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Machine Learning for Energy Systems 2021

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 39543

Special Issue Editor


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Guest Editor
Russian Academy of Sciences, Melentiev Energy Systems Institute, 664033 Irkutsk, Russia
Interests: inverse problems; integral equations; machine learning; nonlinear systems; bifurcation; numerical methods; energy systems engineering; optimal design and operation; forecasting; energy storage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Energies Special Issue on “Machine Learning for Energy Systems 2021”.

Future energy systems will grow in complexity, causing both higher demands in reliability and increase in the degrees of freedom for functional improvement of integrated energy systems. Progress in heterogeneous data acquisition, data fusion, and mathematical modeling opens new perspectives in modern energy systems rethinking and improvement. This Special Issue of Energies aims at addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. Special attention will be given to mathematical modeling and control of cyber-physical systems, including digital twins, the data-driven black box dynamical models integration with classical mathematical and mechanical models and methods.

Original submissions focusing on theoretical and practical issues related to the theory and applications of Machine Learning, including novel optimization and operations research methods and their applications, design techniques and methodologies, reliability analysis, and practical implementation aspects are welcome.

The issue will include but is not be limited to:

  • Mathematical modeling and control of cyber-physical systems
  • Multiphysics measurements-based decision making and control of integrated energy systems
  • Energy systems flexibility, efficiency and power quality
  • Uncertainty quantification and inverse problems in energy systems
  • Data-driven energy management strategies and unit commitment problem solvers

Prof. Dr. Denis N. Sidorov
Guest Editor

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

  • cyber-physical system
  • optimization
  • classification
  • optimal design and operation
  • energy systems
  • forecasting
  • multiple criteria decision-making
  • uncertainty in design and operation
  • operations research
  • inverse problems
  • clustering
  • digital twin

Published Papers (14 papers)

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Editorial

Jump to: Research, Review

8 pages, 243 KiB  
Editorial
Machine Learning for Energy Systems Optimization
by Insu Kim, Beopsoo Kim and Denis Sidorov
Energies 2022, 15(11), 4116; https://doi.org/10.3390/en15114116 - 3 Jun 2022
Cited by 8 | Viewed by 4062
Abstract
This editorial overviews the contents of the Special Issue “Machine Learning for Energy Systems 2021” and review the trends in machine learning (ML) techniques for energy system (ES) optimization [...] Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)

