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The model-based methods of maximum power point (MPP) tracking in photovoltaic installations are widely known. One of these methods proposes the use of tracking by direct estimation of the maximum power point resistance using irradiance measurement processing. It proposes six different models for this estimate. In the present work, an exhaustive analysis to determine the robustness and accuracy of the different models was carried out. To perform the analysis, irradiance data sets, used to fit the parameters of the models, were collected. In addition, tests were done to determine MPP tracking accuracy of each of the six models. To carry out the tests, all models were compared with a widely used maximum power point tracking algorithm, , for different values of irradiance, temperature, and load.

A three-phase multi-level multi-input power converter topology is presented for grid-connected applications. It encompasses a three-phase transformer that is operated on the primary side in an open-end winding configuration. Thus, the primary winding is supplied on one side by a three-phase N-level neutral point clamped inverter and, on the other side, by an auxiliary two-level inverter. A key feature of the proposed approach is that the N-level inverter is able to perform independent management of N − 1 input power sources, thus avoiding the need for additional dc/dc power converters in hybrid multi-source systems. Moreover, it can manage an energy storage system connected to the dc-bus of the two-level inverter. The N-level inverter operates at a low switching frequency and can be equipped with very low on-state voltage drop Insulated-Gate Bipolar Transistor (IGBT) devices, while the auxiliary inverter is instead operated at low voltage according to a conventional high-frequency two-level Pulse Width Modulation (PWM) technique and can be equipped with very low on-state resistance Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) devices. Simulations and experimental results confirm the effectiveness of the proposed approach and its good performance in terms of grid current harmonic content and overall efficiency.

Photovoltaic (PV) cells are very costly because of the silicon element which is not cheaply available. Usually, PV cells are preferred to be used at maximum efficiency. Therefore, PV plants are emphasized to extract maximum power from PVcells. When inertia free PV plants are integrated into the grid in large numbers, the problem of maintaining system stability subjected to load perturbation is quite difficult. In response to this, a control topology is being an approach to make available the PV cells in maintaining system stability by utilizing the system frequency deviation as feedback to the controller. To implement this, the PVs are operated at Maximum Power Point Tracking (MPPT). This allows the PV to operate at Pseudo Maximum Power Point tracking (PMPPT) which makes it possible to run the PV with reserve power capacity without employing a battery for storage. The control strategy has been implemented over a two-stage power conversion model of the PV system. The simulation results showed that the proposed control PMPPT topology is effective in frequency regulation capability as compared to the MPPT technique.

The model-based methods of maximum power point (MPP) tracking in photovoltaic installations are widely known. One of these methods proposes the use of tracking by direct estimation of the maximum power point resistance using irradiance measurement processing. It proposes six different models for this estimate. In the present work, an exhaustive analysis to determine the robustness and accuracy of the different models was carried out. To perform the analysis, irradiance data sets, used to fit the parameters of the models, were collected. In addition, tests were done to determine MPP tracking accuracy of each of the six models. To carry out the tests, all models were compared with a widely used maximum power point tracking algorithm, perturb & observe, for different values of irradiance, temperature, and load.

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.

The shift towards sustainable energy sources is gaining momentum due to their environmental cleanliness, abundant availability, and eco-friendly characteristics. Solar energy, specifically harnessed through photovoltaic (PV) systems, emerges as a clean, abundant, and environmentally friendly alternative. However, the efficacy of PV systems is subjective depending on two critical factors: irradiance and temperature. To optimize power output, maximum power point tracking (MPPT) strategies are essential, allowing operation at the system’s optimal point. In the presence of partial shading, the power–voltage curve exhibits multiple peaks, yet only one global maximum power point (GMPP) can be identified. Existing algorithms for GMPP tracking often encounter challenges, including overshooting during transient periods and chattering during steady states. This study proposes the utilization of fuzzy sliding mode controllers (FSMC) and fuzzy proportional-integral (FPI) control to enhance global MPPT reference tracking under partial shading conditions. Additionally, the system’s performance is evaluated considering potential sensor malfunctions. The proposed techniques ensure precise tracking of the reference voltage and maximum power in partial shading scenarios, facilitating rapid convergence, improved system stability during transitions, and reduced chattering during steady states. The usefulness of the proposed scheme is confirmed through the use of performance indices. FSMC has the lowest integral absolute error (IAE) of 946.94, followed closely by FPI (947.21), in comparison to the sliding mode controller (SMC) (1241.6) and perturb and observe (P&O) (2433.1). Similarly, in integral time absolute error (ITAE), FSMC (56.84) and FPI (57.06) excel over SMC (91.03) and P&O (635.50).

