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RISS 인기검색어
- 검색키워드
- 저자 : Sree Renga Raja, T (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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Time Domain Based Digital Controller for Buck-Boost Converter
Vijayalakshmi, S,Sree Renga Raja, T 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.5
Design, Simulation and experimental analysis of closed loop time domain based Discrete PWM buck-boost converter are described. To improve the transient response and dynamic stability of the proposed converter, Discrete PID controller is the most preferable one. Discrete controller does not require any precise analytical model of the system to be controlled. The control system of the converter is designed using digital PWM technique. The proposed controller improves the dynamic performance of the buck-boost converter by achieving a robust output voltage against load disturbances, input voltage variations and changes in circuit components. The converter is designed through simulation using MATLAB / Simulink and performance parameters are also measured. The discrete controller is implemented, and design goal is achieved and the same is verified against theoretical calculation using LabVIEW.
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Implementation of Robust Prediction Observer Controller for DC-DC Converter
Shenbagalakshmi, R.,Raja, T. Sree Renga The Korean Institute of Electrical Engineers 2013 Journal of Electrical Engineering & Technology Vol.8 No.6
A discrete controller is designed for low power dc-dc switched mode power supplies. The approach is based on time domain and the control loop continuously and concurrently tunes the compensator parameters to meet the converter specifications. A digital state feedback control combined with the load estimator provides a complete compensation, which further improves the dynamic performance of the closed loop system. Simulation of digitally controlled Buck converter is performed with MATLAB/Simulink. Experimental results are given to demonstrate the effectiveness of the controller using LabVIEW with a data acquisition card (model DAQ Pad - 6009).
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Thanigaiselvan, R.,Raja, T. Sree Renga,Karthik, R. The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.6
Transformer mineral oil which is normally hydrocarbon based is non- biodegradable and pollutes the environment in all aspects. Though vegetable oils are eco-friendly in nature and potentially could be used in transformers as a replacement for the mineral oil, there usage is restricted because of their oxidative instability. The present work focuses on using rapeseed oil and pongamia (pongamia pinnata) oil as effective alternatives for the traditional mineral oil in power transformer. The oxidative stability of the rapeseed oil and pongamia oil is increased by using combinations of the natural and synthetic anti-oxidants as additives. The parameters like breakdown voltage, viscosity, flash point, fire point are measured for the rapeseed oil and pongamia oil with and without the additives as per IEC and ASTM standards. The results shown encouraging changes in the parameter values and ensures the use of the oils as a potential alternative insulation in power transformers.
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Gopakumar S.,Sree Renga Raja T. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.4
Transformer defects are defined by their severity which is the intrinsic property of the transformer. Several approaches for identifying the severity of Power Transformer (PT) problems have previously been proposed; however, most published research does not incorporate Gas Level (GL), Gas Rate (GR), and DGA interpretation into a unified strategy. A novel technique in the form of fuzzy logic (FL) has been offered as a new way to assess faults’ severity by utilizing the combination of GL, GR, and DGA interpretation from the Duval Pentagon Method (DPM) to increase the reliability of the faults’ severity evaluation of PT. Based on the local population, a four-level typical concentration and rate were created. A Deep Learning (DL) oriented Convolutional Neural Network (CNN) based DPM and Harris Hawks Optimization (HHO) method with a high agreement to that same graphical DPM has also been devised to enable the evaluation of hundreds of PT information easy. The proposed method was applied to 448 PTs, and it was then used to assess the severity of problems in PTs using historical DGA data. Due to the integration of GL, GR, and DGA interpretation results in one technique, this novel strategy yields good agreement with earlier methods, but with better sensitivity. Transformer defects are defi ned by their severity which is the intrinsic property of the transformer. Several approaches for identifying the severity of Power Transformer (PT) problems have previously been proposed; however, most published research does not incorporate Gas Level (GL), Gas Rate (GR), and DGA interpretation into a unifi ed strategy. A novel technique in the form of fuzzy logic (FL) has been off ered as a new way to assess faults’ severity by utilizing the combination of GL, GR, and DGA interpretation from the Duval Pentagon Method (DPM) to increase the reliability of the faults’ severity evaluation of PT. Based on the local population, a four-level typical concentration and rate were created. A Deep Learning (DL) oriented Convolutional Neural Network (CNN) based DPM and Harris Hawks Optimization (HHO) method with a high agreement to that same graphical DPM has also been devised to enable the evaluation of hundreds of PT information easy. The proposed method was applied to 448 PTs, and it was then used to assess the severity of problems in PTs using historical DGA data. Due to the integration of GL, GR, and DGA interpretation results in one technique, this novel strategy yields good agreement with earlier methods, but with better sensitivity.
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R. Thanigaiselvan,T. Sree Renga Raja,R.Karthik 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.6
Transformer mineral oil which is normally hydrocarbon based is non- biodegradable and pollutes the environment in all aspects. Though vegetable oils are eco-friendly in nature and potentially could be used in transformers as a replacement for the mineral oil, there usage is restricted because of their oxidative instability. The present work focuses on using rapeseed oil and pongamia (pongamia pinnata) oil as effective alternatives for the traditional mineral oil in power transformer. The oxidative stability of the rapeseed oil and pongamia oil is increased by using combinations of the natural and synthetic anti-oxidants as additives. The parameters like breakdown voltage, viscosity, flash point, fire point are measured for the rapeseed oil and pongamia oil with and without the additives as per IEC and ASTM standards. The results shown encouraging changes in the parameter values and ensures the use of the oils as a potential alternative insulation in power transformers.
-
Implementation of Robust Prediction Observer Controller for DC-DC Converter
R. Shenbagalakshmi,T. Sree Renga Raja 대한전기학회 2013 Journal of Electrical Engineering & Technology Vol.8 No.6
A discrete controller is designed for low power dc-dc switched mode power supplies. The approach is based on time domain and the control loop continuously and concurrently tunes the compensator parameters to meet the converter specifications. A digital state feedback control combined with the load estimator provides a complete compensation, which further improves the dynamic performance of the closed loop system. Simulation of digitally controlled Buck converter is performed with MATLAB/Simulink. Experimental results are given to demonstrate the effectiveness of the controller using LabVIEW with a data acquisition card (model DAQ Pad . 6009).
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