34 research outputs found

    Impact of Geomagnetically Induced Currents on Power Transformers

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    This thesis deals with the impact of Geomagnetically Induced Current (GIC) on power transformers in electrical power systems. A simulator to calculate the flows of GIC in an electrical power network, based on an assumed or measured induced geoelectric potential is proposed. This simulator includes all needed mapping techniques to handle a system that cover a large geographical area. A correlation between GIC and the reactive power absorbed in the core of the saturated transformer is proposed. That correlation is used to estimate GIC in a transformer utilizing existing reactive power measuring infrastructure within the electrical grid without the need for dedicated measurement equipment. This technique is validated by simulations with electromagnetic transients software, laboratory work and through data recorded during a GIC event on the Hydro One network. The slope correlating reactive power absorption to GIC from an electromagnetic transient model of the transformer may be used to predict GIC levels in the actual transformers. The application of the technique correlating GIC with reactive power absorption is examined on a segment of a real 500 kV power transmission system. This technique allows GIC to be taken into account during load flow studies. Additionally, some benefits of increased visibility of GIC in the system are shown. A method to determine the frequency and magnitude of the harmonic currents generated by a saturated transformer is also proposed. It is expected that studies conducted in this thesis will be of value to utilities like Hydro One in planning mitigation measures against GICs

    Monitoring Dan Analisis Dampak Pembebanan Terhadap Posisi Peletakan Sensor Vibrasi Pada Tangki Transformator Distribusi 20 kV

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    Transformator distribusi adalah salah satu peralatan yang sangat penting pada sistem tenaga [1]. Vibrasi adalah salah satu peristiwa besar pada transformator yang memulai setelah dekompresi (pengurangan) pada magnet core dan winding [2]. Monitoring vibrasi pada tangki transformator adalah cara yang paling efektif guna merepresentasikan vibrasi yang terjadi pada bagian dalam transformator [3]. Dari sekian penelitian terkait vibrasi pada tangki transformator hanya transformator daya yang banyak diujikan dan beberapa dilakukan pada uji laboratorium. Perlu adanya kajian lebih lanjut untuk dapat diterapkan pada transformator distribusi serta penggunaan sensor vibrasi yang dapat diimplementasikan pada tangki transformator distribusi. Oleh sebab itu penelitian selanjutnya yakni monitoring dan analisis dampak pembebanan terhadap posisi peletakan sensor vibrasi pada tangki transformator distribusi 20 kV dengan mengimplementasikan elemen piezoelectric 20 mm sebagai tranduser vibrasi dengan rata – rata % error pengukuran ± 17,8 % dari desain yang telah dibuat. Hasil eksperimen menunjukkan semakin meningkatnya pembebanan, maka vibrasi juga akan mengalami peningkatan sesuai dengan vibrasi yang terukur pada masing – masing sensor vibrasi. Sensor vibrasi yang terletak di 1/3 bagian atas (A0, A3, A6) dan 1/3 bagian bawah (A2, A5, A8) yang masing – masing memiliki nilai koefisien korelasi positif kuat lebih sensitif terhadap kenaikan pembebanan dari pada di 1/3 bagian tengah (A1, A4, A5) dengan nilai koefisien korelasi cukup/lemah dan sensor vibrasi yang terletak di 1/3 bagian bawah (A2, A5, A8) memiliki nilai vibrasi yang lebih tinggi dari keseluruhan peletakan sensor vibrasi =============================================================================================== Distribution transformers are one of the most important equipment in the power system [1]. Vibration is one of the major events in the transformer that starts after decompression (reduction) on the magnetic core and winding [2]. Vibration monitoring in a transformer tank is the most effective way to represent vibrations that occur on the inside of the transformer [3]. Of the various studies related to the vibrations in the transformer tank, only power transformers are tested and some are carried out in laboratory tests.Further studies are needed to be applied to distribution transformers and the use of vibration sensors that can be implemented in distribution transformers. Therefore the next study is impact monitoring and analysis of loading on the position of the vibration sensor placement in a 20 kV distribution transformer tank by implementing a 20 mm piezoelectric element as a vibrational transducer with an average% measurement error ± 17.8% of the design made. The experimental results show that the higher the load, the vibration will also increase according to the vibrations measured on each vibration sensor.Vibration sensors located in the 1/3 upper part (A0, A3, A6) and 1/3 lower part (A2, A5, A8) which each have a strong positive correlation coefficient are more sensitive to the increase in loading than at 1 / 3 the middle part (A1, A4, A5) with a sufficient / weak correlation coefficient and vibration sensor located in the 1/3 lower part (A2, A5, A8) has a higher vibration value than the overall vibration sensor placement

    International Study Group Progress Report On Linear Collider Development

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    Selected Papers from 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020)

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    The 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020) was held on 6–10 September 2020 in Beijing, China. The conference was organized by the Tsinghua University, China, and endorsed by the IEEE Dielectrics and Electrical Insulation Society. This conference has attracted a great deal of attention from researchers around the world in the field of high voltage engineering. The forum offered the opportunity to present the latest developments and different emerging challenges in high voltage engineering, including the topics of ultra-high voltage, smart grids, and insulating materials

    Bushing diagnosis using artificial intelligence and dissolved gas analysis

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    This dissertation is a study of artificial intelligence for diagnosing the condition of high voltage bushings. The techniques include neural networks, genetic algorithms, fuzzy set theory, particle swarm optimisation, multi-classifier systems, factor analysis, principal component analysis, multidimensional scaling, data-fusion techniques, automatic relevance determination and autoencoders. The classification is done using Dissolved Gas Analysis (DGA) data based on field experience together with criteria from IEEEc57.104 and IEC60599. A review of current literature showed that common methods for the diagnosis of bushings are: partial discharge, DGA, tan- (dielectric dissipation factor), water content in oil, dielectric strength of oil, acidity level (neutralisation value), visual analysis of sludge in suspension, colour of the oil, furanic content, degree of polymerisation (DP), strength of the insulating paper, interfacial tension or oxygen content tests. All the methods have limitations in terms of time and accuracy in decision making. The fact that making decisions using each of these methods individually is highly subjective, also the huge size of the data base of historical data, as well as the loss of skills due to retirement of experienced technical staff, highlights the need for an automated diagnosis tool that integrates information from the many sensors and recalls the historical decisions and learns from new information. Three classifiers that are compared in this analysis are radial basis functions (RBF), multiple layer perceptrons (MLP) and support vector machines (SVM). In this work 60699 bushings were classified based on ten criteria. Classification was done based on a majority vote. The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The work also proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The relevance and redundancy detection methods were able to prune the redundant measured variables and accurately diagnose the condition of the bushing with fewer variables. Experimental results from bushings that were evaluated in the field verified the simulations. The results of this work can help to develop real-time monitoring and decision making tools that combine information from chemical, electrical and mechanical measurements taken from bushings

    Intelligent on-line transformer monitoring, diagnostics, and decision making

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. [231]-235).by Mary Jane Boyd.Ph.D

    Pertanika Journal of Science & Technology

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