Maximum power point tracking (MPPT) techniques extract the ultimate power from the photovoltaic (PV) source. Therefore, it is a fundamental control algorithm in any PV configuration. The research in this area is rich and many MPPT methods have been presented in the literature. However, in the current study, we focus on the PV model-based MPPT algorithms. In this regard, the classification of this category can be mainly divided into curve fitting methods and techniques based on the mathematical model or characteristics of the PV source. The objective of the PV model-based MPPT algorithm is to allocate the position of the maximum power point (MPP). Thus, no searching efforts are required to capture that point, which makes it simple and easy to implement. Consequently, the aim of this study is to give an overview of the most commonly utilized model-based MPPT methods. Furthermore, discussion and suggestions are also addressed to highlight the gap in this area. The main methods from the literature are compared together. The comparison and evaluation are validated using an experimental hardware-in-the-loop (HIL) system, where high efficiency (more than 99%) can be obtained with a simple calculation procedure and fast convergence speed.

Maximum power point tracking (MPPT) is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i) slope (of solar power-versus-solar voltage) and changes of the slope; (ii) slope and variation of the power; (iii) variation of power and variation of voltage; (iv) variation of power and variation of current; (v) sum of conductance and increment of the conductance; and (vi) sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i)–(iv) have two input variables each while algorithms (v) and (vi) use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV) cell properties. The range of the input variable of Algorithm (vi) is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.

Grid-tied solar photovoltaic systems use a PWM-VSI STATCOM to regulate active and reactive power. Due to high reactive power demand, often it has been experienced that there is a decay in reactive power supply which may cause malfunction in the load side equipments. The STATCOM balances power variations caused by solar irradiation and ensures constant DC bus voltage for efficient power conversion and optimal MPPT performance. It also provides dynamic reactive power support, balancing imbalanced loads and filtering harmonics. The modified PWM-VSI controlled by Genetic Algorithm optimized Fractional Order based STATCOM approach enhances dynamic response, improves system efficiency, and integrates with MPPT (SAR) for simultaneous reactive power compensation and extraction. The proposed system ensures grid stability during variable solar generation and outperforms the P&O MPPT controller in active and reactive power management. The proposed system uses a modified PWM-VSI STATCOM controller (FOSTATCOM) to regulate PV system voltage and current waveforms, ensuring grid stability during variable solar generation. The SAR MPPT connected SPV system tied utility grid also outperforms the P&O MPPT controller in active and reactive power management, allowing for 109.1 KW active power supply and 360.2 VAR reactive power supply by integrating modified STATCOM as compared to the P&O MPPT controller with standard PWM-VSI STATCOM which is supplying 108.1 KW and 865.3 VAR.

Maximum power point tracker (MPPT) methods work to maximize the output power of a PV system under changes in meteorological conditions. The performance of these methods depends on the complexity of the algorithm and the number of used variable inputs for obtaining the MPP value. Moreover, they oscillate around the MPP in steady-state operations, causing a waste of power and power loss. Moreover, they do not work perfectly for a PV system running under partial shading conditions. Therefore, this paper proposes modifications to the global maximum power point bald eagle search-based (GMPP BES) method so that it runs as an MPPT as well. The modifications enable the GMPP BES method to detect minor changes in insolation and temperature by observing the changes in the PV array output voltage and, accordingly, trigger the search for the suitable MPP voltage. An experimental setup using a real-time digital simulator (RTDS) was utilized to evaluate the modified GMPP BES-based method under real changes in insolation and ambient temperature. The RTDS simulations confirm the capability of the modified method to accurately and efficiently locate the MPP values. Furthermore, the results demonstrate that the proposed method performs better than the perturb and observe (PO) method concerning its ability to respond to changes in insolation and ambient temperature and its ability to arrive at correct MPP values with nearly zero oscillation around the maximum power point. Thus, with these advantages, the proposed method can be considered a practical solution for solar farms that have to harvest large amounts of energy.