A5047754053- Power Systems Fault Detection
- Power Transformer Diagnostics and Insulation
- Electrical Fault Detection and Protection
- Machine Fault Diagnosis Techniques
- Islanding Detection in Power Systems
- Advanced Control Systems Design
- Adaptive Control of Nonlinear Systems
- Neural Networks and Applications
- Extremum Seeking Control Systems
- Advanced Decision-Making Techniques
- Cybersecurity and Information Systems
- Sensorless Control of Electric Motors
- Power Quality and Harmonics
- Sensor Technology and Measurement Systems
Banaras Hindu University
2010-2014
Indian Institute of Technology BHU
2014
The proposed work presents the use of Artificial Neural Network (ANN) as a pattern classifier for differential protection power transformer, which makes discrimination among normal, magnetizing inrush, over-excitation and internal fault currents. This scheme has been realized through two separate customized Parallel-Hidden Layered ANN architectures in Master-slave mode. Back Propagation (BP) Algorithm Genetic (GA) are used to train multi-layered feed forward neural network their simulated...
This paper presents the use of ANN as a pattern classifier for differential protection power transformer, which makes discrimination among normal, magnetizing inrush, over-excitation, external fault and internal currents. scheme has been realized through two architectures, are designed trained using feed forward back propagation algorithm with experimental data finally one architecture is selected. The results amply demonstrate capabilities in terms accuracy speed identification different...
Stabilization of inverted pendulum is a classical benchmark problem in control systems. This work comprises modeling an followed by applying three techniques like Linear Quadratic Regulator (LQR), Regional pole placement, and 2-loop PID controller techniques. The said are applied to the real-time model, robustness system analyzed. Finally, all controlling compared for performance.
Rotor flux measurement is needed for the control of induction motor by methods like field oriented control. But it difficult tomeasure rotor in motors. Hence estimated using neural networks this paper. simulated model equations and compared with output network.