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Artificial Intelligence Techniques Applications on Power Systems

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 6089

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Special Issue Editor

Prof. Dr. Stavros D. Kaminaris

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Guest Editor

Department of Electrical and Electronics Engineering, Faculty of Engineering, University of West Attica, Athens-Egaleo, Greece
Interests: applications of artificial intelligence; expert systems; fuzzy logic techniques in power systems; advanced building and industrial automation applications; smart electrical installations; troubleshooting and equipment maintenance

Special Issue Information

Dear Colleagues,

A continuous, reliable, and definitive supply of electricity is essential in today’s modern and advanced society. Power systems are showing a continual increasing trend throughout geographical regions with the addition of assets and the introduction of new technologies for the generation, transmission, and distribution of electricity. Moreover, modern power systems operate close to their limits due to ever-increasing energy consumption and the extension of extant electrical transmission networks and lines. Since the early–mid-1980s, most research in power system analysis has turned to the less rigorous and less tedious techniques of artificial intelligence (AI). Over the past 25 years or so, the feasibility of the various applications of AI in power systems has been explored by a number of investigators. Consequently, AI techniques have become popular for solving different problems in power systems: economic load dispatch, load forecasting, optimization of generation and scheduling, transmission capacity and optimal power flow, real and reactive power limits of generators, bus voltages and transformer taps, load demand in interconnected large power systems and their protections, etc. The application of these techniques in solving several power system problems can overcome the drawbacks of traditional solutions.

This Special Issue aims to provide a venue for researchers to interact, share ideas, and discuss their state-of-the-art research on AI techniques applications in power systems. Both original research papers and review articles are welcome.

Prof. Dr. Stavros D. Kaminaris
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence techniques
power system engineering
power systems
neural network
fuzzy systems
expert systems

Published Papers (5 papers)

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Research

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33 pages, 6791 KiB

Open AccessArticle

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

by Vasiliki Rokani, Stavros D. Kaminaris, Petros Karaisas and Dimitrios Kaminaris

Mathematics 2023, 11(22), 4693; https://doi.org/10.3390/math11224693 - 19 Nov 2023

Cited by 2 | Viewed by 1106

Abstract

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. The IEC-599 standard Ratio Method is an accurate method that evaluates the DGA. All the classical approaches have limitations because they cannot diagnose all faults accurately. Precisely diagnosing defects in power transformers is a significant challenge due to their extensive quantity and dispersed placement within the power network. To deal with this concern and to improve the reliability and precision of fault diagnosis, different Artificial Intelligence techniques are presented. In this manuscript, an artificial neural network (ANN) is implemented to enhance the accuracy of the Rogers Ratio Method. On the other hand, it should be noted that the complexity of an ANN demands a large amount of storage and computing power. In order to address this issue, an optimization technique is implemented with the objective of maximizing the accuracy and minimizing the architectural complexity of an ANN. All the procedures are simulated using the MATLAB R2023a software. Firstly, the authors choose the most effective classification model by automatically training five classifiers in the Classification Learner app (CLA). After selecting the artificial neural network (ANN) as the sufficient classification model, we trained 30 ANNs with different parameters and determined the 5 models with the best accuracy. We then tested these five ANNs using the Experiment Manager app and ultimately selected the ANN with the best performance. The network structure is determined to consist of three layers, taking into consideration both diagnostic accuracy and computing efficiency. Ultimately, a (100-50-5) layered ANN was selected to optimize its hyperparameters. As a result, following the implementation of the optimization techniques, the suggested ANN exhibited a high level of accuracy, up to 90.7%. The conclusion of the proposed model indicates that the optimization of hyperparameters and the increase in the number of data samples enhance the accuracy while minimizing the complexity of the ANN. The optimized ANN is simulated and tested in MATLAB R2023a—Deep Network Designer, resulting in an accuracy of almost 90%. Moreover, compared to the Rogers Ratio Method, which exhibits an accuracy rate of just 63.3%, this approach successfully addresses the constraints associated with the conventional Rogers Ratio Method. So, the ANN has evolved a supremacy diagnostic method in the realm of power transformer fault diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques Applications on Power Systems)

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18 pages, 5447 KiB

Open AccessArticle

Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network

by Zhiyuan Zhang and Zhanshan Wang

Mathematics 2023, 11(20), 4350; https://doi.org/10.3390/math11204350 - 19 Oct 2023

