CAJ | 학술논문

提高风电出力的预测精度可降低含高渗透率风电电力系统调度、优化、规划等策略的保守性和控制策略的复杂性.该文在分析风电出力历史数据与气象因素关系的基础上,建立了基于风电出力数据驱动的短期风电功率预估-校正预测模型.采用具有较高精度的小波神经网络预测模型实现预估环节,以自适应动态规划作为附加优化结构,利用风电出力实测数据及时更新预估模型中的参数,实现校正环节,使得预估模型能够适应风机在额定风速以下运行区域内多变的运行点.测试结果表明,该方法在风机出力变化频繁时,能获得比BP、GABP预测模型更高的精度.
제고풍전출력적예측정도가강저함고삼투솔풍전전력계통조도、우화、규화등책략적보수성화공제책략적복잡성.해문재분석풍전출력역사수거여기상인소관계적기출상,건립료기우풍전출력수거구동적단기풍전공솔예고-교정예측모형.채용구유교고정도적소파신경망락예측모형실현예고배절,이자괄응동태규화작위부가우화결구,이용풍전출력실측수거급시경신예고모형중적삼수,실현교정배절,사득예고모형능구괄응풍궤재액정풍속이하운행구역내다변적운행점.측시결과표명,해방법재풍궤출력변화빈번시,능획득비BP、GABP예측모형경고적정도.
Improving the prediction accuracy of wind power is of great value in reducing the complexity and conservative of dispatch, optimization and control of power system with high percentage penetration of wind power. Motivated by the idea of data-driven, a short-time predictor-corrector model with an additional learning architecture was proposed based on the analysis of the coupled relationship between wind power historical data and meteorological factors. Wavelet neural network was taken to perform as a predictor while additional learning structure with adaptive dynamic programming that makes use of the wind power data to update the state parameters of predictor was also introduced to form the corrector. Consequently, this model could adapt to a great variety of the operating points of wind system. The test results indicate that this method could both adapt to the frequent change of environment and get better forecast accuracy than BP and GABP methods.