Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 26047

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Department of Electrical and Computer Engineering, University of Western Macedonia (UOWM), 50150 Kozani, Greece
Interests: telecommunication networks; simulation programming; Internet of Things; cybersecurity
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Special Issue Information

Dear Colleagues,

Intelligent energy management, conversion and control are vital to optimize the generation, distribution and consumption of electrical energy and the corresponding necessity of using solid and liquid fossil fuels. In the last several years, many research organizations and institutions around the world have made efforts towards the realization of innovative and cost-effective energy conversion and utilization. With the technological improvements in renewable energy sources (RESs), electricity production is transitioning from the traditional centralized systems to distributed energy systems.

The introduction of renewable sources and high-capacity accumulator batteries to electricity power grids, together with traditional energy sources, has led to new requirements related to prediction, coordination, conversion and control of energy flows. Along with the other utilized techniques, artificial intelligence, neural networks and machine learning are highly applicable for more efficient management, forecasting, optimization and control of smart power grids.

This Special Issue aims to collect new research information and contributions on intelligent energy management, conversion, prediction and control, including, but not limited to: smart applications for power grid control, renewable energy sources, power electronic converters, fuel cells and others.

Smart energy management and control can be effectively realized in various ways, including:

  • Effective load prediction and management, applying machine learning, neural networks and artificial intelligence;
  • Fuel consumption forecasting and optimization;
  • Efficiency optimization in power flow management;
  • Power electronics energy conversion with loss minimization;
  • Monitoring and timely troubleshooting of intelligent energy systems;
  • Energy and power system management and optimization;
  • Energy and power resiliency and trust.

Prof. Dr. Valeri Mladenov
Dr. Panagiotis Sarigiannidis
Guest Editors

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Related Special Issue

Published Papers (11 papers)

