深度学习辅助的区域交直流配电网区间状态估计

针对区域交直流混合配电网中实时量测覆盖率低、量测误差分布具有不确定性的问题,提出了基于深度神经网络(DNN)伪量测建模的交直流配电网区间状态估计方法。该方法首先对DNN进行离线训练,然后将实时量测数据和电压源换流器控制的变量值作为DNN的输入特征,建立伪量测模型;接着,在实时量测更新时,利用已训练好的DNN快速生成伪量测;最后,对伪量测和实时量测的不确定性采用区间形式建模并进行区间状态估计,进而准确监测交直流系统状态。算例仿真结果表明,所提方法能够避免对量测误差的概率分布进行假设,并且能够在低冗余量测配置或量测缺失时,准确获得交直流配电网状态变量的上下界信息。

Deep-learning-assisting Interval State Estimation of Regional AC/DC Distribution Network

Aiming at the problem of low coverage of real-time measurements and uncertainty of probability distribution of measurement errors, an interval state estimation method based on a pseudo measurement modeling method using deep neural networks (DNN) is proposed for regional AC/DC distribution network. Firstly, DNN is trained offline in this method. Then the real-time measurement data and the variable values controlled by VSC are used as the input features of DNN to establish a pseudo-measurement model. Secondly, the trained DNN is used to generate the pseudo measurements quickly when the real-time measurements are updated. Finally, the uncertainty of the pseudo-measurement and the real-time measurement is modeled in the interval form and the interval state estimation is carried out in order to accurately monitor the states of the AC/DC distribution system. The simulation results of the calculation example show that the proposed method can avoid assumptions about the probability distribution of the measurement errors, and it can obtain the accurate upper and lower bounds of the state variables in the case of low real-time measurement redundancy or insufficient configuration.