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Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (25 August 2024) | Viewed by 13678

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Special Issue Editors

Dr. Mateo Bašić

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Guest Editor

Department of Power Engineering - Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Rudjera Boskovica 32, HR-21000 Split, Croatia
Interests: induction machines; vector control; power electronics; microgrids; renewable generation

Dr. Dejan Jokić

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Guest Editor

Department of Electrical and Electronics Engineering, InteRnational Burch University, Francuske Revolucije bb, Ilidža, 71210 Sarajevo, Bosnia and Herzegovina
Interests: programmable devices; embedded systems; AI hardware; intelligent control system; mechatronics

Special Issue Information

Dear Colleagues,

The past few decades have seen a significant rise in investments in clean, renewable, and sustainable energy technologies as a result of concerns about current and emerging environmental issues and dangers, the depletion of fossil fuel supplies, and the rapidly escalating global energy demand. In addition, the topic of reliability and resilience has recently drawn a lot of attention in an effort to lessen community vulnerability to temporary power outages or total collapse of the main grid brought on by natural disasters, wars, technical issues, physical and cyberattacks, etc. Hence, policymakers have prioritized developing new action plans to address these concerns, including the use of renewable energy and improving energy conversion efficiency and management. Although it is often challenging to put these new action plans into practice due to issues with technology, the economy, or society, process systems engineers continuously strive to enhance systems of all types, from large industrial to small household.

Future energy systems are expected to include many different types of energy storage technology and renewable sources of energy. The availability of renewable resources, in particular, has a significant impact on economic growth potential. However, despite the fact that they can assist with energy issues, the challenges associated with managing, controlling, and monitoring renewable energy sources prevent them from being fully integrated into power systems. The use of sophisticated control algorithms, such as those based on machine learning, artificial intelligence, or cloud computing, can help solve these problems.

Original research papers, reviews, case studies, and technical notes relevant to the scope of this Special Issue include, but are not limited to, the following topics:

Integration of renewable energy sources;
Solar and wind energy technology and applications;
Hybrid renewable energy systems;
Grid stability;
Power electronics in renewable energy systems and smart grids;
Modeling of energy systems and processes;
Policy issues related to sustainability;
Energy conversion and storage technologies;
System reliability and resilience;
Techno-economic and energy/exergy analyses;
Edge computing in smart grids and other applications;
Artificial intelligence applications in smart grids;
PLC and SCADA systems;
Increasing energy efficiency in industrial motor drives.

Dr. Mateo Bašić
Dr. Dejan Jokić
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Processes is an international peer-reviewed open access monthly 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 2400 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

sustainability
intelligent control
renewable energy
reliability and resilience
smart grid
energy storage
edge computing
SCADA

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Published Papers (13 papers)

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14 pages, 5515 KiB

Open AccessArticle

Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network

by Ming Gou, Hao Tang, Lei Song, Zhong Chen, Xiaoming Yan, Xiangwen Zeng and Wenlong Fu

Processes 2024, 12(9), 1832; https://doi.org/10.3390/pr12091832 - 28 Aug 2024

Viewed by 459

Abstract

The current manual inspection of transmission line images captured by unmanned aerial vehicles (UAVs) is not only time-consuming and labor-intensive but also prone to high rates of false detections and missed inspections. With the development of artificial intelligence, deep learning-based image recognition methods can automatically detect various defect categories of transmission lines based on images captured by UAVs. However, existing methods are often constrained by incomplete feature extraction and imbalanced sample categories, which limit the precision of detection. To address these issues, a novel method based on multi-strategy image processing and an improved deep network is proposed to conduct defect diagnosis of transmission lines. Firstly, multi-strategy image processing is proposed to extract the effective area of transmission lines. Then, a generative adversarial network is employed to generate images of transmission lines to enhance the trained samples’ diversity. Finally, the deep network GoogLeNet is improved by superseding the original cross-entropy loss function with a focal loss function to achieve the deep feature extraction of images and defect diagnosis of transmission lines. An actual imbalance transmission line dataset including normal, broken strands, and loose strands is applied to validate the effectiveness of the proposed method. The experimental results, as well as contrastive analysis, reveal that the proposed method is suitable for recognizing defects of transmission lines. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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18 pages, 2898 KiB

