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Intelligent Control in Energy Systems
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Intelligent Control in Energy Systems

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

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 119119

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Prof. Dr. Anastasios Dounis

E-Mail Website
Guest Editor
Department of Automation Engineering, Piraeus University of Applied Sciences (Technological Education Institution of Piraeus), 12244 Egaleo, Greece
Interests: computational intelligence; intelligent control; intelligent buildings; renewable energy polygeneration; smart microgrids
Special Issues, Collections and Topics in MDPI journals

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 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

  • 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 (28 papers)

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Editorial

Jump to: Research

9 pages, 202 KiB  
Editorial
Special Issue “Intelligent Control in Energy Systems”
by Anastasios Dounis
Energies 2019, 12(15), 3017; https://doi.org/10.3390/en12153017 - 05 Aug 2019
Cited by 3 | Viewed by 2996
Abstract
The editor of this special issue on “Intelligent Control in Energy Systems” have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which [...] Read more.
The editor of this special issue on “Intelligent Control in Energy Systems” have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which 27 were published submissions and 33 were rejections. This book contains 27 technical articles and one editorial. All have been written by authors from 15 countries (China, Netherlands, Spain, Tunisia, United States of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and Czech Republic), which elaborated several aspects of intelligent control in energy systems. It covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural network for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision tree for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust μ-synthesis for microgrid, and neuro-fuzzy systems in energy storage. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)

