Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

MPPT Schemes for PV System Under Normal and Partial Shading Condition: a Review

The photovoltaic system is one of the renewable energy device, which directly converts solar radiation into electricity. The I-V characteristics of PV system are nonlinear in nature and under variable Irradiance and temperature, PV system has a single operating point where the power output is maximum, known as Maximum Power Point (MPP) and the point varies on changes in atmospheric conditions and electrical load. Maximum Power Point Tracker (MPPT) is used to track MPP of solar PV system for maximum efficiency operation. The various MPPT techniques together with implementation are reported in literature. In order to choose the best technique based upon the requirements, comprehensive and comparative study should be available. The aim of this paper is to present a comprehensive review of various MPPT techniques for uniform insolation and partial shading conditions. Furthermore, the comparison of practically accepted and widely used techniques has been made based on features, such as control strategy, type of circuitry, number of control variables and cost. This review work provides a quick analysis and design help for PV systems. Article History: Received March 14, 2016; Received in revised form June 26th 2016; Accepted July 1st 2016; Available online How to Cite This Article: Sameeullah, M. and Swarup, A. (2016). MPPT Schemes for PV System under Normal and Partial Shading Condition: A Review. Int. Journal of Renewable Energy Development, 5(2), 79-94. http://dx.doi.org/10.14710/ijred.5.2.79-9

Enhanced MPPT Controllers for Smart Grid Applications

Over the past years, the energy demand has been steadily growing and so methods of how to cope with this staggering increase are being researched and utilized. One method of injecting more energy to the grid is renewable energy, which has become in recent years an integral part of any country’s power generation plan. Thus, it is a necessity to enhance renewable energy resources and maximize their grid utilization, so that these resources can step up and reduce the over dependency of global energy production on depleting energy resources. This thesis focuses on solar power and effective means to enhance its efficiency through the use of different controllers. In this regard, substantial research efforts have been done. However, due to the current market and technological development, more options are made available that are able to boast the efficiency and utilization of renewables in the power mix. In this thesis, an enhanced maximum power point tracking (MPPT) controller has been designed as part of a Photovoltaic (PV) system to generate maximum power to satisfy load demand. The PV system is designed and simulated using MATLAB (consisting of a solar panel array, MPPT controller, boost converter, and a resistive load). The solar panel chosen for the array is Sun Power SPR- 440NE-WHT-D and the array is designed to produce 150 kW of power. The MPPT controller is designed using three different algorithms and the results are compared to identify each controller’s fortes and drawbacks. The three designed controllers used are based on Perturb and Observe (P&O) algorithm, Incremental Conductance (INC) with an Integral Regulator (IR) and Fuzzy Logic Control (FLC). Each controller was tested under two different scenarios; the first is when the panel array is subjected to constant amount of solar irradiance along with a constant atmospheric temperature and the second scenario has varying solar irradiance and atmospheric temperature. The performance of these controllers is analyzed and compared in terms of the output power efficiency, system dynamic response and finally the oscillations behavior. After analyzing the results, it is shown that Fuzzy Logic Controller design performed better compared to the other controllers as it had in most cases the highest mean power efficiency and fastest response

Modelling and Tracking of the Global Maximum Power Point in Shaded Solar PV Systems Using Computational Intelligence