Viewed by 638

Abstract

Accurate load forecasting can bring economic benefits and scheduling optimization. The complexity and uncertainty arising from the coupling of different energy sources in integrated energy systems pose challenges for simultaneously predicting multiple target load sequences. Existing data-driven methods for load forecasting in integrated energy systems use multi-task learning to address these challenges. When determining the input data for multi-task learning, existing research primarily relies on data correlation analysis and considers the influence of external environmental factors in terms of feature engineering. However, such feature engineering methods lack the utilization of the characteristics of multi-target sequences. In leveraging the characteristics of multi-target sequences, language generation models trained on textual logic structures and other sequence features can generate synthetic data that can even be applied to self-training to improve model performance. This provides an idea for feature engineering in data-driven time-series forecasting models. However, because time-series data are different from textual data, existing transformer-based language generation models cannot be directly applied to generating time-series data. In order to consider the characteristics of multi-target load sequences in integrated energy system load forecasting, this paper proposed a generative tractive network (GTN) model. By selectively utilizing appropriate autoregressive feature data for temporal data, this model facilitates feature mining from time-series data. This model is capable of analyzing temporal data variations, generating novel synthetic time-series data that align with the intrinsic temporal patterns of the original sequences. Moreover, the model can generate synthetic samples that closely mimic the variations in the original time series. Subsequently, through the integration of the GTN and autoregressive feature data, various prediction models are employed in case studies to affirm the effectiveness of the proposed methodology. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques Applications on Power Systems)

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0 pages, 15947 KiB

Open AccessArticle

Optimized Sizing of Energy Management System for Off-Grid Hybrid Solar/Wind/Battery/Biogasifier/Diesel Microgrid System

by Ali M. Jasim, Basil H. Jasim, Florin-Constantin Baiceanu and Bogdan-Constantin Neagu

Mathematics 2023, 11(5), 1248; https://doi.org/10.3390/math11051248 - 04 Mar 2023

Cited by 13 | Viewed by 2536 | Correction

Abstract

Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO₂ than traditional power systems, which have significant operational and fuel expenses. In this paper, the proposed hybrid MG adopts renewable energies, including solar photovoltaic (PV), wind turbines (WT), biomass gasifiers (biogasifier), batteries’ storage energies, and a backup diesel generator. The energy management system of the adopted MG resources is intended to satisfy the load demand of Basra, a city in southern Iraq, considering the city’s real climate and demand data. For optimal sizing of the proposed MG components, a meta-heuristic optimization algorithm (Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO)) is applied. The simulation results are compared with those achieved using Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), and Antlion Optimization (ALO) to evaluate the optimal sizing results with minimum costs. Since the adopted GWCSO has the lowest deviation, it is more robust than the other algorithms, and their optimal number of component units, annual cost, and Levelized Cost Of Energy (LCOE) are superior to the other ones. According to the optimal annual analysis, LCOE is 0.1192 and the overall system will cost about USD 2.6918 billion. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques Applications on Power Systems)

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Review

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36 pages, 736 KiB

Open AccessReview

A Survey of Combined Heat and Power-Based Unit Commitment Problem: Optimization Algorithms, Case Studies, Challenges, and Future Directions

by Hamdi Abdi

Mathematics 2023, 11(19), 4170; https://doi.org/10.3390/math11194170 - 05 Oct 2023

Viewed by 863

Abstract

Combined generation units of heat and power, known as CHP units, are one of the most prominent applications of distributed generations in modern power systems. This concept refers to the simultaneous operation of two or more forms of energy from a simple primary source. Due to the numerous environmental, economic, and technical advantages, the use of this technology in modern power systems is highly emphasized. As a result, various issues of interest in the control, operation, and planning of power networks have experienced significant changes and faced important challenges. In this way, the unit commitment problem (UCP) as one of the fundamental studies in the operation of integrated power, and heat systems have experienced some major conceptual and methodological changes. This work, as a complementary review, details the CHP-based UCP (CHPbUCP) in terms of objective functions, constraints, simulation tools, and applied hardwares. Furthermore, some useful data on case studies are provided for researchers and operators. Finally, the work addresses some challenges and opens new perspectives for future research. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques Applications on Power Systems)

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