Intelligent Control in Smart Energy Systems

Topic Information

Dear Colleagues,

The Topic on “Intelligent Control in Smart Energy Systems” aims to present the most advanced and latest research developments, focusing on intelligent control techniques and their potential applications in smart energy systems. The topics include, but are not limited to:

• Intelligent control
• Computational intelligence for modelling and control
• Energy management
• Integrated energy systems
• Smart grids
• Sustainable energy
• Energy efficiency
• Control theory
• Control systems
• Adaptive control
• Robust control
• Smart energy systems
• Renewable energy
• Algorithm

Prof. Dr. Eduard Petlenkov
Dr. Larbi Chrifi-Alaoui
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	-	4.8	2020	27.9 Days	CHF 1000
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400
Symmetry symmetry	2.2	5.4	2009	17.3 Days	CHF 2400

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Published Papers (4 papers)

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All Electricity Energies Mathematics Sustainability Symmetry

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16 pages, 6448 KiB  

Open AccessArticle

Real-Time Control of Sintering Moisture Based on Temporal Fusion Transformers

by Xinping Chen, Jinyang Cheng, Ziyun Zhou, Xinyu Lu, Binghui Ye and Yushan Jiang

Symmetry 2024, 16(6), 636; https://doi.org/10.3390/sym16060636 - 21 May 2024

Viewed by 1295

Abstract

The quality of sintered ore, which serves as the primary raw material for blast furnace ironmaking, is directly influenced by the moisture in the sintering mixture. In order to improve the precision of water addition in the sintering process, this paper proposes an [...] Read more.

The quality of sintered ore, which serves as the primary raw material for blast furnace ironmaking, is directly influenced by the moisture in the sintering mixture. In order to improve the precision of water addition in the sintering process, this paper proposes an intelligent model for predicting water-filling volume based on Temporal Fusion Transformer (TFT), whose symmetry enables it to effectively capture long-term dependencies in time series data. Utilizing historical sintering data to develop a prediction model for the amount of mixing and water addition, the results indicate that the TFT model can achieve the R squared of 0.9881, and the root mean square error (RMSE) of 3.5951. When compared to the transformer, long short-term memory (LSTM), and particle swarm optimization–long short-term memory (PSO-LSTM), it is evident that the TFT model outperforms the other models, improving the RMSE by 8.5403, 6.9852, and 0.453, respectively. As an application, the TFT model provides an effective interval reference for moisture control in normal sintering processes, which ensures that the error is within 1 t. Full article

(This article belongs to the Topic Intelligent Control in Smart Energy Systems)

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18 pages, 2468 KiB  

Open AccessArticle

Operation Assessment of a Hybrid Distribution Transformer Compensating for Voltage and Power Factor Using Predictive Control

by Esteban I. Marciel, Carlos R. Baier, Roberto O. Ramírez, Carlos A. Muñoz, Marcelo A. Pérez and Mauricio Arevalo

Mathematics 2024, 12(5), 774; https://doi.org/10.3390/math12050774 - 5 Mar 2024

Cited by 1 | Viewed by 1502

Abstract

Hybrid Distribution Transformers (HDTs) offer a compelling alternative to traditional low-frequency transformers (LFTs), providing auxiliary services in addition to standard functionalities. By integrating LFTs with power converters, HDTs enhance the operational capabilities of the system. The specific configuration in which converters are connected [...] Read more.

Hybrid Distribution Transformers (HDTs) offer a compelling alternative to traditional low-frequency transformers (LFTs), providing auxiliary services in addition to standard functionalities. By integrating LFTs with power converters, HDTs enhance the operational capabilities of the system. The specific configuration in which converters are connected to the transformer allows for the provision of multiple services. This can not only prevent network failures but also extend the lifespan of its components, an outcome that is highly desirable in a distribution grid. This article discusses an HDT developed to mitigate voltage fluctuations in the grid and to decrease the reactive power drawn from the secondary side of traditional LFTs. A finite-control-set model predictive control (FCS-MPC), in conjunction with linear controllers, is utilized for the effective management of the HDT converters. Two separate control loops are established to regulate voltage and reactive power on the secondary side of the transformer. Results from Hardware-in-the-Loop (HIL) testing affirm the proficiency of HDT in reducing grid voltage variations by 15% and in cutting reactive power consumption by up to 94%. The adopted control strategy and topology are demonstrated to be effective in stabilizing voltage and reactive power fluctuations while concurrently facilitating the charging of the converters’ DC link directly from the grid. Full article

