IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i7p1844-d344059.html
   My bibliography  Save this article

Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data

Author

Listed:
  • Yih-Der Lee

    (The Institute of Nuclear Energy Research, Taoyuan 325, Taiwan)

  • Jheng-Lun Jiang

    (The Institute of Nuclear Energy Research, Taoyuan 325, Taiwan)

  • Yuan-Hsiang Ho

    (The Institute of Nuclear Energy Research, Taoyuan 325, Taiwan)

  • Wei-Chen Lin

    (Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan)

  • Hsin-Ching Chih

    (Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan)

  • Wei-Tzer Huang

    (Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan)

Abstract

An increase in the neutral current results in a malfunction of the low energy over current (LCO) protective relay and raises the neutral-to-ground voltage in three-phase, four-wire radial distribution feeders. Thus, the key point for mitigating its effect is to keep the current under a specific level. The most common approach for reducing the neutral current caused by the inherent imbalance of distribution feeders is to rearrange the phase connection between the distribution transformers and the load tapped-off points by using the metaheuristics algorithms. However, the primary task is to obtain the effective load data for phase rearrangement; otherwise, the outcomes would not be worthy of practical application. In this paper, the effective load data can be received from the feeder terminal unit (FTU) installed along the feeder of Taipower. The net load data consisting of customers’ power consumption and the power generation of distributed energy resources (DERs) were measured and transmitted to the feeder dispatch control center (FDCC). This paper proposes a method of establishing the equivalent full-scale net load model based on FTU data format, and the long short-term memory (LSTM) was adopted for monthly load forecasting. Furthermore, the full-scale net load model was built by the monthly per hour load data. Next, the particle swarm optimization (PSO) algorithm was applied to rearrange the phase connection of the distribution transformers with the aim of minimizing the neutral current. The outcomes of this paper are helpful for the optimal setting of the limit current of the LCO relay and to avoid its malfunction. Furthermore, the proposed method can also improve the three-phase imbalance of distribution feeders, thus reducing extra power loss and increasing the operating efficiency of three-phase induction motors.

