Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid

Abstract

In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro-fuzzy wavelet-based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower–top level arrangements. The top-level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro-fuzzy wavelet-based controller is applied to the inverter. The new proposed wavelet-based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro-fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response.

A Brief Review on Identification, Categorization and Elimination of Power Quality Issues in a Microgrid Using Artificial Intelligent Techniques

“…2. Also, by using the suggested controller, the harmonic contents (2.37%) and frequency variation (less than 0.02%) were way lesser, shown in (Table 20) (Kamal et al 2020), than their respective standard limits of 5% and ±0.8% as set by the IEEE. Based on the above interpretations, the authors concluded that the suggested control technique had superior performance in handling power transfers, regulating grid frequency as well as minimizing system harmonics as compared to the other methods.…”

“…The performance comparisons for both the cases are tabulated in (Table 18) and (Table 19) (Tephiruk et al 2018) Based on the above observations, the authors concluded that the Fuzzy-aided BESS control technique had better performance than the robust BESS method in maintaining a stable grid operation. In order to obtain better power transfer capabilities and system frequency regulation, higher efficiency of microgrid inverter output as well as lesser harmonic contents in a PV-integrated microgrid system, the authors Tariq Kamal et al (Kamal et al 2020) devised a microgrid control based on a combination of supervisory and Adaptive Neuro-Fuzzy Jacobi Wavelet (ANFJW) control methods. They divided the supervisory control into upper and lower-level positions such that the upper position produced control signals by taking into consideration the variation of load and weather conditions whereas the grid converters and energy sources were under the control of the lower position.…”

Different DC-Link Control Methods with Multilevel Inverter for Low Harmonic and Efficient Power Transfer in Grid-Tied Hydrogen Fuel Cell Systems

“…On the other hand, while fuzzy logic control is an alternative and effective solution for the analysis of complex, nonlinear and ill-defined systems, artificial neural networks is defined as an intelligent system that has effective advantages such as learning, adaptation, speed. Morover, adaptive neuro-fuzzy inference system technique has taken its place in the literature as a hybrid research alternative that incorporates efficient solution proposals of fuzzy logic and artificial neural network methods [8], [9]. In this study, a three-phase structure is designed for the grid connection of hydrogen fuel cells.…”

Comparative Performance Analysis of the DC-AC Converter Control System Based on Linear Robust or Nonlinear PCH Controllers and Reinforcement Learning Agent

“…Traditionally, PI-type controllers are used, which offer relatively good performance and parametric robustness, but only around static operating points established after the tuning of the PI-type controller [ 11 ]. Naturally, to obtain superior control performances, a series of modern types of controllers have been developed and implemented specifically for the control of the main elements of the microgrid described above, including adaptive controllers [ 12 ], robust controllers [ 13 , 14 , 15 , 16 , 17 ] in case of significant parametric variations, neuro-fuzzy controllers [ 18 ], as well as nonlinear controllers based on the Passivity theory, including nonlinear PCH [ 19 , 20 , 21 , 22 , 23 ].…”