Solar photovoltaic (PV) systems are attracting a huge focus in the current energy scenario. Various maximum power point tracking (MPPT) methods are used in solar PV systems in order to achieve maximum power. In this article, a clear analysis of conventional MPPT techniques such as variable step size perturb and observe (VSS-P&O), modified incremental conductance (MIC), fractional open circuit voltage (FOCV) has been carried out. In addition, the soft computing MPPT techniques such as fixed step size radial basis functional algorithm (FSS-RBFA), variable step size radial basis functional algorithm (VSS-RBFA), adaptive fuzzy logic controller (AFLC), particle swarm optimization (PSO), and cuckoo search (CS) MPPT techniques are presented along with their comparative analysis. The comparative analysis is done under static and dynamic irradiation conditions by considering algorithm complexity, tracking speed, oscillations at MPP, and sensing parameters. The single-diode model PV panel and double-diode model PV panel are also compared in terms of fill factor (FF) and maximum power extraction. Clear insight is presented supporting the suitability of MPPT techniques for different types of converter configurations.

https://www.mdpi.com/1996-1073/13/2/371/pdf

Different Conventional and Soft Computing MPPT Techniques for Solar PV Systems with High Step-Up Boost Converters: A Comprehensive Analysis

Due to environmental concerns, power system generation is shifting from traditional fossil-fuel resources to renewable energy such as wind, solar and geothermal. Some of these technologies are very location specific while others require high upfront costs. Photovoltaic (PV) generation has become the rising star of this pack, thanks to its versatility. It can be implemented with very little upfront costs, e.g., small solar home systems, or large solar power plants can be developed to generate MWs of power. In contrast with wind or tidal generation, PV can be deployed all around the globe, albeit with varying potentials. These merits have made PV the renewable energy technology with highest installed capacity around the globe. However, PV penetration into the grid comes with its drawbacks. The inverter-interfaced nature of the PV systems significantly impacts the power system operation from protection, power flow and stability perspectives. There must be strategies to mitigate these negative impacts so that PV penetration into the grid can continue. This paper gives a thorough overview of such strategies from different research fields: such as communication, artificial intelligence, power electronics and electric vehicle charging coordination. In addition, possible research directions are given for future work.

A Review of Strategies to Increase PV Penetration Level in Smart Grids

Maximum power point trackers (MPPT) are required to obtain the accurate and high tracking efficiency from the photovoltaic (PV) panel. In this paper, a genetic algorithm (GA) based MPPT is proposed because of its simple design implementation, high convergence speed and excellent capability to get global peak power point under variable weather conditions. Also, cuk converter based PV power system is selected because of excellent buck-boost power capability, better voltage regulation and lower power losses compared to other dc-dc converter. The proposed MPPT tracker has been tested under steady as well as transient operating conditions. The experimental responses are validated through control data acquisition system of real time dSPACE DS 1104 kit, which provides better performance characteristics with small steady state error compared to conventional MPPT algorithm.

https://www.ijrer.ijrer.org/index.php/ijrer/article/download/5379/pdf

An Experimental Implementation and Testing of GA based Maximum Power Point Tracking for PV System under Varying Ambient Conditions Using dSPACE DS 1104 Controller

The non-conventional energy sources received great attention to the researchers because of the limitations of fossil fuels and the requirement for eco-friendly atmosphere. The recent photovoltaic (PV) power system has only 15–20% conversion efficiency to obtain electric power from solar radiation. In order to enhance the energy conversion efficiency of the PV system, a maximum power point tracking (MPPT) system is required. By the application of MPPT methods, the power tracking ability of PV panels is improved and optimal power is obtained. This chapter presents in detail the development of PV system using zeta buck–boost converter as most attractive potential application in distributed generation. Novel fuzzy logic control (FLC) algorithm has been developed to extract maximum power with improved steady-state and dynamic performance. The zeta converter is employed to track peak power from PV system and works as a MPPT tracker. The output DC power from zeta converter is injected to utility grid through voltage source inverter (VSI). The feasibility of the developed system has been experimentally verified using a laboratory 200 W prototype of PV system under steady-state, varying weather, and partial shading conditions .

An Experimental Study on Zeta Buck–Boost Converter for Application in PV System

Harmonics and waveform distortion is a significant power quality problem in modern power systems with high penetration of Renewable Energy Sources (RES). This problem has attracted more attention in recent decades, owing to the increasing integration of power electronic devices and nonlinear loads into power systems. In this paper, Artificial Intelligence (AI) techniques used in different aspects of analyzing harmonics in electrical power networks are reviewed. The tasks of spectrum analysis and waveform estimation or prediction, harmonic source classification, harmonic source location and estimation, determination of harmonic source contributions, harmonic data clustering, filter-based harmonic elimination, and Distributed Generation (DG) hosting capacity in the context of harmonics are considered. The applications of AI in these tasks have been addressed within the literature and are reviewed in this paper. Different AI techniques applied in the study of harmonics such as artificial neural networks, fuzzy systems, support vector machine and decision tree are reviewed. AI techniques mostly outperformed traditional methods in harmonic analysis, particularly under varying operating condition. However, there is still room for improvement regarding the use of combinations of techniques, ensemble learning, optimal structures, training algorithms and further comprehension. This review provides researchers with an insight into research trends in harmonic analysis and outlines opportunities for further research on this increasingly important topic. 

