A Hybrid Model Based on CNN-LSTM to Detect and Forecast Harmonics: A Case Study of an Eskom Substation in South Africa

Kuyumani, Excellence Mutetwa; Hasan, Ali N.; Shongwe, Thokozani

doi:10.1080/15325008.2023.2181883

Cited by 15 publications

(9 citation statements)

References 19 publications

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“…These ML techniques require low-end hardware [9]. ML and DL methods are used to enhance network management, and these methods are bioinspired mathematical [10]. Support vector machines (SVMs), Random Forest, ARIMA, decision trees, and logistic regression are the most frequently used ML time series data approaches, and they tend to work better on small-scale data [11].…”

Section: Review Of Harmonics Forecastingmentioning

confidence: 99%

“…DL methods perform better when used to solve power quality disturbance challenges [12]. The DL method has shown good performance when dealing with high-dimension data, non-stationary data, and non-linear time series data [10]. The most popular deep learning (DL) methods are recurrent neural networks (RNNs), LSTM, BiLSTM, and CNN.…”

Section: Review Of Harmonics Forecastingmentioning

confidence: 99%

“…To the best of our knowledge, the application of CNN-BiLSTM-AM has not been extensively utilized in the prediction of electrical load harmonics, marking this research as pioneering in this specific field. This work is built on our previous work on the application of ANN, LSTM, and CNN-LSTM in the detection and prediction of harmonics in an electrical power system [8][9][10]. In this paper, six (6) models are used to predict the load harmonics and are separately trained and tested using the historical harmonics data.…”

Section: Introductionmentioning

confidence: 99%

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A Combined Model of Convolutional Neural Network and Bidirectional Long Short-Term Memory with Attention Mechanism for Load Harmonics Forecasting

Kuyumani,

Hasan,

Shongwe

2024

Energies

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In the increasingly complex and dynamic electrical power system, forecasting harmonics is key to developing and ensuring a clean power supply. The traditional methods have achieved some degree of success. However, they often fail to forecast complex and dynamic harmonics, highlighting the serious need to improve the forecasting performance. Precise forecasting of electrical power system harmonics is challenging and demanding, owing to the increased frequency with harmonic noise. The occurrence of harmonics is stochastic in nature; it has taken a long time for the development of dependable and efficient models. Several machine learning and statistical methods have produced positive results with minimal errors. To improve the prognostic accuracy of the power supply system, this study proposes an organic hybrid combination of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) method (CNN-BiLSTM-AM) to forecast load harmonics. CNN models intricate non-linear systems with multi-dimensionality aspects. LSTM performs better when dealing with exploding gradients in time series data. Bi-LSTM has two LSTM layers: one layer processes data in the onward direction and the other in the regressive direction. Bi-LSTM uses both preceding and subsequent data, and as a result, it has better performance compared to RNN and LSTM. AM’s purpose is to make desired features outstanding. The CNN-BiLSTM-AM method performed better than the other five methods, with a prediction accuracy of 92.366% and a root mean square error (RMSE) of 0.000000222.

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Section: Review Of Harmonics Forecastingmentioning

confidence: 99%

Section: Review Of Harmonics Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Combined Model of Convolutional Neural Network and Bidirectional Long Short-Term Memory with Attention Mechanism for Load Harmonics Forecasting

Kuyumani,

Hasan,

Shongwe

2024

Energies

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“…Convolutional neural networks (CNNs), which employ convolutional and pooling operations, serve as key discriminative models capable of capturing coherent structures in power-system measurements. In reference [38], harmonic detection is carried out in this way, but the effect can still be improved.…”

Section: Has Beenmentioning

confidence: 99%

EhdNet: Efficient Harmonic Detection Network for All-Phase Processing with Channel Attention Mechanism

Deng,

Wang,

et al. 2024

Energies

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The core of harmonic detection is the recognition and extraction of each order harmonic in the signal. The current detection methods are seriously affected by the fence effect and spectrum aliasing, which brings great challenges to the detection of each order harmonic in the signal. This paper proposes an efficient harmonic detection neural network based on all-phase processing. It is based on three crucial designs. First, a harmonic signal-processing module is developed to ensure phase invariance and establish the foundation for subsequent modules. Then, we constructed the backbone network and utilized the feature-extraction module to extract deep abstract harmonic features of the target. Furthermore, a channel attention mechanism is also introduced in the weight-selection module to enhance the energy of the residual convolution stable spectrum feature, which facilitates the accurate and subtle expression of intrinsic characteristics of the target. We evaluate our method based on frequency, phase, and amplitude in two environments with and without noise. Experimental results demonstrate that the proposed EhdNet method can achieve 94% accuracy, which is higher than the compared methods. In comparison experiments with actual data, the RMSE of EhdNet is also lower than that of other recent methods. Moreover, the proposed method outperforms ResNet, BP, and other neural network approaches in data processing across diverse working conditions due to its incorporation of a channel attention mechanism.

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“…The IBA architecture has greatly expanded the application scope of the Internet of Things, big data, and artificial intelligence, while improving the application quality of the three, maximizing the value of data, and achieving the sublimation of data quantity to quality. The expansion and innovation of applications are the core of the IBA integration architecture and an effective driving force for the development of new industries, laying a solid foundation for achieving the intelligence of the Internet of Things and the interconnection of everything [3]. Under the IBA fusion technology architecture, by abstracting and integrating data, business, and technology, reusing services and support capabilities, a cross departmental, cross hierarchical, and cross domain mid level support is constructed to meet various personalized front-end business needs.…”

Section: Introductionmentioning

confidence: 99%

Design of Smart Grid System Based on IBA Architecture

Li,

Yu,

Huang

et al. 2024

Advances in Transdisciplinary Engineering

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To process the difficulty of equipment operation and maintenance supervision caused by the large number and wide distribution of smart grid power equipment, an innovative architecture integrated IBA is proposed, which combines big data and AI technology in different dimensions of the architecture to achieve hierarchical intelligence as needed. We provided the design process and key modules of the power grid system based on the IBA architecture: firstly, we introduce human-machine data under the architecture and propose the concept of hierarchical cognition. Then, real-time data collection is performed through the Internet of Things, and further mining and analysis of the data is achieved using cloud platforms. By Spark clustering based on k-means to partition state data, we establish corresponding Bayesian causal network models, and achieve data-driven modeling of smart grid equipment monitoring and operation. Finally, an application case of artificial intelligence is used to demonstrate the feasibility and effectiveness of the system. The experimental results demonstrate that the IBA fusion technology architecture has expanded the application scope of the IoTs in smart grids, and improved the intelligent level of smart grid construction and management.

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A Hybrid Model Based on CNN-LSTM to Detect and Forecast Harmonics: A Case Study of an Eskom Substation in South Africa

Cited by 15 publications

References 19 publications

A Combined Model of Convolutional Neural Network and Bidirectional Long Short-Term Memory with Attention Mechanism for Load Harmonics Forecasting

A Combined Model of Convolutional Neural Network and Bidirectional Long Short-Term Memory with Attention Mechanism for Load Harmonics Forecasting

EhdNet: Efficient Harmonic Detection Network for All-Phase Processing with Channel Attention Mechanism

Design of Smart Grid System Based on IBA Architecture

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