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Research

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22 pages, 1918 KiB  
Article
Data-Driven Dynamics Learning on Time Simulation of SF6 HVDC-GIS Conical Solid Insulators
by Kenji Urazaki Junior, Francesco Lucchini and Nicolò Marconato
Electronics 2025, 14(3), 616; https://doi.org/10.3390/electronics14030616 - 5 Feb 2025
Viewed by 234
Abstract
An HVDC-GIL system with a conical spacer in a radioactive environment is studied in this work using simulated data on COMSOL® Multiphysics. Electromagnetic simulations on a 2D model were performed with varying ion-pair generation rates and potential applied to the system. This [...] Read more.
An HVDC-GIL system with a conical spacer in a radioactive environment is studied in this work using simulated data on COMSOL® Multiphysics. Electromagnetic simulations on a 2D model were performed with varying ion-pair generation rates and potential applied to the system. This article explores machine learning methods to derive time to steady state, dark current, gas conductivity, and surface charge density expressions. The focus was on constructing symbolic representations, which could be interpretable and less prone to overfitting, using the symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy) algorithms. The study successfully derived the intended expressions, demonstrating the power of symbolic regression. Predictions of dark currents in the gas–ground electrode interface reported an absolute error and mean absolute percentage error (MAPE) of 1.04 × 104 pA and 0.01%, respectively. The solid–ground electrode interface reported an error of 8.99 × 105 pA and MAPE of 0.04%, showing strong agreement with simulation data. Expressions for time to steady state had a test error of approximately 110 h with MAPE of around 3%. Steady-state gas conductivity expression achieved an absolute error of 0.55 log(S/m) and MAPE of 1%. An interpretable equation was created with SINDy to model the time evolution of surface charge density, achieving a root mean squared error of 1.12 nC/m2/s across time-series data. These results demonstrate the capability of SR and SINDy to provide interpretable and computationally efficient alternatives to time-consuming numerical simulations of HVDC systems under radiation conditions. While the model provides useful insights, performance and practical applications of the expressions can improve with more diverse datasets, which might include experimental data in the future. Full article
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20 pages, 3334 KiB  
Article
Interpretable State Estimation in Power Systems Based on the Kolmogorov–Arnold Networks
by Shuaibo Wang, Wenhao Luo, Sixing Yin, Jie Zhang, Zhuohang Liang, Yihua Zhu and Shufang Li
Electronics 2025, 14(2), 320; https://doi.org/10.3390/electronics14020320 - 15 Jan 2025
Viewed by 453
Abstract
Power system state estimation is a critical task for ensuring stable grid operation and serves as the foundation of grid control and analysis. Conventional approaches largely involve field measurements, network topology, and manual anomaly detection, which present significant limitations, particularly while dealing with [...] Read more.
Power system state estimation is a critical task for ensuring stable grid operation and serves as the foundation of grid control and analysis. Conventional approaches largely involve field measurements, network topology, and manual anomaly detection, which present significant limitations, particularly while dealing with dynamic and complex power systems. In recent years, deep learning techniques have been progressively applied in this field to overcome the shortcomings of conventional approaches, which are based on mathematical models and static analysis. However, existing deep learning techniques primarily focus on power system security analysis and computational resource management. In spite of the powerful capabilities in supervised learning tasks, the lack of interpretability still makes deep learning models less convincing, and thus might hinder their practical applications. In response to this issue, we apply a computational model for power system state estimation based on the Kolmogorov–Arnold network (KAN) model with learnable activation functions, visualization capabilities, and pruning features. From the perspective of feature interpretability, we find the influence of bus features on the model output, such as bus voltage magnitude. Moreover, through analysis of the internal structure of the model, we uncover a possibility of potential mechanisms of power system state estimation. Experimental results show that our study not only enhances the interpretability of power system state estimation but also effectively ensures grid security and stability through state estimation. Full article
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21 pages, 15716 KiB  
Article
A Novel Wind Power Prediction Model That Considers Multi-Scale Variable Relationships and Temporal Dependencies
by Zhanyang Xu, Hong Zhao, Chengxi Xu, Hongyan Shi, Jian Xu and Zhe Wang
Electronics 2024, 13(18), 3710; https://doi.org/10.3390/electronics13183710 - 19 Sep 2024
Viewed by 1346
Abstract
Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. [...] Read more.
Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. In this paper, a multi-scale frequency decomposition module is designed to split the raw data into high-frequency and low-frequency parts. Subsequently, features are extracted from the high-frequency information using a multi-scale temporal graph neural network combined with an adaptive graph learning module and from the low-frequency data using an improved bidirectional temporal network. Finally, the features are integrated through a cross-attention mechanism. To validate the effectiveness of the proposed model, extensive comprehensive experiments were conducted using a wind power dataset provided by the State Grid. The experimental results indicate that the MSE of the model proposed in this paper has decreased by an average of 7.1% compared to the state-of-the-art model and by 48.9% compared to the conventional model. Moreover, the improvement in model performance becomes more pronounced as the prediction horizon increases. Full article
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18 pages, 1002 KiB  
Article
Optimizing Parameter Extraction in Grid Information Models Based on Improved Convolutional Neural Networks
by Xintong Li and Xiangjun Liu
Electronics 2024, 13(14), 2717; https://doi.org/10.3390/electronics13142717 - 11 Jul 2024
Cited by 1 | Viewed by 1064
Abstract
With the rapid advancement of digital technology, three-dimensional designs of Grid Information Models (GIMs) are increasingly applied in the power industry. Addressing the challenges of extracting key parameters during the GIM’s process for power grid equipment, this paper explores an innovative approach that [...] Read more.
With the rapid advancement of digital technology, three-dimensional designs of Grid Information Models (GIMs) are increasingly applied in the power industry. Addressing the challenges of extracting key parameters during the GIM’s process for power grid equipment, this paper explores an innovative approach that integrates artificial intelligence with image recognition technologies into power design engineering. The traditional methods of “template matching, feature extraction and classification, and symbol recognition” have enabled the automated processing of electrical grid equipment engineering drawings, allowing for the extraction of key information related to grid equipment. However, these methods still rely on manually designed and selected feature regions, which limits their potential for achieving full automation. This study introduces an optimized algorithm that combines enhanced Convolutional Neural Networks (CNNs) with Depth-First Search (DFS) strategies, and is specifically designed for the automated extraction of crucial GIM parameters from power grid equipment. Implemented on the design schematics of power engineering projects, this algorithm utilizes an improved CNN to precisely identify component symbols on schematics, and continues to extract text data associated with these symbols. Utilizing a scene text detector, the text data are matched with corresponding component symbols. Finally, the DFS strategy is applied to identify connections between these component symbols in the diagram, thus facilitating the automatic extraction of key GIM parameters. Experimental results demonstrate that this optimized algorithm can accurately identify basic GIM parameters, providing technical support for the automated extraction of parameters using the GIM. This study’s recognition accuracy is 91.31%, while a traditional CNN achieves 71.23% and a Faster R-CNN achieves 89.59%. Compared to existing research, the main innovation of this paper lies in the application of the combined enhanced CNN and DFS strategies for the extraction of GIM parameters in the power industry. This method not only improves the accuracy of parameter extraction but also significantly enhances processing speed, enabling the rapid and effective identification and extraction of critical information in complex power design environments. Moreover, the automated process reduces manual intervention, offering a novel solution in the field of power design. These features make this research broadly applicable and of significant practical value in the construction and maintenance of smart grids. Full article
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18 pages, 6437 KiB  
Article
Detection and Classification of Rolling Bearing Defects Using Direct Signal Processing with Deep Convolutional Neural Network
by Maciej Skowron, Oliwia Frankiewicz, Jeremi Jan Jarosz, Marcin Wolkiewicz, Mateusz Dybkowski, Sebastien Weisse, Jerome Valire, Agnieszka Wyłomańska, Radosław Zimroz and Krzysztof Szabat
Electronics 2024, 13(9), 1722; https://doi.org/10.3390/electronics13091722 - 29 Apr 2024
Cited by 4 | Viewed by 1596
Abstract
Currently, great emphasis is being placed on the electrification of means of transportation, including aviation. The use of electric motors reduces operating and maintenance costs. Electric motors are subjected to various types of damage during operation, of which rolling bearing defects are statistically [...] Read more.
Currently, great emphasis is being placed on the electrification of means of transportation, including aviation. The use of electric motors reduces operating and maintenance costs. Electric motors are subjected to various types of damage during operation, of which rolling bearing defects are statistically the most common. This article focuses on presenting a diagnostic tool for bearing conditions based on mechanic vibration signals using convolutional neural networks (CNN). This article presents an alternative to the well-known classical diagnostic tools based on advanced signal processing methods such as the short-time Fourier transform, the Hilbert–Huang transform, etc. The approach described in the article provides fault detection and classification in less than 0.03 s. The proposed structures achieved a classification accuracy of 99.8% on the test set. Special attention was paid to the process of optimizing the CNN structure to achieve the highest possible accuracy with the fewest number of network parameters. Full article