Open AccessArticle

Study on Short-Term Electricity Load Forecasting Based on the Modified Simplex Approach Sparrow Search Algorithm Mixed with a Bidirectional Long- and Short-Term Memory Network

by Chenjun Zhang, Fuqian Zhang, Fuyang Gou and Wensi Cao

Processes 2024, 12(9), 1796; https://doi.org/10.3390/pr12091796 - 23 Aug 2024

Viewed by 420

Abstract

In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low prediction accuracy resulting from power load volatility and nonlinearity. It suggests optimizing the number of hidden layer nodes, number of iterations, and learning rate of bi-directional long- and short-term memory networks using the improved sparrow search algorithm, and predicting the actual load data using the load prediction model. Using actual power load data from Wuxi, Jiangsu Province, China, as a dataset, the model makes predictions. The results indicate that the model is effective because the enhanced sparrow algorithm optimizes the bi-directional long- and short-term memory network model for predicting the power load data with a relative error of only 2%, which is higher than the prediction accuracy of the other models proposed in the paper. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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14 pages, 2250 KiB

Open AccessArticle

Optimal Operation Strategy for Wind–Photovoltaic Power-Based Hydrogen Production Systems Considering Electrolyzer Start-Up Characteristics

by Ben Ma, Jianfeng Zheng, Zhongye Xian, Bo Wang and Hengrui Ma

Processes 2024, 12(8), 1756; https://doi.org/10.3390/pr12081756 - 20 Aug 2024

Viewed by 431

Abstract

Combining electrolytic hydrogen production with wind–photovoltaic power can effectively smooth the fluctuation of power and enhance the schedulable wind–photovoltaic power, which provides an effective solution to solve the problem of wind–photovoltaic power accommodation. In this paper, the optimization operation strategy is studied for the wind–photovoltaic power-based hydrogen production system. Firstly, to make up for the deficiency of the existing research on the multi-state and nonlinear characteristics of electrolyzers, the three-state and power-current nonlinear characteristics of the electrolyzer cell are modeled. The model reflects the difference between the cold and hot starting time of the electrolyzer, and the linear decoupling model is easy to apply in the optimization model. On this basis, considering the operation constraints of the electrolyzer, hydrogen storage tank, battery, and other equipment, the optimization operation model of the wind–photovoltaic power-based hydrogen production system is developed based on the typical scenario approach. It also considers the cold and hot starting time of the electrolyzer with the daily operation cost as the goal. The results show that the operational benefits of the system can be improved through the proposed strategy. The hydrogen storage tank capacity will have an impact on the operation income of the wind–solar hydrogen coupling system, and the daily operation income will increase by 0.32% for every 10% (300 kg) increase in the hydrogen storage tank capacity. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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23 pages, 4028 KiB

Open AccessArticle

Future Prospects of MeOH and EtOH Blending in Gasoline: A Comparative Study on Fossil, Biomass, and Renewable Energy Sources Considering Economic and Environmental Factors

