The extensive use of renewable energy sources (RESs) in energy sectors plays a vital role in meeting the present energy demand. The widespread utilization of allocated resources leads to multiple usages of converters for synchronization with the power grid, introducing poor power quality. The integration of distributed energy resources produces uncertainties which are reflected in the distribution system. The major power quality problems such as voltage sag/swell, voltage unbalancing, poor power factor, harmonics distortion (THD), and power transients appear during the transition of micro-grids (MGs). In this research, a single micro-grid is designed with PVs, wind generators, and fuel cells as distributed energy resources (DERs). A nonlinear auto regressive exogenous input neural network (NARX-NN) controller has been investigated in this micro-grid in order to maintain the above power quality issues within the specific standard range (IEEE/IEC standards). The performance of the NARX-NN controller is compared with PID and fuzzy-PID controllers. The single micro-grid is extended to design a three-phase large-scale realistic micro-grid structure to test the feasibility of the proposed controller. The realistic micro-grid is verified through addition of line-impedance, communication delay, demand response, and off-nominal situations. The proposed controller is also validated by simulating different test scenarios using MATLAB/Simulink and TMS320-based processor-in-loop (PIL) for real-time implementation.

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller’s reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform.

The evolution of non-renewable energy sources in modern era leads to the perception of Micro Grid (MG). The enormous energy demand of the exponentially increasing population requires alternative energy resources. The distributed resources are eco-friendly and emission less or nonhazardous in nature hence gaining more attention for present as well as future energy mandate. In this aspect the resources like solar, wind, geothermal and fuel cell energy are of prime importance because of their easy accessibility. The distributed and small scale energy production of MGs makes it very difficult for them to be tied and synchronized to utility grid. Further even after the coupling of MG with traditional grid system proper monitoring and maintenance of the power quality is indispensable. In this study the active power (P), reactive power (Q) and the Power Factor (PF) of a grid tied MG system is monitored and maintained with the help of Artificial Neural Network (ANN) trained Distributed Static Compensator (DSTATCOM). The superiority of the ANN is compared with that of traditional fuzzy logic controller (FLC). A brief analysis is executed by the simulation data of the proposed system, for multiple operating circumstances using Matlab/Simulink architecture. The results obtained confirms that the ANN controller is more efficient than FLC for improving the system characteristics in terms of better efficiency, stability, and dynamic response.