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Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Author

Listed:
  • Mantas Plienis

    (Department of Electric Power Systems, Kaunas University of Technology, Studentu Str. 48, LT-51367 Kaunas, Lithuania)

  • Tomas Deveikis

    (Department of Electric Power Systems, Kaunas University of Technology, Studentu Str. 48, LT-51367 Kaunas, Lithuania)

  • Audrius Jonaitis

    (Department of Electric Power Systems, Kaunas University of Technology, Studentu Str. 48, LT-51367 Kaunas, Lithuania)

  • Saulius Gudžius

    (Department of Electric Power Systems, Kaunas University of Technology, Studentu Str. 48, LT-51367 Kaunas, Lithuania)

  • Inga Konstantinavičiūtė

    (Department of Electric Power Systems, Kaunas University of Technology, Studentu Str. 48, LT-51367 Kaunas, Lithuania)

  • Donata Putnaitė

    (Department of Electric Power Systems, Kaunas University of Technology, Studentu Str. 48, LT-51367 Kaunas, Lithuania)

Abstract

The decline in power quality within electrical networks is adversely impacting the energy efficiency and safety of transmission elements. The growing prevalence of power electronics has elevated harmonic levels in the grid to an extent where their significance cannot be overlooked. Additionally, the increasing integration of renewable energy sources introduces heightened fluctuations, rendering the prediction and simulation of working modes more challenging. This paper presents an improved algorithm for calculating power transformer losses attributed to harmonics, with a comprehensive validation against simulation results obtained from the Power Factory application and real-world measurements. The advantages of the algorithm are that all evaluations are performed in real-time based on single-point measurements, and the algorithm was easy to implement in a Programmable Logic Controller (PLC). This allows us to receive the exchange of information to energy monitoring systems (EMSs) or with Power factor Correction Units (PFCUs) and control it. To facilitate a more intuitive understanding and visualization of potential hazardous scenarios related to resonance, an extra Dijkstra algorithm was implemented. This augmentation enables the identification of conditions, wherein certain branches exhibit lower resistance than the grid connection point, indicating a heightened risk of resonance and the presence of highly distorted currents. Recognizing that monitoring alone does not inherently contribute to increased energy efficiency, the algorithm was further expanded to assess transformer losses across a spectrum of Power Factory Correction Units power levels. Additionally, a command from a PLC to a PFCU can now be initiated to change the capacitance level and near-resonance working mode. These advancements collectively contribute to a more robust and versatile methodology for evaluating power transformer losses, offering enhanced accuracy and the ability to visualize potentially critical resonance scenarios.

Suggested Citation

  • Mantas Plienis & Tomas Deveikis & Audrius Jonaitis & Saulius Gudžius & Inga Konstantinavičiūtė & Donata Putnaitė, 2023. "Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis," Energies, MDPI, vol. 16(23), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7837-:d:1290364
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    References listed on IDEAS

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    1. Adedayo Owosuhi & Yskandar Hamam & Josiah Munda, 2023. "Maximizing the Integration of a Battery Energy Storage System–Photovoltaic Distributed Generation for Power System Harmonic Reduction: An Overview," Energies, MDPI, vol. 16(6), pages 1-22, March.
    2. Ayman Agha & Hani Attar & Ashish Kr. Luhach, 2021. "Optimized Economic Loading of Distribution Transformers Using Minimum Energy Loss Computing," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, December.
    3. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    4. Tamer Khatib & Lama Sabri, 2021. "Grid Impact Assessment of Centralized and Decentralized Photovoltaic-Based Distribution Generation: A Case Study of Power Distribution Network with High Renewable Energy Penetration," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-16, July.
    5. Rosalia Sinvula & Khaled Mohamed Abo-Al-Ez & Mohamed Tariq Kahn, 2020. "A Proposed Harmonic Monitoring System for Large Power Users Considering Harmonic Limits," Energies, MDPI, vol. 13(17), pages 1-18, September.
    6. Minh Nguyen Dat & Kien Duong Trung & Phap Vu Minh & Chau Dinh Van & Quynh T. Tran & Trung Nguyen Ngoc, 2023. "Assessment of Energy Efficiency Using an Energy Monitoring System: A Case Study of a Major Energy-Consuming Enterprise in Vietnam," Energies, MDPI, vol. 16(13), pages 1-15, July.
    7. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    8. Ueno, Tsuyoshi & Sano, Fuminori & Saeki, Osamu & Tsuji, Kiichiro, 2006. "Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data," Applied Energy, Elsevier, vol. 83(2), pages 166-183, February.
    9. Mantas Plienis & Tomas Deveikis & Audrius Jonaitis & Saulius Gudžius, 2023. "Design of IOT-Based Framework for Evaluation of Energy Efficiency in Power Transformers," Energies, MDPI, vol. 16(11), pages 1-15, May.
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