by Xiaofei Shi, Zihao Yu, Tangmao Lin, Sikan Wu, Yujiang Fu and Bo Chen

Processes 2024, 12(8), 1751; https://doi.org/10.3390/pr12081751 - 20 Aug 2024

Viewed by 596

Abstract

Alcohol-blended gasoline is recognized as an effective strategy for reducing carbon emissions during combustion and enhancing fuel performance. However, the carbon footprint associated with its production process in refineries deserves equal attention. This study introduces a refinery modeling framework to evaluate the long-term economic and environmental performance of utilizing alcohols derived from fossil, biomass, and carbon capture sources in gasoline blending processes. The proposed framework integrates Extreme Learning Machine-based models for gasoline octane blending, linear programming for optimization, carbon footprint tracking, and future trends in feedstock costs and carbon taxes. The results indicate that gasoline blended with coal-based alcohol currently exhibits the best economic performance, though its carbon footprint ranges from 818.54 to 2072.89 kgCO₂/t. Gasoline blended with biomass-based alcohol leads to a slight reduction in benefits and an increase in the carbon footprint. Blending gasoline with CCUM (CO₂ capture and utilization to methanol) results in the lowest economic performance, with a gross margin of 8.91 CNY/t_oil at a 30% blending ratio, but achieves a significant 62.4% reduction in the carbon footprint. In long-term scenarios, the additional costs brought by increased carbon taxes result in negative economic performance for coal-based alcohol blending after 2040. However, cost reductions driven by technological maturity lead to biomass-based alcohol and CCUM blending gradually showing economic advantages. Furthermore, owing to the negative carbon emissions characteristic of CCUM, the blending route with CCUM achieves a gross margin of 440.60 CNY/t_oil and a gasoline carbon footprint of 282.28 kgCO₂/t at a 20% blending ratio by 2050, making it the best route in terms of economic and environmental performance. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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23 pages, 3175 KiB

Open AccessArticle

Economic Dispatch of Integrated Energy Systems Considering Wind–Photovoltaic Uncertainty and Efficient Utilization of Electrolyzer Thermal Energy

by Ji Li, Lei Xu, Yuying Zhang, Yang Kou, Weile Liang, Alihan Bieerke and Zhi Yuan

Processes 2024, 12(8), 1627; https://doi.org/10.3390/pr12081627 - 2 Aug 2024

Viewed by 649

Abstract

Currently, high levels of output stochasticity in renewable energy and inefficient electrolyzer operation plague IESs when combined with hydrogen energy. To address the aforementioned issues, an IGDT-based economic scheduling strategy for integrated energy systems is put forth. Firstly, this strategy establishes an IES consisting of coupled electricity, heat, hydrogen, and gas taking the hydrogen production electrolyzer’s thermal energy utilization into account. Second, to minimize the system’s overall operating costs, a deterministic scheduling model of the IES is built by taking into account the stepped carbon trading mechanism and the integrated demand response. Lastly, an optimal dispatch model is built using the information gap decision theory under the two strategies of risk aversion and risk seeking, taking into account the uncertainty of renewable energy generation. CPLEX is the solver used to solve the proposed model. After taking into account the effective use of thermal energy from the electrolyzer and loads demand response, the results show that the system carbon emission is reduced by 2597.68 kg and the operating cost is lowered by 44.65%. The IES scheduling model based on IGDT can effectively manage costs while maintaining system risk control, all while accommodating decision-makers’ varying risk preferences. This study can provide a useful reference for the research related to the scheduling of the IES low-carbon economy. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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26 pages, 4827 KiB

Open AccessArticle

Energy, Exergy, and Economic Analysis of a New System for Simultaneous Power Production and Cooling Operating with an Ammonia–Water Mixture

by Alejandro Pacheco-Reyes, José C. Jiménez-García, J. Alejandro Hernández-Magallanes, Raman Shankar and Wilfrido Rivera

Processes 2024, 12(7), 1288; https://doi.org/10.3390/pr12071288 - 21 Jun 2024

Viewed by 573

Abstract

This paper presents the energy, exergy, and economic analysis of a new cogeneration cycle for the simultaneous production of power and cooling operating with an ammonia–water mixture. The proposed system consists of an absorption cooling system integrating a reheater, a separation tank, a compressor, a turbine, and an expansion valve. In addition, internal rectification is applied, improving the system’s performance. Mass, energy, and exergy balances were applied to each system’s component to evaluate its performance. Additionally, the costs of each component were determined based on economic equations, which take into account mass, heat flows, and temperature differences. A parametric analysis found that the system reached an energy utilization factor of 0.58 and an exergy efficiency of 0.26 using internal rectification at T_G = 120 °C, T_A = 30 °C, and T_E = 10 °C. The power produced by the turbine was 26.28 kW, and the cooling load was 366.8 kW. The output costs were estimated at 0.071 $/kW. The condenser was found to be the most expensive component of the system, contributing 28% of the total cost. On the other hand, it was observed that the generator was the component with the highest exergy destruction, with 38.16 kW. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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23 pages, 3527 KiB

