dc.description.abstract |
Power systems are often seen as complex, nonlinear, dynamic systems with a variety inherent
perturbation. Those operations may trigger steady-state instability in a power system, which
may also result in the accumulation of poorly damped electromechanical modes or system
collapse. To fix this problem, power system stabilizers are applied to generate extra
excitation control system signals. Since gain settings are determined for the specific working
conditions, a conventional power system stabilizer (CPSS) utilizes lead-lag compensation
strategies. Fuzzy logic uses linguistic information and avoids complex mathematical models.
A multi-level fuzzy-based power system stabilizer is addressed in this thesis to mitigate low-frequency oscillations in single and multi-machine power systems.
The suggested controller uses the variation of rotor speed and acceleration as inputs. To show
fuzzy controller performance, different membership functions, such as triangular, gaussian,
generalized bell, and trapezoidal are taken. Electrical torque deviations using multi-level
fuzzy with triangular, gaussian, and generalized bell membership function-based power
system stabilizer reduce the settling time by 95.670 %, 55.961 %, and 2.231 % than a single
fuzzy-based power system stabilizer. The multi-level fuzzy based PSS with triangular
membership function reduced the settling time by 34.601 %, 27.030 %, and 95.670 % than
single fuzzy-based PSS for rotor angle, rotor speed, and electrical torque deviations,
respectively. The Multi-level Fuzzy based PSS with triangular membership function settled
the rotor angle, rotor speed, and electrical torque deviations 29.517 %, 5.785 %, and 39.709
% faster than the gaussian membership function fuzzy-based PSS, respectively. When there
is a 5 % change in both mechanical torque and reference voltage at a time in multi-machine
system, a Multi-level Fuzzy based PSS achieved a settling time better than a single fuzzy-based power system stabilizer for machine-1, machine-2, and machine-3 for each state
variable (power angle, rotor speed, and accelerating torque deviations). Triangular
membership function resulted better result than the gaussian for single and multi-machine
power system low frequency oscillation damping.
Keywords: Low-Frequency Oscillation, Multi-Level Fuzzy, Multi-Machine, Power System
Stabilizer |
en_US |