Four heuristic optimization algorithms applied to wind energy: determination of Weibull curve parameters for three Brazilian sites

Abstract

Minimizing errors in wind resource analysis brings significant reliability gains for any wind power generation project. The characterization of the wind regime is one of fundamental importance, and the two parameters Weibull distribution is the most applied function for it. This study aims to determine the scale and shape factor in an attempt to establish acceptable criteria to a better utilization of wind power in the states of Pernambuco and Rio Grande do Sul, which is a national prominence in the use of renewable sources for electricity generation in Brazil. The following heuristic optimization algorithms were applied: Harmony Search, Cuckoo Search Optimization, Particle Swarm Optimization and Ant Colony Optimization. The fit tests were performed with data from the Brazilian Federal Government's SONDA (National System of Environmental Data Organization) project, referring to Triunfo, Petrolina and São Martinho da Serra, states of Pernambuco and Rio Grande do Sul, cities in the northeast and south regions of Brazil, during the period of 1 year. The tests were made in 2006 and 2010, all at 50 m from ground level. The results were analyzed and compared with those obtained by the maximum likelihood method, moment method, empirical method and equivalent energy method, methods that presented significant results in regions with characteristics similar to the regions studied in this study. The performance of each method was evaluated by the RMSE (root mean square error), MAE (mean absolute error), R 2 (coefficient of determination) and WPD (wind production deviation) tests. The statistical tests showed that ACO is the most efficient method for determining the parameters of the Weibull distribution for Triunfo and São Martinho da Serra and CSO is the most efficient for Petrolina.

Mesoscale Wind Farm Placement via Linear Optimization Constrained by Power System and Techno-economics

“…In (9), the matrices obtained for each technology are recalculated according to the target height value of the wind power plants to be established before being converted into (10).…”

“…The matrices in (10) are collected by (11) and a connection matrix with n s rows and n w columns is obtained for each busbar where the connection and the non-connection values are 1 and 0, respectively.…”

“…The wind atlas analysis and application program (WAsP) was used in [7] to evaluate the measured wind data before estimating the energy produced at different points. With this program, the analysis on potential wind energy was conducted in regions where no measurements were available [8] - [10]. Another important parameter for the accurate evaluation of wind energy is the turbine type used in wind power plants [11], [12].…”

Review on the cost optimization of microgrids via particle swarm optimization

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Artificial Intelligence Techniques for Estimation of Optimum Weibull Parameters for Wind Speed Distribution

“…The most critical issue with the application of the Weibull distribution is the estimation of the Weibull scale parameter c and shape parameter k [12]. Numerous techniques are used to estimate these parameters.…”