Open AccessArticle

Thermodynamic Modeling of a Solar-Driven Organic Rankine Cycle-Absorption Cooling System for Simultaneous Power and Cooling Production

by José C. Jiménez-García, Isaías Moreno-Cruz and Wilfrido Rivera

Processes 2024, 12(3), 427; https://doi.org/10.3390/pr12030427 - 20 Feb 2024

Cited by 1 | Viewed by 1318

Abstract

Humanity is facing the challenge of reducing its environmental impact. For this reason, many specialists worldwide have been studying the processes of production and efficient use of energy. In this way, developing cleaner and more efficient energy systems is fundamental for sustainable development. The present work analyzed the technical feasibility of a solar-driven power-cooling system operating in a particular location in Mexico. The theoretical system integrates organic Rankine and single-stage absorption cooling cycles. A parabolic trough collector and a storage system integrated the solar system. Its performance was modeled for a typical meteorological year using the SAM software by NREL. The analyzed working fluids for the organic cycle include benzene, cyclohexane, toluene, and R123, while the working fluid of the absorption system is the ammonia-water mixture. The cycle’s first and second-law performances are determined in a wide range of operating conditions. Parameters such as the energy utilization factor, turbine power, COP, and exergy efficiency are reported for diverse operating conditions. It was found that the highest energy utilization factor was 0.68 when the ORC utilized benzene as working fluid at ORC and ACS condensing temperatures of 80 °C and 20 °C, respectively, and at a cooling temperature of 0 °C. The best exergy efficiency was 0.524 at the same operating conditions but at a cooling temperature of −10 °C. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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0 pages, 4468 KiB

Open AccessArticle

A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

by Ivan Radaš, Luka Matić, Viktor Šunde and Željko Ban

Processes 2024, 12(2), 404; https://doi.org/10.3390/pr12020404 - 17 Feb 2024

Cited by 2 | Viewed by 791 | Correction

Abstract

This paper proposes a new method for assessing the state of charge (SoC) and identifying the types of different lithium-ion cells used in the battery systems of light electric vehicles. A particular challenge in the development of this method was the SoC estimation time, as the method is intended for implementation in the control system of a bicycle charging station, where the state of charge must be determined immediately after the bicycle is plugged in in order to start the charging process as quickly as possible according to the appropriate charging algorithm. The method is based on the identification of the transfer function, i.e., the dynamic response of the battery voltage to the current pulse. In the learning phase of this method, a database of reference transfer functions and corresponding SoCs for a specific type of battery cell is created. The transfer functions are described by coefficients determined through the optimization procedure. The algorithm for estimating the unknown battery cell SoCs is based on the comparison of the measured voltage response with the responses of the reference transfer functions from the database created during the learning process to the same current signal. The comparison is made by calculating the integral of the square error (ISE) between the response of the specific reference transfer function and the measured voltage response of the battery cell. Each transfer function corresponds to a specific SoC and cell type. The specific SoC of the unknown battery is determined by quadratic interpolation of the SoC near the reference point with the smallest ISE for each battery type. The cell type detection algorithm is based on the fact that the integral squared error criterion near the actual SoC for the actual cell type changes less than the squared error criterion for any other battery cell type with the same SoC. An algorithm for estimating the SoC and cell type is described and tested on several different cell types. The relative error between the estimated SoC and the actual SoC was used as a measure of the accuracy of the algorithm, where the actual SoC was calculated using the Coulomb counting method. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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12 pages, 1618 KiB