Solar Photovoltaic (PV) systems are renewable energy sources that are environmentally friendly and are now widely used as a source of power generation. The power produced by solar PV varies with temperature, solar irradiance and load. This variation is nonlinear and it is difficult to predict how much power will be produced by the solar PV system. When the solar panel is directly coupled to the load, the power delivered is not optimal unless the load is properly matched to the PV system. In the case of a matched load the variation of irradiance and temperature will change this matching so a maximum peak power point tracking is therefore necessary for maximum efficiency. The complete PV system with a maximum power point tracking (MPPT) includes the solar panel array, MPPT algorithm and a DC-DC converter topology. Each subsystem is modelled and simulated in MATLAB/Simulink environment. The components are then combined with a DC resistive load to assess the overall performance when the PV panels are subjected to different weather conditions. The PV panel is modelled based on the Shockley diode equation and is used to predict the electrical characteristic curves under different irradiances and temperatures. In this dissertation, five MPPT algorithms were investigated. These algorithms include the standard Perturb and Observe (PnO), Incremental conductance (IC), Fuzzy Logic (FL), Particle Swarm Optimisation (PSO) and the Firefly Optimisation (FA). The algorithms are tested under different weather conditions including partial shading. The Particle Swarm and Firefly algorithm performed relatively the same and were chosen to be the best under all test conditions as they were the most efficient and were able to track the global maximum power point under partial shading. The PnO and IC performed well under static and varying irradiance, the PnO was seen to lose track of the MPP under rapid increasing irradiance. The PnO was tested under partial shaded conditions and it was seen that it is not reliable under these conditions. The Fuzzy logic performed better than the PnO and IC but was not as good as the PSO and FA. Since the fuzzy logic requires extensive tuning to converge it was not tested under partial shaded conditions. A DC-DC boost converter interface study between a DC source and the DC load are performed. This includes the steady state and dynamic analysis of the Boost converter. The converter is linearised about its steady state operating point and the transfer function is obtained using the state space averaged model. The simulation results of the complete PV system show that PSO and Firefly algorithm provided the best results under all weather conditions compared to other algorithms. They provided less oscillations at steady state, high efficiency in tracking (99%), quick convergence time at maximum power point and where able to track global power under partial shaded weather conditions for all partial shaded patterns. The Fuzzy logic performed well for what it was tested for which are static irradiance and rapid varying irradiance. The PnO and IC also performed relatively well but showed a lot of ringing at steady state. The PnO failed to track the MPP at certain instances under rapid increasing irradiance and the IC was shown to be unstable at low irradiance. The PnO was not reliable in tracking the global maximum power point under partial shaded conditions as it converged at local maximum power points for some partial shaded patterns

Performance of maximum power point tracking by using conventional and soft computing techniques during partial shading conditions

Characteristics curves of solar module represent the characteristic of particular photovoltaic (PV) module. These curves reveal the ability of a specific solar module conversion and efficiency to extract maximum available power. In this study, the MSX-64 PV module and boost DC/DC converter are used for simulation and modeling the maximum power point tracking system. Maximum power extraction from PV system can be achieved by using maximum power point tracking (MPPT) techniques that are classified into conventional and soft computing. This work demonstrates the performance of three types of MPPT techniques for the PV system during partial shading conditions (PSC), where 25% and 50% shaded weather profile applied to the system. The system also subjected to a non-shaded real weather profile to examine the proposed MPPTs performance under changing weather conditions. The techniques used are fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS) as soft computing technique which are compared with the conventional perturb and observe technique. The performance has evaluated under the same conditions of irradiance and atmospheric temperature by using the same operating conditions. These MPPT techniques are compared in terms of power extracted, MPPT efficiency, rise time, steady-state oscillation, its ability to track global maximum power point and the response to a varied weather. Simulation results of soft computing MPPT techniques have shown the ability to track the maximum power point during partial shading conditions and the response to weather changes when non-shaded real weather profile applied to the system. The performance of the conventional perturb and observe based MPPT has depicted the failure of this controller to track the global maximum power point during partial shading and lack response to real weather changes. The proposed system has implemented by using MATLAB/SIMULINK environment