Research

Jump to: Editorial

23 pages, 12267 KiB  
Article
Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm
by Mahmoud S. AbouOmar, Hua-Jun Zhang and Yi-Xin Su
Energies 2019, 12(8), 1435; https://doi.org/10.3390/en12081435 - 14 Apr 2019
Cited by 44 | Viewed by 6450
Abstract
The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding [...] Read more.
The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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15 pages, 3038 KiB  
Article
Corrective Control by Line Switching for Relieving Voltage Violations Based on A Three-Stage Methodology
by Zhengwei Shen, Yong Tang, Jun Yi, Changsheng Chen, Bing Zhao and Guangru Zhang
Energies 2019, 12(7), 1206; https://doi.org/10.3390/en12071206 - 28 Mar 2019
Cited by 2 | Viewed by 2372
Abstract
An online line switching methodology to relieve voltage violations is proposed. This novel online methodology is based on a three-stage strategy, including screening, ranking, and detailed analysis and assessment stages for high speed (online application) and accuracy. The proposed online methodology performs the [...] Read more.
An online line switching methodology to relieve voltage violations is proposed. This novel online methodology is based on a three-stage strategy, including screening, ranking, and detailed analysis and assessment stages for high speed (online application) and accuracy. The proposed online methodology performs the tasks of rapidly identifying effective candidate lines, ranking the effective candidates, performing detailed analysis of the top ranked candidates, and supplying a set of solutions for the power system. The post-switching power systems, after executing the proposed line switching action, meet the operational and engineering constraints. The results provided by the exact Alternating Current (AC) power flow are used as a benchmark to compare the speed and accuracy of the proposed three-stage methodology. One feature of the methodology is that it can provide a set of high-quality switching solutions from which operators may choose a preferred solution. The effectiveness of the proposed online line switching methodology in providing single-line switching and multiple-line switching solutions to relieve voltage violations is evaluated on the IEEE 39-bus and 2746-bus power system. The CPU time of the proposed methodology compared with that under AC power flow constitutes a speed-up of 9905.32% on a 2746-bus power system, showing good potential for online application in a large-scale power system. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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17 pages, 4885 KiB  
Article
A Novel Control Architecture for Hybrid Power Plants to Provide Coordinated Frequency Reserves
by Daniel Vázquez Pombo, Florin Iov and Daniel-Ioan Stroe
Energies 2019, 12(5), 919; https://doi.org/10.3390/en12050919 - 09 Mar 2019
Cited by 16 | Viewed by 4228
Abstract
The inertia reduction suffered by worldwide power grids, along with the upcoming necessity of providing frequency regulation with renewable sources, motivates the present work. This paper focuses on developing a control architecture aimed to perform frequency regulation with renewable hybrid power plants comprised [...] Read more.
The inertia reduction suffered by worldwide power grids, along with the upcoming necessity of providing frequency regulation with renewable sources, motivates the present work. This paper focuses on developing a control architecture aimed to perform frequency regulation with renewable hybrid power plants comprised of a wind farm, solar photovoltaic, and a battery storage system. The proposed control architecture considers the latest regulations and recommendations published by ENTSO-E when implementing the first two stages of frequency control, namely the fast frequency response and the frequency containment reserve. Additionally, special attention is paid to the coordination among sub-plants inside the hybrid plant and also between different plants in the grid. The system’s performance is tested after the sudden disconnection of a large generation unit (N-1 contingency rules). Thus, the outcome of this study is a control strategy that enables a hybrid power plant to provide frequency support in a system with reduced inertia, a large share of renewable energy, and power electronics-interfaced generation. Finally, it is worth mentioning that the model has been developed in discrete time, using relevant sampling times according to industrial practice. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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16 pages, 2688 KiB  
Article
A Recursive Least Squares Method with Double-Parameter for Online Estimation of Electric Meter Errors
by Xiangyu Kong, Yuying Ma, Xin Zhao, Ye Li and Yongxing Teng
Energies 2019, 12(5), 805; https://doi.org/10.3390/en12050805 - 28 Feb 2019
Cited by 36 | Viewed by 3527
Abstract
In view of the existing verification methods of electric meters, there are problems such as high maintenance cost, poor accuracy, and difficulty in full coverage, etc. Starting from the perspective of analyzing the large-scale measured data collected by user-side electric meters, an online [...] Read more.
In view of the existing verification methods of electric meters, there are problems such as high maintenance cost, poor accuracy, and difficulty in full coverage, etc. Starting from the perspective of analyzing the large-scale measured data collected by user-side electric meters, an online estimation method for the operating error of electric meters was proposed, which uses the recursive least squares (RLS) and introduces a double-parameter method with dynamic forgetting factors λa and λb to track the meter parameters changes in real time. Firstly, the obtained measured data are preprocessed, and the abnormal data such as null data and light load data are eliminated by an appropriate clustering method, so as to screen out the measured data of the similar operational states of each user. Then equations relating the head electric meter in the substation and each users’ electric meter and line loss based on the law of conservation of electric energy are established. Afterwards, the recursive least squares algorithm with double-parameter is used to estimate the parameters of line loss and the electric meter error. Finally, the effects of double dynamic forgetting factors, double constant forgetting factors and single forgetting factor on the accuracy of estimated error of electric meter are discussed. Through the program-controlled load simulation system, the proposed method is verified with higher accuracy and practicality. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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23 pages, 11084 KiB  