Research

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13 pages, 3364 KiB  
Article
Enhancement of the Technique for Calculation and Assessment of the Condition of Major Insulation of Power Transformers
by Olga Melnikova, Alexandr Nazarychev and Konstantin Suslov
Energies 2022, 15(4), 1572; https://doi.org/10.3390/en15041572 - 21 Feb 2022
Cited by 9 | Viewed by 1981
Abstract
The findings of the analysis of data on the accident rate of power transformers indicate that one of the main causes of their failures is a decrease in the dielectric strength of the insulation. To reduce failures and extend the service life of [...] Read more.
The findings of the analysis of data on the accident rate of power transformers indicate that one of the main causes of their failures is a decrease in the dielectric strength of the insulation. To reduce failures and extend the service life of power transformers in operation, the issue of enhancing the techniques for assessing the condition of their internal insulation becomes relevant. Currently, when selecting the major insulation of transformers, one takes into account the dependency of the dielectric strength of the oil passage on its width. Experts discuss the issues involved in the choice of major insulation while taking into account the effect of the generalized factor being the volume of the oil passage. The solution to that problem largely depends on the study of the statistical characteristics of the dielectric strength of oil passages of different volumes and the effect rated parameters of transformers have on them. The efficiency of the application of such diagnostic characteristics depends on the extent of studies available on them and the establishment of their standardized parameters. The paper proposes a method for estimating the change in the transformer oil volume in stressed oil passages of major insulation of high-voltage power transformers and statistical characteristics of the dielectric strength of these passages while taking into account the effect of the rated values of capacity and voltage of transformers. It is shown that the degree of effect of transformer technical parameters on the statistical characteristics of the dielectric strength of oil passages depends on the quality of transformer oil, which undergoes a change in operating conditions. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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16 pages, 22445 KiB  
Article
Low-Cost Sensors for Indoor PV Energy Harvesting Estimation Based on Machine Learning
by Bastien Politi, Alain Foucaran and Nicolas Camara
Energies 2022, 15(3), 1144; https://doi.org/10.3390/en15031144 - 3 Feb 2022
Cited by 8 | Viewed by 1971
Abstract
With the number of communicating sensors linked to the Internet of Things (IoT) ecosystem increasing dramatically, well-designed indoor light energy harvesting solutions are needed. A first step in this direction would be to be able to accurately estimate the harvestable energy in a [...] Read more.
With the number of communicating sensors linked to the Internet of Things (IoT) ecosystem increasing dramatically, well-designed indoor light energy harvesting solutions are needed. A first step in this direction would be to be able to accurately estimate the harvestable energy in a specific light environment. However, inside, this energy varies in spectral composition and intensity, depending on the emission source as well as the time of day. These challenging conditions mean that it has become necessary to obtain accurate information about these variations and determine their impact on energy recovery performance. In this context, this manuscript presented a method to apply an innovative energy harvesting estimation method to obtain practical and accurate insight for the design of energy harvesting systems in indoor environments. It used a very low-cost device to obtain spectral information and fed it to supervised machine learning classification methods to recognize light sources. From the recognized light source, a model developed for flexible GaAs solar cells was able to estimate the harvestable energy. To validate this method in real indoor conditions, the estimates were compared to the energy harvested by an energy harvesting prototype. The mean absolute error percentage between estimates and the experimental measurements was less than 5% after more than 2 weeks of observation. This demonstrated the potential of this low-cost estimation system to obtain reliable information to design energetically autonomous devices. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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24 pages, 4254 KiB  
Article
Towards the Flexible Distribution Networks Design Using the Reliability Performance Metric
by Ilia Shushpanov, Konstantin Suslov, Pavel Ilyushin and Denis N. Sidorov
Energies 2021, 14(19), 6193; https://doi.org/10.3390/en14196193 - 28 Sep 2021
Cited by 22 | Viewed by 1712
Abstract
At present, the entire world is moving towards digitalization, including in the electric power industry. Digitalization is in its heyday and a lot of articles and reports are devoted to this topic. At the same time, the least digitalized of the electrical networks [...] Read more.