Review of AI applications in harmonic analysis in power systems

This paper presents comparison analysis of artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) artificial intelligence (AI) based maximum power point tracking (MPPT) techniques for tracking maximum power from the Photovoltaic (PV) array. These algorithms are essential since PV arrays have non-linear characteristics with its firm dependence on changing solar irradiation and temperature. To increase the power extracted from solar panel, PV array must operate at a maximum power point (MPP) under given load conditions. Conventional algorithms such as Perturb and Observe (P&O) and Incremental-Conductance (Inc-Cond) suffers, with high oscillations during changing solar irradiation leading to low efficiency, therefore AI based techniques are designed and presented in this paper. ANFIS is more efficient in tracking MPP with less settling time, less overshoot, less oscillations and less time taken to track MPP than ANN based Controller.

Comparison of ANN and ANFIS based MPPT Controller for grid connected PV systems

This study presents the development of Legendre-based functional neural network algorithm for shunt compensation in photovoltaic (PV)-based grid-connected system. The controller is developed for improving power quality (PQ) and the compensator is controlled to work in current control mode. It injects the requisite compensating current depending on the nature of the load current. The compensator is also interfaced with PV source and the controller design incorporates its contribution too. Some of the PQ problems studied include curtailment of harmonics, providing necessary reactive power, power factor improvement and so on. Results under distorted grid, varying solar irradiation and variety of loads have been presented. The proposed algorithm is designed using non-linear functional Legendre expansion of load current and has not been used for compensation or PQ problem alleviation till date. Both simulation and experimental results verify that the proposed algorithm performs far better than the adaptive popular backpropagation multilayer perceptron neural network, recurrent neural network and non-adaptive conventional synchronous reference frame theory based techniques.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2019.0269

Design and implementation of Legendre-based neural network controller in grid-connected PV systems

Here, a new control algorithm based on functional link artificial neural network (FLANN) has been designed and studied for shunt compensation. FLANN controller is synonymous to extended trigonometric functional non-linear expansion and is trained online to achieve mitigation of different power quality (PQ) problems. Increased use of power electronics-based loads has contributed to harmonic pollution in power distribution systems and the PQ issues raised need to be effectively addressed. Several other PQ problems exist at the load end such as poor power factor, unregulated voltage, load unbalancing etc. and these can be effectively controlled using a shunt compensator. The designed FLANN controller is based on individual non-linear functional expansion of the input signal and it is updated online by the adaptive least mean square (LMS) corrective learning algorithm. The controller is designed to predict the weighted active component of load current to generate reference supply current for the grid. The algorithm converges fast and has good transient as well as steady-state response. Simulation as well as experimental results are shown for shunt compensator installed for the single-phase grid-connected system and the performance results are compared with some conventional algorithms usually utilised for compensation.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2018.6070

Design and analysis of functional link artificial neural network controller for shunt compensation

This paper presents the artificial neural network (ANN), fuzzy logic controller (FLC) maximum power point tracking (MPPT) methods in grid connected photovoltaic (PV) systems for optimizing the solar energy efficiency All the methods are simulated in MATLAB-Simulink, respectively together with SunPower-SPR305 PV module connected to single-ended primary inductor converter (SEPIC) Performance assessment covers efficiency, overshoot, settling time response, oscillations and stability

AI based MPPT methods for grid connected PV systems under non linear changing solar irradiation

Multilevel converters are getting increasingly popular these days due to their high-power capability under medium voltage applications. In this paper, the modelling and analysis of a seven-level cascaded H-bridge multilevel inverter connected with photovoltaic (PV) cells is presented. Conventional modulating techniques give large total harmonic distortion (THD), so the focus of this paper is on the analysis and study of different modulating techniques. The presented techniques viz. phase disposition (PD) carrier based PWM, phase opposition disposition (POD) carrier based PWM, alternate phase opposition disposition (APOD) carrier based PWM and phase shifting (PS) carrier based PWM are discussed and compared in terms of total harmonic distortion (THD). It is observed that phase shifting modulating technique results in lowest THD as compared to other techniques.

Ankita Arora

Papers

Comparison of ANN and ANFIS based MPPT Controller for grid connected PV systems

Design and implementation of Legendre-based neural network controller in grid-connected PV systems

Design and analysis of functional link artificial neural network controller for shunt compensation

AI based MPPT methods for grid connected PV systems under non linear changing solar irradiation

Performance Analysis of Different Modulating Techniques for Multilevel Inverter