Article
Comparison of Intelligence Control Systems for Voltage Controlling on Small Scale Compressed Air Energy Storage
by Widjonarko, Rudy Soenoko, Slamet Wahyudi and Eko Siswanto
Energies 2019, 12(5), 803; https://doi.org/10.3390/en12050803 - 28 Feb 2019
Cited by 7 | Viewed by 3356
Abstract
This study presents the strategy of controlling the air discharge in the prototype of small scale compressed air energy storage (SS-CAES) to produce a constant voltage according to the user set point. The purpose of this study is to simplify the control of [...] Read more.
This study presents the strategy of controlling the air discharge in the prototype of small scale compressed air energy storage (SS-CAES) to produce a constant voltage according to the user set point. The purpose of this study is to simplify the control of the SS-CAES, so that it can be integrated with a grid based on a constant voltage reference. The control strategy in this study is carried out by controlling the opening of the air valve combined with a servo motor using three intelligence control systems (fuzzy logic, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS)). The testing scenario of this system will be carried out using two scenes, including changing the voltage set point and by switching the load. The results that were obtained indicate that ANN has the best results, with an average settling time of 2.05S in the first test scenario and 6.65S in the second test scenario. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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16 pages, 4239 KiB  
Article
Multiple-Point Voltage Control to Minimize Interaction Effects in Power Systems
by Yun-Hyuk Choi and Yoon-Sung Cho
Energies 2019, 12(2), 274; https://doi.org/10.3390/en12020274 - 16 Jan 2019
Cited by 3 | Viewed by 2672
Abstract
This paper proposes an advanced continuous voltage control method that implements multiple-point control to ensure peak power system performance. Most control schemes utilize generators to regulate the pilot point voltage of a control area. However, exact control of a single pilot point is [...] Read more.
This paper proposes an advanced continuous voltage control method that implements multiple-point control to ensure peak power system performance. Most control schemes utilize generators to regulate the pilot point voltage of a control area. However, exact control of a single pilot point is difficult because of the influence of adjacent areas in a meshed power system. To address this challenge, the proposed method accesses multiple pilot points to mitigate the effects of the neighboring area. In simulations of the Korean power system, the proposed control scheme offered a considerable improvement in performance when compared with the conventional, currently implemented voltage control system. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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18 pages, 2985 KiB  
Article
Adaptive Damping Control Strategy of Wind Integrated Power System
by Jun Deng, Jun Suo, Jing Yang, Shutao Peng, Fangde Chi and Tong Wang
Energies 2019, 12(1), 135; https://doi.org/10.3390/en12010135 - 01 Jan 2019
Cited by 3 | Viewed by 3201
Abstract
Random variation of grid-connected wind power can cause stochastic variation of the power system operating point. This paper proposes a new scheme to design an adaptive damping controller by tracking the variation of system operating points and updating the controller’s functions to achieve [...] Read more.
Random variation of grid-connected wind power can cause stochastic variation of the power system operating point. This paper proposes a new scheme to design an adaptive damping controller by tracking the variation of system operating points and updating the controller’s functions to achieve a robust damping control effect. Firstly, the operating space is classified into different modes according to the classification of wind power outputs. Multiple power system stabilizers (PSSs) are then designed. Secondly, the method of optimal classification and regression decision tree (CART) is utilized for classifying subspaces of system operating point and it is proposed that the on-line measurements from wide area measurement system (WAMS) are used for tracking the dynamic behaviors of stochastic drifting point and thus guide the updating of appropriate PSSs be switched on adaptively. A 16-generator-68-bus power system integrated with wind power is presented as a test system to demonstrate that the adaptive control scheme by use of the CART can damp multi-mode oscillations effectively when the wind power output changes. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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17 pages, 7867 KiB  
Article
Design and Analysis of a Repetitive Current Controller for a Single-Phase Bridgeless SEPIC PFC Converter
by Jinwoo Kim, Sanghun Han, Wontae Cho, Younghoon Cho and Hyunsoo Koh
Energies 2019, 12(1), 131; https://doi.org/10.3390/en12010131 - 31 Dec 2018
Cited by 6 | Viewed by 3690
Abstract
This paper studies a repetitive controller design scheme for a bridgeless single-ended primary inductor converter (SEPIC) power factor correction (PFC) converter to mitigate input current distortions. A small signal modeling of the converter is performed by a fifth-order model. Since the fifth-order model [...] Read more.
