Voltage Instability Prediction Using a Deep Recurrent Neural Network

Abstract

This paper develops a new method for voltage instability prediction using a recurrent neural network with long short-term memory. The method is aimed to be used as a supplementary warning system for system operators, capable of assessing whether the current state will cause voltage instability issues several minutes into the future. The proposed method uses a long sequence-based network, where both real-time and historic data are used to enhance the classification accuracy. The network is trained and tested on the Nordic32 test system, where combinations of different operating conditions and contingency scenarios are generated using time-domain simulations. The method shows that almost all N-1 contingency test cases were predicted correctly, and N-1-1 contingency test cases were predicted with over 95 % accuracy only seconds after a disturbance. Further, the impact of sequence length is examined, showing that the proposed long sequenced-based method provides significantly better classification accuracy than both a feedforward neural network and a network using a shorter sequence.

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

“…Prates et al [27] performed an analysis to identify defects in distribution lines, which is the same goal of this paper; using a laboratory-produced dataset, they achieved 85.48% accuracy when identifying defects in insulators. There are also modern failure assessment models that are based on predicting the development of an anomaly regarding the increase in an adverse condition [28].…”

Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network

“…The overall architecture of the LSTM module is shown in Figure 7, which consists of an input gate, forget gate, candidate gate and the output gate. The detail mechanism and operation of the LSTM block can be found in [16,37]. For time series data, an LSTM network may be created by constructing a sequence of many LSTM blocks.…”

“…Deep learning algorithms have recently been used to learn high-level features from data, alleviating the need for domain expertise, and a separate feature extraction technique, which are required in a traditional machine learning approach. In addition, deep learning algorithms works well on prediction the time series data compared to machine learning algorithms [15,16]. Furthermore, deep learning algorithms such as the convolutional neural network (CNN), the long short-term memory (LSTM) neural network, and the combined graph convolutional network and long short-term memory (GCN-LSTM) have been developed to address the problem related to short-term voltage instability [17][18][19].…”

Enhancing Transient Stability of Power Synchronization Control via Deep Learning

“…The main concern is that the standard vector current control fails to ensure converter stability when the converter is connected to a weak highimpedance grid [3]. Basically, voltage and frequency volatility are the main characteristics of weak power grids which jeopardize stability of the converter and power quality of the grid [4], [5]. Over time, control schemes based on emulation of synchronous generators have been introduced to provide supportive services for weak grids as well as maintaining the converter stability under weak grid operating condition [6], [7].…”