Intelligent Techniques for Maximum Power Point Tracking

Abstract

Renewable energy (RE) is an ultimate energy source, which plays an important role nowadays due to its advantages over non-RE. Compared to other RE resources, the PV array or module can only convert about 30%–40% of the solar radiation into electrical energy due to the nonlinear behavior of the irradiation and temperature. Many types of research have been conducted in recent years to improve the effectiveness of PV systems and address the issues related to solar PV systems, one of which is the Maximum Power Point Tracking (MPPT) control algorithm. The MPPT helps in extracting the maximum capable power of the modules having irradiation and temperature as its main parameters. The parameters that help in tracking the maximum power point are maximum power (P m), maximum power voltage (V mp), open-circuit voltage (V oc), maximum power current (I mp), and short-circuit current (I sc). The output of the MPPT is generally in terms of duty cycle that changes with the variation with environmental conditions. Various MPPT control algorithms are used nowadays to control the operating point to maximum mower point at higher efficiency. Still, due to nonlinear behavior and shading effect on PV array, photovoltaic arrays have complicated multiple peak (local minima) patterns in output I–V and P–V. The maximum power point shifts with time, which reduces the effectiveness of the system. So, to track the maximum power (global maxima), MPPT is used that has less settling time and transients in the output terminals of converters. MPPT controller algorithms are divided into three categories in this chapter: classical, soft computing, and hybrid-based MPPT controller algorithms. Classical-based MPPT algorithms are used due to their simplicity and directly controlling the PV system array/module parameters. Still, with time new techniques came into existence, i.e., soft computing-based control algorithm with hybrid methods. These controllers employ different algorithms with improved efficiency, performance, modernity, complexity, tracking speed, handling capability, self-learning approach, and less dependency on the system, which act as an advantage over classical algorithms. Hybrid methods combine classical and artificial intelligence (AI) methods, which have the benefit of both classical and AI techniques. The different soft-computing MPPT control algorithms are highlighted in this. Soft-computing algorithm comprises AI-based and Nature-inspired MPPT algorithms. These methods or strategies are used to control and vary the duty cycle for the converter section based on the performance of the PV array and to optimize the output performance in less amount of time. Nature-inspired methods and algorithms are more efficient in all conditions. This review of different MPPT will help the researcher in selecting the suitable MPPT algorithm for enhancing the performance of the PV system; the advantages and disadvantages of the different MPPT algorithms are also highlighted.