Soft-Computing Techniques for Voltage Regulation of Grid-Tied Novel PV Inverter at Different Case Scenarios
In this paper, the voltage regulation of large-scale grid-tied photovoltaic power plant (GTPVPP) operating during nonlinear PV generation has been discussed. This research proposes the comparative voltage regulation of a novel multilevel inverter with soft-computing techniques such as fuzzy and adaptive neuro-fuzzy inference system (ANFIS)-based control for regulating the voltage of GTPVPP. Due to the interruptible PV generation and at worst-case scenarios, the proposed control scheme is useful to satisfy the load demand by grid integration. In this comparison, the ANFIS-based control scheme improves the dynamic performance, reduces the THD, and improves the efficiency. The fuzzy and proposed ANFIS-based control schemes are developed in MATLAB/Simulink environment and are compared at worst-case solar generation, rapid change of loads, and grid faults.
KeywordsSolar PV generation DC–DC converter Resonant switched-capacitor converter (RSCC) Multilevel inverter Soft-computing techniques
- 8.Jang, J.-S., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice-Hall, Englewood Cliffs, NJ, USA (1997)Google Scholar
- 11.Mirhosseini, M., Agelidis, V.G.: Performance of Large-Scale Grid-Connected Photovoltaic System under Various Fault Conditions. IEEE (2013)Google Scholar
- 13.Suroso, S., Noguchi, T.: New generalized multilevel current-source PWM inverter with no-isolated switching devices. In: Proceedings of the IEEE International Conference Power Electronics Drives Systems (PEDS), pp. 314–319 (2009)Google Scholar
- 15.Basarir, H., Elchalakani, M., Karrech, A.: The Prediction of Ultimate Pure Bending Moment of Concrete-Filled Steel Tubes by Adaptive Neuro-Fuzzy Inference System (ANFIS). Springer, Australia (2017)Google Scholar