近海风电场短期功率预测研究
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摘要
在全球大力提倡节能减排的新形势下,以风能为代表的可再生能源得到了广泛关注。我国风能资源储量丰富,近年来风电事业发展迅速。作为风电技术研究的一个方面,风电场短期功率预测对风电电网的稳定运行及合理调度具有极其重要的意义,而近海风电场由于其特殊的地理环境和风速特点,使得短期功率预测更加迫切。传统的预测方法在风电场短期功率预测中各具优点,但是在近海风电场中不能很好的应用,而且原有预测研究内容很少包含功率预测结果的不确定性分析,预测结果并不能很好的体现出其科学指导价值。因此,本文的主要研究内容是构建新的功率预测模型并对预测结果进行不确定性分析。
从历史功率和风速序列的非线性,非平稳性角度出发,提出了改进的经验模态分解法和神经网络法相结合的短期功率预测模型。采用分段三次Hermite插值作为经验模态分解中新的包络拟合算法,解决包络拟合时出现的过冲/欠冲问题,提高分解精度。将改进后的经验模态分解法用于功率序列的分解,降低了序列的非平稳性,然后各分量再使用神经网络预测。仿真实例表明该模型具有较高的预测精度。
从实测序列受多种外界因素干扰以及它们之间的相互影响性角度出发,提出了基于独立成分分析和最小二乘支持向量机的短期功率预测模型。将独立成分分析法应用于功率预测领域,保证提取出的各成分相互独立,然后经最小二乘支持向量机预测后进行重构,最后通过已有信息进行回归修正。通过实例仿真并与其他方法的比较,表明了此模型的精确性,合理性和有效性。
在预测出功率数值的基础上,对其进行了不确定性分析,提出了基于独立成分分析和条件概率理论为基础的预测结果置信概率确定新模型。针对确定置信概率时传统方法存在的不足,提出采用独立成分分析法得到功率独立影响事件集,将置信概率求解问题转化为多个功率独立影响事件的条件概率和无条件概率问题,思路清晰,求解方便。通过仿真实例,新模型充分考虑了目标功率的发生条件,并充分挖掘了原有信息内容,得到的置信概率符合实际意义,使得对近海风电场短期功率预测技术的研究更加完善。
Under the new situations and circumstances of a worldwide avocation of energy conservation and reduction of pollutant emission, the wind energy, as the representative of the renewable energy sources has caused extensive concern. China has pretty rich wind resources and wind power industry has developed rapidly in recent years. As one research field of wind-electric technology, the short-term forecast of wind power field, which plays an important role in the stable operation and the proper modulation of wind power network, gets more compelling due to the special geographical conditions and the wind speed characteristics of wind power field. Although the traditional methods of forecast have their own merits in the short-term forecast of wind power field, they couldn't be a good application in the coastal wind farms. The content of the traditional research covers a limited area of the uncertainty analysis on the prediction results of power. In addition, prediction results not well reflect the value of their scientific guidance. Therefore, the main contents of this paper are to build new power prediction model and make uncertainty analysis of power prediction results.
For the difficult forecasting problems of non-linear, non-stationary power and wind speed series, this paper proposes a short-term power prediction model based on improved Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN). Power series can be decomposed into different series using the improved fitting algorithm in the process of fitting the envelope, in order to reduce the sequence of non-stationary, and then ANN is used to forecast power by using each component. The simulation example shows that the model has higher prediction accuracy.
For the difficult problems of measuring values interfered by a number of external factors and the nature of the interaction between their, this paper proposes a short-term power prediction model based on Independent Component Analysis (ICA) and Least Squares Support Vector Machine (LS-SVM). ICA is applied to power forecasting to ensure that the extracted components are independent of each, and then each component is predicted using LS-SVM, the final results obtained by modifying the preliminary predicting power according to existing information. The simulation example shows that this model has higher accuracy, rationality and effectiveness, compared with other methods.