This paper studies a repetitive controller design scheme for a bridgeless single-ended primary inductor converter (SEPIC) power factor correction (PFC) converter to mitigate input current distortions. A small signal modeling of the converter is performed by a fifth-order model. Since the fifth-order model is complex to be applied in designing a current controller, the model is approximated to a third-order model. Using the third-order model, the repetitive controller is designed to reduce the input current distortion. Then, the stability of the repetitive controller is verified with an error transfer function. The proposed controller performance is validated by simulation, and the experiment results show that the input current total harmonic distortion (THD) is improved by applying the proposed controller for an 800 W bridgeless SEPIC PFC converter prototype. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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18 pages, 7204 KiB  
Article
A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications
by El Manaa Barhoumi, Ikram Ben Belgacem, Abla Khiareddine, Manaf Zghaibeh and Iskander Tlili
Energies 2018, 11(12), 3423; https://doi.org/10.3390/en11123423 - 06 Dec 2018
Cited by 28 | Viewed by 4497
Abstract
This paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference [...] Read more.
This paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference value under the strong fluctuations of the fuel flow rate, fuel supply pressure, and temperature, a neural network controller is developed and implemented using Matlab-Simulink (R2012b, MathWorks limited, London, UK). The advantage of this controller resides in its simplicity, where limited number of tests are carried out using Matlab-Simulink to construct it. To investigate the robustness of the proposed converter and the neural network controller, strong variations of the fuel flow rate, fuel supply pressure, temperature and air supply pressure are applied to both the fuel cell and the neural network controller of the converter. The simulation results show the effectiveness and the robustness of the both the proposed controller and converter to control the load voltage and minimize the current and voltage ripples. As a result of that, fuel cell current oscillations are considerably reduced on the one hand, while on the other hand, the load voltage is stabilized during transient variations of the fuel cell inputs. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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20 pages, 6511 KiB  
Article
Study of the Intelligent Behavior of a Maximum Photovoltaic Energy Tracking Fuzzy Controller
by Gul Filiz Tchoketch Kebir, Cherif Larbes, Adrian Ilinca, Thameur Obeidi and Selma Tchoketch Kebir
Energies 2018, 11(12), 3263; https://doi.org/10.3390/en11123263 - 23 Nov 2018
Cited by 14 | Viewed by 3368
Abstract
The Maximum Power Point Tracking (MPPT) strategy is commonly used to maximize the produced power from photovoltaic generators. In this paper, we proposed a control method with a fuzzy logic approach that offers significantly high performance to get a maximum power output tracking, [...] Read more.
The Maximum Power Point Tracking (MPPT) strategy is commonly used to maximize the produced power from photovoltaic generators. In this paper, we proposed a control method with a fuzzy logic approach that offers significantly high performance to get a maximum power output tracking, which entails a maximum speed of power achievement, a good stability, and a high robustness. We use a fuzzy controller, which is based on a special choice of a combination of inputs and outputs. The choice of inputs and outputs, as well as fuzzy rules, was based on the principles of mathematical analysis of the derived functions (slope) for the purpose of finding the optimum. Also, we have proved that we can achieve the best results and answers from the system photovoltaic (PV) with the simplest fuzzy model possible by using only 3 sets of linguistic variables to decompose the membership functions of the inputs and outputs of the fuzzy controller. We compare this powerful controller with conventional perturb and observe (P&O) controllers. Then, we make use of a Matlab-Simulink® model to simulate the behavior of the PV generator and power converter, voltage, and current, using both the P&O and our fuzzy logic-based controller. Relative performances are analyzed and compared under different scenarios for fixed or varied climatic conditions. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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30 pages, 2168 KiB  
Article
A Load-Balance System Design of Microgrid Cluster Based on Hierarchical Petri Nets
by Jose R Sicchar, Carlos T. Da Costa, Jr., Jose R. Silva, Raimundo C. Oliveira and Werbeston D. Oliveira
Energies 2018, 11(12), 3245; https://doi.org/10.3390/en11123245 - 22 Nov 2018
Cited by 10 | Viewed by 4663
Abstract
In the new paradigm of urban microgrids, load-balancing control becomes essential to ensure the balance and quality of energy consumption. Thus, phase-load balance method becomes an alternative solution in the absence of distributed generation sources. Development of efficient and robust load-balancing control algorithms [...] Read more.
In the new paradigm of urban microgrids, load-balancing control becomes essential to ensure the balance and quality of energy consumption. Thus, phase-load balance method becomes an alternative solution in the absence of distributed generation sources. Development of efficient and robust load-balancing control algorithms becomes useful for guaranteeing the load balance between phases and consumers, as well as to establish an automatic integration between the secondary grid and the supervisory center. This article presents a new phase-balancing control model based on hierarchical Petri nets (PNs) to encapsulate procedures and subroutines, and to verify the properties of a combined algorithm system, identifying the load imbalance in phases and improving the selection process of single-phase consumer units for switching, which is based on load-imbalance level and its future state of load consumption. A reliable flow of automated procedures is obtained, which effectively guarantees the load equalization in the low-voltage grid. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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24 pages, 9108 KiB  
