scholarly journals Stability Enhancement by Comparative Analysis of AI Techniques in Power System Stablizer Integrated With Hybrid System

2019 ◽  
Vol 5 (6) ◽  
pp. 4
Author(s):  
Harsh Vardhan Singh ◽  
Dr. Ranjeeta Khare
Keyword(s):  
Power System ◽  
Hybrid System ◽  
Active Power ◽  
Power System Stabilizer ◽  
Swarm Optimization ◽  
Distortion Level ◽  
Voltage Output ◽  
Power Enhancement ◽  
System Stabilizer ◽  
Stability Enhancement

Hybrid system has been modeled in MATLAB/SIMULINK environment which is then integrated with two generators based power system. The work has done over analysis of THD level in voltage output from the hybrid system with various controls being proposed for the power system stabilizer. Various controls like PI-Hysteresis, particle swarm optimization (PSO) and PSO with neural network (NN) have been implemented for comparative study. It was found that the distortion level in voltage output waveform was least in stabilizer having PSO-NN control which is 3.36%. Also the active power enhancement reached a whooping value of 9.4 KW from the hybrid system.

Desain Power System Stabilizer Berbasis Fuzzy dan Particle Swarm Optimization

2019 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Tamaji
Keyword(s):  
Particle Swarm Optimization ◽  
Feedback Control ◽  
Power System ◽  
State Feedback ◽  
The State ◽  
State Feedback Control ◽  
Power System Stabilizer ◽  
Feedback Gain ◽  
Swarm Optimization ◽  
System Stabilizer

One important factor to produce  a qualified electricity is the stability of the system.  An unstable system resulted  an undamped oscilation of system, and the stable system can damp the oscilation quickly. Therefore, it is necessary to apply  a stability device to a power system and it is called a Power System Stabilizer (PSS). One of stability design is a feedback control design. Here, in this research, the state feedback control are designed for Single Machine Infinite Bus (SMIB) . The SMIB model is non linear therefore the feedback control can’t be designed directly. Some researchers do linearize the system before design the feedback control.  In this research, a nonlinear model of SMIB is build in a state space form. Subsequently, a fuzzification Takagi-Sugeno is applied. The state feedback controls are applied to design the control of SMIB fuzzy system, a state feedback gain is determined using method Routh Hurwitz. The determining the parameter of state feedback gain influence the performance of SMIB. Therefore, it is important to determine the suitable parameter such that the SMIB has the optimal performance. The Particle Swarm Optimization (PSO) is applied to optimaze the performance of SMIB. In these research, it is compared the performance of SMIB by applying between Routh Hurwitz, fuzzy Routh Hurwitz, PSO fuzzy Routh Hurwitz for state feedback control. The simulation result show that Performance of SMIB using The PSO Fuzzy Routh  Hurwitz state feedback can improve the performance of SMIB, but the performance of Efd become oscillate and this method influence by the chosen parameter.

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