Open AccessArticle

Methods of Measuring Air Pollution in Cities and Correlation of Air Pollutant Concentrations

by Milan Bodić, Vladimir Rajs, Marko Vasiljević Toskić, Jovan Bajić, Branislav Batinić and Miloš Arbanas

Processes 2023, 11(10), 2984; https://doi.org/10.3390/pr11102984 - 15 Oct 2023

Viewed by 1838

Abstract

The monitoring of air quality continues to be one of the most important tasks when ensuring the safety of our environment. This paper aims to look at correlations between different types of pollutants, so that robust air quality measurement systems can be deployed in remote, inaccessible areas, at a reduced cost. The first matter at hand was to design an affordable and portable system capable of measuring different air pollutants. A custom PCB was designed that could support the acquisition of readings of, among others, particulate and CO sensors. Then, correlations between the concentrations of different pollutants were analyzed to identify if measuring the concentration of one type of pollutant can allow the extrapolation of the concentration of another. This particular study focuses on the correlations between the concentrations of particulate matter and CO. Finally, after observing a moderate correlation, it was proposed to measure the concentrations of pollutants that require less expensive sensors, and to extrapolate the concentrations of pollutants that require a more expensive sensor to measure their concentration. The link between particulate pollution and CO concentrations was identified and discussed as the result of this study. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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25 pages, 7983 KiB

Open AccessMore Editor’s choice articles in journal Processes.">Editor’s ChoiceArticle

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

by Mateo Bašić, Dinko Vukadinović and Ivan Grgić

Processes 2023, 11(10), 2917; https://doi.org/10.3390/pr11102917 - 5 Oct 2023

Cited by 2 | Viewed by 1312

Abstract

The paper considers the model predictive current control (MPCC) of an induction motor (IM) drive and evaluates five IM models of different complexities—from conventional to magnetic saturation, iron losses, and stray-load losses—for the MPCC design. The validity of each considered IM model and the corresponding MPCC algorithm is evaluated by comparison of the following performance metrics: the total harmonic distortion of the stator current, the average switching frequency, the rotor flux magnitude error, the rotor flux angle error, and the product of the first two metrics. The metrics’ values are determined in wide ranges of the rotor speed (0.1–1 p.u.) and load torque (0–1 p.u.) through simulations performed in the MATLAB Simulink environment. The obtained results allow us to identify the IM model that offers the best tradeoff between the practicability and accuracy. Furthermore, a control effort penalization (CEP) is suggested to reduce the average switching frequency and, hence, the power converter losses. This involves constraining the simultaneous switching to a maximum of two branches of the three-phase power converter, as well as inclusion of the weighted switching penalization term in the cost function. Finally, the performance—both steady-state and dynamic—of the proposed MPCC system with CEP is compared with that of the analogous field-oriented controlled (FOC) IM drive. The inverter switching frequency is reduced more than twice by including the frequency-dependent iron-loss resistance in the MPCC. It is additionally reduced by implementing the proposed CEP strategy without sacrificing many other performance metrics, thus achieving a performance comparable to the FOC IM drive. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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16 pages, 2761 KiB

Open AccessMore Editor’s choice articles in journal Processes.">Editor’s ChoiceArticle

Exergy and Energy Analysis of the Shell-and-Tube Heat Exchanger for a Poultry Litter Co-Combustion Process