Experimental evaluation of Kalman filter based MPPT in grid-connected PV system

Mestrado de dupla diplomação com a Ecóle Supérieur en Sciences ApliquéesPhotovoltaic (PV) energy is becoming an important alternative energy source, since it is abundant in nature, non-polluting and requires low maintenance. However, it suffers from low energy conversion efficiency, which can be even lower if the photovoltaic generator does not operate around a so-called Maximum Power Point (MPP). Tracking this point, which changes its location depending on weather conditions, is a very important step in the design of a photovoltaic system. Several techniques have been investigated in the literature in the MPP context. However, some techniques such as the Kalman filter are steel unknown with a lack of information in real test conditions, since their evaluation is limited only in simulation and literature review. This work presents an experimental evaluation of the Kalman filter based on a comparison with two well-known maximum power point tracking (MPPT) algorithms, which are the Perturbation and observation (among the simplest techniques) and the Particle Swarm Optimization (among the most complex techniques). The experimental tests were carried out under real atmospheric conditions, using Matlab/Simulink and the 1103 dSPACE real-time controller board. The results show that the Kalman filter has a higher aptitude to operate closer to the MPP, with a low oscillation in steady-state compared to the other MPPT evaluated in this work. However, the technique’s flaw lies in the shadow situation where it can not differentiate between the local and global optimums, unlike the particle swarm optimization.A energia fotovoltaica (PV) está a tornar-se uma importante fonte de energia alternativa, uma vez que é abundante na natureza, não poluente, e requer pouca manutenção. No entanto, sofre de uma baixa eficiência de conversão energética, que pode ser ainda mais baixa se o gerador PV não operar em torno do chamado Ponto de Potência Máxima (MPP). O rastreio deste ponto, que muda a sua localização dependendo das condições meteorológicas, é um passo muito importante na concepção de um sistema PV. Várias técnicas têm sido investigadas na literatura no contexto do MPP. No entanto, o desempenho de algumas técnicas, como o filtro Kalman, em condições reais de teste, ainda desconhecido, ou existe pouca informação, uma vez que a sua avaliação é limitada apenas na simulação e revisão da literatura. Este trabalho apresenta uma avaliação experimental do filtro de Kalman com base numa comparação com dois seguidores de ponto de potência máxima (MPPT) bem conhecidos, que são a Perturbação e observação e a Optimização do Enxame de Partículas. Os testes experimentais foram realizados em condições atmosféricas reais, utilizando o Matlab/Simulink e a carta de controlo em tempo real dSPACE. Os resultados mostram que o filtro de Kalman tem uma maior aptidão para operar mais perto do MPP, com uma baixa oscilação em regime permenente, comparativemente com os outros algoritmos MPPT avaliados neste trabalho. No entanto, a desvantagem ocorre aquando da ocorãncia de sombra, onde a técnica não consegue diferenciar entre os óptimos locais e global, ao contrário da optimização do enxame de partículas. Palavras-chave: Fotovoltaico (PV), Seguimento do Ponto de Potência Máxima (MPPT), Perturbação e Observação (PO), Optimização de enxame de partículas (PSO), Filtro de Kalman (KF), Sistema PV ligado à Rede, dSPACE 1103

Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition

© 2019 Elsevier Ltd This paper proposes a novel bio-inspired optimization method named memetic salp swarm algorithm (MSSA). It is developed by extending the original salp swarm algorithm (SSA) with multiple independent salp chains, thus it can implement a wider exploration and a deeper exploitation under the memetic computing framework. In order to enhance the convergence stability, a virtual population based regroup operation is used for the global coordination between different salp chains. Due to partial shading condition (PSC) and fast time-varying weather conditions, photovoltaic (PV) systems may not be able to generate the global maximum power. Hence, MSSA is applied for an effective and efficient maximum power point tracking (MPPT) of PV systems under PSC. To evaluate the MPPT performance of the proposed algorithm, four case studies are undertaken using Matlab/Simulink, e.g., start-up test, step change of solar irradiation, ramp change of solar irradiation and temperature, and field atmospheric data of Hong Kong. The obtained PV system responses are compared to that of eight existing MPPT algorithms, such as incremental conductance (INC), genetic algorithm (GA), particle swarm optimization (PSO), artificial bees colony (ABC), cuckoo search algorithm (CSA), grey wolf optimizer (GWO), SSA, and teaching-learning-based optimization (TLBO), respectively. Simulation results demonstrate that the output energy generated by MSSA in Spring in HongKong is 118.57%, 100.73%, 100.96%, 100.87%, 101.35%, 100.36%, 100.81%, and 100.22% to that of INC, GA, PSO, ABC, CSA, GWO, SSA, and TLBO, respectively. Lastly, a hardware-in-the-loop (HIL) experiment using dSpace platform is undertaken to further validate the implementation feasibility of MSSA

Salp Swarm Optimized Hybrid Elman Recurrent Neural Network (SSO-ERNN) based MPPT Controller for Solar PV

Renewable energy technologies provide clean and abundant energy that can be self-renewed from natural sources; more support from the public to replace fossil fuels with various renewable energy sources to protect the environment. Although solar energy has less impact on the environment than other renewable sources, the output efficiency is lower due to the different weather conditions. So to overcome that, the MPPT controller is used for tracking peak power and better efficiency. Some conventional methods in MPPT controllers provide less tracking efficiency, and steady-state oscillations occur in maximum power tracking due to the sudden variations in solar irradiance. Thus, in this work salp swarm optimized (SSO) based Elman recurrent neural network (ERNN) controller is proposed to track the maximum power form PV with high efficiency. The weight parameter of ERNN layer is optimized with the help of SSO, which solve the complex problems and give maximum efficiency. The proposed method is performed in MATLAB/Simulink environment, which differs from existing plans and gives a better output efficiency. Using this proposed controller, the system can achieve high tracking efficiency of 99.74% compared to conventional processes