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Special Issue "Intelligent Control in Energy Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: 30 April 2019

Special Issue Editor

Guest Editor
Prof. Dr. Anastasios Dounis

Department of Industrial Design and Production Engineering, University of West Attica, University Campus 2, P. Ralli & Thivon 250, 12244, Egaleo-Athens, Greece
Website | E-Mail
Interests: computational intelligence; intelligent control; intelligent buildings; renewable energy polygeneration; smart microgrids

Special Issue Information

Dear Colleagues,

Energy systems (ES) are a complex and constantly evolving research area. Because energy systems are multi-layered and distributed, there is a growing interest in integrating heterogeneous entities (energy sources, energy storage, micro-grids, grid networks, buildings, electrical vehicles, etc.) into distribution systems. The challenge in handling the vast volume of information is the requirement the use of modern efficient management control strategies such as intelligent control technologies.

Intelligent control (IC) describes a class of control techniques that use various artificial intelligence techniques such as neural network control, Bayesian control, fuzzy logic control, neuro-fuzzy control, evolutionary computation, machine learning and intelligent agents. IC systems are very useful when no mathematical model is available a priori. IC is inspired by the intelligence and genetics of living beings.

IC, communications infrastructure and wireless networking play an important role in a smart grid network in achieving reliable, efficient, secure, distribution, cost-effective generation and consumption. IC on energy storage devices provide reliability and economic impacts on the energy systems.

Buildings consume a large portion of the world’s energy and they are a source of greenhouse gas emissions. The concept of sustainable and zero energy buildings is emerging as an important area for the smart micro-grid initiative. In addition, effective energy management is becoming more feasible using the innovative smart micro-grid technologies and IC. These changes have resulted an environment of high complexity, uncertainty and imprecision. The IC can play a remarkable and vital role in handling a significant part of this high uncertainty and nonlinearity by providing new smart solutions for a more efficient and reliable operation of ESs.  

This Special Issue is focused on to bring together innovative developments and synergies in the fields of intelligent control and energy systems.

Potential topics include, but are not limited to:

  • Energy management and IC in energy micro-grids;
  • ESs modeling and IC;
  • IC and optimization for zero energy buildings;
  • Evolutionary control in ESs;
  • IC in hybrid ESs of isolated areas;
  • Fuzzy logic control in ESs;
  • Intelligent multiagent control systems in ESs;
  • Artificial neural networks for control in ESs;
  • IC of holonic ESs;
  • IC in energy storage systems;
  • IC in sustainable smart ESs;
  • Fault diagnosis and IC in ESs;
  • Chaos control in ESs;
  • Bayesian control in renewable energy systems;
  • Neuro-fuzzy control in ESs;
  • Machine learning in ESs;
  • IC in distributed electrical energy generation system;
  • IC in smart grid network;
  • IC and ESs stability;
  • IC and demand side forecasting in ESs;
  • IC and uncertainty analysis of ESs.

Prof. Dr. Anastasios Dounis
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 papers will be 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 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 1600 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

  • Intelligent control
  • Evolutionary control
  • Fuzzy logic control
  • Energy systems
  • Machine learning
  • Artificial neural network
  • Intelligent energy management systems
  • Intelligent buildings
  • Micro-grids
  • Energy storage systems
  • Smart grid network

Published Papers (16 papers)