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27 pages, 14497 KiB  
Article
Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization
by Hamdi A. Al-Jamimi, Galal M. BinMakhashen, Muhammed Y. Worku and Mohamed A. Hassan
Electronics 2023, 12(24), 4909; https://doi.org/10.3390/electronics12244909 - 6 Dec 2023
Cited by 7 | Viewed by 1919
Abstract
Accurate load forecasting is of utmost importance for modern power generation facilities to effectively meet the ever-changing electricity demand. Predicting electricity consumption is a complex task due to the numerous factors that influence energy usage. Consequently, electricity utilities and government agencies are constantly [...] Read more.
Accurate load forecasting is of utmost importance for modern power generation facilities to effectively meet the ever-changing electricity demand. Predicting electricity consumption is a complex task due to the numerous factors that influence energy usage. Consequently, electricity utilities and government agencies are constantly in search of advanced machine learning solutions to improve load forecasting. Recently, deep learning (DL) has gained prominence as a significant area of interest in prediction efforts. This paper introduces an innovative approach to electric load forecasting, leveraging advanced DL techniques and making significant contributions to the field of energy management. The hybrid predictive model has been specifically designed to enhance the accuracy of multivariate time series forecasting for electricity consumption within the energy sector. In our comparative analysis, we evaluated the performance of our proposed model against ML-based and state-of-the-art DL models, using a dataset obtained from the Distribution Network Station located in Tetouan City, Morocco. Notably, the proposed model surpassed its counterparts, demonstrating the lowest error in terms of the Root-Mean-Square Error (RMSE). This outcome underscores its superior predictive capability and underscores its potential to advance the accuracy of electricity consumption forecasting. Full article
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17 pages, 7866 KiB  
Article
Novel Neural-Network-Based Fuel Consumption Prediction Models Considering Vehicular Jerk
by Licheng Zhang, Jingtian Ya, Zhigang Xu, Said Easa, Kun Peng, Yuchen Xing and Ran Yang
Electronics 2023, 12(17), 3638; https://doi.org/10.3390/electronics12173638 - 28 Aug 2023
Viewed by 1876
Abstract
Conventional fuel consumption prediction (FCP) models using neural networks usually adopt driving parameters, such as speed and acceleration, as the training input, leading to a low prediction accuracy and a poor correlation between fuel consumption and driving behavior. To address this issue, the [...] Read more.
Conventional fuel consumption prediction (FCP) models using neural networks usually adopt driving parameters, such as speed and acceleration, as the training input, leading to a low prediction accuracy and a poor correlation between fuel consumption and driving behavior. To address this issue, the present study introduced jerk (an acceleration derivative) as an important variable in the training input of four selected neural networks: long short-term memory (LSTM), recurrent neural network (RNN), nonlinear auto-regressive model with exogenous inputs (NARX), and generalized regression neural network (GRNN). Furthermore, the root-mean-square error (RMSE), relative error (RE), and coefficient of determination (R2) were used to evaluate the prediction performance of each model. The results from the comparison experiment show that the LSTM model outperforms the other three models. Specifically, the four selected neural network models exhibited an improved accuracy in fuel consumption prediction after the jerk was added as a new variable to the training input. LSTM exhibited the greatest improvement under the high-speed expressway scenario, in which the RMSE decreased by 14.3%, the RE decreased by 28.3%, and the R2 increased by 9.7%. Full article
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15 pages, 5468 KiB  
Article
Evaluation of VSC Impact on Power System Using Adequate P-Q Capability Curve
by Michal Brodzicki, Jacek Klucznik and Stanislaw Czapp
Electronics 2023, 12(11), 2462; https://doi.org/10.3390/electronics12112462 - 30 May 2023
Cited by 2 | Viewed by 2047
Abstract
Renewable energy sources, which are becoming increasingly popular, often use a voltage source converter (VSC) for connection to the power system. Assessing the effects of connecting such a source to the power system is essential to ensure the proper operation of the power [...] Read more.
Renewable energy sources, which are becoming increasingly popular, often use a voltage source converter (VSC) for connection to the power system. Assessing the effects of connecting such a source to the power system is essential to ensure the proper operation of the power system and the connected source. For this purpose, it is necessary to know the range of active and reactive power generation by the converter. The authors indicate that the interaction between the power system and the converter affects its range of available active and reactive power. Therefore, a strictly defined range of the converter’s generating capability should not be assumed as invariant, but its capability for a given operating condition of the power system should be determined iteratively. In order to confirm this thesis, the authors analyzed the operation of the VSC-based energy source in an example power system using the PowerFactory software. Extending the calculation procedure to include iterative determination of the converter’s available power range showed a significant influence of the system’s operating state on the converter’s generating capabilities. The results obtained in this work extend the knowledge, and thanks to them, the operation of VSC systems can be modelled more accurately. Full article
