A Distributed Voltage Control Method for a Remote Microgrid Using Adaptive Fuzzy Controllers System Supported by Batteries and Solar Energy

The integration of renewable energy sources, particularly solar PV (PV) power systems and battery energy storage is an effective solution to address energy supply challenges in remote and off-grid areas. However, the intermittent nature and non-linearity of solar power generation, along with the ever-evolving demands of loads and the capabilities of conventional control systems, can lead to voltage instability and power quality deterioration. In order to overcome these problems and ensure the dependable, stable, and sustainable functioning of microgrids powered by renewable energy, this research emphasizes the use of sophisticated intelligent control approaches. The proposed adaptive Fuzzy-ANN control method integrates the interpretability and ability to deal with uncertainty of fuzzy logic with the adaptive learning and nonlinear mapping capabilities of artificial neural networks. Hybrid intelligent controllers are better than the traditional PI/PID controllers and also better than the stand-alone fuzzy logic controllers in the voltage regulation, transient response improvement, reduction of total harmonic distortion and efficient utilization of the battery energy storage. The adaptive Fuzzy-ANN controller continuously adjusts the operating conditions of the control parameters to enhance the compensating effect of the voltage drop due to the change of solar radiation, sudden load increase or decrease, and battery SOC. The extensive literature review and simulated tests revealed that adaptive intelligent controllers are definitely a good solution to enhance the operational stability of far-off microgrids consisting of renewables. Furthermore, the proposed control structure could be easily implemented with more distributed renewable energy resources and improved energy management methods. They are not only technically superior but also increase the social and economic benefits of decentralised energy supply and rural electrisation projects, reduce operating and maintenance costs and extend battery life. In conclusion, robust, smart and sustainable microgrid systems could be obtained by adaptive Fuzzy-ANN control approaches. Future work should focus on hardware implementation, experimental validation in real time and integration with optimization techniques such as metaheuristic algorithms and predictive control techniques to improve the system's durability and efficiency. The key to building a fully-fledged solar, wind and advanced energy storage hybrid renewable energy system is important to overcome global energy sustainability, reliability and environmental protection goals towards the future autonomous microgrid.

[1] M. Kumar, S. Sen, and S. Kumar, “A robust performance analysis of a solar PV-battery based islanded microgrid inverter output voltage control using dual-loop pid controller,” in Proc. IEEE IAS Glob. Conf. Emerg. Technol., 2022, pp. 6–11.   [2] A. Bani-Ahmed, M. Rashidi, A. Nasiri, and H. Hosseini, “Reliability analysis of a decentralized microgrid control architecture,” IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 3910–3918, Jul. 2019.   [3] X. Ge, H. Han,W. Xiong, M. Su, Z. Liu, and Y. Sun, “Locally-distributed and globally-decentralized control for hybrid series parallel microgrids,” Elect. Power Energy Syst., vol. 116, pp. 1–10, 2020.   [4] M. Kumar and B. Tyagi, “A state of art review of microgrid control and integration aspects,” in Proc. IEEE 7th Int. Conf. Power Electron., 2016, pp. 1–6.   [5] K.-Y. Huang, H.-C. Chin, and Y.-C. Huang, “A model reference adaptive control strategy for interruptible load management,” IEEE Trans. Power Syst., vol. 19, no. 1, pp. 1661–1662, Feb. 2004.   [6] A. Abraham and N. Pappa, “An improved MRAC scheme for non-linear design of adaption gain γ using heuristic algorithms,” in Proc. Int. Conf. Adv. Control Optim. Dynamical Syst., 2014, pp. 734–739.   [7] A. B. Siddique, S. Munsi, S. K. Sarkar, and S. K. Das, “Model reference modified adaptive PID controller design for voltage and current control of is landed microgrid,” in Proc. IEEE 4th Int. Conf. Elect. Eng. Inf. Commun. Technol., 2018, pp. 130–135.   [8] M. Akter, M. A. Rahman, M. S. Munsi, A. B. Siddique, S. K. Sarkar, and S. K. Das, “Multi-objective model reference modified adaptive PID framework to islanded microgrid control under various load conditions,” in Proc. IEEE Int. Conf. Advance. Elect. Electron. Eng., 2018, pp. 1–4.   [9] S. Ahmad, S.Mekhilef, and H. Mokhlis, “DQ-axis synchronous reference frame-based P-Q control of grid connected AC microgrid,” in Proc. IEEE Int. Conf. Comput., Power Commun. Technol., 2020, pp. 842–847.   [10] M. Nasir, H. A. Khan, A. Hussain, L. Mateen, and N. A. Zaffar, “Solar PV-Based scalable DC microgrid for rural electrification in developing regions,” IEEE Trans. Sustain. Energy, vol. 9, no. 1, pp. 390–399, Jan. 2018.