MPPT technique based on neural network for photovoltaic system

Mestrado de dupla diplomação com a Superior School of Applied Sciences of TlemcenThe using of an efficient MPPT (Maximum Power Point Tracking) algorithm influences a lot in the global efficiency of the PV system. This thesis presents a detailed study based on simulation of different MPPT algorithms with their features using two systems (off-grid and on-grid). The off-grid system contains a PV array connected to a boost converter and a resistive load. On the off-grid system a simulation is presented using MATLAB/SIMULINK platform with several MPPT algorithms. The simulated MPPT algorithms are the conventionals Incremental Conductance (IncCond), Perturb and Observe (P&O), Open Circuit Voltage (OCV) and a new developed Neural Network (NN) under different environmental conditions of temperature and irradiance. As a result of the simulation, the NN algorithm has a quick response, i.e, it requires less time to reach the MPP and high efficiency and less oscillation comparing with the conventional methods. On the other hand, a single-phase two-stage photovoltaic grid-connected system is simulated which contains a PV array, a boost converter, a dc link capacitor, an inverter, an output L filter and the utility grid. In that system a control of dc link voltage, the injected current and the MPPT is made. Another MPPT algorithm based on NN (modified- NN) was also established. Showed later that is the most suitable for the system. The maximum of power is achieved when the irradiance is maximal and the temperature is minimal. Finally, a study of the influence of the variation in the climatic conditions on the output performance of the system is done.O uso de um algoritmo de MPPT (Maximum Power Point Tracking-Rastreio do Ponto de Potência Máxima) eficiente influencia muito na eficiência global do sistema fotovoltaico. Esta tese apresenta um estudo detalhado com simulação de diferentes algoritmos MPPT com suas características utilizando dois sistemas (off-grid e on-grid). O sistema off-grid contém um painel fotovoltaico conectado a um conversor boost e uma carga resistiva. No sistema fora da rede, é apresentada uma simulação usando a plataforma MATLAB / SIMULINK com vários algoritmos MPPT. Os algoritmos MPPT simulados são os convencionais, Condutância Incremental (IncCond), Perturb e Observe (P&O), Tensão em Circuito Aberto (OCV) e uma nova Rede Neural (NN) desenvolvida sob diferentes condições ambientais de temperatura e irradiância. Como resultado da simulação, o algoritmo NN tem uma resposta rápida, ou seja, requer menos tempo para atingir o MPP, alta eficiência e menos oscilação em comparação com os métodos convencionais. Por outro lado, é simulado um sistema monofásico de dois estágios conectado à rede fotovoltaica que contém um painel fotovoltaico, um conversor boost, um barramento CC, um inversor, um filtro L de saída e a rede elétrica. Nesse sistema é feito um controle da tensão do barramento CC, da corrente injetada e do MPPT. Foi estabelecido também outro algoritmo MPPT baseado em NN (NN modificado). Posteriormente foi mostrado que é o mais adequado para o sistema. O máximo de potência é alcançado quando a irradiância é máxima e a temperatura é mínima. Por fim, é feito um estudo da influência da variação das condições climáticas no desempenho de saída do sistema

Maximum power point tracking technique under partial shading condition for photovoltaic systems

Maximum Power Point Tracking (MPPT) technique extracts the maximum available power from the photovoltaic array (PV). Perturb and Observe (P&O) is the most preferable type of MPPT algorithm due to its simplicity, accuracy and low cost. However, when partial shading condition occurs, it produces multiple local maximum power points (MPPs) on the PV characteristics curve. This causes confusion for the conventional P&O algorithm to track the true MPP. This thesis studies the impact of partial shading on the PV system and to improve the P&O algorithm by adding a checking algorithm into the variable step size. This checking algorithm determines the global maximum power by first comparing all existing peak points before the P&O algorithm identifies the voltage at MPP to calculate the duty cycle of the boost converter. The PV power and voltage rating used for this research are 42 W and 17 V, respectively. The boost converter can double the PV output voltage. The simulation results have proven that the proposed algorithm is able to track the global MPP with a tracking efficiency of 99.96%. This has been verified by hardware implementation of the proposed algorithm using Arduino Mega 2560. The proposed MPPT algorithm also provides better stability with less percentage error on the PV output voltage and power compared to using the conventional P&O MPPT algorithm

