## 7 Simulation Results
### Comparison with Other Methods  
To further show the superiority of the proposed technique we compared it to the most commonly used UFLS schemes which are traditional UFLS, adaptive UFLS, and semi-adaptive or ROCOF-based schemes. It should be noted that the choice of settings for the used UFLS schemes will give different results. For consistence in results, we implemented the traditional UFLS with the settings shown in Appendix A. For ROCOF-based UFLS we considered the requirements of IEEE Std. C37.118.1, and Appendix A show the considered settings [10]. With adaptive UFLS, we simply estimated the disturbance using the swing equation and the deficit is shed at once when the frequency reaches 49.0 Hz. Two scenarios are compared which are the most serious contingency of 0.5 p.u power deficit and a medium disturbance of 0.2 p.u. Firstly, we consider a large disturbance of 0.5 p.u. When this disturbance occurs, the frequency dropped to about 46.5 Hz as in Figure12. From Figure 12, it can be observed that with traditional  
Figure 11: Frequency behavior due to time delay. Figure 10: The amount of load to be shed in third scenario: a comparison between cases 5 & 6. UFLS scheme, the frequency of the system dropped rapidly to 47 Hz in almost 5 s, an under-shedding situation occurred. At this rate of frequency decay if no drastic measures are taken, total system collapse will be experienced. Using adaptive and ROCOF-based UFLS techniques, the frequency was restored to a steady state of above 49.5 Hz; however, the frequency drop with ROCOF technique was much higher. With WAMS-based optimal UFLS scheme where the ICA algorithm is used, frequency only dropped to 49.1 Hz and it is restored to a steady state of about 49.5 Hz in almost 5 s. Therefore, it can be observed that the optimal UFLS scheme is more superior in that the frequency can be restored in a short period thereby avoiding system collapse, in the traditional case usually under-shedding or over-shedding occurs and they both affect system stability. In the second scenario, we considered a medium disturbance of 0.2 p.u. After the disturbance occurred, Figure 13 shows that without UFLS the frequency would drop to almost 48.5 Hz. This is a more common disturbance in power systems. In this scenario, all the considered UFLS techniques restored the frequency to above 49.5 Hz. With traditional UFLS, there was frequency overshoot due to over-shedding and the frequency finally settled at about 50.1 Hz. However, our proposed scheme was more superior if we consider the minimum frequency reached with other techniques. We also compared the proposed ICA algorithm with another stochastic search algorithm. Meta-heuristic algorithms include GA, bacteria foraging, and PSO. These algorithms have proven to be efficient in handling complex nonlinear constraints and to provide high-quality solutions. However, it has been proven that PSO has better performance compared to GA and bacteria foraging algorithms [39; 40]. They however have their challenges on convergence and issues to do with  
Figure 12: Frequency behavior of the proposed UFLS technique compared to others: large disturbance. Figure 13: Frequency behavior of the proposed UFLS technique compared to others: small disturbance. dimensionality [39]. We chose the PSO algorithm in our comparisons as this algorithm is recently used to solve the UFLS in [40]. Again, the most severe disturbance in the studied system, i.e., 0.5 p.u is considered. Figure 14 shows the results of the three compared scenarios. Using our proposed ICA-based UFLS specifically case 2 is used and comparing it to PSO suggested in [40] it gave better results. With the PSO suggested in [40] there was delayed shedding and the minimum frequency reached was almost 48.5 Hz.