Resumen
The stochastic fluctuations of wind speed and wind power curve modeling are complex tasks due to fluctuations in the difference between actual and theoretical power output, leading to a reduction in the accuracy of wind-power curve models. To address this issue, this paper proposes a normal distribution-modeling method based on relative volatility, which extracts the wind-speed variation patterns from the onsite SCADA (Supervisory Control And Data Acquisition) data, analyzes the correlation between wind-speed relative volatility and power relative volatility, and establishes a wind-power volatility-curve model to provide a basis for evaluating the efficiency of wind turbines. First, the definitions of relative volatility and probability vectors are provided, and a probability vector volatility-assessment function is designed to calculate the volatility-assessment index of the probability vector. Then, the relative volatility and probability vectors of wind speed are modeled, and features extracted from the onsite SCADA data, and characteristic parameters such as mean, standard deviation, and confidence interval of wind-speed relative volatility are statistically analyzed, as well as the wide-window coefficient, volatility-assessment index, attribute features (volatility center and volatility boundary), normal distribution features (mean and standard deviation) of the probability vectors of wind-speed relative volatility with different periods. The visualization descriptions of six typical probability vector distributions show that there is a correlation between the volatility assessment index of the probability vector based on relative volatility and the standard deviation of its distribution. Finally, the correlation between wind-speed relative volatility and power relative volatility is analyzed: in the maximum wind-energy tracking area, the derivative of power is linearly related to the derivative of wind speed, while in the constant power area, the derivative of the wind-energy utilization coefficient is linearly related to the derivative of wind speed. The conclusions obtained in this paper will provide a method reference for data processing to mine the parameter variation patterns and interrelationships of wind farm SCADA data and provide a basis for evaluating the power generation efficiency of wind turbines.