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Research

Open AccessArticle A High-Efficiency Bidirectional Active Balance for Electric Vehicle Battery Packs Based on Model Predictive Control
Energies 2018, 11(11), 3220; https://doi.org/10.3390/en11113220 (registering DOI)
Received: 28 October 2018 / Revised: 14 November 2018 / Accepted: 15 November 2018 / Published: 20 November 2018
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Abstract
This study designs an active equilibrium control strategy based on model predictive control (MPC) for series battery packs. To shorten equalisation time and reduce unnecessary energy consumption, bidirectional active equalisation is modelled and analysed, and the model predictive control algorithm is then applied
[...] Read more.
This study designs an active equilibrium control strategy based on model predictive control (MPC) for series battery packs. To shorten equalisation time and reduce unnecessary energy consumption, bidirectional active equalisation is modelled and analysed, and the model predictive control algorithm is then applied to the established state space equation. The optimisation problem that minimises the equilibrium time is transformed to a linear programming form in each cycle. By solving the linear programming problem online, a group of control optimal solutions is found and the series equalisation problem is decoupled. The equalisation time is shortened by dynamically adjusting the equalisation current. Simulation results show that the MPC algorithm can avoid unnecessary energy transfer and shorten equalisation time. The bench experimental result shows that the equilibrium time is reduced by 31%, verifying the rationality of the MPC strategy. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Total Suspended Particle Emissions Modelling in an Industrial Boiler
Energies 2018, 11(11), 3097; https://doi.org/10.3390/en11113097
Received: 3 October 2018 / Revised: 1 November 2018 / Accepted: 4 November 2018 / Published: 9 November 2018
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Abstract
Particulate matter emission into the atmosphere is a massive-scale problem. Fossil fuel combustion is an important source of this kind of pollution. The knowledge of total suspended particle (TSP) emissions is the first step for TSP control. The formation of TSP emissions is
[...] Read more.
Particulate matter emission into the atmosphere is a massive-scale problem. Fossil fuel combustion is an important source of this kind of pollution. The knowledge of total suspended particle (TSP) emissions is the first step for TSP control. The formation of TSP emissions is poorly understood; therefore new approaches for TSP emissions source modelling are required. TSP modelling is a multi-variable non-linear problem that would only require basic information on boiler operation. This work reports the development of a non-linear model for TSP emissions estimation from an industrial boiler based on a one-layer neural network. Expansion polynomial basic functions combined with an orthogonal least-square and model structure selection approach were used for modelling. The model required five independent boiler variables for TSP emissions estimation. Data from the data acquisition system of a 350 MW industrial boiler were used for model development and validation. The results show that polynomial expansion basic functions are an excellent approach to solve modelling problems related to complex non-linear systems in the industry. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy
Energies 2018, 11(11), 3089; https://doi.org/10.3390/en11113089
Received: 30 September 2018 / Revised: 31 October 2018 / Accepted: 7 November 2018 / Published: 8 November 2018
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Abstract
Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties
[...] Read more.
Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessFeature PaperArticle Integrated Energy Micro-Grid Planning Using Electricity, Heating and Cooling Demands
Energies 2018, 11(10), 2810; https://doi.org/10.3390/en11102810
Received: 12 September 2018 / Revised: 1 October 2018 / Accepted: 17 October 2018 / Published: 18 October 2018
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Abstract
Many research works have demonstrated that taking the combined cooling, heating and power system (CCHP) as the core equipment, an integrated energy system (IES), which provides multiple energy flows by a combination of different energy production equipment can bring obvious benefit to energy
[...] Read more.
Many research works have demonstrated that taking the combined cooling, heating and power system (CCHP) as the core equipment, an integrated energy system (IES), which provides multiple energy flows by a combination of different energy production equipment can bring obvious benefit to energy efficiency, CO2 emission reduction and operational economy in urban areas. Compared with isolated IES, an integrated energy micro-grid (IEMG) which is formed by connecting multiple regions’ IES together, through a distribution and thermal network, can further improve the reliability, flexibility, cleanliness and the economy of a regional energy supply. Based on the existing IES model, this paper describes the basic structure of IEMG and built an IEMG planning model. The planning was based on the mixed integer linear programming. Economically, construction planning configuration are calculated by using known electricity, heating and cooling loads information and the given multiple equipment selection schemes. Finally, the model is validated by a case study, which includes heating, cooling, transitional and extreme load scenarios, proved the feasibility of planning model. The results show that the application of IEMG can effectively improve the economy of a regional energy supply. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Improved Adaptive Backstepping Sliding Mode Control of Static Var Compensator
Energies 2018, 11(10), 2750; https://doi.org/10.3390/en11102750
Received: 27 August 2018 / Revised: 5 October 2018 / Accepted: 12 October 2018 / Published: 14 October 2018
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Abstract
