Maximum power point tracking techniques for photovoltaic systems: a comparative study

Solar photovoltaic (PV) system contributes a significant role in meeting power requirements and helps the decentralization of power generation and distribution. The output of the individual photovoltaic module depends on local temperature and irradiance. Since the variation of these parameters depends on the environmental factors, it is difficult to obtain maximum power in all instances. The maximum power point tracking (MPPT) technique is used for tracking the maximum power available at a particular instant. This paper presents the maximum power extraction from a photovoltaic system using Artificial Intelligence (AI) techniques. AI based methods provide a fast dynamic response with high accuracy and mitigate the effects of partial shading. A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). PV module output is interfaced with the resistive load through the Z-Source inverter (ZSI). The ZSI boost the input voltage and provides an AC output voltage. The AI based MPPT techniques have been investigated with conventional Z-source Inverter (ZSI) and Switched Inductor Z-Source inverter (SL-ZSI). A simple boost control technique is employed to control, both conventional and SL Z-source inverter. The performance of the system is verified using MATLAB Simulink.

This paper focuses on the intelligent techniques used for tracking the maximum power point of the solar photovoltaic (SPV) system for varying environmental conditions. The most widely used perturb and observe (P&O) maximum power point tracking (MPPT) technique is discussed briefly for the comparison with the intelligent techniques. The paper proposes control technique for the SPV system by using fuzzy logic controller (FLC)-based MPPT algorithm and the optimisation of its various parameters by genetic algorithm (GA). The performance of the FLC optimised with GA is compared with the P&O and the fuzzy-based MPPT technique. MATLAB/simulink software is used to design the different stages of the MPPT controllers. Simulation results reported that GA optimised FLC perform much better than the P&O and fuzzy logic-based MPPT controllers.

In renewable system control engineering, Maximum Power Point Tracking (MPPT) techniques are considered as one of the key steps in photovoltaic (PV) system development. Indeed, some MPPT techniques provide high performance under uniform irradiation and temperature conditions. However, their performance significantly degrades when the two preceding climatic conditions suddenly change. Therefore, this paper proposes a new voltage regulation based on Fuzzy Logic Control (FLC) strategy and small-signal model of DC–DC booster converter. The proposed control scheme is mainly based on the indirect incremental conductance (Inc) technique, equipped with an optimal integrator controller (OIC). It is implemented in a PV system, consisting of a DC–DC boost converter connected to a resistive load, with the main objective of ensuring a good MPP tracking behavior for variable operating conditions. To achieve this objective, the proposed MPPT strategy is designed around two stages. First, the smooth reference voltage is ensured by a Single-Input Single-Output (SISO) fuzzy logic controller using the Inc technique based on the MPPT approach. Then, the optimal integrator controller stabilizing the closed-loop system is integrated with optimization of its parameter according to Routh’s criterion for resistive load variations. Simulation results confirm the effectiveness of the proposed MPPT technique compared to some commonly used MPPT approaches. The superiority of the proposal is proven by using efficiency ratio, which rises more than 98% in all case studies. These objectives are ensured for both uniform and critical sudden change in weather conditions, regardless of resistive load variations.

To mitigate problems like energy shortage and environmental degradation caused by conventional energy sources, it needs to shift from conventional energy to non-conventional energy sources. The solar photovoltaic (PV) technology emerges as one of the leading non-conventional energy sources. To operate a PV system at its maximum efficiency, it requires the operating module at maximum power output point (MPP). There are many conventional as well as advanced maximum power point tracking (MPPT) techniques developed and implemented for a solar PV system. This work provides a comparative analysis of the conventional and fuzzy logic controller (FLC)-based MPPT techniques for a solar PV system. In this study, performance of two conventional techniques is considered, i.e., perturb and observe (P & O) and incremental conductance (IC) MPPT technique. The analysis of the PV system is performed for different solar irradiance levels. The comparative study is evaluated for complexity, tracking time, efficiency, range of power, voltage, and current oscillations. The DC-DC boost converter is used for operation of the proposed PV system at MPP. The implementation of the entire system and control algorithms has been modeled in MATLAB Simulink.

This study offers a thorough examination of a photovoltaic (PV) system using a variety of maximum power point tracking (MPPT) methods, including fuzzy logic control (FLC), adaptive neuro-fuzzy inference systems (ANFIS), perturb and observe (P&O), and artificial neural networks (ANN). Optimizing power extraction from PV systems under various environmental circumstances, including temperature variations and irradiance, is the main goal of these MPPT algorithms. Despite its widespread use and affordability, the P&O algorithm may have performance issues in dynamic circumstances. By using fuzzy logic to adjust to non-linear changes in environmental conditions, FLC improves P&O and offers more dependable and seamless operation. Although they demand a large amount of data and processing power, ANN-based MPPT approaches provide sophisticated capabilities by predicting optimal operating points by learning from historical system actions. By fusing fuzzy logic and neural networks, ANFIS offers a reliable solution that can more accurately adjust in real time to changing circumstances. These algorithms' incorporation into a PV system allows for more flexible and effective power management, guaranteeing peak performance in a range of climatic conditions. By combining many MPPT techniques, hybrid approaches can further reduce the drawbacks of individual approaches and improve the overall dependability and efficiency of PV systems.

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In the work a study is performed between different maximum power point tracking (MPPT) techniques for DC grid connected solar PV applications. To harness the maximum power from available input under changing weather conditions MPPT controller is used. Different MPPT techniques namely Perturb and Observe (P&O), Incremental Conductance (In &C), Fuzzy logic and Random Search Method (RSM) based MPPT techniques are implemented here. Due to simple computation, absence of derivatives RSM guarantees global convergence, unlike other methods. Simulation is performed in MATLAB/Simulink environment to identify efficient MPP tracker.

