Performance Of Maximum Power Point Tracker Research Articles

Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems

Due to the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade conditions (PSCs). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPTs) may become locked in one of the LPs, significantly reducing the PV system’s generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms’ key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA’s convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA’s superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time was reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, the best initialization of search agents, and the optimal design of the dc–dc converter are introduced for optimal MPPT performance.

An adaptive power point tracker in wind photovoltaic system using an optimized deep learning framework

ABSTRACT Over the past two decades, the integration and generation of photovoltaic power plants have risen to a huge level. The reason is increased pollution, which tends to cause ozone damage. The input source is a wind turbine, in which wind converts mechanical energy into electrical energy. The grid and solar array links have been optimized using the Maximum Power Point Tracker (MPPT), demonstrating the DC to DC converter. The deep learning framework could improve the performance and efficiency of MPPT. The RLC filter has been utilized for the specialized deduction in the current harmonic and voltage. The complexity, voltage fluctuation, and load imbalance were the causes to reduce power quality performance. Therefore, the proposed method has been designed to get error-free output. Furthermore, the MPPT based on a novel Vulture-based convolution neural model (VbCNM) is utilized to improve the efficiency and extract the power. The proposed method has resolved the open issues and technical implementation using the VBCNN technique. Furthermore, the precise error-free output has been determined by simulating MATLAB. The results obtained are more efficient, fast convergence, and clear. Finally, the simulation output was compared with various conventional methods and has attained a lower power loss as 0.1 kW and less sample times as 9 µs.

Assessment of maximum power point trackers performance using direct and indirect control methods

The direct and indirect control methods are commonly used for maximum power point tracker (MPPT) of photovoltaic (PV) systems. Due to the absence of the literature that assess their performance in a comprehensive way, this work attempts to analyze the effectiveness of both control methods for the perturb and observe (P&O) MPPT algorithm. The MATLAB/Simulink simulation is verified experimentally using a PV array simulator and the dSPACE DS1104 DSP board driving a buck-boost converter. The results show that the indirect method exhibits lower steady-state oscillation and is less sensitive to rapid irradiance change and load variations. It increases the steady-state efficiency by 1.6%. Furthermore, under irradiance and load step changes, the transient efficiency increases by 29% and 30%, respectively. Based on these findings, it is envisaged that the indirect method is more suitable to be used as the controller in conjunction with the MPPT algorithm.

Implementasi Algoritma Perturb and Observe untuk Mengoptimasi Daya Keluaran Solar Cell Menggunakan MPPT di Laboratorium Energi Baru Terbarukan

Photovoltaic systems produce very intermittent energy and highly dependent on the wheather conditions. To optimize the power of solar cell, Maximum Power Point (MPP) is usually used . This project presented implementation of Perturb and Observe algorithm for Maximum Power Point Tracker (MPPT) as controller for solar power generator. Constructed system harnessed Arduino Uno R3 to extract maximum power of solar cell. Microcontroller controlled DC buck converter for storing the energy using 6V battery. This methode provided perturbing (increase or decrease), voltage or current of terminal array, and compared power produced with solar cell output power. Appropriate perturbing is necessary to receive the most optimimal power. Validity of the photovoltaic module with P & O method allowed better performance of MPPT due to variation of both power and voltage. Using perturbation parameter 0.1 V for proposed method in the MPPT, solar panel was able to optimize power output up to 24.49W using load 5? in 523 W/m2 irradiance level compared with conventional methode using identical load and irradiance level 527 W/m2 providing power output 15.37W