The stability of a single machine infinite bus system with a static var compensator is proposed by an improved adaptive backstepping algorithm, which includes error compensation, sliding mode control and a κ-class function. First, storage functions of the control system are constructed
[...] Read more.
The stability of a single machine infinite bus system with a static var compensator is proposed by an improved adaptive backstepping algorithm, which includes error compensation, sliding mode control and a κ -class function. First, storage functions of the control system are constructed based on modified adaptive backstepping sliding mode control and Lyapunov methods. Then, adaptive backstepping method is used to obtain nonlinear controller and parameter adaptation rate for static var compensator system. The results of simulation show that the improved adaptive backstepping sliding mode variable control based on error compensation is effective. Finally, we get a conclusion that the improved method differs from the traditional adaptive backstepping method. The improved adaptive backstepping sliding mode variable control based on error compensation method preserves effective non-linearities and real-time estimation of parameters, and this method provides effective stability and convergence. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Medium-Voltage AC Static Switch Solution to Feed Neutral Section in a High-Speed Railway System
Energies 2018, 11(10), 2740; https://doi.org/10.3390/en11102740
Received: 12 September 2018 / Revised: 4 October 2018 / Accepted: 9 October 2018 / Published: 12 October 2018
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Abstract
A high-speed train (HST) is a single-phase load supplied by a three-phase AC grid. The HST produces unbalanced three-line currents affecting the power quality of the grid. To balance the asymmetries on average, railway feeding sections are supplied that rotate the three phases
[...] Read more.
A high-speed train (HST) is a single-phase load supplied by a three-phase AC grid. The HST produces unbalanced three-line currents affecting the power quality of the grid. To balance the asymmetries on average, railway feeding sections are supplied that rotate the three phases of the grid. An electric isolation segment, called the neutral section (NS), between different sections is necessary. The HST must pass through this 1.6 km NS without power supply. In this paper, a medium-voltage AC static switch solution to feed the high-speed train in the NS is proposed. Thyristor technology is selected to design the 25 KVAC static switch. A medium-voltage power electronics procedure design is proposed to ensure proper operation in the final application. An NS operation is analyzed to identify impacts within the electric system and solution requirements are developed. Then, a low-scale prototype is used to experimentally validate the solution based on thyristor technology and the medium-voltage AC static switch is designed. Limitations on power and voltage at the Mondragon University Medium-Voltage Laboratory do not allow testing of the AC static switch at nominal conditions. A partial test procedure to test sections of the AC static switch is proposed and applied to validate the solution. Finally, experimental results for the Cordoba–Malaga (Spain) high-speed railway in real conditions with an HST crossing the NS at 300 km/h are shown. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessFeature PaperArticle Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management
Energies 2018, 11(10), 2514; https://doi.org/10.3390/en11102514
Received: 25 August 2018 / Revised: 15 September 2018 / Accepted: 17 September 2018 / Published: 21 September 2018
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Abstract
Along with the emerging development of demand side management applications, it is still a challenge to exploit flexibility realistically to resolve or prevent specific geographical network issues due to limited situational awareness of the (unbalanced low-voltage) network as well as complex time dependent
[...] Read more.
Along with the emerging development of demand side management applications, it is still a challenge to exploit flexibility realistically to resolve or prevent specific geographical network issues due to limited situational awareness of the (unbalanced low-voltage) network as well as complex time dependent constraints. To overcome these problems, this paper presents a time-horizon three-phase grid-supportive demand side management methodology for low voltage networks by using a universal interface that is established between the demand side management application and the monitoring and network analysis tools of the network operator. Using time-horizon predictions of the system states that the probability of operational limit violations is identified. Since this analysis is computationally intensive, a data driven approach is adopted by using machine learning. Time-horizon flexibility is procured, which effectively prevents operation limit violation from occurring independent of the objective that the demand side management application has. A practical example featuring fair power sharing demonstrates the effectiveness of the presented method for resolving over-voltages and under-voltages. This is followed by conclusions and recommendations for future work. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle New Monitoring System for Photovoltaic Power Plants’ Management
Energies 2018, 11(10), 2495; https://doi.org/10.3390/en11102495
Received: 4 July 2018 / Revised: 6 September 2018 / Accepted: 17 September 2018 / Published: 20 September 2018
Cited by 1 | PDF Full-text (3000 KB) | HTML Full-text | XML Full-text
Abstract
An innovative solar monitoring system has been developed. The system aimed at measuring the main parameters and characteristics of solar plants; collecting, diagnosing and processing data. The system communicates with the inverters, electrometers, metrological equipment and additional components of the photovoltaic arrays. The
[...] Read more.
