| 引用本文: | 张颖超,郭晓杰,叶小岭,等.一种短期风电功率集成预测方法[J].电力系统保护与控制,2016,44(7):90-95. |
| ZHANG Yingchao,GUO Xiaojie,YE Xiaoling,et al.An integrated forecasting method of short-term wind power[J].Power System Protection and Control,2016,44(7):90-95 |
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| 摘要: |
| 为提高短期风电功率预测精度，缩短模型训练时间，提出了一种短期风电功率集成预测方法。根据风速功率曲线和风速频率特征，将风速划分为高、中、低三段，并对每段的风速功率特征进行统计分析。高、低风速段功率波动较大，使用最小二乘支持向量机(Least Squares Support Vector Machine，LSSVM)方法可取得较高的预测精度。中风速段风速数据点较多，且风速和功率有明显的物理关系，使用高斯(Gaussian)模型预测。并用风速功率等级表对各段预测的结果进行订正，保证了算法的稳定性。用上海某风电场2014年的历史数据，验证了Gaussian模型以及高、中、低风速段对应的预测算法选取的合理性。与LSSVM预测方法相比较，集成预测方法既提高了预测精度又缩短了预测时间，适合风电场短期功率的实时预测。 |
| 关键词: 短期风电功率预测 集成预测方法 Gaussian模型 LSSVM Weibull |
| DOI：10.7667/PSPC151030 |
| 分类号: |
| 基金项目:江苏省六大人才高峰资助项目(WLW-021)；国家公益性行业(气象)科研专项资助项目(GYHY201106040)；江苏省高校优势学科建设工程资助项目 |
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| An integrated forecasting method of short-term wind power |
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ZHANG Yingchao1,2, GUO Xiaojie1, YE Xiaoling1,2, DENG Hua1,2
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1.Nanjing University of Information Science & Technology, Nanjing 210044, China;2.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
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| Abstract: |
| An integrated forecasting method of short-term wind power is presented for improving prediction accuracy and shortening the model training time. Based on the characteristic of wind power curve and wind speed frequency, the wind speed is divided into high, medium and low three segments, and each wind power characteristic is analyzed. As the predicted power shows larger fluctuated statuses in segments of high and low wind speed, so Least Squares Support Vector Machine is used to achieve better prediction accuracy. Much more data can be accessed in the medium segment, and there is an obvious physical relationship between wind speed and power, so Gaussian Model is used under this sort of circumstance. At the same time, the level table of wind and power is used to revise the predicted power in each section to ensure the stability of the algorithm. The rationality of Gaussian model and selection of algorithm in high, medium and low segment is verified by using the historical data of a wind farm of Shanghai in 2014. The simulated result compared with LSSVM’S shows that the proposed algorithm can not only improve the prediction accuracy, but also shorten the model training time. It can be well used to predict short-term wind power in real-time. |
| Key words: short-term wind power prediction integrated prediction method Gaussian model LSSVM Weibull |
