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1. A Data-Based Learning and Control Method for Long-Term Voltage Stability.

Author

Cai, Huaxiang, Ma, Haomin, and Hill, David J.

Subjects
*FEATURE extraction, *ARTIFICIAL neural networks, *ELECTRIC potential, *DATA structures, *SYSTEM dynamics, *VOLTAGE control
Abstract

Based on the knowledge accumulated off-line and feature extractions, a novel data-based learning and control method is proposed for the long-term voltage stability problem in this paper. All the spatial-temporal data is considered and the features of different emergency events are extracted by principle component analysis which can reduce the dimension and reveal the significant internal structure of the data. An artificial neural network is used to build a classifier to reinforce the relationship directly between the system dynamics and optimal control actions. With the prepared control knowledge, it is faster to find an optimal control action online with a good system performance. Simulation results on the 6-bus system, New England 39-bus system and Iceland 189-bus system are given to show the potential of this method for on-line control. [ABSTRACT FROM AUTHOR]

Published
2020
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2. A Novel Equivalent Model of Active Distribution Networks Based on LSTM.

Author

Zheng, Chao, Wang, Shaorong, Liu, Yilu, Liu, Chengxi, Xie, Wei, Fang, Chen, and Liu, Shu

Subjects
*RECURRENT neural networks, *SHORT-term memory, *ALGEBRAIC equations, *PLUG-in hybrid electric vehicles
Abstract

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy. [ABSTRACT FROM AUTHOR]

Published
2019
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