An innovative solar monitoring system has been developed. The system aimed at measuring the main parameters and characteristics of solar plants; collecting, diagnosing and processing data. The system communicates with the inverters, electrometers, metrological equipment and additional components of the photovoltaic arrays. The developed and constructed long working system is built on special data collecting technologies. At the generating plants, a special data logger BBbox is installed. The new monitoring system has been used to follow 65 solar plants in the Czech Republic and elsewhere for 175 MWp. As an example, we have selected 13 PV plants in this paper that are at least seven years old. The monitoring system contributes to quality management of plants, and it also provides data for scientific purposes. Production of electricity in the built PV plants reflects the expected values according to internationally used software PVGIS (version 5) during the previous seven years of operation. A comparison of important system parameters clearly shows the new solutions and benefits of the new Solarmon-2.0 monitoring system. Secured communications will increase data protection. A higher frequency of data saving allows higher accuracy of the mathematical models. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings
Energies 2018, 11(9), 2427; https://doi.org/10.3390/en11092427
Received: 24 August 2018 / Revised: 6 September 2018 / Accepted: 6 September 2018 / Published: 13 September 2018
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Abstract
This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure
[...] Read more.
This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers’ different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method
Energies 2018, 11(9), 2404; https://doi.org/10.3390/en11092404
Received: 25 August 2018 / Revised: 6 September 2018 / Accepted: 7 September 2018 / Published: 11 September 2018
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Abstract
The winding is the core component of a transformer, and the technology used to diagnose its current state directly affects the operation and maintenance of the transformer. The mechanical vibration characteristics of a dry-type transformer winding are studied in this paper. A short-circuit
[...] Read more.
The winding is the core component of a transformer, and the technology used to diagnose its current state directly affects the operation and maintenance of the transformer. The mechanical vibration characteristics of a dry-type transformer winding are studied in this paper. A short-circuit test was performed on an SCB10-1000/10 dry-type transformer, and the vibration signal at the surface was measured. Based on actual experimental conditions, a vibration-simulation model of the transformer was established using COMSOL Multiphysics software. A multiphysics coupling simulation of the circuit, magnetic field, and solid mechanics of the transformer was performed on this model. The simulation results were compared with measured data to verify the validity of the simulation model. The simulation model for a transformer operating under normal conditions was then used to develop simulation models of transformer-winding looseness, winding deformation, and winding-insulation failure, and the winding fault vibration characteristics were analyzed. The results provide a basis for detecting and analyzing the mechanical state of transformer windings. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor
Energies 2018, 11(9), 2212; https://doi.org/10.3390/en11092212
Received: 15 July 2018 / Revised: 19 August 2018 / Accepted: 21 August 2018 / Published: 23 August 2018
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Abstract
A dynamic optimization energy management strategy called Hybrid Electric Vehicle Based on Compound Structured Permanent-Magnet Motor (CSPM-HEV) is investigated in this paper. CSPM-HEV has obvious advantages in power density, heat dissipation efficiency, torque performance and energy transmission efficiency. This paper describes the topology
[...] Read more.
A dynamic optimization energy management strategy called Hybrid Electric Vehicle Based on Compound Structured Permanent-Magnet Motor (CSPM-HEV) is investigated in this paper. CSPM-HEV has obvious advantages in power density, heat dissipation efficiency, torque performance and energy transmission efficiency. This paper describes the topology and working principle of the CSPM-HEV, and analyzes its operating mode and corresponding energy flow laws. On this basis, the relationship about the power loss of the vehicle, the CSPM transmission ratio iCSPM and the CSPM-HEV power distribution coefficient f1 were derived. According to the optimal combination of (iCSPM, f1), the engine power and speed which minimize the power loss of the vehicle, were calculated, thus realizing the instantaneous optimal control of the vehicle. In addition, in order to improve the instantaneously optimized control processing speed, a neural network controller was established. The drive axle demand power, speed and battery State of Charge (SOC), were taken as input variables. Then, the engine power and speed were taken as output variables. The simulation results show that the average speed of the instantaneous optimization strategy after BP neural network optimization is increased by 98.1%, the control effect is significant, and it has high application value. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Low Cost Position Controller for Exhaust Gas Recirculation Valve System
Energies 2018, 11(8), 2171; https://doi.org/10.3390/en11082171
Received: 30 July 2018 / Revised: 17 August 2018 / Accepted: 17 August 2018 / Published: 20 August 2018
PDF Full-text (4796 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a position control method for a low-cost exhaust gas recirculation (EGR) valve system for automotive applications. Generally, position control systems used in automotive applications have many restrictions, such as cost and space. The mechanical structure of the actuator causes high
[...] Read more.
This paper proposes a position control method for a low-cost exhaust gas recirculation (EGR) valve system for automotive applications. Generally, position control systems used in automotive applications have many restrictions, such as cost and space. The mechanical structure of the actuator causes high friction and large differences between static friction and coulomb friction. When this large friction difference occurs, the position control vibrates when the controller uses a conventional linear controller such as the P or PI controller. In this paper, we introduce an inexpensive position control method that can be applied under the high-difference-friction mechanical systems. The proposed method is verified through the use of experiments by comparing it with the results obtained when using a conventional control system. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid