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23 pages, 8457 KiB  
Article
Long Short-Term Memory Network-Based HVDC Systems Fault Diagnosis under Knowledge Graph
by Qian Chen, Jiyang Wu, Qiang Li, Ximing Gao, Rongxing Yu, Jianbao Guo, Guangqiang Peng and Bo Yang
Electronics 2023, 12(10), 2242; https://doi.org/10.3390/electronics12102242 - 15 May 2023
Cited by 4 | Viewed by 1659
Abstract
To enhance the precision of fault diagnosis for high-voltage direct-current (HVDC) systems by effectively extracting various types of fault characteristics, a fault diagnosis method based on the long short-term memory network (LSTM) is proposed in this paper. The method relies on a knowledge [...] Read more.
To enhance the precision of fault diagnosis for high-voltage direct-current (HVDC) systems by effectively extracting various types of fault characteristics, a fault diagnosis method based on the long short-term memory network (LSTM) is proposed in this paper. The method relies on a knowledge graph platform and is developed using measured data from four fault types in an HVDC substation located in southwest China. Firstly, a knowledge graph for the HVDC systems is constructed, then the fault waveform data is preprocessed and divided into a training set and a test set. Various optimizers are employed to train and test the LSTM. The proposed strategy’s accuracy is calculated and compared with recurrent neural network (RNN), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), Naive Bayes classifier, probabilistic neural networks (PNN), and classification learner (CL), which are commonly used in fault diagnosis. Results indicate that the proposed method achieves an accuracy of over 95%, which is 30% higher than RNN, 8% higher than XGBoost, 4% higher than SVM, 7% higher than Naive Bayes, 40% higher than PNN, and 42% higher than classification learner (CL), respectively; the method also has the minimum time cost, fully demonstrating its superiority and effectiveness compared to other methods. Full article
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23 pages, 2761 KiB  
Article
Predictive Maintenance for Distribution System Operators in Increasing Transformers’ Reliability
by Vasiliki Vita, Georgios Fotis, Veselin Chobanov, Christos Pavlatos and Valeri Mladenov
Electronics 2023, 12(6), 1356; https://doi.org/10.3390/electronics12061356 - 12 Mar 2023
Cited by 25 | Viewed by 8548
Abstract
Power transformers’ reliability is of the highest importance for distribution networks. A possible failure of them can interrupt the supply to consumers, which will cause inconvenience to them and loss of revenue for electricity companies. Additionally, depending on the type of damage, the [...] Read more.
Power transformers’ reliability is of the highest importance for distribution networks. A possible failure of them can interrupt the supply to consumers, which will cause inconvenience to them and loss of revenue for electricity companies. Additionally, depending on the type of damage, the recovery time can vary and intensify the problems of consumers. This paper estimates the maintenance required for distribution transformers using Artificial Intelligence (AI). This way the condition of the equipment that is currently in use is evaluated and the time that maintenance should be performed is known. Because actions are only carried out when necessary, this strategy promises cost reductions over routine or time-based preventative maintenance. The suggested methodology uses a classification predictive model to identify with high accuracy the number of transformers that are vulnerable to failure. This was confirmed by training, testing, and validating it with actual data in Colombia’s Cauca Department. It is clear from this experimental method that Machine Learning (ML) methods for early detection of technical issues can help distribution system operators increase the number of selected transformers for predictive maintenance. Additionally, these methods can also be beneficial for customers’ satisfaction with the performance of distribution transformers, which would enhance the highly reliable performance of such transformers. According to the prediction for 2021, 852 transformers will malfunction, 820 of which will be in rural Cauca, which is consistent with previous failure statistics. The 10 kVA transformers will be the most vulnerable, followed by the 5 kVA and 15 kVA transformers. Full article
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Review

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17 pages, 292 KiB  
Review
Reinforcement Learning Techniques in Optimizing Energy Systems
by Stefan Stavrev and Dimitar Ginchev
Electronics 2024, 13(8), 1459; https://doi.org/10.3390/electronics13081459 - 12 Apr 2024
Cited by 3 | Viewed by 3407
Abstract
Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various [...] Read more.
Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization. Full article
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