Suggested Citation

  • Yih-Der Lee & Jheng-Lun Jiang & Yuan-Hsiang Ho & Wei-Chen Lin & Hsin-Ching Chih & Wei-Tzer Huang, 2020. "Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data," Energies, MDPI, vol. 13(7), pages 1-20, April.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1844-:d:344059
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/7/1844/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/7/1844/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Juan A. Martinez-Velasco & Gerardo Guerra, 2016. "Reliability Analysis of Distribution Systems with Photovoltaic Generation Using a Power Flow Simulator and a Parallel Monte Carlo Approach," Energies, MDPI, vol. 9(7), pages 1-21, July.
    2. Hao Xiao & Wei Pei & Zuomin Dong & Li Kong & Dan Wang, 2018. "Application and Comparison of Metaheuristic and New Metamodel Based Global Optimization Methods to the Optimal Operation of Active Distribution Networks," Energies, MDPI, vol. 11(1), pages 1-29, January.
    3. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2020. "Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting," Energies, MDPI, vol. 13(2), pages 1-21, January.
    4. Chia-Sheng Tu & Ming-Tang Tsai, 2020. "Optimal Phase Arrangement of Distribution Transformers for System Unbalance Improvement and Loss Reduction," Energies, MDPI, vol. 13(3), pages 1-15, January.
    5. Khikmafaris Yudantaka & Jung-Su Kim & Hwachang Song, 2019. "Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction," Energies, MDPI, vol. 13(1), pages 1-17, December.
    6. Arpita Samanta Santra & Jun-Lin Lin, 2019. "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(11), pages 1-11, May.
    7. Alena Otcenasova & Andrej Bolf & Juraj Altus & Michal Regula, 2019. "The Influence of Power Quality Indices on Active Power Losses in a Local Distribution Grid," Energies, MDPI, vol. 12(7), pages 1-31, April.
    8. Krzysztof Lowczowski & Jozef Lorenc & Jerzy Andruszkiewicz & Zbigniew Nadolny & Jozef Zawodniak, 2019. "Novel Earth Fault Protection Algorithm Based on MV Cable Screen Zero Sequence Current Filter," Energies, MDPI, vol. 12(16), pages 1-20, August.
    9. Amir Farughian & Lauri Kumpulainen & Kimmo Kauhaniemi, 2019. "Earth Fault Location Using Negative Sequence Currents," Energies, MDPI, vol. 12(19), pages 1-14, September.
    10. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    11. Seon Hyeog Kim & Gyul Lee & Gu-Young Kwon & Do-In Kim & Yong-June Shin, 2018. "Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting," Energies, MDPI, vol. 11(12), pages 1-17, December.
    12. Jorge Arias & Maria Calle & Daniel Turizo & Javier Guerrero & John E. Candelo-Becerra, 2019. "Historical Load Balance in Distribution Systems Using the Branch and Bound Algorithm," Energies, MDPI, vol. 12(7), pages 1-14, March.
    13. Ritam Misra & Sumit Paudyal & Oğuzhan Ceylan & Paras Mandal, 2017. "Harmonic Distortion Minimization in Power Grids with Wind and Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-13, July.
    14. Wenquan Shao & Jie Bai & Yuan Cheng & Zhihua Zhang & Ning Li, 2019. "Research on a Faulty Line Selection Method Based on the Zero-Sequence Disturbance Power of Resonant Grounded Distribution Networks," Energies, MDPI, vol. 12(5), pages 1-18, March.
    15. Wei-Tzer Huang & Tsai-Hsiang Chen & Hong-Ting Chen & Jhih-Siang Yang & Kuo-Lung Lian & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2015. "A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(12), pages 1-17, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chien-Kuo Chang & Shih-Tang Cheng & Bharath-Kumar Boyanapalli, 2022. "Three-Phase Unbalance Improvement for Distribution Systems Based on the Particle Swarm Current Injection Algorithm," Energies, MDPI, vol. 15(9), pages 1-16, May.
    2. Yih-Der Lee & Wei-Chen Lin & Jheng-Lun Jiang & Jia-Hao Cai & Wei-Tzer Huang & Kai-Chao Yao, 2021. "Optimal Individual Phase Voltage Regulation Strategies in Active Distribution Networks with High PV Penetration Using the Sparrow Search Algorithm," Energies, MDPI, vol. 14(24), pages 1-22, December.
    3. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Krzysztof Lowczowski & Jozef Lorenc & Jozef Zawodniak & Grzegorz Dombek, 2020. "Detection and Location of Earth Fault in MV Feeders Using Screen Earthing Current Measurements," Energies, MDPI, vol. 13(5), pages 1-24, March.
    2. Nasir Ayub & Muhammad Irfan & Muhammad Awais & Usman Ali & Tariq Ali & Mohammed Hamdi & Abdullah Alghamdi & Fazal Muhammad, 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler," Energies, MDPI, vol. 13(19), pages 1-21, October.
    3. Piotr Hoduń & Michał Borecki, 2021. "Reliability Assessment of MV Power Connections," Energies, MDPI, vol. 14(21), pages 1-15, October.
    4. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    5. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    6. Gabriel Trierweiler Ribeiro & João Guilherme Sauer & Naylene Fraccanabbia & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting," Energies, MDPI, vol. 13(9), pages 1-19, May.
    7. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    8. Fitsum Salehu Kebede & Jean-Christophe Olivier & Salvy Bourguet & Mohamed Machmoum, 2021. "Reliability Evaluation of Renewable Power Systems through Distribution Network Power Outage Modelling," Energies, MDPI, vol. 14(11), pages 1-25, May.
    9. Fei Teng & Yafei Song & Xinpeng Guo, 2021. "Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model," Mathematics, MDPI, vol. 9(19), pages 1-21, September.
    10. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
    11. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    12. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    13. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    14. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    15. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    16. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Automatic Selection of Temperature Variables for Short-Term Load Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    17. Wagner A. Vilela Junior & Antonio P. Coimbra & Gabriel A. Wainer & Joao Caetano Neto & Jose A. G. Cararo & Marcio R. C. Reis & Paulo V. Santos & Wesley P. Calixto, 2021. "Analysis and Adequacy Methodology for Voltage Violations in Distribution Power Grid," Energies, MDPI, vol. 14(14), pages 1-21, July.
    18. Bulent Haznedar & Huseyin Cagan Kilinc & Furkan Ozkan & Adem Yurtsever, 2023. "Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 681-701, May.
    19. Xingjie Liu & Yamin Zeng & Baoping An & Xiaolei Zhang, 2019. "Research on Characteristics of ECVT for Power Quality Detection and Optimum Design of Its Parameter," Energies, MDPI, vol. 12(12), pages 1-20, June.
    20. Gayo-Abeleira, Miguel & Santos, Carlos & Javier Rodríguez Sánchez, Francisco & Martín, Pedro & Antonio Jiménez, José & Santiso, Enrique, 2022. "Aperiodic two-layer energy management system for community microgrids based on blockchain strategy," Applied Energy, Elsevier, vol. 324(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1844-:d:344059. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.