(This article belongs to the Topic Intelligent Control in Smart Energy Systems)

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24 pages, 7048 KiB  

Open AccessArticle

Estimation of an Extent of Sinusoidal Voltage Waveform Distortion Using Parametric and Nonparametric Multiple-Hypothesis Sequential Testing in Devices for Automatic Control of Power Quality Indices

by Aleksandr Kulikov, Pavel Ilyushin, Aleksandr Sevostyanov, Sergey Filippov and Konstantin Suslov

Energies 2024, 17(5), 1088; https://doi.org/10.3390/en17051088 - 24 Feb 2024

Cited by 1 | Viewed by 1123

Abstract

Deviations of power quality indices (PQI) from standard values in power supply systems of industrial consumers lead to defective products, complete shutdown of production processes, and significant damage. At the same time, the PQI requirements vary depending on the industrial consumer, which is [...] Read more.

Deviations of power quality indices (PQI) from standard values in power supply systems of industrial consumers lead to defective products, complete shutdown of production processes, and significant damage. At the same time, the PQI requirements vary depending on the industrial consumer, which is due to different kinds, types, and composition of essential electrical loads. To ensure their reliable operation, it is crucial to introduce automatic PQI control devices, which evaluate the extent of distortion of the sinusoidal voltage waveform of a three-phase system. This allows the power dispatchers of grid companies and industrial enterprises to quickly make decisions on the measures to be taken in external and internal power supply networks to ensure that the PQI values are within the acceptable range. This paper proposes the use of an integrated indicator to assess the extent of distortion of the sinusoidal voltage waveform in a three-phase system. This indicator is based on the use of the magnitude of the ratio of complex amplitudes of the forward and reverse rotation of the space vector. In the study discussed, block diagrams of algorithms and flowcharts of automatic PQI control devices are developed, which implement parametric and nonparametric multiple-hypothesis sequential analysis using an integrated indicator. In this case, Palmer’s algorithm and the nearest neighbor method are used. The calculations demonstrate that the developed algorithms have high speed and high performance in detecting deviations of the electrical power quality. Full article

(This article belongs to the Topic Intelligent Control in Smart Energy Systems)

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19 pages, 1212 KiB  

Open AccessArticle

Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion

by Chun-Che Huang, Wen-Yau Liang, Roger R. Gung and Pei-An Wang

Sustainability 2023, 15(20), 14984; https://doi.org/10.3390/su152014984 - 17 Oct 2023

Cited by 1 | Viewed by 1194

Abstract

As developing economies become more industrialized, the energy problem has become a major challenge in the twenty-first century. Countries around the world have been developing renewable energy to meet the Sustainable Development Goals (SDGs) of the United Nations (UN) and the 26th UN [...] Read more.

As developing economies become more industrialized, the energy problem has become a major challenge in the twenty-first century. Countries around the world have been developing renewable energy to meet the Sustainable Development Goals (SDGs) of the United Nations (UN) and the 26th UN Climate Change Conference of the Parties (COP26). Leaders of enterprises have been made aware of the need to protect the environment and have been practicing environmental marketing strategies and green information systems (GISs) as part of ESG practices. With the rapid growth of the available data from renewable electricity suppliers, the analyses of multi-attribute characteristics across different fields of studies use data mining to obtain viable rule induction and achieve adaptive management. Rough set theory is an appropriate method for multi-attribute classification and rule induction. Nevertheless, past studies for Big Data analytics have tended to focus on incremental algorithms for dynamic databases. This study entails rough set theory from the perspective of the decrement decay alternative rule-extraction algorithm (DAREA) to explore rule induction and present case evidence with managerial implications for the emerging renewable energy industry. This study innovates rough set research to handle data deletion in a Big Data system and promotes renewable energy with valued managerial implications. Full article

(This article belongs to the Topic Intelligent Control in Smart Energy Systems)

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