## 1 Introduction  
Modern power systems have become so sophisticated that research in the area of protection and control remains of major interest. Due to the ever-increasing electrical demand, large electrical power systems (EPS) are usually loaded close to their steady-state stability limit and thus are more vulnerable to network disturbances [1; 2]. The operation at points close to the steady-state stability limit is a huge threat to power system security, such that in the event of a disturbance, the probability of cascading events, which might lead to total system collapse, is inevitable if not rectified in time. The most common disturbances are generator tripping, transmission line outages, lightning strikes, and short circuits. These disturbances are attributed in literature as the major causes of the system frequency to fall below set thresholds and triggers corrective action [3; 4]. During severe disturbances, frequency stability, control, and protection can be a subject of primary concern [5; 6; 7]. In the past, several under-frequency load-shedding (UFLS) techniques have been introduced which can be mainly classified as traditional, adaptive, semi-adaptive, and computational-based UFLS techniques. In traditional techniques, relays make use of locally measured frequency which is constantly compared against a certain threshold as the input [8; 9]. The frequency thresholds may differ from one power system to another and a violation of each threshold triggers relay action. The most important advantages of these schemes are simplicity, the ease of implementation, and their reliability. The major limitation in these schemes is that a steep frequency gradient (df/dt) and a gradual one is usually treated equally and may result in over or under-shedding [10]. Over-shedding affects power quality and under-shedding usually leads to tripping of electricity services. This is due to its inability to estimate the actual amount of the power imbalance since they simply follow a rigid pre-set rule in which a fixed amount of load is shed when frequency deviates from the nominal value. Besides the local frequency measurements, traditional UFLS schemes can make use of a variety of input information which have an influence on system frequency stability such as governors, load characteristics, exciters [11] and system inertia, and this can pose huge complexities in calculations. In comparison to traditional methods, with adaptive UFLS schemes the complexity can be reduced and optimum load-shedding can be achieved. While traditional schemes are designed for the worst-case scenarios, adaptive methods are situation-based and works well under almost all cases. Based on dynamic behavior of the system's frequency and swing equation, the actual active power imbalance can be determined, and consequently appropriate actions can be taken. An improved technique based on the swing equation to estimate the magnitude of generation loss was recently proposed in [12]. The authors accounted for the inertia variations both before and soon after the disturbance to improve estimation accuracy. Adaptive schemes can be semi-adaptive, power imbalance adaptive, or adaptive schemes with predictive capabilities. Predictive UFLS schemes introduces a new concept where it is not necessary to find the root cause for frequency variations such as demand-generation imbalance [13]. In this scheme, a forecast in frequency behavior is made a few seconds before corrective action, if prediction indicates the violation of allowable frequency tolerances, load-shedding is triggered. The main limitation with this scheme is that predictive UFLS always have inherent prediction uncertainties and as time increase the prediction becomes even less reliable. However, the advantage of frequency prediction is that it can be performed in real time. Adaptive schemes only work when the operator can gather information in real time.