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Advances in Optimization and Control of Electric Motors for Energy...
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Advances in Optimization and Control of Electric Motors for Energy Savings

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F3: Power Electronics".

Deadline for manuscript submissions: closed (24 August 2023) | Viewed by 10529

Special Issue Editors

Dr. Huiwei Wang

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Interests: distributed estimation; optimization game; its application in smart grid
Dr. Guo Chen

E-Mail Website
Guest Editor
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Interests: power system security assessment; sustainable energy system modeling; artificial intelligence
Dr. Huaqing Li

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Interests: machine learning; artificial intelligence; smart grids

Special Issue Information

Dear Colleagues,

Motor-driven components used in heating, ventilation, and air-conditioning (HVAC) and refrigeration are the highest energy consumers in the industrial sector. Industrial motors compose a major fraction of total industrial energy uses.  Different types of losses that occur in a motor have been identified and ways to overcome these losses explained. Advanced motor technologies provide various opportunities to reduce overall energy consumption in these sectors.

Technical energy savings potential is the savings achieved by instantaneous replacement of the entire technically suitable installed based. Throughout this Special Issue, it is expected to characterize the state and type of motor technologies used in industrial appliances and equipment, and identify opportunities to reduce the energy consumption of electric motor-driven systems in the industrial sectors through the use of advanced motor technologies.

This Special Issue aims to present and disseminate the most recent advances related to the theory, design, modelling, application, control, and condition monitoring of all types of energy savings on electric motors.

Topics of interest for publication include, but are not limited to:

  • All aspects of energy savings on induction machines, permanent magnet synchronous machines, synchronous reluctance machines, switched reluctance machines, brushless DC machines, and emerging PM machines, among others;
  • Energy savings on electric motors for more industrial scenarios, such as pumps, compressor, hydraulics, and fans, and so on;
  • Improving the electric motor efficiency by advanced speed‐regulating approaches and variable frequency drive approaches;
  • New approaches to motor monitoring, alignment, testing, and connections;
  • Artificial intelligence technology for smart drives, such as the application of soft starters, reactive power compensation devices, and computer‐aided automatic control system

Dr. Huiwei Wang
Dr. Guo Chen
Dr. Huaqing Li
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. Energies 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

  • electric motors
  • energy savings
  • artificial intelligence technology
  • optimization of the operation and control

Published Papers (6 papers)