by Samuel O. Alamu, Seong W. Lee and Xuejun Qian

Processes 2023, 11(8), 2249; https://doi.org/10.3390/pr11082249 - 26 Jul 2023

Cited by 1 | Viewed by 1575

Abstract

Increasing production of poultry litter, and its associated problems, stimulates the need for generating useful energy in an environmentally friendly and efficient energy system, such as the use of shell-and-tube heat exchangers (STHE) in a fluidized-bed combustion (FBC) system. A holistic approach which involves the integration of the First Law of Thermodynamics (FLT) and Second Law of Thermodynamics (SLT) is required for conducting effective assessment of an energy system. In this study, the STHE designed by the CAESECT research group, which was integrated into the lab-scale FBC, was investigated to determine the maximum available work performed by the system and account for the exergy loss due to irreversibility. The effects of varying operating parameters and configuration of the space heaters connected to the STHE for space heating purposes were investigated in order to improve the thermal efficiency of the poultry litter-to-energy conversion process. Exergy and energy analysis performed on the STHE using flue gas and water media showed higher efficiency (75–92%) obtained via energy analysis, but much lower efficiency (12–25%) was obtained when the ambient conditions were factored into the exergy analysis, thus indicating huge exergy loss to the surroundings. From the obtained experimental data coupled with the simulation on parallel arrangement of air heaters, it was observed that exergy loss increased with increasing flue gas flow rate from 46.8–57.6 kg/h and with increasing ambient temperature from 8.8 °C to 25 °C. To lower the cost of STHE during final design, a larger temperature difference between the hot and cold flue gas is needed throughout the exchanger, which further increases the exergetic loss while maintaining an energy balance. In addition, this study also found the optimal conditions to reduce exergy loss and improve energy efficiency of the designed STHE. This study shows the possibility to evaluate energy systems using integration of exergy and energy analysis. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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Review

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21 pages, 2736 KiB

Open AccessReview

The Evolving Technological Framework and Emerging Trends in Electrical Intelligence within Nuclear Power Facilities

by Yao Sun, Zhijian Wang, Yao Huang, Jie Zhao, Bo Wang, Xuzhu Dong and Chenhao Wang

Processes 2024, 12(7), 1374; https://doi.org/10.3390/pr12071374 - 1 Jul 2024

Viewed by 977

Abstract

This paper thoroughly explores the feasibility of integrating a variety of intelligent electrical equipment and smart maintenance technologies within nuclear power plants to enhance the currently limited level of intelligence of these systems and better support operational and maintenance tasks. Initially, this paper outlines the demands and challenges of intelligent electrical systems in nuclear power plants, highlighting the current state of development of intelligent electrical systems, including new applications of artificial intelligence and big data technologies in power grid companies, such as intelligent defect recognition through image recognition, intelligence-assisted inspections, and intelligent production commands. This paper then provides a detailed introduction to the architecture of intelligent electrical equipment, encompassing the smart electrical equipment layer, the smart control system layer, and the cloud platform layer. It discusses the intelligentization of medium- and low-voltage electrical equipment, such as smart circuit breakers, smart switchgear, and low-voltage distribution systems, emphasizing the importance of intelligentization in improving the safety, reliability, and maintenance efficiency of medium- and low-voltage distribution equipment in nuclear power plants. Furthermore, this paper addresses issues in the intelligentization of nuclear power plant electrical systems, such as information silos, the inefficiency of traditional manual inspection processes, and the lack of comprehensive intelligent design and evaluation standards, proposing corresponding solutions. Additionally, this paper presents the trends in intelligent operation and maintenance technology and applications, including primary and secondary fusion technology, intelligent patrol system architecture, intelligent inspection based on non-destructive testing, and a comprehensive solution based on inspection robots. The application of these technologies aids in achieving automated inspection, real-time monitoring, and the intelligent diagnosis of electrical equipment in nuclear power plants. Finally, this paper proposes basic principles for the development of intelligent electrical systems in nuclear power plants, including intelligent architecture, the evolutionary path, and phased goals and key technologies. It emphasizes the gradual transition from automation to digitization and then to intelligentization and presents a specific implementation plan for the intelligentization of the electrical systems in nuclear power plants. This paper concludes with a summary of short-term and long-term goals for improving the performance of nuclear power plant electrical systems through intelligent technologies and prospects for the application of intelligent technologies in the operation and maintenance of nuclear power plants in the future. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)

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