These values are realized by the stator power converter for \(f_{OPTIM}\) and by the rotor power converter for \(R_{ad-OPTIM}\). In the second range, for limiting the electrical and mechanical stress within admissible bounds, under high wind speeds, both the WT power and MAS are saturated; the WT power is regulated through the blade pitch angle \(\beta\), while the generator power is characterized by the stator working at constant frequency \(f\) and the rotor working at constant \(R_{ad}\) actuated by the rotor converter. In this regime, the generator power changes only subject to the MAS value \(n\) but it is the WT power characteristic selected as the controller quantity. **Remark 3**: _In the first interval corresponding to small up to medium wind speeds, the blade pitch angle \(\beta=0\) is constant, while in the second interval, it is changed as it enters the active control role._  
Recent results on blade pitch angle control are analyzed from the literature. Improved WT operation under individual pitch control was proposed by the authors of [28]. A linear quadratic pitch angle controller was designed for a variable speed WT of 850 [kW] in [29]. Intelligent genetic algorithms were used to optimize the parameters of a PID for WT blade pitch controller in [30], while for WT load mitigation, individual pitch controllers were proposed by [PERSON] et al. in [31]. Comparisons of different metaheuristic optimizers were provided by the authors of [32] for the same blade pitch control problem. A similar optimal-based control approach is followed by [33] but for a doubly fed induction generator in WT, based on evolutionary algorithm. Machine learning is a novel approach dedicated to pitch angle control in WTs and was among the first reported in the work of [34; 35]. Actuator faults were considered for improving the load mitigation in WTs through fault-tolerant pitch control by the authors of [36]. Yet another machine learning control, this time a reinforcement learning approach, was proposed for the same control problem in the work [37]. A wider and theoretically oriented work towards stability analysis under several controllers is proposed in [38]. Under large wind speed variation, which is also the topic of concern in this work, the pitch control was considered by [PERSON] et al. in [39]. Adaptive control for improved tracking performance of WTs through blade pitch angle control is proposed by [PERSON] et al. in [40]. For offshore WTs, the control robustness issue under high wind and waves was considered and proven feasible by the authors of the work [41]. For variable speed wind turbine operation, the wind speed is the most important factor, as it is simultaneously a time-varying parameter in the dynamical model and also imposes the optimal MAS reference input value to ensure MPP tracking. On short time intervals lasting from several seconds to several tens of seconds, the wind speed can be accepted to follow a ramp-like temporal variation schedule. In effect, this also drives a ramp-like MAS reference input variation. The internal model principle in control requires a double integrator to ensure null steady state tracking error; however, the WT model analyzed in this work shows that this is not possible in the operating region where MAS control is required, so a tradeoff must be made. On the other hand, at higher wind speeds, corresponding to the operating region where the blade pitch angle is used to control the WT power, the linearized dynamics show that a double integrator is feasible and therefore implemented. Different from the analyzed literature, this paper works out several distinct contributions. First, a real-world case study analysis is proposed, based on WT models operating in the Oravita region, Romania. Secondly, the high wind speed variation in continental locations forces the WTs to operate in different regions, aimed at either maximum operation efficiency (maximum power point) or at maximally reliable operation through saturation of the powers and MAS. This aspect is clearly analyzed in our work.