An efficient maximum power point tracking (MPPT) controller is a crucial part of solar photovoltaic (PV) system, which can handle the non-linear characteristics of a solar PV array. In this study, a proposed solar PV system with boost converter and dc load is modeled and compared according to the operating characteristics (i.e. voltage, current, power and efficiency under varying solar irradiance and cell temperature) using five conventional and modern MPPT techniques. The proposed solar PV system and MPPT controllers are modeled and simulated in MATLAB Simulink platform. Among these five MPPT controllers, artificial neural network (ANN) MPPT controller provides the highest efficiency of 97.55% and produces less voltage and power fluctuations. The novelty of this paper is that it focuses on the key characteristics and simulated results of the five MPPT techniques to make a comparison between them.

Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC

This paper presents a novel Simulink models with an evaluation study of more widely used On-Line Maximum Power Point tracking (MPPT) techniques for Photo-Voltaic based Battery Storage Systems (PV-BSS). To have a full comparative study in terms of the dynamic response, battery state of charge (SOC), and oscillations around the Maximum Power Point (MPP) of the PV-BSS to variations in climate conditions, these techniques are simulated in Matlab/Simulink. The introduced methodologies are classified into two types; the first type is conventional hill-climbing techniques which are based on instantaneous PV data measurements such as Perturb & Observe and Incremental Conductance techniques. The second type is a novel proposed methodology is based on using solar irradiance and cell temperature measurements with pre- build Adaptive Neuro-Fuzzy Inference System (ANFIS) model to predict DC–DC converter optimum duty cycle to track MPP. Then evaluation study is introduced for conventional and proposed On-Line MPPT techniques. This comparative study can be useful in specifying the appropriateness of the MPPT techniques for PV-BSS. Also the introduced model can be used as a valued reference model for future research related to Soft Computing (SC) MPPT techniques. A significant improvement of SOC is achieved by the proposed model and methodology with high accuracy and lower oscillations.

Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.

Solar Photo-voltaic (PV) arrays have non-linear characteristics with distinctive maximum power point (MPP) which relies on ecological conditions such as solar radiation and ambient temperature. In order to obtain continuous maximum power (MP) from PV arrays under varying ecological conditions, maximum power point tracking (MPPT) control methods are employed. MPPT is utilized to extract MP from the solar-PV array; high-performance soft computing techniques can be used. In this paper, the proposed hybrid MPPT algorithm is used in the solar-PV system with variable climatic conditions. The performance of the proposed hybrid MPPT algorithm with different membership functions is analyzed to optimize the MPP. Simulation results establish that with the application of MPPT controller such as Perturb and Observe, Fuzzy Logic and a proposed hybrid MPPT for the solar-PV system, the proposed hybrid MPPT controller provides more accurate performance and also reduces the fluctuation about the MPP as compared to other MPPT techniques.

This paper reviews the existing models, connection schemes and maximum power point tracking (MPPT) methods of PV arrays. The partial shading causes significant power losses. It is very difficult to maintain uniform irradiance over the entire PV array, especially for rooftop or building-integrated PV systems. The aspects such as connection scheme of PV array, shading pattern and maximum power point tracking (MPPT) techniques, etc., decide the power generated by the PV array. The conventional MPPT methods are only capable of reaching maximum power point (MPP) in identical insolation situations. Hence, conventional MPP tracking becomes inept due to the existence of several peaks in the P-V characteristics. There are different global maximum power point (GMPP) tracking techniques available in the literature that operate in partial shading scenarios. The reconfiguration of the PV array reduces the number of multiple peaks. This reduction in several peaks makes conventional MPP tracking techniques competent even under partial shading conditions with low implementation complexity and higher tracking speed. This paper can assist scientists in choosing a precise objective-based PV module model, connection schemes and MPP tracking methods out of numerous schemes available in the literature.

The best alternative to the conventional sources of energy is ever lasting solar energy and it is one among the cheapest and widely used energy. The present paper deals with the design and analysis of zeta converters, as it has been used as intermediate between voltage source inverter (VSI) and solar PV array. Maximum power point tracking (MPPT) technique has been used for gaining maximum efficiency from solar PV array. In this paper, Artificial Intelligence(AI) Technique based Fuzzy logic controller is used for better results. When compared to other methods of MPPT technique, Fuzzy logic based MPPT technique is best because it provides better results for randomly varying atmospheric conditions. In our proposed system, BLDC motor which has higher efficiency and noise less operation is considered. Maximum power loss of PV generator is well matched with the load characteristic of BLDC motor. Matlab/Simulink results carried out for the different topology of the DC-DC converters and the results are analyzed and compared. Keywords: Photovoltaic(PV) system, Maximum power point tracking (MPPT), Artificial Intelligence (AI) Technique, Fuzzy logic controller, BLDC motor, zeta converters.

Numerous strategies exist for improving maximum power point tracking (MPPT) techniques, which vary in terms of tracking speed, accuracy, sensor usage, complexity, and cost. Due to the importance of sustainable energy use and related challenges, it is essential to choose an appropriate algorithm that can reliably provide energy without exhausting resources. While much research has examined the benefits and drawbacks of different MPPT algorithms, guidance on selecting the best suited algorithm for a particular solar system application is lacking. For a fair and thorough comparison, this study analyzes four MPPT algorithms, including two artificial intelligence-based techniques, an adaptive neuro-fuzzy inference system, and a conventional technique. A decision matrix model and requirement analysis are used to determine their suitability for a standalone PV application, using particularly ranked evaluation criteria, which include tracking efficiency, implementation costs, rise time, settling time, tracking error and variance. Simulation results show that the perturb and observe technique has the fastest settling and rise times and high tracking efficiency for varying irradiance and temperature levels, with little implementation effort required for a stand-alone photovoltaic application.