Optimal Designing Grid-Connected PV Systems

Photovoltaic systems, direct conversion of solar energy to electrical energy, are produced in the form of DC power by photovoltaic arrays bathed in sunlight and converted into AC power through an inverter system, which is more convenient to use. There are two main paradigms for optimal designing of photovoltaic systems. First, the system can be designed such that the generated power and the loads, that is, the consumed power, match. A second way to design a photovoltaic system is to base the design on economics, as pinpointed in the following. Photovoltaic grid connected through shunt active filter by considering maximum power point tracking for these systems is known as the optimal design. This chapter is organized as follows: First, we discuss an overview of grid-connected photovoltaic systems. After that, we take a more detailed look on grid-connected photovoltaic system via active filter; in this section, we explain the modeling of photovoltaic panel and shunt active filter. In the next section, we learn different maximum power point tracking methods and also learn how to design DC link as a common bus of shunt active filter and photovoltaic system. Finally, MATLAB/Simulink simulations verify the performance of the proposed model performance

An improved maximum power point tracking for PV system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Working very far from maximum power point diminishes the created power from photovoltaic (PV) system. It is therefore vital, in order to ensure ideal operating conditions, to constantly track the Maximum Power Point (MPP) of the PV panel array. However, this is not easy to identify, due to considerable changes in external influences and the nonlinear relationship of the electrical attributes of PV panels. Therefore, Maximum Power Point Tracking (MPPT) methods can be used to uphold the PV panel operating at its MPP. To date, a number of MPPT methods have been developed, ranging from the simple to the more complex, depending on the weather conditions and the control strategies employed. This current study offers a novel approach to augment the MPPT method for the PV system, based on the Lagrange Interpolation (LI) formula and the Particle Swarm Optimisation (PSO) method. The LI method is used initially to determine the optimum value of the duty cycle in the case of the MPP, according to the operating point. Starting from that point, the PSO method can then be used to search for the true Global Peak (GP). The proposed MPPT controller essentially initialises the particles surrounding the MPP, thereby providing the initial swarm with information concerning the most effective position. This has the ability to improve PSO efficiency and lead to a more rapid convergence, with zero steady-state oscillations. Additionally, there is no need to restrict particle velocity, as the initial values are closer to MPP. Thus, the proposed technique aims to increase efficiency without adding additional complexity, thereby substantially enhancing potential tracking speeds, while also reducing the steady-state oscillation (i.e. to practically zero) once the MPP is located. This offers a number of significant improvements over the conventional PSO method, in which new operating points are at too great a distance from MPP, and thus require additional iterations. The algorithm put forward in this work is verified with an OPAL-RT real time simulator and Matlab Simulink tool. A number of simulations are undertaken and compared to: (1) the Perturb and Observe (P&O) method; (2) the Incremental Conductance (IncCond) method; and (3) the PSO based algorithm. The simulation results indicate that the proposed algorithm can effectively enhance stability and fast tracking capability under fast-changing non-uniform insolation conditions

On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions

This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between a Neural Network and the Perturb and Observe method (NN-P&amp;O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as voltage and power, are obtained. The MPPT techniques were simulated using the MATLAB/Simulink environment. A comparison of the performance of the IPSO and NN-P&amp;O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy, and simplicity.</p

A Review and Classification of Most Used MPPT Algorithms for Photovoltaic Systems

Energy has become an indispensable need to sustain our lives. Approximately 80% of the energy consumed in the world is produced from fossil sources. For the reasons such as the depletion of fossil resources and their damages to the environment, the interest in renewable resources is increasing and the importance of solar systems, which draws attention with unlimited energy resource, is increasing day by day. The biggest disadvantages of solar systems are seen as low production efficiency and high setup cost. A PV cell can convert only 5-20% of the solar energy coming on it to electricity. Based on this, it is very important to provide the power obtained from PV with maximum efficiency and minimum cost. Accordingly, many different maximum power point tracking (MPPT) algorithms have been proposed over the years. Although the purpose of all proposed algorithms is the same, they have many advantages and disadvantages compared to each other. In this study, the most used MPPT algorithms have been examined and compared by considering many parameters such as tracking speed, stability, and cost etc. and a new classification of these algorithms is proposed

