Classification of Power Quality Disturbances Using Convolutional Network and Long Short-Term Memory Network

Rodrigues, Wilson L.; Borges, Fábbio A. S.; Rabelo, Ricardo A. L.; Lima, Bruno Vicente Alves de; Alencar, José Eduardo Almeida de

doi:10.1109/ijcnn.2019.8852287

Cited by 22 publications

(9 citation statements)

References 19 publications

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“…Robustness to noisy data: LSTMs are robust to noisy or missing data, making them suitable for real-world applications where data are often noisy or incomplete. The three major disadvantages of the LSTM algorithm are as follows [64]. Computational complexity: LSTMs can be computationally expensive due to their large number of parameters, making them difficult to train on large datasets.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

et al. 2023

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Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

et al. 2023

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“…To improve the performance of classification by learning additional features, a hybrid architecture combining CNN with LSTM (long short-term memory) is proposed for learning spatial information and temporal characteristics [21]. To deal well with noisy PQD signals, a hybrid architecture, which is composed by convolutional layers, a pooling layer, an LSTM layer, and batch normalization, is proposed to extract features automatically [22]. Also, a novel detection framework based on multifusion convolution neural network (MFCNN) was proposed for complex PQ disturbances, in which the time domain and frequency domain information were fused [23].…”

Section: Related Workmentioning

confidence: 99%

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Yang

Chen

Tang

et al. 2021

IET Generation Trans & Dist

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Type identification and time location of power quality disturbances (PQDs) is the key to adopting corresponding measures to suppress disturbances. More complex multiple disturbances caused by the overlapping of different micro-grids make it a challenging task. The paper proposes a hybrid approach combing KF-ML (Kalman filter based on maximum likelihood) with deep belief network (DBN) for dealing with PQDs. To be specific, the KF-ML is firstly applied to reduce noise from the original distorted signal, and the innovation sequence obtained by KF-ML can be used to locate starting-ending times of PQDs. Then, the DBN, which fuses feature extraction and classification into a single block, is capable of recognizing the type of PQDs accurately. To verify the effectiveness of the proposed method, 20 classes of PQDs with noise interference are tested, and experiment results show that the detection time of the proposed method is very close to the set time, and the absolute error of time location is less than 0.3 ms. The average classification accuracy at different noise levels reaches about 95%, and is very high even with more disturbances combined. Thus, the proposed method is immune to noise and less affected with more disturbances combined relative to other methods.1 PQDs obviously emerged [3].Multiple power quality disturbances (MPQDs), which are composed of several single disturbances with different type, dif-This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…Due to the typical temporal features of PQD signals, some scholars use deep learning algorithms, which are more suitable for sequences, to detect them. A hybrid architecture of CNN and LSTM is put forward in [13,14] to automatically extract features and detect PQD signals. Document [15] uses a sequence-to-sequence deep learning model for PQD detection.…”

Section: Figure 1 Block Diagram Representation Of Traditional Pqd Detmentioning

confidence: 99%

A simple gated recurrent network for detection of power quality disturbances

Wei

2020

IET Generation Trans & Dist

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This paper presents a new concise deep learning-based sequence model to detect the power quality disturbances (PQD), which only uses original signals and does not require pre-processing and complex artificial feature extraction process. A simple gated recurrent network (SGRN) with a new recurrent cell structure is developed, which consists of only two gates: forget gate and input gate, and two weight matrices. Compared with the standard Recurrent Neural Network (RNN) model, the training process of the proposed method is more stable and the prediction accuracy is higher. In addition, this special structure retains basic non-linearity and long-term memory, while enabling the simple gated recurrent network model to be superior to Long Short-Term Memory (LSTM) Network and Gated Recurrent Unit (GRU) Network in terms of the number of parameters (i.e. memory cost) and detection speed. In the light of the experimental results, the simple gated recurrent network algorithm can achieve 99.07% detection accuracy, and contains only 18,959 parameters, which indicates that our proposed method is easier to deploy in resource-constrained internet of things (IoT) micro-controllers.

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Classification of Power Quality Disturbances Using Convolutional Network and Long Short-Term Memory Network

Cited by 22 publications

References 19 publications

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

A simple gated recurrent network for detection of power quality disturbances

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