Method of Quartile for determination of Weibull parameters and assessment of wind potential

Abstract

Weibull Distribution is the most widely used distribution in wind power assessment. Two parameters Weibull distribution is commonly used for wind distribution modeling. The wind turbine converts wind energy into electrical energy. According to Betz law, No wind turbine can convert more than 59% of the available wind energy into electrical energy. The available method to find the parameters, e.g., Empirical Method (EM), Method of Moment (MoM), Energy Pattern Factor Method (EPFM), Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), measure an overestimated value of wind power. An attempt has been made to develop a new method to evaluate Weibull parameters to measure wind potential close to the actual one. The new methods depend on the Quartiles of the wind distribution, Method of Quartile. Wind speed data for twelve months, January to December of 2016, for the cities Hyderabad, Karachi, and Quetta is used in this study. The new method results are compared with the five methods of Weibull parameter determination, EM, MoM, EPFM, MLM, and MMLM. The new method’s average wind speed is closer to the actual average wind speed than those measured by other methods. The Root Mean Square Error (RMSE), Mean Absolute Error (MABE), and chi-square statistic calculated by all methods are close. The Akaike Information Criterion (AIC) model selection criterion was used for each method and month. It is found that the AIC values for every month and every city are the lowest for MoQ. It also suggests that the new method, MoQ, is the best among the existing method. Keywords: Weibull Distribution, Method of Quartile, Method of Moments, Maximum Likelihood, Empirical Method, Energy Pattern Factor Method

A new mathematical technique and its Python program to assess wind potential

“…Regarding Sindh, Pakistan, [24] considered the wind site of Babaurband to evaluate the wind production perspective and estimate Weibull parameters. Recently, [25] innovated the quartile method for assessing wind potential and determining Weibull parameters for three cities: Karachi, Hyderabad, and Quetta.…”

Short-term wind power prediction based on anomalous data cleaning and optimized LSTM network

“…At present, due to the limitation of technical means, the power prediction by neural network is generally a short-term prediction. In this case, a lot of data from the SCADA system, but the system records and stores the operation data in the inevitable existence of noise and faults and other anomalous data, according to the distribution of the data in the power curve of the unit, the anomalous data is divided into: the bottom of the pile-up type, deviation from the power band type, the middle of the pile-up type, the type of discrete several categories Zhao et al (2022); Uddin and Sadiq (2022). These data cannot truly reflect the operating state of the unit, and only the data after removing the abnormal data can reflect the unit's will-con state, and can be used to train the prediction model of the unit Wang et al (2022a).…”