Article
Automation & Control Systems
Ali Abdali, Ali Abedi, Kazem Mazlumi, Abbas Rabiee, Josep M. Guerrero
Summary: In this article, a novel formula is proposed to predict the hotspot temperature (HST) of distribution transformers (DTs) by considering the impact of electrical and mechanical parameters on heat dissipation capacity. Complete and accurate 3D modeling based on computational fluid dynamics (CFD) is utilized to validate the proposed formula. Experimental results show that the proposed formula is highly accurate and has a good correlation with empirical values.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Information Systems
Mingyang Li, Zezhong Wang, Junshuang Zhang, Zhengze Ni, Ruijuan Tan
Summary: This study investigates the impact of DC bias on temperature rise in power transformers, finding that errors are small when DC current is 0 A, but increase at 8 or 16 A. The methods used provide a foundation for further research on large-capacity power transformers under DC bias.
Article
Engineering, Electrical & Electronic
Lijun Zhou, Wei Liao, Dongyang Wang, Yi Cui, Lujia Wang, Liqing Zhang, Lei Guo
Summary: This study investigates the effects of traction loads on the thermal aging of oil-paper insulation in traction transformers through accelerated aging experiments and data analysis, revealing the relationship between traction load and insulation aging.
IEEE TRANSACTIONS ON POWER DELIVERY
(2021)
Article
Engineering, Electrical & Electronic
Mudabbir Badar, Ping Lu, Qirui Wang, Thomas Boyer, Kevin P. Chen, Paul R. Ohodnicki
Summary: Timely detection of incipient faults in power transformers is crucial to prevent malfunction. Monitoring transformer insulation oil, particularly its temperature, is essential as it provides key diagnostic information. Distributed optical fiber sensors have advantages over traditional methods, as shown in this study on a 100 kVA distribution transformer. Results indicate good agreement between conventional methods and distributed fiber temperature sensors.
IEEE SENSORS JOURNAL
(2021)
Article
Automation & Control Systems
Xianhao Fan, Jiefeng Liu, Hui Hwang Goh, Yiyi Zhang, Chaohai Zhang, Saifur Rahman
Summary: A novel method is proposed to evaluate the aging of transformer oil-immersed insulation using frequency-domain spectroscopy (FDS). The multiobjective function is constructed based on the dielectric response equivalent circuit, and a genetic algorithm is used to acquire FDS data of the hotspot region. The verification results show that the method provides reliable aging evaluation.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Enze Zhang, Jiefeng Liu, Boshu Song, Heng Zhang, Xianhao Fan, Yiyi Zhang, Qiang Fu
Summary: This paper investigates the variation law of methanol concentration in transformer oil caused by operational defects and hotspot temperature from 500 kV field transformers. The correlations between various operational defects and methanol in oil are explained in detail for the first time. It is observed that bushing heating, fan failure, and breather failure can lead to an increase in methanol concentration in oil. Furthermore, the associations between annual load factor and hotspot temperature with methanol are investigated. The results show that the methanol concentration in oil at the bottom of the winding can indicate the hotspot temperature of the winding. The correlation between methanol concentration and hotspot temperature is also proved through accelerated non-uniform thermal aging experiments. The uneven distribution of methanol concentration along the axial height of the winding is also discovered. This work provides valuable insights for the maintenance of transformer components and contributes to a better understanding of the distribution mechanism of methanol in insulating oil.
IEEE TRANSACTIONS ON POWER DELIVERY
(2023)
Article
Engineering, Electrical & Electronic
Cenk Gezegin, Okan Ozgonenel, Hasan Dirik
Summary: This paper discusses the importance of transformer monitoring systems and researches two methods to determine transformer temperature. These methods have been experimentally verified and proven to be more accurate than traditional methods.
IEEE TRANSACTIONS ON POWER DELIVERY
(2021)
Article
Automation & Control Systems
Jiefeng Liu, Xianhao Fan, Chaohai Zhang, Chun Sing Lai, Yiyi Zhang, Hanbo Zheng, Loi Lei Lai, Enze Zhang
Summary: This article introduces a new intelligent model for moisture diagnosis in transformers, which utilizes a genetic algorithm support vector machine (GA-SVM) and obtains feature parameters through frequency-domain spectroscopy. The feasibility and accuracy of this model are demonstrated in lab and field conditions.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Muhammad Aslam, Inzamam Ul Haq, Muhammad Saad Rehan, Abdul Basit, Muhammad Arif, Muhammad Iftikhar Khan, Muhammad Sadiq, Muhammad Naeem Arbab
Summary: The paper presents a comparative analysis of different thermal models for predicting the hotspot temperature and top oil temperature of power transformers, as well as proposes a new thermal model for monitoring transformer operation in real-time. The model is experimentally validated and found to be in good agreement with actual field data.
