Renewable energy resources are gaining a lot of popularity. Several researchers have worked on the tracking and extraction of energy from these sources. In the past few decades, among the available green energy resources, wind energy has been the most attractive option among the resources available. It is imperative to use the maximum power available in the wind to achieve the wind turbine (WT) operation at maximum power. The maximum power point tracking (MPPT) algorithms are a pioneer in this context. Many research papers are contributed in this domain which necessitates a thorough review while choosing an appropriate technique. This paper comprehensively focuses on reviewing different algorithms in the past and present for tracking maximum power point, and capturing maximized output power from the wind energy conversion system (WECS). In this paper, the algorithms are classified based on the direct and indirect power measurement, hybrid and smart algorithms for tracking maximum power point, and they are compared, considering the parameters like complexity, convergence speed, use of sensors, memory requirement, need for knowledge of system parameters, etc. The immense popularity of the different versions of perturb and observe (P&O) based algorithms due to their various features is evident from the literature. The review reveals that the hybrid maximum power point tracking algorithms can use the advantages of the conventional methods and eliminate their drawbacks.

This paper outlines a novel design of low-cost, portable, fast, and precise Current-Voltage Curve Tracer (IVCT) with automated parameter extraction for high power rated Solar Photovoltaic (SPV) modules to effectively and efficiently determine the outdoor operating status of SPV power generators. The developed IVCT is based on a Raspberry Pi microprocessor, a super-capacitive load, heat sinkable discharge resistances, and sensors with high sensitivity and resolution for measuring light irradiance, module temperature, current, and voltage. The proposed Outdoor Test Facility (OTF) consists of an Current-Voltage ( $I-V$ ) and a Power-Voltage ( $P-V$ ) curve tracer that uses a dynamic loading supercapacitor to safely and quickly scan the SPV module performance characteristics under real-world operating conditions. It also helps to achieve uniform sampling with better data accuracy. It uses Raspberry Pi as a central processing unit for low-cost data acquisition, data logging, and data computation. Furthermore, results from on-field testing of various small-scale SPV modules show that the $I-V$ tracer can acquire higher-resolution characteristics curves and perform accurate model parameter recognition in real-time. Proposed IVCT can measure individual SPV modules without altering the electrical interconnection circuit, and the operating point can be shifted to 20 $A$ and $45~V$ in few seconds. The proposed system recomposes the SPV module $I-V$ characteristics based on this variance, with accuracies of 1 to 3% for the region near maximum power.

The capacitor based load method possess the capability to perform during non-uniform environmental conditions with auto-sweep functionality in the process

This work presents a new trapezoidal-rule-based variation of the perturb and observe algorithm to track the point with maximum power for a wind energy conversion system. The algorithm works in three steps. In the first step, the trapezoidal-rule-based division of the power curve into trapezoids of equal width is carried out. In the second step, areas of the adjacent trapezoids are compared to identify the trapezoid with the largest area. In the third step, the conventional perturb and observe algorithm is employed in the trapezoid having the largest area to capture the point of maximum power. The algorithm is simulated in MATLAB/SIMULINK to check the efficacy in capturing the maximum power. The simulation results suggest that the proposed method performs well under fluctuating wind conditions with improved yielded power. An effort to achieve simplicity for implementation and effectively track the maximum power point is made and presented.

The conventional techniques employed for tracking maximum power suffer from several issues. The perturb and observe (P&O) algorithm is extremely popular among the conventional techniques for WECS to track and capture the maximum power point. The major issue with this algorithm is opting for an appropriate step size. A new technique is developed and described in this paper, which combines the trapezoidal rule and the fuzzy logic control technique, to address this issue. Two techniques are compared with the proposed algorithm: the trapezoidal rule-based P&O (TRPO) algorithm and the conventional P&O method to prove the superiority of the technique presented in this work. MATLAB/Simulink simulation showcases the enhancement of power by this technique. Randomly varying wind speeds were used to examine the power output of all three algorithms. The results obtained after simulation portray significant enhancement in DC output current, voltage, and power with considerable reduction in the oscillations by employing the proposed technique.

One of the key reasons contributing to a reduction in the performance of a solar photovoltaic (SPV) system is the presence of partial shade on the solar panels. It is necessary to use maximum power point tracking (MPPT) in SPV systems to get around the non-linear behaviour of photovoltaic panels. This research analyses the reduction in power output of SPV systems caused by partial shading. In the latter part of the paper, the improved dynamic behaviour-based ant colony optimization MPPT approach under the effect of module-level partial shading is implemented in MATLAB®/Simulink®. Various partial shading levels were applied to SPV modules to evaluate the system performance. The proposed system follows the maximum power point with 99.9% accuracy and a response time of 2 ms. The addition of a weight coefficient accelerates the convergence of the algorithm.