Article
A High-Efficiency Bidirectional Active Balance for Electric Vehicle Battery Packs Based on Model Predictive Control
by Shixin Song, Feng Xiao, Silun Peng, Chuanxue Song and Yulong Shao
Energies 2018, 11(11), 3220; https://doi.org/10.3390/en11113220 - 20 Nov 2018
Cited by 11 | Viewed by 4560
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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17 pages, 4050 KiB  
Article
Total Suspended Particle Emissions Modelling in an Industrial Boiler
by Guillermo Ronquillo-Lomeli, Gilberto Herrera-Ruiz, José Gabriel Ríos-Moreno, Irving Alfredo Alejandro Ramirez-Maya and Mario Trejo-Perea
Energies 2018, 11(11), 3097; https://doi.org/10.3390/en11113097 - 09 Nov 2018
Cited by 5 | Viewed by 3039
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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15 pages, 2087 KiB  
Article
Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy
by Ke Yan, Xudong Wang, Yang Du, Ning Jin, Haichao Huang and Hangxia Zhou
Energies 2018, 11(11), 3089; https://doi.org/10.3390/en11113089 - 08 Nov 2018
Cited by 133 | Viewed by 9255
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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20 pages, 1628 KiB  
Article
Integrated Energy Micro-Grid Planning Using Electricity, Heating and Cooling Demands
by He Huang, DaPeng Liang and Zhen Tong
Energies 2018, 11(10), 2810; https://doi.org/10.3390/en11102810 - 18 Oct 2018
Cited by 17 | Viewed by 2664
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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12 pages, 382 KiB  
Article
Improved Adaptive Backstepping Sliding Mode Control of Static Var Compensator
by Qingyu Su, Fei Dong and Xueqiang Shen
Energies 2018, 11(10), 2750; https://doi.org/10.3390/en11102750 - 14 Oct 2018
Cited by 8 | Viewed by 2490
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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17 pages, 7415 KiB  
Article
Medium-Voltage AC Static Switch Solution to Feed Neutral Section in a High-Speed Railway System
by Jose Maria Canales, Iosu Aizpuru, Unai Iraola, Jon Andoni Barrena and Manex Barrenetxea
Energies 2018, 11(10), 2740; https://doi.org/10.3390/en11102740 - 12 Oct 2018
Cited by 9 | Viewed by 4594
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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18 pages, 2759 KiB  
Article
Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management
by Niels Blaauwbroek, Phuong Nguyen and Han Slootweg
Energies 2018, 11(10), 2514; https://doi.org/10.3390/en11102514 - 21 Sep 2018
Cited by 4 | Viewed by 2373
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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13 pages, 3000 KiB  
Article
New Monitoring System for Photovoltaic Power Plants’ Management
by Václav Beránek, Tomáš Olšan, Martin Libra, Vladislav Poulek, Jan Sedláček, Minh-Quan Dang and Igor I. Tyukhov
Energies 2018, 11(10), 2495; https://doi.org/10.3390/en11102495 - 20 Sep 2018
Cited by 45 | Viewed by 4604
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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20 pages, 1105 KiB  
Article
Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings
by Jin Dong, Christopher Winstead, James Nutaro and Teja Kuruganti
Energies 2018, 11(9), 2427; https://doi.org/10.3390/en11092427 - 13 Sep 2018
Cited by 51 | Viewed by 5225
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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19 pages, 10045 KiB  
Article
Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method
by Xiaomu Duan, Tong Zhao, Jinxin Liu, Li Zhang and Liang Zou
Energies 2018, 11(9), 2404; https://doi.org/10.3390/en11092404 - 11 Sep 2018
Cited by 25 | Viewed by 5535
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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17 pages, 5717 KiB  
Article
A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor
by Qiwei Xu, Yunqi Mao, Meng Zhao and Shumei Cui
Energies 2018, 11(9), 2212; https://doi.org/10.3390/en11092212 - 23 Aug 2018
Cited by 12 | Viewed by 3855
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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11 pages, 4796 KiB  
Article
Low Cost Position Controller for Exhaust Gas Recirculation Valve System
by Habib Bhuiyan and Jung-Hyo Lee
Energies 2018, 11(8), 2171; https://doi.org/10.3390/en11082171 - 20 Aug 2018
Cited by 6 | Viewed by 3748
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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15 pages, 576 KiB  
Article
Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid
by Mohammed M. Olama, Teja Kuruganti, James Nutaro and Jin Dong
Energies 2018, 11(7), 1852; https://doi.org/10.3390/en11071852 - 16 Jul 2018
Cited by 35 | Viewed by 5250
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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18 pages, 6090 KiB  
Article
Decision Tree-Based Preventive Control Applications to Enhance Fault Ride Through Capability of Doubly-Fed Induction Generator in Power Systems
by Dione Vieira, Marcus Nunes and Ubiratan Bezerra
Energies 2018, 11(7), 1760; https://doi.org/10.3390/en11071760 - 04 Jul 2018
Cited by 7 | Viewed by 3472
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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19 pages, 3284 KiB  
Article
Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems
by Hongyue Li, Xihuai Wang and Jianmei Xiao
Energies 2018, 11(7), 1686; https://doi.org/10.3390/en11071686 - 27 Jun 2018
Cited by 18 | Viewed by 3095
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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18 pages, 2462 KiB  
Article
Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell
by Minhwan Seo, Taedong Goh, Minjun Park and Sang Woo Kim
Energies 2018, 11(7), 1669; https://doi.org/10.3390/en11071669 - 27 Jun 2018
Cited by 43 | Viewed by 8044
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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