Title :
Power system stabilizer based on artificial neural network
Author :
Kumar, Jagdish ; Kumar, P. Pavan ; Mahesh, Aeidapu ; Shrivastava, Ankit
Author_Institution :
Dept. of Electr. Eng., PEC Univ. of Technol., Chandigarh, India
Abstract :
This paper describes a systematic approach for designing a self-tuning adaptive power system stabilizer (PSS) based on artificial neural network (ANN). An ANN is used for self-tuning the parameters of PSS e.g. stabilizing gain Kstab and time constant (T1) for Lead PSS in realtime. The inputs to the ANN are generator terminal active power (P) and reactive power (Q). Investigations are carried out to assess the dynamic performance of the system with self-tuning PSS based on ANN (ST-ANNPSS) over a wide range of loading conditions. The simulations are performed using Matlab/Simulink´s neural network toolbox. The simulation and experimental results demonstrate the effective dynamic performance of the proposed system.
Keywords :
neural nets; power engineering computing; power system stability; reactive power; Matlab-Simulink neural network toolbox; ST-ANNPSS; artificial neural network; generator terminal active power; lead PSS; reactive power; self-tuning PSS; self-tuning adaptive power system stabilizer; stabilizing gain; systematic approach; time constant; Artificial neural networks; Generators; Neurons; Oscillators; Power system stability; Rotors; Torque; Artificial Neural Network (ANN); Kstab and T1; power system stabilizer;
Conference_Titel :
Power and Energy Systems (ICPS), 2011 International Conference on
Conference_Location :
Chennai
Print_ISBN :
INAVLID ISBN
DOI :
10.1109/ICPES.2011.6156656