Overview of power converter designs feasible for high voltage transformer-less wind turbine

2011 ◽  
Author(s):  
Michal Sztykiel
Keyword(s):  
Wind Turbine ◽  
High Voltage ◽  
Power Converter ◽  
Voltage Transformer ◽  
High Voltage Transformer

Investigation of Plasma Generator Driving by High-Frequency High-Voltage Electric Source

2013 ◽  
Vol 284-287 ◽  
pp. 1136-1140
Author(s):  
Yong Nong Chang ◽  
Chih Ming Kuo
Keyword(s):  
High Voltage ◽  
High Frequency ◽  
Electric Circuit ◽  
Plasma Generator ◽  
Power Converter ◽  
Resonant Circuit ◽  
Resonant Converter ◽  
Voltage Transformer ◽  
High Voltage Transformer ◽  
Electric Source

In this paper, a series resonant converter with high-frequency high-voltage transformer for plasma generator is presented. The high-frequency, high-voltage electric source is designed by employing a series load-resonant converter with the plasma generator as part of the electric circuit. The equivalent circuit of this high-frequency transformer modeled by the introduction of the stray capacitance is proposed and the circuit model of the high-voltage plasma generator is conducted as well. Thus, the overall model of the high-voltage plasma generator is built and the designing procedures for appropriate selections of the corresponding resonant-circuit parameters can be established. Finally, a high-voltage plasma generator with 220V, 60Hz, and 1kW input, plus a 22 kHz and over 8kV power converter output, is realized and implemented.

scholarly journals Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7426
Author(s):  
Imene Mitiche ◽  
Tony McGrail ◽  
Philip Boreham ◽  
Alan Nesbitt ◽  
Gordon Morison
Keyword(s):  
Anomaly Detection ◽  
High Voltage ◽  
Short Term Memory ◽  
Absolute Error ◽  
Real World Data ◽  
Voltage Transformer ◽  
Power Outage ◽  
Phase Measurements ◽  
High Voltage Transformer ◽  
Supply Industry

The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment’s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified.

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