## 1 Introduction
In such cases where frequency prediction is necessary, wide-area measurement system (WAMS) can be an essential tool for transmission system operators (TSOs) in power system dynamic studies [14; 15; 16; 17; 18]. WAMS is based on the optical communication infrastructure and global positioning system (GPS), and can significantly contribute in assuring safe and reliable power system operation. The measurement data from WAMS is time tagged with GPS synchronization as a result data samples can be arranged to give the real-time status of the power system. The easy availability of data reduces delays significantly, hence the objective of enhancing UFLS to minimize the risk of ultimate system collapse due to extremely low frequency can be achieved. Since the measurements are gathered and processed in real time in the event of a severe disturbance, frequency can be easily restored [19; 20]. Based on WAMS frequency measurements, the prediction is constantly being updated and is very accurate for short prediction periods. The use of WAMS in power systems protection and control is widely known as wide-area monitoring, protection and control (WAMPAC) and it can be used in under-frequency protection [21]. Presently, many TSOs have taken advantage of WAMPAC in implementing robust UFLS schemes to curtail the severe effects of frequency collapse [22]. With WAMS, the implementation of adaptive techniques is possible. Practical electrical grids cover large geographical areas and without the use of WAMS, it was difficult to quickly pick the root cause of system collapse. The extensive use of WAMS has brought flexibility to TSOs in power networks monitoring [23; 24; 25]. With WAMS event monitoring, control and effects mitigation can now be performed more rapidly [26]. By making use of wide-area measurements, the adaptive schemes can be further enhanced by incorporating the optimization of the load to be shed. In the past, optimization techniques have been implemented to solve the UFLS problem. Based on the power flow data, a genetic algorithm is proposed to obtain the optimum amount of load to be shed in [27]. The main aim of the authors was to maintain the steady-state frequency within acceptable thresholds. In [28], genetic algorithm is also applied to calculate the amount of load to be shed whenever a frequency threshold is violated. The fuzzy logic is used is used in the modeling of bus-load uncertainties. Though with genetic algorithm (GA) a global optimum solution can be achieved, the main problem is on its convergence rate [29]. A comprehensive learning particle swarm optimization (CLPSO) technique is proposed in [30] to implement planned load-shedding and maintain stability of the islanded system. In the event of power loss from the main grid, the islanded system generators must be able to continue in operation supplying the islanded system. The algorithm in [30] is successfully applied to meshed and radial networks but it is also noted that the technique is affected by partial optimization. A review of GA and particle swarm optimization (PSO) shows that the techniques are mainly meant for offline applications and can be interrupted easily in determining the load to be shed. With the availability of PMU measurements, imperialist competitive algorithm (ICA) algorithm can be enhanced to determine the load to be shed since it has been proven in [31; 32] that ICA has a better convergence rate as compared to other algorithms such as PSO and GA. Also, for avoiding the cascading and blackout events in modern power systems, UFLS techniques need to be enhanced by adopting WAMS technologies and make use of real-time measurement data available from PMUs. Surveys on the existence UFLS techniques showed that there is a knowledge gap regarding developing optimal UFLS techniques based on WAMS technologies. In this paper, a WAMS-based frequency model is setup and it is coupled together with an optimization algorithm to perform optimum UFLS.