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Selected papers from the Fourth IEEE International Symposium on Computer, Consumer and Control, 2018 (IS3C 2018)

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 14685

Special Issue Editors

Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Interests: photovoltaic system; energy cnversion; power converter; photovoltaic module array diagonosis; maximum power point tracking technology of PV systems
Special Issues, Collections and Topics in MDPI journals
Department of Electrical Engineering, National Chin-Yi University of Technology, Taiping District, Taichung City 41170, Taiwan
Interests: biomedical electronics and signal processing; artificial intelligence and its application; embedded system application; pattern recognition; demand response; power quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 4th IEEE International Symposium on Computer, Consumer and Control, 2018 (IS3C 2018), will be held on 6-8 December 2018 in Taichung City, Taiwan, and is organized by IEEE and National Chin-Yi University of Technology, and is sponsored by IEEE Computer Society. Details of this conference can be found on the website, http://is3c2018.ncuteecs.org/. This conference offers a great opportunity for scientists, engineers, and practitioners to present the latest research results, ideas, developments, and applications, as well as to facilitate interactions between scholars and practitioners. As suggested by the name of the conference, the theme of this conference covers computer, consumer electronics, renewable energy, systems and control, and digital signal processing. Original high-quality papers related to this theme are especially solicited, including theories, methodologies, and applications in Computing, Consumer and Control for renewable energy. All accepted papers will be published in the conference proceedings. Selected papers will be recommended to Energies journals for a special issue publication.

Topics to be covered in this Special Issue include, but are not limited to, the following areas:

  • Renewable Energy Technologies
  • Photovoltaic and Wind Energy Technologies
  • Power Conversions for Renewable Energy
  • Applications of Power Electronics in Renewable Energy
  • Smart Grid Systems
  • Computer Networks for Renewable Energy
  • Artificial Intelligence, and Knowledge Discovery for Renewable Energy
  • Internet of Thing Applications for Renewable Energy
  • Computer and Microprocessor-Based Control for Renewable Energy
  • System Modeling and Simulation, Dynamics and Control for Renewable Energy
  • Intelligent and Learning Control for Renewable Energy
    Robust and Nonlinear Control for Renewable Energy
  • Digital Signal Processing Theory and Methods for Renewable Energy
  • Statistical Signal Processing and Applications for Renewable Energy
  • Neural Networks, Fuzzy Systems, Expert Systems, Genetic Algorithms and Data Fusion for Renewable Energy

Prof. Dr. Kuei-Hsiang Chao
Prof. Chia-Hung Lin
Guest Editors

Manuscript Submission Information

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

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Photovoltaic and Wind Energy Technologies
  • Power Conversions
  • Applications of Power Electronics
  • Smart Grid Systems
  • Artificial Intelligence
  • Internet of Thing
  • Intelligent and Learning Control

Published Papers (4 papers)

