Design and analysis of robust fuzzy logic maximum power point tracking based isolated photovoltaic energy system

Abstract

Photovoltaic (PV) energy is highly promising because of its renewable, green, and environment-friendly nature. In this article, the design and analysis of an isolated PV system using a push-pull converter with a fuzzy logic-based maximum power point tracking (MPPT) algorithm is presented. Furthermore, DC-DC converters, along with intelligent controllers fed with MPPT algorithms, are used to ensure the maximum extraction of incident energy. The proposed methodology utilizes fuzzy logic MPPT techniques based on an isolated push-pull boost converter to optimize the power output of PV modules, as well as to achieve isolation and high DC gain for DC/AC inversion. This work also presents a single-phase inverter with fuzzy logic close loop control analysis with LCL filter design. A Canadian solar panel of 250 W is assumed in this research work, which has an open circuit voltage 59.9 V, short circuit current 5.49 A at 25 • C temperature, and 1000 W/m 2 irradiance. The voltages are tracked, through the MPPT algorithm. These voltages represent a boost to 340 V DC through push-pull boost converter and are inverted up to 220 V AC through fuzzy logic voltage source inverter. In addition, a unipolar switching technique is used to remove the total harmonic distortion under linear load. The proposed methodology is simulated in MATLAB/Simulink. The simulation results verify that the proposed methodology can efficiently track the MPPT. Finally, the hardware prototype of the proposed system has been experimentally validated. K E Y W O R D S fuzzy logic control, maximum power point tracking, pulse width modulation, total harmonic distortion 1 INTRODUCTION Energy is the need of the modern world and there is a need to explore new means of energy, which are cheap and environment-friendly. The energy demand is increasing every year and already existing resources including thermal, hydro, nuclear, and gas are unable to meet the demand. There is a need for some alternate sources of energy that can meet This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

“…In previous research, the AI technique has been implemented for the long-term load and PF of electricity conventionally. In [23][24][25][26], a short-term load and PF using an NN fitting tool were used to compute hourly and daily data of weather temperature and electricity load as input features. Furthermore, the generalized-RNN indicates temperature statistics and price signals as input parameters.…”

Implementation of ANN-Based Embedded Hybrid Power Filter Using HIL-Topology with Real-Time Data Visualization through Node-RED

“…After the analysis of the results obtained from [20], it is observed that this algorithm successfully meets the required design specification of the harmonic eliminator filter. A Fast Transverse Recursive Least Squares (FT-RLS) is proposed for harmonic estimation in [21]. This algorithm helps to calculate the amplitude, frequency, and phase of the power signal that changes over time.…”

Hardware-in-the-Loop Implementation and Performance Evaluation of Three-Phase Hybrid Shunt Active Power Filter for Power Quality Improvement

“…Various control techniques, such as ANN, fuzzy logic control technique (FLC), an adaptive neuro-fuzzy-based inference system (ANFIS)-based controller maximum power point tracking (MPPT) to extract maximum power from PV-integrated SAPF, with SRRF-based control algorithm for reference current extraction are executed and validated with strong computational engine OPAL-RT 4510 based on field programmable gate array (FPGA) for real-time testing. e performance evaluation and comparison of various control algorithms (for balanced and unbalanced load) based on THD, losses, voltage drop, and pf have been presented along with optimized cost analysis by Kumar and Bansal [14] and Asif et al [15].…”