A Hybrid Photovoltaic-Fuel Cell for Grid Integration With Jaya-Based Maximum Power Point Tracking:Experimental Performance Evaluation

This paper deals the grid integration of photovoltaic (PV), fuel cell, and ultra-capacitor with maximum power point tracking (MPPT). The voltage oriented control for the grid-integrated inverter is proposed to regulate dc link voltage. Here, the fuel cell is employed as the main renewable energy source and PV as an auxiliary source with ultra-capacitor, which compensates power variation. An integrated CUK converter is proposed for peak power extraction from PV modules. The Jaya-based MPPT method is employed to achieve fast PV tracking ability with zero deviation around maximum power point (MPP) and has accelerated searched performance in equated with particle swarm optimization (PSO) and artificial bee colony (ABC) techniques. The hybrid PV-fuel cell with ultra-capacitor as energy storage works effectively under varying operating conditions. Compared to other energy storing devices, ultra-capacitor provides a fast dynamic response by absorbing/delivering power fluctuations. The hybrid PV-fuel storage control methodologies are experimentally validated using dSPACE (DS1104) board that provides optimal power extraction with stable power affirmation for a standalone/grid-connected system

Partial shading conditions for photovoltaic system using artificial neural networks technique

Partial shading condition in solar photovoltaic (PV) systems is an inevitable problem due to the behavior of high nonlinear and unpredictable characteristics due to different shading states. However, several scientific works and research aimed to find approximate and expressive models of this nonlinear behavior using modern methods and techniques to allow researchers to find effective solutions to these critical situations. This paper aims to obtain the appropriate model for partial shading cases using artificial intelligence techniques through machine learning of neural network technology, based on experimental data of PV characteristics for different cases. This model allows for diagnosing the state of faults of Partial shading (PV) systems. Moreover, it allows the development of appropriate algorithms in order to maintain, perform, and prevent the complete shutdown of the systems. All results of the model photovoltaic partial shading characteristics for different situations based on the machine learning process confirm the effectiveness of the adopted technique after comparing it with the real data with a very acceptable margin of error

A Novel MPPT Technique based on Hybrid Radial Movement Optimization with Teaching Learning Based Optimization for PV system

Because of its pure and plentiful accessibility, solar power is a remarkable resource of energy for the generation of electrical power. The solar photovoltaic mechanism transforms sunlight striking the photovoltaic solar panel or array of photovoltaic panels directly into non-linear DC power. Due to the nonlinear characteristics of solar photovoltaic panels, power must be tracked for their effective usage. When the photovoltaic arrays are shaded, the problem of nonlinearity becomes more pronounced, resulting in large power loss and intensive heating in a few areas of the photovoltaic arrangement. The tracking challenge is made more difficult by the fact that bypass diodes, which are used to completely eradicate the shading effect, generate numerous power peak levels on the power vs. voltage (P-V) curve. Traditional methods for tracing the global peak point are unable to examine the entire P-V curve as they frequently get stuck at the local peak point. Recently, machine learning or optimization algorithms have been used to determine the global peak point. Because these algorithms are random, they search the entire search area, reducing the possibility of being caught in the local maximum value. This article proposes a hybrid of two optimization approaches: radial movement optimization and teaching-learning optimization (HRMOTLBO). The proposed MPPT method was thoroughly investigated and tested in a wide range of photovoltaic partial shading combinations. The recommended HRMOTLBO MPPT approach outperforms and is more reliable than a recent Jaya-based MPPT approach in terms of tracing time and power variation under dynamic and static partial shading conditions. Experimental as well as simulation outcomes demonstrate that the proposed MPPT successfully traces the global peak point in less time and with fewer fluctuations during various partial shading conditions

Hybrid Improved Differential Evolution and Splinebased Jaya for Photovoltaic MPPT Technique

Some Soft Computing algorithms to solve themaximum power point tracking (MPPT) method problem ofthe photovoltaic system under partially shaded conditions willstop tracking Global Maxima and produce reference voltage orthe best duty-cycle if the difference between the worst and thebest candidate solution is smaller than the specified threshold.A large threshold value will produce fast converging, but theaccuracy value will be low, and vice versa, then thedetermination of the threshold value will be very dilemma.Therefore, this study proposed a combination of ImprovedDifferential Evolution (IDE) and Jaya optimization based onpredictive curves using cubic spline interpolation to determinethe best particles after the IDE reaches convergent criteria, sothat with a large threshold value it will still get high accuracyand high convergent speed. Furthermore, the algorithmproposed in this study is known as Improved DifferentialEvolution and Jaya Based Spline (IDESJaya). The proposedalgorithm is compared with conventional P&O, Jaya based onSpline, and IDE. Simulation results show that the IDESJayatechnique is faster converging, provides a better overalltracking efficiency and higher accuracy

Solar array fed synchronous reluctance motor driven water pump : an improved performance under partial shading conditions