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Research

15 pages, 3956 KiB  
Article
Dynamical Modelling of a Centrifugal Fan Driven by an Induction Motor and Experimental Validation
by Cebrail Turkeri and Oleh Kiselychnyk
Energies 2023, 16(18), 6658; https://doi.org/10.3390/en16186658 - 16 Sep 2023
Viewed by 1450
Abstract
The application of electrical drives in the control of centrifugal fans and pumps is a well-established area that leads to substantial energy savings. It requires electrical and automation engineers to have some knowledge relevant to drives about fan and pump modelling since ignoring [...] Read more.
The application of electrical drives in the control of centrifugal fans and pumps is a well-established area that leads to substantial energy savings. It requires electrical and automation engineers to have some knowledge relevant to drives about fan and pump modelling since ignoring or oversimplifying fan/pump modelling for the intended drive design compromises the required control quality. This paper improves the existing dynamical models of fans and pumps integrated with induction motors via neural network estimation of overall fan/pump efficiency; this estimation is based on the voltage frequency of the driving induction motor, pressure, and flow rate, followed by the separation of the fan’s or pump’s own efficiency for the motor load torque computation, enhancing the accuracy due to the correct reflection of the power balance. The model is developed with a view to being convenient for control design applications. For the first time for dynamical models, it is verified experimentally, justifying the necessity of the first-order nonlinear differential equation for the flow rate. The validation includes a direct approach based on analysis of transient behaviour caused by a small-step perturbation of the frequency of the motor voltages and an indirect approach based on the introduced concept of the dynamical flow rate and pressure estimators. Full article
(This article belongs to the Special Issue Advances in Optimization and Control of Electric Motors for Energy Savings)
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29 pages, 6813 KiB  
Article
Energy-Saving Control for Asynchronous Motor Motion System Based on Direct Torque Regulator
by Stanimir Valtchev, Viktor Meshcheryakov, Elena Gracheva, Alexey Sinyukov and Tatyana Sinyukova
Energies 2023, 16(9), 3870; https://doi.org/10.3390/en16093870 - 2 May 2023
Cited by 3 | Viewed by 1824
Abstract
Energy saving issues occupy a leading position in all control systems. This article provides a detailed analysis of control systems and is conducted by considering the complexity of implementation and response to the control action. The implementation of energy-saving control systems is directly [...] Read more.
Energy saving issues occupy a leading position in all control systems. This article provides a detailed analysis of control systems and is conducted by considering the complexity of implementation and response to the control action. The implementation of energy-saving control systems is directly related to the selected control system and the proposed energy-efficient algorithm. A system with direct torque control is proposed, which provides energy savings in the mechanisms for moving goods. A detailed analysis of the implementation of systems with direct torque control is carried out. New methods to save energy in the control system by minimizing the stator current have been proposed. Full article
(This article belongs to the Special Issue Advances in Optimization and Control of Electric Motors for Energy Savings)
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16 pages, 15975 KiB  
Article
Peer-to-Peer Trading for Energy-Saving Based on Reinforcement Learning
by Liangyi Pu, Song Wang, Xiaodong Huang, Xing Liu, Yawei Shi and Huiwei Wang
Energies 2022, 15(24), 9633; https://doi.org/10.3390/en15249633 - 19 Dec 2022
Cited by 3 | Viewed by 1722
Abstract
This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price [...] Read more.
This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price of the electric energy to sell/buy. There are two aspects we need to examine: the profits for the individual user and the utility for the community. For a single user, we consider that they want to realise both a comfortable living environment to enhance happiness and satisfaction by adjusting usage loads and certain economic benefits by selling the surplus electric energy. Taking the whole community into account, we care about the balance between energy sellers and consumers so that the surplus electric energy can be locally absorbed and consumed within the community. To this end, MARL is applied to solve the problem, where the decision making of each user in the community not only focuses on their own interests but also takes into account the entire community’s welfare. The experimental results prove that our method is profitable both both the sellers and buyers in the community. Full article
(This article belongs to the Special Issue Advances in Optimization and Control of Electric Motors for Energy Savings)
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13 pages, 2465 KiB  
Article
Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System
by Lijuan Sun, Menggang Chen, Yawei Shi, Lifeng Zheng, Songyang Li, Jun Li and Huijuan Xu
Energies 2022, 15(24), 9364; https://doi.org/10.3390/en15249364 - 10 Dec 2022
Viewed by 1126
Abstract
As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented [...] Read more.
As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles. Full article
(This article belongs to the Special Issue Advances in Optimization and Control of Electric Motors for Energy Savings)
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15 pages, 792 KiB  
Article
Energy Loss Reduction for Distribution Networks with Energy Storage Systems via Loss Sensitive Factor Method
by Xiangming Wu, Chenguang Yang, Guang Han, Zisong Ye and Yinlong Hu
Energies 2022, 15(15), 5453; https://doi.org/10.3390/en15155453 - 27 Jul 2022
Cited by 8 | Viewed by 1977
Abstract
The loss of distribution networks caused by various electrical motors including transformers and generators can significantly affect the efficiency and economical operation of the power grid, especially for new power systems with high penetration of renewable energies. In this paper, the potential of [...] Read more.
The loss of distribution networks caused by various electrical motors including transformers and generators can significantly affect the efficiency and economical operation of the power grid, especially for new power systems with high penetration of renewable energies. In this paper, the potential of using an energy storage system (ESS) for loss reduction is investigated, where a novel two-stage method for key-bus selection and ESS scheduling is proposed. At the first stage, the most sensitive key buses to the variation of load are selected by using the loss sensitive factors (LSF) method. At the second stage, ESS scheduling is conducted by solving an optimization problem with uncertainties caused by high penetration of renewable energies, where the uncertainties are characterized by confidence levels. The optimal scheduling of ESS including locations, capacities, and working modes are obtained at the second stage. The effectiveness of the proposed method is demonstrated via numerical simulations. The influences of capacities of ESS and confidence levels with respect to uncertainties are also analyzed. It is demonstrated that the loss-reduction performances can be improved if the ESSs are deployed on the buses selected by the LSF method and operated under the developed optimal scheduling method. Full article
(This article belongs to the Special Issue Advances in Optimization and Control of Electric Motors for Energy Savings)
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16 pages, 468 KiB  
Article
Event-Triggered Security Consensus for Multi-Agent Systems with Markov Switching Topologies under DoS Attacks
by Yuan Tian, Sheng Tian, Huaqing Li, Qi Han and Xiaonan Wang
Energies 2022, 15(15), 5353; https://doi.org/10.3390/en15155353 - 23 Jul 2022
Cited by 7 | Viewed by 1583
Abstract
This paper studies secure consensus control for multi-agent systems subject to denial-of-service (DoS) attacks. The DoS attacks cause changes in topologies, which will destroy the channels of communication and result in network paralysis. Unlike the existing publications with Markov switching, this paper mainly [...] Read more.
This paper studies secure consensus control for multi-agent systems subject to denial-of-service (DoS) attacks. The DoS attacks cause changes in topologies, which will destroy the channels of communication and result in network paralysis. Unlike the existing publications with Markov switching, this paper mainly studies the topological structure changes of the subsystem models after DoS attacks. To ensure the consensus of systems, this paper designs an event triggered to reduce the use of the controller and decrease the influence of channel breaks off caused by DoS attacks. On this basis, different Lyapunov functions are established in each period of attack. Then, stochastic and Lyapunov stable theory is used to form the consensus criteria. Moreover, Zeno behavior is excluded by theoretical analysis. Finally, the simulation experiment proves the effectiveness of the proposed protocol. Full article
(This article belongs to the Special Issue Advances in Optimization and Control of Electric Motors for Energy Savings)
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