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained

Intelligent Global Maximum Power Point Tracking Strategies Based on Shading Perception for Photovoltaic Systems

When a Photovoltaic (PV) system is partially shaded in the environment, the current-voltage (I-V) and power-voltage (P-V) curves exhibit multiple stairs/peaks and the locus of Maximum Power Point (MPP) varies over a wide range. Such Partial Shading Conditions (PSC) bring challenges to the Maximum Power Point Tracking (MPPT) systems. This thesis presents some novel shading information to characterize the complex PSC and MPPT techniques based on the shading perception. Shading information is the mathematical indicator to express the shading patterns. The existing shading information, such as shading rate and shading strength, has the limitations that they can only characterize the PSC with two irradiation levels. To improve the application range of the shading information, the shading matrix and shading vector are proposed in this thesis. The identification and detection methods for the proposed shading information are also included. Results from simulations and experiments have shown the effectiveness and accuracy of the proposed shading detection methods. Under PSC, the power characteristics of the PV systems are too complicated that there exist multiple MPPs. The traditional MPPT techniques may be trapped in the Local MPPs (LMPPs) instead of the Global MPP (GMPP). In this thesis, some novel methods are proposed to estimate the GMPP location from the detected shading information. The proposed MPPT techniques based on the shading perception are capable of tracking the GMPP fast and accurately. Simulations and experiments are conducted to validate the performance of the proposed MPPT methods with the comparison with some well-known MPPT methods

New Three Phase Photovoltaic Energy Harvesting System for Generation of Balanced Voltages in Presence of Partial Shading, Module Mismatch, and Unequal Maximum Power Points

The worldwide energy demand is growing quickly, with an anticipated growth rate of 48% from 2012 to 2040. Consequently, investments in all forms of renewable energy generation systems have been growing rapidly due to growth rate and climate concerns. Increased use of clean renewable energy resources such as hydropower, wind, solar, geothermal, and biomass is expected to noticeably alleviate many present environmental concerns associated with fossil fuel-based energy generation. In recent years, wind and solar energies have gained the most attention among all other renewable resources. As a result, both have become the target of extensive research and development for dynamic performance optimization, cost reduction, and power reliability assurance. The performance of Photovoltaic (PV) systems is highly affected by environmental and ambient conditions such as irradiance fluctuations and temperature swings. Furthermore, the initial capital cost for establishing the PV infrastructure is very high. Therefore, it is essential that the PV systems always harvest the maximum energy possible by operating at the most efficient operating point, i.e. Maximum Power Point (MPP), to increase conversion efficiency to reach 100% and thus result in lowest cost of captured energy. The dissertation is an effort to develop a new PV conversion system for large scale PV grid-connected systems which provides 99.8% efficacy enhancements compared to conventional systems by balancing voltage mismatches between the PV modules. Hence, it analyzes the theoretical models for three selected DC/DC converters. To accomplish this goal, this work first introduces a new adaptive maximum PV energy extraction technique for PV grid-tied systems. Then, it supplements the proposed technique with a global search approach to distinguish absolute maximum power peaks within multi-local peaks in case of partially shaded PV module conditions. Next, it proposes an adaptive MPP tracking (MPPT) strategy based on the concept of model predictive control (MPC) in conjunction with a new current sensor-less approach to reduce the number of required sensors in the system. Finally, this work proposes a power balancing technique for injection of balanced three-phase power into the grid using a Cascaded H-Bridge (CHB) converter topology which brings together the entire system and results in the final proposed PV power system. The developed grid connected PV solar system is evaluated using simulations under realistic dynamic ambient conditions, partial shading, and fully shading conditions and the obtained results confirm its effectiveness and merits comparted to conventional systems. The resulting PV system offers enhanced reliability by guaranteeing effective system operation under unbalanced phase voltages caused by severe partial shading