Energies 2018, 11(7), 1852; https://doi.org/10.3390/en11071852
Received: 11 June 2018 / Revised: 6 July 2018 / Accepted: 9 July 2018 / Published: 16 July 2018
Cited by 1 | PDF Full-text (576 KB) | HTML Full-text | XML Full-text
Abstract
Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we
[...] Read more.
Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Decision Tree-Based Preventive Control Applications to Enhance Fault Ride Through Capability of Doubly-Fed Induction Generator in Power Systems
Energies 2018, 11(7), 1760; https://doi.org/10.3390/en11071760
Received: 2 June 2018 / Revised: 29 June 2018 / Accepted: 2 July 2018 / Published: 4 July 2018
Cited by 1 | PDF Full-text (6090 KB) | HTML Full-text | XML Full-text
Abstract
The development of a preventive control methodology to increase the capacity of voltage sag recovery (Fault Ride Through Capability (FRTC)) of a doubly-fed induction generator (DFIG) connected in an electrical network is presented. This methodology, which is based on the decision trees (DT)
[...] Read more.
The development of a preventive control methodology to increase the capacity of voltage sag recovery (Fault Ride Through Capability (FRTC)) of a doubly-fed induction generator (DFIG) connected in an electrical network is presented. This methodology, which is based on the decision trees (DT) technique, assists with monitoring and support for security and preventive control, ensuring that wind systems remain connected to the power system even after the occurrence of disturbances in the electric system. Based on offline studies, DT discovers inherent attributes of the FRTC scenario related to electrical system behavior and provides a quick prediction model for real-time applications. From the obtained results, it is possible to check that the DFIG is contributing to a system’s operation security from the availability of power dispatch and participation in the voltage control. It is also noted that the use of DT, in addition to classifying the system’s operational state with good accuracy, also significantly facilitates the operator´s task, by directing him to monitor the most critical variables of the monitored operation state for a given system’s topological configuration. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems
Energies 2018, 11(7), 1686; https://doi.org/10.3390/en11071686
Received: 28 May 2018 / Revised: 24 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
Cited by 1 | PDF Full-text (3284 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, the secondary load frequency controller of the power systems with renewable energies is investigated by taking into account internal parameter perturbations and stochastic disturbances induced by the integration of renewable energies, and the power unbalance caused between the supply side
[...] Read more.
In this paper, the secondary load frequency controller of the power systems with renewable energies is investigated by taking into account internal parameter perturbations and stochastic disturbances induced by the integration of renewable energies, and the power unbalance caused between the supply side and demand side. For this, the μ-synthesis robust approach based on structure singular value is researched to design the load frequency controller. In the proposed control scheme, in order to improve the power system stability, an ultracapacitor is introduced to the system to rapidly respond to any power changes. Firstly, the load frequency control model with uncertainties is established, and then, the robust controller is designed based on μ-synthesis theory. Furthermore, a novel method using integrated system performance indexes is proposed to select the weighting function during controller design process, and solved by a differential evolution algorithm. Finally, the controller robust stability and robust performance are verified via the calculation results, and the system dynamic performance is tested via numerical simulation. The results show the proposed method greatly improved the load frequency stability of a micro-grid power system. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Open AccessArticle Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell
Energies 2018, 11(7), 1669; https://doi.org/10.3390/en11071669
Received: 8 June 2018 / Revised: 18 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
Cited by 1 | PDF Full-text (2462 KB) | HTML Full-text | XML Full-text
Abstract
Early detection of internal short circuit which is main cause of thermal runaway in a lithium-ion battery is necessary to ensure battery safety for users. As a promising fault index, internal short circuit resistance can directly represent degree of the fault because it
[...] Read more.
Early detection of internal short circuit which is main cause of thermal runaway in a lithium-ion battery is necessary to ensure battery safety for users. As a promising fault index, internal short circuit resistance can directly represent degree of the fault because it describes self-discharge phenomenon caused by the internal short circuit clearly. However, when voltages of individual cells in a lithium-ion battery pack are not provided, the effect of internal short circuit in the battery pack is not readily observed in whole terminal voltage of the pack, leading to difficulty in estimating accurate internal short circuit resistance. In this paper, estimating the resistance with the whole terminal voltages and the load currents of the pack, a detection method for the soft internal short circuit in the pack is proposed. Open circuit voltage of a faulted cell in the pack is extracted to reflect the self-discharge phenomenon obviously; this process yields accurate estimates of the resistance. The proposed method is verified with various soft short conditions in both simulations and experiments. The error of estimated resistance does not exceed 31.2% in the experiment, thereby enabling the battery management system to detect the internal short circuit early. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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