# Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
## 1 Introduction  
Recent trends in wind turbine development emphasize the need for large-diameter rotors and tall towers to efficiently utilize wind resources and maximize energy production. Major global wind turbine manufacturers have been continuously conducting research on the development of ultra-large wind turbine models with capacities exceeding 14 MW, and some models have already been developed and released [1; 2; 3]. On the other hand, with the growing need for micro-grid systems in regions where establishing a national power grid is challenging, such as islands, small- to medium-sized wind turbines are being highlighted as key energy sources [4; 5; 6; 7]. Consequently, continuous research and development of small- and medium-sized wind turbine models are required. While small wind turbines come in various configurations depending on the type of generation and purpose, horizontal-axis lift-type turbines are known to be the most advantageous in terms of power generation efficiency [8; 9]. For this reason, small wind turbines with capacities above several tens of kW and medium-sized wind turbines are generally designed as horizontal-axis lift-type models, similar to large wind turbines [10; 11]. The power control algorithm that determines the operational performance of horizontal-axis lift-type wind turbines can be categorized into generator torque control and blade pitch control. Generally, torque control is used in the operating regions known as Region II and Region II-1/2, where wind speeds are below the rated wind speed. In contrast, blade pitch control is used in the operating region known as Region III, where wind speeds exceed the rated wind speed [12; 13]. The main objective of torque control is to maximize the efficiency of wind turbines or, in the case of turbines equipped with gearboxes, to reduce the loads related to the drive train mode [14]. According to the research by [PERSON] et al., a torque control strategy was designed and proposed to eliminate torque noise acting on the drive train's final stage, and to reduce the torque fluctuations that cause excessive damage. The proposed algorithm was validated using the FAST simulation model. Although the quantitative results were not provided, the reduction in torque fluctuations was confirmed through time-series and frequency domain data analysis [15]. [PERSON] et al. designed an optimal Tip Speed Ratio (TSR) tracking control for torque control. The proposed optimal TSR tracking control calculates the torque command through Proportional-Integral (PI) control based on the error between the operating TSR, derived from predicted wind speed, and the optimal TSR. The simulation results showed a reduction in the overall power by about 2%, but the torque fluctuation rate was reduced by approximately 80% [16]. [PERSON] et al. proposed a maximum power tracking control using an aerodynamic torque estimator. The proposed control algorithm was validated through a test bench, and although no quantitative results were provided in the literature, it was concluded that the control was efficient and successful based on time-series data [17]. Pitch control algorithms have been applied for purposes such as power control and load reduction, using techniques like peak shaving [18; 19], feed-forward control [20], and individual pitch control [21; 22]. In the study validating peak shaving, the control algorithm was updated on a Programmable Logic Controller (PLC) and demonstrated on a 100 kW wind turbine. Peak shaving reduces the thrust and loads acting on the wind turbine by pre-emptively sending pitch commands in the Region II-1/2 operating area. The demonstration results showed that the standard deviation of the rotor speed was reduced by approximately 8% [19]. [PERSON] et al. proposed a feed-forward control using wind speed measurements from LIDAR and designed the algorithm by adding a feed-forward logic to the conventional pitch control algorithm.