Leveraging Deep Learning to Improve Performance of Distributed Optimal Frequency Control Under Communication Failures
Leveraging Deep Learning to Improve Performance of Distributed Optimal Frequency Control Under Communication Failures
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DOI：
10.1109/tsg.2022.3194131
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发表时间：
2023-01
影响因子：
9.6
通讯作者：
S. Xie;M. Nazari;Farinaz Nezampasandarbabi;L. Wang
中科院分区：
文献类型：
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作者：
S. Xie;M. Nazari;Farinaz Nezampasandarbabi;L. Wang
本文提出了一种深度学习方法，以克服通信故障对电力系统分布式最优频率控制（DOFC）性能和收敛速度的影响。拟议框架的新特点有四个方面。首先，考虑电力系统潮流的非线性，建立了自由度潮流控制器的非线性模型。其次，长短期记忆（LSTM）算法用于通信故障期间的动态模型估计。接下来，基于LSTM的DOFC方法，以科普通信故障对分布式控制策略的性能的影响。最后，我们证明了LSTM-DOFC的收敛性，并表明该算法具有上级性能相比，线性预测方法，如自回归移动平均模型。两个现实世界的电力系统进行了仿真，以证明所提出的框架的有效性。
This paper proposes a deep learning approach to overcome the impacts of communication failures on the performance and convergence rate of the distributed optimal frequency control (DOFC) for power systems. Novel features of the proposed framework are fourfold. First, the nonlinear model of the DOFC is developed to consider for nonlinearities of power flows. Second, the long short-term memory (LSTM) algorithm is used for dynamic model estimation during communication failures. Next, the LSTM-based DOFC method is introduced to cope with the impact of communication failures on the performance of the distributed control strategy. Finally, we prove the convergence of LSTM-DOFC and show that the algorithm has superior performance compared to the linearized prediction methods, such as autoregressive-moving-average models. Simulations on two real-world power systems are carried out to demonstrate the effectiveness of the proposed framework.