Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural Networks

Abstract

The voltage source inverters in microgrids often rely on the droop control method integrated with voltage and inner current control loops in order to provide a reliable electric power supply. This research aims to present a Cascade-Forward Neural Network (CFNN) droop control method that manages inverter-based microgrids under grid-connected/islanded operating modes. The proposed method operates the inverter in a bi-directional technique for a wide range of battery energy storage systems or any other distributed generation systems. The proposed strategy uses the CFNN to learn the inverter's nonlinear model to achieve accurate demand and reference power tracking under different operating conditions for smart grid applications. Additionally, it reformulates the grid control concept to drive the inverter based on the optimal conditions by considering the power demand, reference power, equipment size, and disturbances. Also, it does not require any tuning procedure. The power tracking and operating performance of the proposed CFNN controller are evaluated through several experimental tests using the power hardware-in-the-loop (PHIL) methodology in different scenarios. All results are matched with the proven conventional strategy to confirm its effectiveness.INDEX TERMS Distributed generation, droop control, inverter-based power system, microgrid, cascadeforward neural network.

A Review of Control Techniques for Inverter-Based Distributed Energy Resources Applications

“…A microgrid can be connected to several DERs; coordination between these units is required to fulfil the demand. Inverters, though, should meet the load requirements in stand-alone and grid-connected modes of operation and meet the voltage and frequency restrictions [26]. Various methods of control strategies have been proposed to maintain the voltage and frequency stability in the power system, in which droop control techniques are the most used strategy to regulate the exchange of active and reactive power in inverterinterfaced microgrids [27].…”

“…Since conventional power systems have rotational inertia, they respond better to fast load changes to maintain stability, whereas RESs are inverter-based sources with no rotational inertia. Thus, the control strategy becomes essential to maintain system reliability [26]. Droop control techniques are the most often utilized strategy for managing the exchange of active and reactive power in inverter-interfaced microgrids.…”

Assessment of Cyber-Physical Inverter-Based Microgrid Control Performance under Communication Delay and Cyber-Attacks

“…This purposeful simplification omitted the intricate dynamics of renewable energy sources integrated into the DG system, such as solar variations and wind speed fluctuations, aligning with the proposed research goal and centering focus on the cyber-physical MG assessment. The inverters work under voltage control mode as the system works in an islanded mode, and the voltage and current control loops manage their operation; detailed equations governing these control loops are available in [30,34]. The two parallel-connected inverters operate under a droop control loop for power-sharing.…”

“…The proposed scheme proved its effectiveness under different operating conditions, though they did not include the cyber aspect impact on the presented analysis. A cascade-forward neural networks-based droop control algorithm for the inverter-based MG was developed in [30]; the authors proposed real-time experimental tests. However, they did not introduce the impact of cyberattacks on the proposed system.…”

Conservation Voltage Reduction Strategy for Autonomous Microgrid with Improved Voltage-CurrentDroop-Based Inverter Control Framework

“…Te virtual oscillator control technique is recently used for improved power sharing, faster synchronization, enhances the dynamic characteristics of the system, and ensures better performance than the droop control [12]. In [13], the droop method known as cascade forward neural network technique is utilized to adopt the nonlinear model of the inverter to track reference power and demand at diferent operating characteristics. A nonlinear droop method is proposed for parallel converters in microgrid which utilizes the probability distribution function of the load current to optimize the droop characteristics [14].…”