抗噪性高斯过程用于风电系统暂态电压稳定性的评估

Transient Voltage Stability Assessment of Wind Power System Based on Noisy Input Multi-Class Gaussian Process

基于机器学习的风电系统安全评估方法已成为当下热点,但对样本噪声影响不予以充分考虑,则难以保证系统暂态电压稳定性评估的准确性和可靠性。为此构建抗噪性高斯过程多分类模型(noisy input multi-class Gaussian process,NIMGP),首先,引入稀疏高斯过程,选用诱导点代替部分原输入点进行训练,以降低模型计算的复杂度;其次,对模型中的输入数据引入可加性高斯噪声实现抗噪处理,通过泰勒级数法近似求解含噪声输入的高斯过程,使输入噪声转化为输出噪声、改善模型评估性能。最后,在含风电场的新英格兰39节点系统进行仿真,对系统暂态电压的稳定、临界和失稳状态及稳定样本的稳定裕度进行预测。多种不同工况仿真结果对比表明,NIMGP具有较强的泛化能力、在不同工况下仍有较好的预测精度。

The safety assessment method of wind power system based on machine learning has become a hot spot at present,but the influence of sample noise is not fully considered.It is difficult to ensure the accuracy and reliability of system transient voltage stabil⁃ity assessment.In this paper,a noisy input multi-class Gaussian process(NIMGP)is constructed,which introduces a sparse Gaussian process and selects induction points instead of some original input points for training to reduce the complexity of model calculation.Secondly,additive Gaussian noise is introduced into the input data in the model to achieve anti-noise processing,and the Gaussian process with noisy input is approximated by Taylor series method,so that the input noise is converted into output noise and the model evaluation performance is improved.Finally,simulation is carried out in a New England 39-bus system with wind farms,including the stabilization of the transient voltage of the system,critical and unstable states and the stability margin of the stable sample for prediction.The comparison of simulation results under various working conditions show that NIMGP has strong generalization abil⁃ity and good prediction accuracy under different working conditions.