In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13 • 41 30.4" N 79 • 21 34.4" E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260-280 • and wind from 0-4 m/s were present in sector 170-180 • of the Tirumala region in India.optimization is that there exists information exchange among the solutions of candidates. In this way they can handle the local optima, bias of search space and premature convergence easier and faster. Some of the meta-heuristic optimization such as the genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution algorithm (DEA) have overcome the limitations of single solution optimization methods. Therefore, in this paper a multiverse optimization (MVO) technique is implemented to estimate the parameters to adjust and fit the actual wind profile. This methodology serves as an innovative solution for any particular wind conditions provided a well-defined pattern has been provided. The MVO outperforms other optimization techniques and is being applied for wind energy applications to achieve rapid convergence in estimating Weibull parameters. This MVO algorithm is compared with PSO, which is best among the SI based technique and GWO, MFO as one of the most recent algorithms. The proposed algorithm has high exploitation ability due to combination of WEP/TDR constants and wormholes combined to provide high exploitation. Superior exploration of the proposed algorithm is due to white and black holes to exchange different objects.Here, the wind data for a period of six years from January 2012 to December 2017 in the Tirumala region (13 • 41 30.4" N 79 • 21 34.4" E) is being studied which is located in the southern part of India. In order to analyze the wind distribution, scale and shape parameters are determined using MVO. This gives two values, which is further utilized to determine the probability density function of the Weibull and Rayleigh distribution. With this study it has been estimated that there is sufficient wind potential in this region...

This review study focuses on various methods and technologies used in past and present for obtaining maximum output power from a wind energy conversion system. There are plenty of solution for maximum power point (MPP), but the problem lies in the effective choice made among them and it needs the expert knowledge on every technique for picking up the best MPP method as every method on its own has some advantages and disadvantages. A comparison has been made among various MPP methods in terms of convergence time, efficiency, training, complexity and wind measurement. Here, different MPP tracking (MPPT) algorithms are classified based on wind speed measurement (WSR) and without WSR models. In this study, from the literature, a novel maximum electrical power tracking (MEPT) and maximum mechanical power tracking (MMPT) methods are compared with state-of-the-art MPPT algorithms. On basis of the results obtained from the literature available, the MEPT algorithm has fast convergence rate of 15 ms; on the other hand, optimal relation-based method is having large convergence rate of 364 ms and less efficient. A case study has been considered for performance validation, and MEPT and MMPT are having a good response for dynamic variation in wind speed.

For the purpose of better utilization and to have control over varying wind speeds we use variable speed wind turbines. The performance mainly depends on the system operating point. In this paper we implement extremum seeking (ES) which is a non-model based approach for maximum power extraction in the region between cut-in speed and rated speed. The convergence of the system depends mainly on the system dynamics so we go for non-linear control based on field oriented approach and also feedback linearization. For achieving maximum power at all wind speeds the outer loop of ES is used to tune the turbine speed in the sub rated region. By adjusting the voltage magnitude and electrical frequency through matrix converter we can achieve a fast transient response. The transient response can be improved by providing inner loop control based on field oriented control. Through this we can avoid magnetic saturation in the induction generator.

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