This paper makes uncertainty analysis of power prediction results, and proposes a new model to determine the confidence probability based on ICA and conditional probability theory. For the shortcomings of traditional methods that determine the confidence probability, the power independent influence events set can be obtained from ICA, then the problem of determining the confidence probability will transform into the conditional probability and unconditional probability problem whose objects are the power independent influence events. The model is clear and easy, which is fully takes into account occurrence conditions of the target power and the original content. The simulation example shows the confidence probability result has realistic sense, making the study of short-term power forecasting in coastal wind farm more perfect.
从历史功率和风速序列的非线性,非平稳性角度出发,提出了改进的经验模态分解法和神经网络法相结合的短期功率预测模型。采用分段三次Hermite插值作为经验模态分解中新的包络拟合算法,解决包络拟合时出现的过冲/欠冲问题,提高分解精度。将改进后的经验模态分解法用于功率序列的分解,降低了序列的非平稳性,然后各分量再使用神经网络预测。仿真实例表明该模型具有较高的预测精度。
从实测序列受多种外界因素干扰以及它们之间的相互影响性角度出发,提出了基于独立成分分析和最小二乘支持向量机的短期功率预测模型。将独立成分分析法应用于功率预测领域,保证提取出的各成分相互独立,然后经最小二乘支持向量机预测后进行重构,最后通过已有信息进行回归修正。通过实例仿真并与其他方法的比较,表明了此模型的精确性,合理性和有效性。
在预测出功率数值的基础上,对其进行了不确定性分析,提出了基于独立成分分析和条件概率理论为基础的预测结果置信概率确定新模型。针对确定置信概率时传统方法存在的不足,提出采用独立成分分析法得到功率独立影响事件集,将置信概率求解问题转化为多个功率独立影响事件的条件概率和无条件概率问题,思路清晰,求解方便。通过仿真实例,新模型充分考虑了目标功率的发生条件,并充分挖掘了原有信息内容,得到的置信概率符合实际意义,使得对近海风电场短期功率预测技术的研究更加完善。
Under the new situations and circumstances of a worldwide avocation of energy conservation and reduction of pollutant emission, the wind energy, as the representative of the renewable energy sources has caused extensive concern. China has pretty rich wind resources and wind power industry has developed rapidly in recent years. As one research field of wind-electric technology, the short-term forecast of wind power field, which plays an important role in the stable operation and the proper modulation of wind power network, gets more compelling due to the special geographical conditions and the wind speed characteristics of wind power field. Although the traditional methods of forecast have their own merits in the short-term forecast of wind power field, they couldn't be a good application in the coastal wind farms. The content of the traditional research covers a limited area of the uncertainty analysis on the prediction results of power. In addition, prediction results not well reflect the value of their scientific guidance. Therefore, the main contents of this paper are to build new power prediction model and make uncertainty analysis of power prediction results.
For the difficult forecasting problems of non-linear, non-stationary power and wind speed series, this paper proposes a short-term power prediction model based on improved Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN). Power series can be decomposed into different series using the improved fitting algorithm in the process of fitting the envelope, in order to reduce the sequence of non-stationary, and then ANN is used to forecast power by using each component. The simulation example shows that the model has higher prediction accuracy.
For the difficult problems of measuring values interfered by a number of external factors and the nature of the interaction between their, this paper proposes a short-term power prediction model based on Independent Component Analysis (ICA) and Least Squares Support Vector Machine (LS-SVM). ICA is applied to power forecasting to ensure that the extracted components are independent of each, and then each component is predicted using LS-SVM, the final results obtained by modifying the preliminary predicting power according to existing information. The simulation example shows that this model has higher accuracy, rationality and effectiveness, compared with other methods.
This paper makes uncertainty analysis of power prediction results, and proposes a new model to determine the confidence probability based on ICA and conditional probability theory. For the shortcomings of traditional methods that determine the confidence probability, the power independent influence events set can be obtained from ICA, then the problem of determining the confidence probability will transform into the conditional probability and unconditional probability problem whose objects are the power independent influence events. The model is clear and easy, which is fully takes into account occurrence conditions of the target power and the original content. The simulation example shows the confidence probability result has realistic sense, making the study of short-term power forecasting in coastal wind farm more perfect.
引文
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