In recent years, with the advent of globalization, the world is witnessing a steep rise in its energy consumption. The world is transforming itself into an industrial and knowledge society from an agricultural one which in turn makes the growth, energy intensive resulting in emissions. Energy modeling and energy planning is vital for the future economic prosperity and environmental security. Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted in energy modeling to precisely map the energy systems. In this paper, an attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates.

Applications of fuzzy logic in renewable energy systems – A review

Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.

State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review

This study presents an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc-dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with PV module of Kyocera KD210GH-2PU connected to PV boost dc-dc converter. For hardware implementation, the proposed adaptive P&O-fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O-fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.

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Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost dc-dc converter

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Electronic Control Of Switched Reluctance Machines

String inverter photovoltaic (PV) systems with bypass diodes require improved global maximum power point tracking (GMPPT) algorithms to effectively reach the absolute maximum power operating point. Several GMPPT algorithms have been proposed to deal with this problem, but most of them require scanning wide voltage ranges of the PV array from nearly zero voltage to open-circuit voltage that increases the scanning time and, in turn, causes energy loss. This paper presents a novel GMPPT method which significantly restricts the voltage window search range and tracks the global power peak rapidly in all shading conditions. Simulation tests and experimental comparisons with another GMPPT algorithm are presented to highlight the features of the presented approach.

Global MPPT Scheme for Photovoltaic String Inverters Based on Restricted Voltage Window Search Algorithm

Maximum power point tracking of partially shaded solar PV system using modified Fibonacci search method with fuzzy controller

Evolutionary computation based multi-objective pole shape optimization of switched reluctance machine

This paper presents the modelling, design and power management of a hybrid energy storage system for a three-wheeled light electric vehicle under Indian driving conditions. The hybrid energy storag...

https://journals.sagepub.com/doi/pdf/10.1177/0020294019858212

Modelling, design and control of a light electric vehicle with hybrid energy storage system for Indian driving cycle:

This paper presents Particle Swarm Optimization (PSO) based design optimization of Switched Reluctance Machine (SRM). The SRM design is formulated as multiobjective constrained optimization problem. The objective functions for obtaining desired design are maximization of average torque and minimization of torque ripple with stator and rotor pole arc as design variables. The optimization procedure is tested on 8/6, four-phase, 5 HP, 1500 rpm SRM. The results are compared and investigated with those obtained from Genetic Algorithm (GA) technique and FEA simulation. The results demonstrate that the proposed method is effective and outperforms GA in terms of solution quality, accuracy, constraint handling and computational time.

Design optimization of Switched Reluctance Machine using Particle Swarm Optimization

This work focuses on adaptive controller design using fuzzy Logic (FL) controller to get better dynamic performance under non-linearities and parameter uncertainties. In the proposed controller, the membership function (MF) parameters are tuned in line with the parameter variations of the motor. The steady state, regulatory and servo response of the proposed controller are analysed to highlight the merits of the controller. The simulation results indicate that the proposed controller possesses good tracking capability and faster response time in comparison with conventional schemes.

M. Balaji

Papers

Maximum power point tracking of partially shaded solar PV system using modified Fibonacci search method with fuzzy controller

Evolutionary computation based multi-objective pole shape optimization of switched reluctance machine

Modelling, design and control of a light electric vehicle with hybrid energy storage system for Indian driving cycle:

Design optimization of Switched Reluctance Machine using Particle Swarm Optimization

Adaptive MF tuned fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique