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Graph showing power and voltage relationship14.
This work aims to make a substantial contribution to the field of solar energy systems and control algorithms.
Specifically, it evaluates a highly advanced PV model for MPPT tacking.
Our focus extends to the rigorous evaluation of ten distinct MPPT controllers, including conventional methods such as INC, P&O, INC-PSO, P&O-PSO and advanced approaches employing AI such as ANN, FLC, ANFIS, PSO, ANN-PSO and FLC-PSO.
The crux of our contribution lies in the comprehensive comparative analysis of these controllers, assessing key performance parameters such as maximum output voltage, extracted maximum power, time response dynamics, design complexity, and system stability.
The ultimate goal of this research is to guide the scientific community in selecting and optimizing MPPT algorithms for improved solar energy harvesting.
A state-of-the-art literature review is conducted to analyze the research gap and present the novelty of the proposed technique. The Study presents a novel MPPT method utilizing Artificial Neural Networks (ANN) to efficiently track the maximum power generated by a PV panel. The proposed ANN-based MPPT algorithm demonstrates rapid and accurate adaptation to changing meteorological conditions, including variations in temperature and solar radiation. Comprehensive research includes the design and modeling of a PV system structure in conjunction with the ANN-MPPT controller. The main goal of the study is to develop a high-performance ANN-based MPPT controller for solar applications.
In15, the authors introduced the Fuzzy Logic (FL) MPPT algorithm, a novel fuzzy logic-based method for monitoring the maximum power point of PV arrays. Unlike standard FL-MPPT methods that employ the change in slope of P–V characteristics, the proposed technique uses a new parameter termed "Ea" that is generated from I–V characteristics. This additional parameter improves tracking performance in a variety of environmental conditions (ECs) and increases the precision with which duty ratio changes may be computed. Using the "Ea" parameter, the approach successfully distinguishes between the operating point''s placement in the Voltage Source Region (VSR) or Current Source Region (CSR) and its proximity to the MPP region.
Another study highlights the importance of MPPT controllers for optimizing the performance of solar (PV) modules16. The authors present a comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller architecture, an FL power controller, a PV module, an ANFIS reference model, and a DC-DC boost converter. Through simulations in the MATLAB/Simulink environment, the proposed ANFIS-based MPPT controller successfully harvests the maximum power from the PV module under a variety of weather circumstances, contributing significantly to the advancement of MPPT methods for solar energy systems.
Another study by21 aims to enhance the performance of microgrid systems by creating a self-adapting energy management model that integrates optimal ANN. The proposed model is composed of a series of artificial neural networks that have been optimized individually through the application of PSO. The model aims to estimate and provide essential data to the energy management system to enhance the microgrid''s integration of various energy sources. After being constructed in MATLAB/Simulink, the model is validated using experimental data.
A novel hybrid Fuzzy Particle Swarm Optimization (FPSO) technique, in conjunction with a photovoltaic-fed shunt active power filter, is proposed by22 to increase power quality and produce clean electricity. MPPT is a function of the FPSO system, which tracks the MPP and extracts as much energy as possible from the PV system. Fuzzy logic and synchronous reference frame theory govern the photovoltaic-fed shunt active power filter, which connects the boost converter output to the grid. The results demonstrate that the recommended controller performs well in a variety of load circumstances, resulting in improved power quality and more environmentally friendly electricity distribution.
The availability of different methods presents issues for maintaining continuous power generation from solar PV systems and ensuring the usage of optimum MPPT controllers. As a result, a thorough comparison study is required. In line with current research trends in renewable energy, our study is to perform a thorough comparative analysis of these controllers by applying real environments and a variety of weather conditions24.
Conventional MPPT methods are fundamental approaches used in solar energy system optimization with the goal of improving PV system efficiency. Of these, the most often used are INC and P&O due to their ease of use and integration. These methods work by dynamically modifying the PV system''s operating point in order to track the MPP in a range of environmental circumstances. While INC is recognized for its rapid convergence and adaptability to dynamic changes, P&O offers a straightforward approach, albeit with potential accuracy challenges in certain scenarios25. Practitioners must have a thorough understanding of these traditional MPPT approaches in order to design and implement PV systems with the best possible energy extraction.
Because it offers the following advantages, the INC MPPT controller is a popular choice for optimizing energy extraction from PV solar systems.
The MPP can be efficiently tracked by the INC MPPT controller across a range of weather conditions and load variations. Ensuring that the PV system is running at the MPP raises energy conversion efficiency. The INC algorithm can readily adapt to variations in temperature and solar irradiation because of its quick reaction time. The controller''s reactivity allows it to maintain system operation at the MPP even in the face of fast environmental changes. The INC MPPT controller accurately tracks the MPP by comparing the instantaneous conductance (I/V) with the incremental conductance (dI/dV)29. The technology continually extracts the greatest amount of power from the PV array thanks to this accurate tracking.
In photovoltaic systems, one of the most used MPPT algorithms is the P&O algorithm. Its basic idea is to gradually alter the PV system''s operating point while closely observing how the power output changes in response. The operating point is changed to improve power output after reaching the maximum power point32. Due to its simplicity and ease of implementation, the P&O algorithm is preferred. It is important to keep in mind, though, that it has a somewhat slow tracking speed and that it might not reach the global maximum power point. The flow diagram of the P&O controller is given in Fig. 3.
Shows the flow diagram of P&O33.
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