An improved performance of a photovoltaic (PV) pumping system employing a synchronous reluctance motor (SynRM) under partial shading conditions is proposed. The system does not include the dc-dc converter that is predominantly being utilized for maximizing the output power of the PV array. In addition, storage batteries are also not contained. A conventional inverter connected directly to the PV array is used to drive the SynRM. Further, a control strategy is proposed to drive the inverter so that the maximum output power of the PV array is achieved while the SynRM is working at the maximum torque per Ampere condition. Consequently, this results in an improved system efficiency and cost. Moreover, two maximum power point tracking (MPPT) techniques are compared under uniform and partial shadow irradiation conditions. The first MPPT algorithm is based on the conventional perturbation and observation (P&O) method and the second one uses a differential evolution (DE) optimization technique. It is found that the DE optimization method leads to a higher PV output power than using the P&O method under the partial shadow condition. Hence, the pump flow rate is much higher. However, under a uniform irradiation level, the PV system provides the available maximum power using both MPPT techniques. The experimental measurements are obtained to validate the theoretical work

Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions Using Bat Algorithm

The vibrant, noiseless, and low-maintenance characteristics of photovoltaic (PV) systems make them one of the fast-growing technologies in the modern era. This on-demand source of energy suffers from low-output efficiency compared with other alternatives. Given that PV systems must be installed in outdoor spaces, their efficiency is significantly affected by the inevitable complication called partial shading (PS). Partial shading occurs when different sections of the solar array are subjected to different levels of solar irradiance, which then leads to a multiple-peak function in the output characteristics of the system. Conventional tracking techniques, along with some nascent/novel approaches used for the tracking maximum power point (MPP), are unsatisfactory when subjected to PS, eventually leading to the reduced efficiency of the PV system. This study aims at investigating the use of the bat algorithm (BA), a nature-inspired metaheuristic algorithm for MPP tracking (MPPT) subjected to PS conditions. A brief explanation of the behavior of the PV system under the PS condition and the advantages of using BA for estimating the MPPT of the PV system under PS condition is discussed. The deployment of the BA for the MPPT in PV systems is then explained in detail highlighting the simulation results which verifies whether the proposed method is faster, more efficient, sustainable and more reliable than conventional and other soft computing-based methods. Three testing conditions are considered in the simulation, and the results indicate that the proposed technique has high efficiency and reliability even when subjected to an acute shading condition

Roach Infestation Optimization MPPT Algorithm of PV Systems for Adaptive to Fast-Changing Irradiation and Partial Shading Conditions

Of all the renewable energy sources, solar photovoltaic (PV) power is considered to be a popular source owing to several advantages such as its free availability, absence of rotating parts, integration to building such as roof tops and less maintenance cost. The nonlinear current–voltage (I–V) characteristics and power generated from a PV array primarily depends on solar insolation/irradiation and panel temperature. The power output depends on the accuracy with which the nonlinear power–voltage (P–V) characteristics curve is traced by the maximum power point tracking (MPPT) controller. A DC-DC converter is commonly used in PV systems as an interface between the PV panel and the load, allowing the follow-up of the maximum power point (MPP). The objective of an efficient MPPT controller is to meet the following characteristics such as accuracy, robustness and faster tracking speed under partial shading conditions (PSCs) and climatic variations. To realize these objectives, numerous traditional techniques to artificial intelligence and bio-inspired techniques/algorithms have been recommended. Each technique has its own advantage and disadvantage. In view of that, in this thesis, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). In addition, the mathematical formulations and operation of the boost converter is investigated. To validate the effectiveness of the proposed RIO MPPT algorithm, MATLAB/Simulink simulations are carried out under varying environmental conditions, for example step changes in solar irradiance, and partial shading of the PV array. The obtained results are examined and compared with the particle swam optimization (PSO). The results demonstrated that the RIO MPPT performs remarkably in tracking with high accuracy as PSO based MPPT. Last but not the least, I am very grateful to the Arctic Centre for Sustainable Energy (ARC), UiT The Arctic University of Norway, Norway for providing an environment to d

Detection and identification of global maximum power point operation in solar PV applications using a hybrid ELPSO-P&O tracking technique

Non-homogeneous irradiation conditions due to environmental changes introduce multiple peaks in non-linear PV characteristics. Hence, to operate PV at the global power point, numerous algorithms have been proposed in the literature. However, due to the insufficient exploitation of control variables, all the MPPT methods presented in literature fail to guarantee Global Maximum Power Point (GMPP) operation. In this paper, a new detection technology to identify global MPP zones using hybrid Enhanced Leader Particle Swarm Optimization (ELPSO) assisted by a conventional Perturb and Observe (P&O) algorithm is proposed. With inherent mutations, ELPSO applied to MPPT excels in exploring global regions at initial stages to determine the global best leader; whilst, P&O is reverted back soon after global solution space is detected. The transition from ELPSO to P&O is mathematically verified and allowed only when ELPSO finds the global optimal zone. Adapting this hybrid strategy, the proposed method has produced interesting results under partial shaded conditions. For further validation, the results of the proposed hybrid ELPSO-P&O are compared with conventional ELPSO and the hybrid PSO-P&O methods. Experimental results along with energy evaluations confirmed the superiority of the ELPSOP&O method in obtaining the maximum available power under all shaded conditions

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods