)-based transition is proposed, which tends to generate a reference trajectory and enable smooth transition. Further, in [130], a model predictive control based transition controller is proposed, which has stable output and is much easier to implement with a small modification to the preexisting control algorithm.…”

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

“…The various traditional charging strategies adhere to the charging interfaces with novel strategies available for minimizing the impact of EVCS on the distribution grid being discussed [16]. The investigation of research gap for different control approaches in comparison with the existing literature [17][18][19][20][21][22][23] is presented in Table 1.…”

“…The investigation of the control strategies for PV‐powered EVCS control in terms of similarities and differences is as follows: In [18], the fuzzy inference system (FIS) was designed for the grid synchronization technique in the GC and SA modes using the droop controller; however, in our proposed work, an unintentional islanding detection and its classification in distributed sources is an add‐on feature as compared with [18]. Further in [23], a deep neural network (DNN)‐based short‐term forecast model of EVs load demand was designed.…”