## 1 Introduction  
In an isolated power system known as a hybrid wind-diesel power system (HWDPS), diesel generators are used in conjunction with wind turbine generators to use less fuel and obtain the best energy output possible from the variable wind resources. This consequently lowers operating costs and has a less negative impact on the environment. The HWDPS system is an attractive solution for supplying electric power to isolated areas due to the technical difficulties and economic challenges of providing electric energy to such areas directly from the grid. Since both wind energy and actual energy needs are intermittent, it is essential to have a suitable control approach for upholding the desired frequency by eliminating any offset between the generation and load. In this regard, the load frequency control (LFC) technique has been developed in the literature in order to balance the power supply and keep the system frequency at a desirable level, see [1; 2; 3] and the references therein. Different LFC strategies have been proposed to reduce the frequency mismatch between generation and load demands, such as PID control [4; 5; 6], artificial neural network (ANN) control [7; 8], fuzzy control [9; 10], optimal control [11; 12; 13; 14], adaptive control [15; 16; 17], and sliding mode control [18; 19; 20]. Most existing LFC techniques are carried out in the continuous-time domain. However, in practice, the feedback signal is often analog and the control setup is digital, i.e., the sampled-data control (SDC) [21; 22]. Hence, it is important to consider the digital implementation effect when the feedback law is constructed. In particular, the sampling effect needs to be considered in the control design, otherwise, the closed-loop stability can no longer be guaranteed. In this context, some results have been reported in the literature regarding the lowest stabilizing sampling rate in a periodic time-triggered implementation [23; 24]. However, since periodic sampling is often based on the worst-case scenario, the derived sampling frequency can be conservative [25]. To overcome this issue, event-triggered control (ETC) has been proposed, such that the sequence of sampling times is produced by state-dependent sampling, as opposed to fixed periodic sampling, such that a new transmission is only generated when it is necessary from the stability/performance perspectives [26]. A critical property of ETC is to ensure the existence of a strictly positive minimum time between the transmission instant to avoid the accumulation of sample intervals over a finite period of time (Zeno phenomenon). This task becomes more difficult to resolve when external disturbances impact the system [27; 28] or are subject to communication delays [29; 30]. Different ETC approaches have been applied to the FLC problem in the literature [31; 32; 33; 34; 35; 36; 37; 38; 39]. In [31; 32], memory-based ETC techniques were designed for the LFC problem for multi-area power systems [31] and T-S fuzzy wind turbine systems [32]. The developed approach in [31] takes into account possible cyber attacks and network delays and an \(H_{\infty}\) stability property has been established. The authors of [32] considered modeling uncertainty and distributed delay. The authors of [33] investigated the LFC problem using an event-triggered sliding mode control (SMC) in the presence of external disturbances and transmission time delays. The plant model in [33] was assumed to be linear time-invariant (LTI) and the analysis was carried out in continuous-time. In [34], an area-based ETC was developed for the LFC of multi-area power systems in an asynchronous fashion. Then, the frequency fluctuation was handled by an SMC. Each area power system was described by an LTI discrete-time model. In [35], an ETC mechanism was developed for the LFC to ensure \(H_{\infty}\) stability against cyber attacks, such as denial-of-service and deception attacks. The system was modeled as a linear switched system and stability was performed in continuous-time.