Real-Time Power Quality Enhancement in a Hybrid Micro-Grid Using Nonlinear Autoregressive Neural Network

Abstract

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.

Control and Improvement of Power Quality in Hybrid Three-Terminal AC/DC Microgrids

“…In order to attain the intended objective, a customized active power filter and a power filter compensator kit modules have been employed to enhance the harmonics of the AC component of the system. In accordance with reference [21], a solitary MG is constructed utilizing PVs, WTs, and FCs as distributed energy resources. An investigation has been conducted on a controller in this MG to ensure that power quality issues are kept within the specified standard range.…”

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

“…[36] has implemented deep learning for the optimal power allocation in microgrids. In addition, neural networks have been used to enhance the power quality in a hybrid microgrid by [37]. In addition, ref.…”

Multiple Grid-Connected Microgrids with Distributed Generators Energy Sources Voltage Control in Radial Distribution Network Using ANFIS to Enhance Energy Management

“…The authors in [42] use the application of a Fuzzy Logic Controlled Static Var Compensator (SVC) for voltage control and Ferranti effect mitigation in transmission systems. However, a hybrid series active power filter was used in reference [43] to enhance Power Quality with ANFIS Controller, and using Nonlinear Autoregressive Neural Network [44], Neuro-based controllers that reduce oscillation [45] for Grid-Connected Microgrid with Solar PV and Wind Turbine. In [46], the ANFIS approach for reconfiguring Distribution Networks was established with the goals of keeping the feeder power balanced, lowering active power loss, and minimizing node voltage volatility.…”