【作者】 张登峰；

【导师】 刘健；

【作者基本信息】 西安科技大学 ， 通信与信息系统， 2005， 硕士

【摘要】 为了解决已有的基于小波—卡尔曼滤波的短期负荷预测方法,在对温度敏感负荷进行预测时,预测准确度较差的问题,提出了一种改进方法:将日负荷表示为日平均负荷与波动部分的乘积,对日平均负荷和波动部分分别进行预测。提出了一种利用人工神经网络预测平均负荷的新方法:将平均负荷表示为温度敏感分量与平稳的温度不敏感分量之和。温度不敏感分量根据温度不敏感季节同时期的若干负荷数据统计得出。根据前若干天的温度敏感分量值、温度信息(包括平均温度、最高温度和最低温度)以及预测日的温度信息,采用 BP 网络构成的负荷预测器,得出预测日的温度敏感分量的预测值。对于波动部分沿用已有的基于小波—卡尔曼滤波的方法,在对波动部分进行多分辨分析的基础上,将小波系数作为状态变量,利用卡尔曼滤波算法得出波动部分的预测结果。实例分析表明,提出的改进方法显著提高了预测准确性。 对于偶然因素引起负荷预测结果较大地偏离实际负荷的情况,为了进行及时的修正以便改善随后的预测结果,提出了局部修正的方法:当检测到连续给定个数的预测误差采样值大于域值时,采用灰色预测等修正算法对随后的预测值进行修正,当检测到连续出现给定个数的预测误差采样值小于域值时,立即退出局部修正模式。实例表明,提出的修正方法达到了预期的目的。 此外,为了删除历史负荷数据中夹杂的异常数据以便提高预测精度,采用了一种伪数据处理方法:利用小波变换,将负荷序列投影到不同的尺度上,在不同的尺度域分别计算模极大值,并根据负荷以天为周期波动的特性对模极大值进行处理,最后通过小波重构得到去处伪数据的负荷序列。对实际负荷数据的计算证明了该方法的有效性。 综合上述方法,编写了完整的短期负荷预测程序,并对西安市区配电网进行了短期负荷预测,并与实际负荷测试结果进行了比较,结果表明提出的方法是可行的。更多还原

【Abstract】 Some disadvantages of the existing Wavelet-Kalman filter based load forecasting methods are pointed out. An improved scheme is presented: A load is expressed as the product of an average component and a varying component. Such two components are forecasted separately. A new method based on artificial neural networks is proposed and applied in forecasting the average load. The average load is expressed as the summation of a temperature-insensitive part being stationary and a temperature-sensitive part. The former is obtained according to the data of temperature insensitive seasons. The latter is obtained by a BP network whose inputs are history information of load, temperature (including the averaged temperature, the highest temperature and the lowest temperature) and the temperature of the predict day. The varying component is forecasted by Wavelet-Kalman filter based method. The wavelet coefficients of the varying load component are obtained by the decomposition scheme of multi-resolution analysis. The wavelet coefficients are modeled as the state variables of the Kalman filter. The best estimation of the predicted varying load component is obtained by the recursive Kalman filter algorithm. Load forecasting results show that the improved method increase the forecast precision. A new method of real time modification on forecasting results is proposed. In case of several forecasted errors are over the threshold successively, the revise algorithm such as grey-forecasting is taken to modify the following forecasted data until a certain number of forecasted errors restore to the allowed range. Load forecasting results show that the revise method is satisfied. In addition, a bad data elimination method based on the wavelet analysis is adopted. By using the wavelet transform, the different load sequence components are projected to the different scales in which the matching modulus maxima can be obtained and eliminated according to the daily-period feature of the power system load. The effectiveness of the method is verified by the results of a practical example. An integrated short-term load forecasting program is completed based on above methods. The loads of Xi’an distribution network are used to test the program. Comparison of the results with the measured values show that the proposed methods and the program are feasible.更多还原