At present, the entire world is moving towards digitalization, including in the electric power industry. Digitalization is in its heyday and a lot of articles and reports are devoted to this topic. At the same time, the least digitalized of the electrical networks are distribution networks that account for a very large share in electric power systems. The article proposes a methodology for creating a flexible distribution network based on the use of digital technology. Additionally, we elaborate a methodology with the identification and collection of the necessary information to create digital networks, develop ways to adapt the required equipment, and suggest methods of recognition of some short circuits. Furthermore, we address the issue of reliability of the information obtained from digital devices, develop a technique for arranging the devices to cover the entire network as required to improve the power system protection of electrical power distribution networks. The above measures make it possible to ensure the flexibility of the active distribution network, as well as to adjust the parameters of the actuation of power system protection depending on changes in external conditions and in the event of emergencies. We propose a technique for controlling the distribution network, based on the factoring-in of the type of damage during an emergency in real time, as well as a technique for arranging the measuring devices and the creation of an information and communication network. We provide recommendations for the design and operation of electric power distribution networks with digital network control technology. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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25 pages, 9233 KiB  
Article
The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers
by Jaemin Park, Haesung Jo and Insu Kim
Energies 2021, 14(18), 5606; https://doi.org/10.3390/en14185606 - 7 Sep 2021
Cited by 6 | Viewed by 1926
Abstract
Distributed generation (DG) using renewable energy sources is of widespread interest. For example, modern centralized conventional fossil fuel power generation commonly adds DG using renewable energy resources to the grid. Therefore, in these changes, it is necessary to optimize renewable energy systems to [...] Read more.
Distributed generation (DG) using renewable energy sources is of widespread interest. For example, modern centralized conventional fossil fuel power generation commonly adds DG using renewable energy resources to the grid. Therefore, in these changes, it is necessary to optimize renewable energy systems to increase energy efficiency and reduce emissions. In previous studies, meta-heuristic algorithms were used to optimize DG location and capacity, but different types of DG systems and integrated energy hub conditions were not considered. Determining the most effective DG type for an integrated energy hub is critical. Accordingly, this study presented a methodology for selecting the most cost-efficient DG for metropolitan residential customers of energy hubs. In this paper, we model energy hubs for residential customers and the most cost-efficient DG type using MATLAB and HOMER software, considering microturbine (MT), photovoltaic (PV), wind turbine, and fuel cell (FC) power sources. For this purpose, the energy hub was modeled as a combined cooling heat and power (CCHP) system and selected a specific metropolitan area as a testbed (Atlanta, USA). For practical simulation, the total active power of the Atlanta community was measured by multiplying the average load profile data of residential houses collected by open energy information (OpenEI). The first case study showed that optimal-blast MTs without absorption chillers (AbCs) were the most cost-efficient compared to other optimal-blast DG systems without AbCs. Additional second case studies for optimal and full-blast MTs with AbCs were performed to verify the results for energy consumption, costs, and emissions savings. As a result, full-blast MTs with AbCs comprise the most cost-efficient DG type in the CCHP system for metropolitan residential customers, reducing energy consumption, cost, and emissions. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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20 pages, 11112 KiB  
Article
Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization
by Haesung Jo, Jaemin Park and Insu Kim
Energies 2021, 14(14), 4135; https://doi.org/10.3390/en14144135 - 8 Jul 2021
Cited by 7 | Viewed by 1808
Abstract
The reliance on coal-fired power generation has gradually reduced with the growing interest in the environment and safety, and the environmental effects of power generation are now being considered. However, it can be difficult to provide stable power to end-users while minimizing environmental [...] Read more.
The reliance on coal-fired power generation has gradually reduced with the growing interest in the environment and safety, and the environmental effects of power generation are now being considered. However, it can be difficult to provide stable power to end-users while minimizing environmental pollution by replacing coal-fired systems with combined cooling, heat, and power (CCHP) systems that use natural gas, because CCHP systems have various power output vulnerabilities. Therefore, purchasing power from external electric grids is essential in areas where CCHP systems are built; hence, optimal CCHP controls should also consider energy purchased from external grids. This study proposes a two-stage algorithm to optimally control CCHP systems. In Stage One, the optimal energy mix using the Lagrange multiplier method for state-wide grids from which CCHP systems purchase deficient electricity was calculated. In Stage Two, the purchased volumes from these grids were used as inputs to the proposed optimization algorithm to optimize CCHP systems suitable for metropolitan areas. We used case studies to identify the accurate energy efficiency, costs, and minimal emissions. We chose the Atlanta area to analyze the CCHP system’s impact on energy efficiency, cost variation, and emission savings. Then, we calculated an energy mix suitable for the region for each simulation period. The case study results confirm that deploying an optimized CCHP system can reduce purchased volumes from the grid while reducing total emissions. We also analyzed the impact of the CCHP system on emissions and cost savings. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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19 pages, 3385 KiB  
