**8.2 Power system model, AVR parameters and PSS block diagram and parameters**

#### **8.2.1 Power system model diagram**

Fig. A1. System model- Single-Machine Infinite Bus (SMIB)

#### **8.2.2 Block diagram of the Automatic Voltage Regulator (AVR)**

Fig. A2. Automatic voltage regulatore structure

#### **8.2.3 Block diagram and parameters of the PSSs**


Fig. A3. Power system stabilizer structure

In Fig. A3, VPSS is the output signal of the PSS, while ∆ω(s) is the input signal, which in this case is the speed deviation.


Table A1. PSS parameters.

#### **8.3 Generator's parameters**

314 Bio-Inspired Computational Algorithms and Their Applications

In Fig. A3, VPSS is the output signal of the PSS, while ∆ω(s) is the input signal, which in this

PSSs *Ks T*<sup>1</sup> *T*<sup>2</sup> *T*<sup>w</sup> CPSS 9.7928 1.1686 0.2846 2.5000 GA-PSS 13.7358 3.5811 1.2654 2.5000 BGA-PSS 18.8838 3.7604 1.7390 2.5000

**8.2 Power system model, AVR parameters and PSS block diagram and parameters** 

**8.2.1 Power system model diagram** 

Fig. A1. System model- Single-Machine Infinite Bus (SMIB)

Fig. A2. Automatic voltage regulatore structure

Fig. A3. Power system stabilizer structure

case is the speed deviation.

Table A1. PSS parameters.

**8.2.3 Block diagram and parameters of the PSSs** 

**8.2.2 Block diagram of the Automatic Voltage Regulator (AVR)** 

*Xl* =0.0742 p.u, , *Xd*=1.72 p.u,, *X'd*=0.45 p.u,, *X"d*=0.33 p.u,*T'd0*=6.3sec., *T"d0* = 0.033 p.u,, *Xq* =1.68 p.u,, *X'q* =0.59 p.u,, *X"q* =0.33 p.u, *T'q0* =0.43 sec

*T"q0* = 0.033sec., *H* = 4.0sec

#### **8.4 Pseudo code for BGA generator's parameters**

```
Begin 
    Randomly initialize a population of N individuals; 
    Initialize mutation rate Rnom
    While termination criterion not met
        evaluate goodness of each individuals 
        save the best individual in the new population 
        select the best T% individuals and discarding the rest; 
        for I =1 to N-1 do 
             randomly select two individuals among the T% best individual 
             recombine the two parents to obtain one offspring 
        end 
        divide the new population into two halves (X and Y) 
        apply mutation rate rnom/2 to X and 2 Rnom to Y 
        evaluate the average fitness value for the two half population (X and Y) 
        If X performs better than Y; assign r= Rnom -0.1 rnom; 
        If Y performs better than X; assign r= Rnom + 0.1 rnom; 
    end
```
**end**