Article
Engineering, Electrical & Electronic
Lucas Pniak, Loic Queval, Bertrand Revol, Jean-Sylvio Ngoua Teu, Cyrille Gautier, Olivier Bethoux
Summary: This article proposes an innovative frequency analytical model for optimizing high-frequency transformers, which accurately computes the current distribution, ac resistance, and leakage inductance. The approach is validated and provides guidelines for engineers designing high-frequency transformers.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Multidisciplinary
Syed Ali Raza, Ahsan Ullah, Shuang He, Yifeng Wang, Jiangtao Li
Summary: The study aims to determine the direct comprehensive thermal distribution in distribution transformers for different loading conditions, with a focus on temperature distribution in the oil, core, and windings. Experimental results show the hottest spot temperature consistently located in the critical section of the primary winding.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Reza Kebriti, S. M. Hassan Hossieni
Summary: The temperature of windings hot spot is a crucial factor that affects the load capacity and useful life of power and distribution transformers. This study conducted temperature rise tests and compared the results with computational fluid dynamics (CFD) analysis, demonstrating that the application of CFD method in transformer design is feasible.
ELECTRICAL ENGINEERING
(2022)
Article
Energy & Fuels
Chao Gao, Lijun Huang, Yuhui Feng, Erya Gao, Zhongqing Yang, Kai Wang
Summary: Oil-immersion transformer plays a crucial role in the power system and its reliable operation is vital for safe and economical system functioning. Internal failures can lead to transformer shutdown and damage, impacting the overall operation of the power system. Due to the harsh working conditions with high temperature and pressure, effectively and accurately protecting the transformer from failures is challenging. Existing methods fail to sensitively and accurately detect and protect transformer faults. Therefore, this paper focuses on the research of detection and protection of oil-immersion transformers. Through analyzing calculation results, the relationship between temperature rise of the transformer structure and the ambient temperature is discussed. It is observed that higher ambient temperatures pose greater harm to the equipment, with the high-temperature area primarily concentrated at the pull plate. This provides valuable insights for developing startup strategies in different environments and helps prevent mechanical structure and insulation aging damages caused by temperature.
FRONTIERS IN ENERGY RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Qingchuan Xu, Shengkang Wang, Fuchang Lin, Hua Li
Summary: This article proposes a method for extracting the frequency spectroscopy of oil-immersed paper from the oil-paper insulation without the known insulation structure. By measuring the frequency spectroscopy of the oil-immersed paper under different conditions and constructing a transformer insulation model, accurate extraction of the frequency spectroscopy of oil-immersed paper can be achieved.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Mohammad Kharezy, Hassan Reza Mirzaei, Yuriy Serdyuk, Torbjorn Thiringer, Morteza Eslamian
Summary: The study presents a prototype of a 50 kW oil-paper insulated Medium Frequency Transformer (MFT) designed for connecting wind turbines to DC/DC converters to reduce the size of transformer stations. This solution requires MFTs to be insulated against high DC voltage to withstand the voltage in limited space.
IEEE TRANSACTIONS ON POWER DELIVERY
(2021)
Article
Engineering, Electrical & Electronic
Yiming Xie, Jiangjun Ruan, Daochun Huang, Yu Shi, Shuo Jin, Yongqing Deng, Yuanchao Hu
IEEE TRANSACTIONS ON POWER DELIVERY
(2020)
Article
Engineering, Electrical & Electronic
Yongqing Deng, Jiangjun Ruan, Xuzhu Dong, Daochun Huang, Chen Zhang
Summary: This study focuses on the overheating faults of oil-immersed transformers and analyzes the heating characteristics of transformers under overheating faults using the electromagnetic thermal fluid multi-physical field indirect coupling analysis method. Based on the inversion of transformer top oil temperature rise, a method for identifying abnormal heating states of transformers is proposed. Through verification, this method can provide certain reference for the detection and maintenance of abnormal internal heating faults in transformers.
IET ELECTRIC POWER APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Jiangjun Ruan, Xuezong Wang, Taotao Zhou, Xuelin Peng, Yongqing Deng, Qiuyu Yang
Summary: In this paper, a method for fault identification of trip mechanism based on vibration signal and coil current signal is proposed. Features are extracted from the vibration signal using phase space reconstruction (PSR) method, and combined with the features from the coil current waveform to form a feature set representing the health condition of the trip mechanism. Fault simulation tests are conducted to study the variation of current vibration characteristics under fault conditions, and a fault identification model based on support vector machine (SVM) is proposed. The identification accuracy of fault identification is 83.3% considering only the features of the current waveform or vibration signal, and the accuracy rises to 96.7% when using the feature set of current and vibration signals.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2023)
Article
Computer Science, Information Systems
Jiangjun Ruan, Yongqing Deng, Yu Quan, Ruohan Gong
Summary: This paper proposes an inversion method using a machine learning model to estimate the transient hot spot temperature of a 10 kV oil-immersed transformer, demonstrating that the inversion results are more accurate compared to other methods.
Article
Computer Science, Information Systems
Yongqing Deng, Jiangjun Ruan, Yu Quan, Ruohan Gong, Daochun Huang, Cihan Duan, Yiming Xie