Article
Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization
by Donghyeon Lee, Seungwan Son and Insu Kim
Energies 2021, 14(11), 3112; https://doi.org/10.3390/en14113112 - 26 May 2021
Cited by 15 | Viewed by 2472
Abstract
Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective [...] Read more.
Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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15 pages, 1420 KiB  
Article
Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine
by Ethelbert Ezemobi, Andrea Tonoli and Mario Silvagni
Energies 2021, 14(8), 2243; https://doi.org/10.3390/en14082243 - 16 Apr 2021
Cited by 16 | Viewed by 2439
Abstract
The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is [...] Read more.
The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 μs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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18 pages, 3528 KiB  
Article
The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method
by Beopsoo Kim, Nikita Rusetskii, Haesung Jo and Insu Kim
Energies 2021, 14(2), 418; https://doi.org/10.3390/en14020418 - 13 Jan 2021
Cited by 11 | Viewed by 3327
Abstract
The power requirements of grids have risen as artificial intelligence and electric vehicle technologies have been used. Thus, the installation of distributed generators (DGs) has become an essential factor to streamline power grids. The objective of this study is to optimize the capacity [...] Read more.
The power requirements of grids have risen as artificial intelligence and electric vehicle technologies have been used. Thus, the installation of distributed generators (DGs) has become an essential factor to streamline power grids. The objective of this study is to optimize the capacity and location of DGs. For this purpose, an objective function was defined, which takes into account the fault current and the levelized cost of energy, and a modified particle swarm optimization method was applied. Then, we analyzed a case of a single line-to-ground fault with a test feeder (i.e., the IEEE 30 bus system) with no DGs connected, as well as a case where the DGs are optimally connected. The effect of the optimally allocated DGs on the system was analyzed. We discuss an optimal layout method that takes the economic efficiency of the DG installation into account. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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13 pages, 764 KiB  
Article
Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads
by Soyeong Park, Seungwook Yoon, Byungtak Lee, Seokkap Ko and Euiseok Hwang
Energies 2021, 14(1), 165; https://doi.org/10.3390/en14010165 - 30 Dec 2020
Cited by 4 | Viewed by 1847
Abstract
Residential electricity load data can include numerous types of bad data, even clustered bad data, as they that are typically captured by simple measurement instruments. For example, in the case of a time-series of Not-a-Number (NaN) errors, the values before or next to [...] Read more.
Residential electricity load data can include numerous types of bad data, even clustered bad data, as they that are typically captured by simple measurement instruments. For example, in the case of a time-series of Not-a-Number (NaN) errors, the values before or next to a NaN may appear as the sum of actual values during the times of the NaN series. To utilize load data that includes such erroneous data for prediction or data mining analysis, customized detection and imputation should be conducted. This study proposes a new joint detection and imputation method for handling clustered bad data in residential electricity loads. Examples of these data are known invalid data points, such as consecutive NaN or zero values followed by or being ahead of an outlier. The proposed joint detection and imputation scheme first investigates the neighbors of the invalid data points, using probabilistic forecasting techniques. These techniques are implemented by the next valid neighbors to determine whether there is an anomaly or not. Then, adaptive imputations are applied on the basis of the detection, the candidate point should be imputed simultaneously or not. To assess the potential of the newly proposed scheme to characterize the clustered bad data, we analyzed the electricity loads of 354 households. Moreover, joint detection and imputations are conducted to test with the randomly injected synthesized clustered bad data (containing NaNs of various lengths) that is followed by the summation of the actual NaN values. The proposed scheme succeeded in detecting clustered bad data with an accuracy of 95.5% and a false alarm rate of 3.6% for all households in the dataset. Outlier detection-assisted imputation schemes are evaluated for NaNs with optional outliers. Results demonstrate that these schemes improve the overall accuracy significantly compared to schemes without outlier detection. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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13 pages, 2552 KiB  
Article
Two-Stage Active and Reactive Power Coordinated Optimal Dispatch for Active Distribution Network Considering Load Flexibility
by Yu Zhang, Xiaohui Song, Yong Li, Zilong Zeng, Chenchen Yong, Denis Sidorov and Xia Lv
Energies 2020, 13(22), 5922; https://doi.org/10.3390/en13225922 - 13 Nov 2020
Cited by 12 | Viewed by 1801
Abstract
A high proportion of renewable energy connected to the power grid has caused power quality problems. Voltage-sensitive loads are extremely susceptible to voltage fluctuations, causing power system safety issues and economic losses. Considering the uncertainty factor and the time-varying characteristic, a linearized random [...] Read more.
