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: 15 October 2024 | Viewed by 12651

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: IoT; 5G mobile communication; UAV; quality of service; radio access networks; computer network security; radio networks; artificial intelligence
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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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Published Papers (7 papers)

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Research

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19 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
Viewed by 551
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 1 | Viewed by 1164
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 1119
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 1 | Viewed by 1251
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 2 | Viewed by 1146
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 15 | Viewed by 5705
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
Viewed by 550
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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