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Research

14 pages, 14023 KiB  
Article
Adaptive Frequency-Based Reference Compensation Current Control Strategy of Shunt Active Power Filter for Unbalanced Nonlinear Loads
by Cheng-I Chen, Chien-Kai Lan, Yeong-Chin Chen and Chung-Hsien Chen
Energies 2019, 12(16), 3080; https://doi.org/10.3390/en12163080 - 09 Aug 2019
Cited by 10 | Viewed by 2310
Abstract
The shunt active power filter (SAPF) is an effective means for the modification of power quality. However, the compensation performance of SAPF would be deteriorated when the unbalanced nonlinear loads are present in the power system. To enhance the compensation performance of SAPF, [...] Read more.
The shunt active power filter (SAPF) is an effective means for the modification of power quality. However, the compensation performance of SAPF would be deteriorated when the unbalanced nonlinear loads are present in the power system. To enhance the compensation performance of SAPF, the adaptive frequency-based reference compensation current control strategy is proposed in this paper. The proposed solution procedure can be divided into three stages including adaptive frequency detection, phase synchronization, and adaptive compensation. With the tracking of power system frequency, the phase synchronization for the SAPF compensation can be effectively modified under the power variation of unbalanced nonlinear loads. Based on the correct synchronization phase angle, the reference compensation current of SAPF can be accurately obtained with the adaptive linear neural network (ALNN) in the stage of adaptive compensation. In addition, the direct current (DC)-link voltage of SAPF can also be effectively regulated to maintain the compensation performance. To verify the effectiveness of the proposed adaptive frequency-based reference compensation current control strategy, the comprehensive case studies implemented with the hardware-in-the-loop (HIL) mechanism are performed to examine the compliance with the specification limits of IEEE Standard 519-2014. The experimental results reveal that the performance of proposed SAPF control strategy is superior to that of the traditional instantaneous reactive power compensation control technique (p-q method) and sliding discrete Fourier transform (DFT). Full article
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15 pages, 3315 KiB  
Article
Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting
by Peng Liu, Peijun Zheng and Ziyu Chen
Energies 2019, 12(12), 2445; https://doi.org/10.3390/en12122445 - 25 Jun 2019
Cited by 48 | Viewed by 5481
Abstract
Accurate short-term electric load forecasting is significant for the smart grid. It can reduce electric power consumption and ensure the balance between power supply and demand. In this paper, the Stacked Denoising Auto-Encoder (SDAE) is adopted for short-term load forecasting using four factors: [...] Read more.
Accurate short-term electric load forecasting is significant for the smart grid. It can reduce electric power consumption and ensure the balance between power supply and demand. In this paper, the Stacked Denoising Auto-Encoder (SDAE) is adopted for short-term load forecasting using four factors: historical loads, somatosensory temperature, relative humidity, and daily average loads. The daily average loads act as the baseline in final forecasting tasks. Firstly, the Denoising Auto-Encoder (DAE) is pre-trained. In the symmetric DAE, there are three layers: the input layer, the hidden layer, and the output layer where the hidden layer is the symmetric axis. The input layer and the hidden layer construct the encoding part while the hidden layer and the output layer construct the decoding part. After that, all DAEs are stacked together for fine-tuning. In addition, in the encoding part of each DAE, the weight values and hidden layer values are combined with the original input layer values to establish an SDAE network for load forecasting. Compared with the traditional Back Propagation (BP) neural network and Auto-Encoder, the prediction error decreases from 3.66% and 6.16% to 2.88%. Therefore, the SDAE-based model performs well compared with traditional methods as a new method for short-term electric load forecasting in Chinese cities. Full article
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12 pages, 5911 KiB  
Article
Implementation of the Low-Voltage Ride-Through Curve after Considering Offshore Wind Farms Integrated into the Isolated Taiwan Power System
by Shiue-Der Lu, Meng-Hui Wang and Chung-Ying Tai
Energies 2019, 12(7), 1258; https://doi.org/10.3390/en12071258 - 01 Apr 2019
Cited by 1 | Viewed by 3571
Abstract
In response to the power impact effect resulting from merging large-scale offshore wind farms (OWFs) into the Taiwan Power (Taipower) Company (TPC) system in the future, this study aims to discuss the situation where the offshore wind power is merged into the power [...] Read more.
In response to the power impact effect resulting from merging large-scale offshore wind farms (OWFs) into the Taiwan Power (Taipower) Company (TPC) system in the future, this study aims to discuss the situation where the offshore wind power is merged into the power grids of the Changbin and Changlin areas, and study a Low-Voltage Ride-Through (LVRT) curve fit for the Taiwan power grid through varying fault scenarios and fault times to reduce the effect of the tripping of OWFs on the TPC system. The Power System Simulator for Engineering (PSS/E) program was used to analyze the Taipower off-peak system in 2018. The proposed LVRT curve is compared to the current LVRT curve of Taipower. The research findings show that if the offshore wind turbine (OWT) set uses the proposed LVRT curve, when a fault occurs, the wind turbines can be prevented from becoming disconnected from the power grid, and the voltage sag amplitude of the connection point during the fault and the disturbances after the fault is cleared are relatively small. In addition, according to the transient stability analysis results, the system can return to stability after fault clearance, thereby meeting the Taipower transmission system planning criteria and technical key points of renewable energy power generation system parallel connection technique. Full article
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19 pages, 9261 KiB  
Article
DG System Using PFNN Controllers for Improving Islanding Detection and Power Control
by Kuang-Hsiung Tan and Chien-Wu Lan
Energies 2019, 12(3), 506; https://doi.org/10.3390/en12030506 - 05 Feb 2019
Cited by 6 | Viewed by 2805
Abstract
In this study, an intelligent controlled distributed generator (DG) system is proposed for tracking control and islanding detection. First, a DC/AC inverter with DC power supply is adopted to emulate a DG system and control the active and reactive power outputs. Moreover, in [...] Read more.
In this study, an intelligent controlled distributed generator (DG) system is proposed for tracking control and islanding detection. First, a DC/AC inverter with DC power supply is adopted to emulate a DG system and control the active and reactive power outputs. Moreover, in order to comply with the standard for interconnection with the power grid, a novel active islanding detection method is proposed for the inverter-based DG system. In the proposed active islanding detection method, a perturbation signal is designed to inject into the d-axis current of the DG system which causes the frequency at the terminal of the RLC load to deviate when the power grid breaks down. The feasibility of the proposed active islanding detection method is verified according to the UL 1741 test configuration. Furthermore, in order to improve the tracking control of the active and reactive powers of the inverter-based DG system, and to effectively reduce the detection time of islanding phenomenon, two probabilistic fuzzy neural network (PFNN) controllers are adopted to take the place of the conventional proportional-integral (PI) controllers. In addition, the network structure and the online learning algorithm of the adopted PFNN are presented in details. Finally, some experimental results of the proposed active islanding detection method using PFNN controllers are proposed to validate the effectiveness and feasibility of the tracking control and islanding detection. Full article
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