In this paper, an innovative hybrid multi-variable generator’s actual-output-power predicting model is proposed based on ant colony optimization algorithm and extreme learning machine network, and a data-driven performance evaluation model is presented based on the two indices, K-means clustering algorithm and Markov chain for the performance evaluation of the wind turbines. Ant colony optimization algorithm is used to optimize the initial weights and thresholds of the extreme learning machine network, then the optimized combinations of weights and thresholds are provided into the extreme learning machine models to overcome the sensitivity problem of initialization setting and the disadvantage of easily falling into local optimum. Through the actual-output-power prediction of the WTs in a wind farm, the results show that the proposed model has more higher prediction accuracy than other methods mentioned in this paper. The optimization process also shows that the prediction accuracy is sensitive to the number of hidden-layer nodes and is relatively insensitive to other model parameters. Then, the data-driven performance evaluation models are proposed based on the error sequences obtained above. The case study is conducted and the results show that the method can evaluate the operating performance of the wind turbines correctly. The effectiveness of the evaluation results is also verified by the actual operation results.

本文提出了一种基于蚁群优化算法和极限学习机网络的混合动力多变量发电机的实际输出功率预测模型，并提出了基于两个指标K-的数据驱动性能评估模型。均值聚类算法和马尔可夫链用于风机性能评估。利用蚁群优化算法对极限学习机网络的初始权重和阈值进行优化，然后将权重和阈值的优化组合提供给极限学习机模型，克服了初始化设置的敏感性问题和容易掉落的缺点。进入局部最优。通过风电场中WT的实际输出功率预测，结果表明，所提出的模型比本文提到的其他方法具有更高的预测精度。优化过程还表明，预测精度对隐层节点的数量敏感，对其他模型参数相对不敏感。然后，基于上面获得的错误序列，提出了数据驱动的性能评估模型。进行了案例研究，结果表明该方法可以正确评估风力发电机的运行性能。评估结果的有效性也得到了实际运营结果的验证。优化过程还表明，预测精度对隐层节点的数量敏感，对其他模型参数相对不敏感。然后，基于上面获得的错误序列，提出了数据驱动的性能评估模型。进行了案例研究，结果表明该方法可以正确评估风力发电机的运行性能。评估结果的有效性也得到了实际运营结果的验证。优化过程还表明，预测精度对隐层节点的数量敏感，对其他模型参数相对不敏感。然后，基于上面获得的错误序列，提出了数据驱动的性能评估模型。进行了案例研究，结果表明该方法可以正确评估风力发电机的运行性能。评估结果的有效性也得到了实际运营结果的验证。