In grid-connected Distributed Generation (DG) systems, with high-penetrations of renewable and energy storage assets, the prediction of grid voltage and frequency plays an important role in enabling the power quality support, the stabilization and monitoring of distribution networks. In this paper, a method based on Artificial Neural Networks (ANNs) and Deep Recurrent Neural Networks (DRNN) has been developed for very short-term prediction of grid voltage and frequency. For different time scales (183ms, 1s, 10s, 60s), one-step and multistep ahead forecasters are developed to predict the future behavior of grid parameters. This type of predictors can be used in distributed generation systems to enhance the control performance, to prevent the occurrence of grid faults and to improve the power systems stability. The data used to establish and validate the ANNs forecasters are provided from grid connected battery storage system installed at the University of Manchester. The developed prediction models have been validated experimentally via a dSPACE real-time controller. The obtained results show that the ANNs forecasters are able to predict in real time the grid voltage and frequency with satisfactory accuracy as the largest mean absolute percent error is 0.32%.

在并网分布式发电 (DG) 系统中，随着可再生能源和储能资产的高渗透率，电网电压和频率的预测在实现电能质量支持、配电网络的稳定和监控方面发挥着重要作用。在本文中，开发了一种基于人工神经网络 (ANN) 和深度循环神经网络 (DRNN) 的方法，用于极短期的电网电压和频率预测。针对不同的时间尺度（183ms、1s、10s、60s），开发了一步和多步提前预报器来预测网格参数的未来行为。这种类型的预测器可用于分布式发电系统，以提高控制性能，防止电网故障的发生，提高电力系统的稳定性。用于建立和验证人工神经网络预测器的数据由安装在曼彻斯特大学的并网电池存储系统提供。开发的预测模型已经通过 dSPACE 实时控制器进行了实验验证。获得的结果表明，人工神经网络预测器能够以令人满意的精度实时预测电网电压和频率，因为最大平均绝对百分比误差为 0.32%。