A high proportion of renewable energy connected to the power grid has caused power quality problems. Voltage-sensitive loads are extremely susceptible to voltage fluctuations, causing power system safety issues and economic losses. Considering the uncertainty factor and the time-varying characteristic, a linearized random ZIP model (constant impedance (Z), constant current (I), and constant power (P)) with time-varying characteristics was proposed. In order to improve the voltage quality of the voltage-sensitive loads in the day-here stage in an active distribution network (ADN), a linearized two-stage active and reactive power coordinated stochastic optimization model was established. The day-ahead active and reactive power coordination optimization was to smooth the large voltage fluctuation and develop a reserve plan to eliminate the unbalanced power caused by the prediction error in the day-here optimization. In the day-here real-time redispatch, the voltage was further improved by the continuous reactive power compensation device. Finally, the simulation results on the IEEE-33 bus system showed that the control strategy could better eliminate the unbalanced power caused by the prediction error and obviously improve the voltage of sensitive loads in the real-time stage on the premise of maintaining economic optimality. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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14 pages, 3309 KiB  
Article
Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network
by Hai Guo, Qun Ding, Yifan Song, Haoran Tang, Likun Wang and Jingying Zhao
Energies 2020, 13(18), 4782; https://doi.org/10.3390/en13184782 - 14 Sep 2020
Cited by 29 | Viewed by 5796
Abstract
The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM) directly affect the its temperature rise. The accurate evaluation and prediction of stator winding temperature is of great significance to the safety and reliability of PMSMs. In order to study [...] Read more.
The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM) directly affect the its temperature rise. The accurate evaluation and prediction of stator winding temperature is of great significance to the safety and reliability of PMSMs. In order to study the influencing factors of stator winding temperature and prevent motor insulation ageing, insulation burning, permanent magnet demagnetization and other faults caused by high stator winding temperature, we propose a computer model for PMSM temperature prediction. Ambient temperature, coolant temperature, direct-axis voltage, quadrature-axis voltage, motor speed, torque, direct-axis current, quadrature-axis current, permanent magnet surface temperature, stator yoke temperature, and stator tooth temperature are taken as the input, while the stator winding temperature is taken as the output. A deep neural network (DNN) model for PMSM temperature prediction was constructed. The experimental results showed the prediction error of the model (MAE) was 0.1515, the RMSE was 0.2368, the goodness of fit (R2) was 0.9439 and the goodness of fit between the predicted data and the measured data was high. Through comparative experiments, the prediction accuracy of the DNN model proposed in this paper was determined to be better than other models. This model can effectively predict the temperature change of stator winding, provide technical support to temperature early warning systems and ensure safe operation of PMSMs. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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35 pages, 2405 KiB  
Article
A Multi-Agent NILM Architecture for Event Detection and Load Classification
by André Eugenio Lazzaretti, Douglas Paulo Bertrand Renaux, Carlos Raimundo Erig Lima, Bruna Machado Mulinari, Hellen Cristina Ancelmo, Elder Oroski, Fabiana Pöttker, Robson Ribeiro Linhares, Lucas da Silva Nolasco, Lucas Tokarski Lima, Júlio Shigeaki Omori and Rodrigo Braun dos Santos
Energies 2020, 13(17), 4396; https://doi.org/10.3390/en13174396 - 26 Aug 2020
Cited by 11 | Viewed by 2851
Abstract
A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by [...] Read more.
A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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Review

Jump to: Editorial, Research

16 pages, 2691 KiB  
Review
Electric Power System Transformations: A Review of Main Prospects and Challenges
by Nikolai Voropai
Energies 2020, 13(21), 5639; https://doi.org/10.3390/en13215639 - 28 Oct 2020
Cited by 37 | Viewed by 3405
Abstract
The paper deals with the main prospects and challenges of radical transformations of electric power systems (EPSs) with changes in their structure and properties conditioned by wide use of innovative energy-related technologies and digitalization and intellectualization of system operation and control. Structural trends [...] Read more.
The paper deals with the main prospects and challenges of radical transformations of electric power systems (EPSs) with changes in their structure and properties conditioned by wide use of innovative energy-related technologies and digitalization and intellectualization of system operation and control. Structural trends of EPS development are the focus of the analysis. Consideration is given to changes in EPS properties driven by the use of new technologies, to the problems of system flexibility and to its enhancement. EPS “resiliency” and “survivability” notions are subjected to comparison. The main factors favoring the formation of future EPSs to cyber-physical systems are discussed. Objective trends of EPS control and protection system development are under consideration. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
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