Categorisation of power quality problems using long short‐term memory networks

Abdelsalam, Abdelazeem A.; Hassanin, Ahmed Mohamed; Hasanien, Hany M.

doi:10.1049/gtd2.12122

Cited by 20 publications

(6 citation statements)

References 33 publications

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“…The issue of classifying power quality disruptions through machine learning based on LSTM, which is a type of RNN, is also described in [101][102][103][104][105][106][107][108]. In [108], an LSTM method combined with a CNN method was proposed, which achieved an accuracy of 97.3%.…”

Section: Power Qualitymentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

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Section: Power Qualitymentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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“…The proposed method achieved an overall accuracy of 97.34% on the benchmark dataset. Abdel Salam et al [65] developed a PQ event categorization method using LSTM networks. They utilized a new feature extraction technique based on the wavelet packet transform (WPT).…”

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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“…In the literature, after feature extraction of PQ events, pattern identification techniques such as artificial neural network (ANN) [4,12,18], probabilistic neural network (PNN) [19,20], fuzzy logic (FL) [21], extreme learning machine (ELM) [22], support vector machine (SVM) [23], deep convolutional network [24], and long short-term memory networks (LSTM) [25]. are used to classify PQ events.…”

Section: Department Of Electrical and Electronics Engineering Erzinca...mentioning

confidence: 99%

Exponential Smoothing of Quadrature Amplitude Modulation for Power Quality Disturbance Detecting and Classification

Altintasi

2023

IEEJ Transactions Elec Engng

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In this study, exponential smoothing (ES)-based quadrature amplitude modulation (QAM) is applied to detect and classify the power quality (PQ) events with high accuracy and low complexity. Considering the IEEE-1159 standards, in the simulation environment, 14 different power quality distortion signals are produced. Each of these signals is modulated at the fundamental frequency by QAM. The high-frequency constituents in the in-phase and quadratic components resulting from the QAM are filtered by the ES. PQ events are detected and classified by extracting features from the filtered in phase and quadrature component. The performance of the proposed method is examined by taking 1000 randomly selected data samples for each PQ event, and its performance is 100% and 98.28%, respectively, at noiseless and 30 dB SNR environments. The obtained results are compared with the others in the literature for the detection and classification of PQ events, and it is shown that the proposed method has better performance in noisy and noiseless conditions. In addition, to verify that the proposed method can be applied to real systems, oscillating transitions produced by capacitor banks in Simulink environment and voltage sag and swell events are generated in experimental environment. The proposed method successfully detected and classified PQ events.

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Categorisation of power quality problems using long short‐term memory networks

Cited by 20 publications

References 33 publications

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

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

Exponential Smoothing of Quadrature Amplitude Modulation for Power Quality Disturbance Detecting and Classification

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