基于ISMC-PSO的风电爬坡输出功率预测系统的研究

目前风电场都是使用单一的功率爬坡预测模型,其泛化能力较差,预测精度低。通过分析支持向量机和极限学习机两种单一功率的爬坡预测模型,研究了这两种模型的权值选取方法,提出了一种组合功率爬坡预测模型。该模型使用基于粒子群算法的改进算法对上述两种单一模型的权值进行权重优化,形成了一种新的高精度的预测系统。然后对该系统进行建模仿真。仿真结果验证了该预测模型的有效性,其预测精度较之单一的预测模型有了很大的提高。

Research on ISMC-PSO based wind uphill power output prediction system

At present, wind farms use a single power ramp prediction model, which has poor generalization ability and low prediction accuracy. By analyzing the two single-power ramp-up prediction models of support vector machine and extreme learning machine, the weight selection methods of these two models are studied, and a combined power hill-climbing prediction model is proposed. The model uses the improved algorithm based on particle swarm optimization algorithm to optimize the weights of the above two models, and forms a new high-precision prediction system. Then the system is modeled and simulated. The simulation results verify the effectiveness of the prediction model, and its prediction accuracy is greatly improved compared with the single prediction model. This work is supported by Open Project of Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (No. 2016AB14) and National Natural Science Foundation of China (No. 51477099).