###### Abstract
They also discussed advantages and drawbacks of the system. [PERSON] et al. [8] considered the effects of reduction in structural loads. A low-authority linear-quadratic (LQ) controller was proposed, and this proposed controller was compared with the baseline controller (BC) utilized by the state-of-the-art wind turbine simulator FAST using a high-fidelity offshore wind turbine model. In [PERSON] et al. [9], a controller that utilized a simplified two-mass model and an adaptive fractional-order non-singular fast terminal sliding mode controller (AFO-NFTSMC), based on individual pitch control strategy considering uncertainties and external disturbances, was proposed. The proposed controller was implemented in the FAST environment considering the wind profiles utilizing TurbSim, and was explored in the presence of parametric uncertainties. [PERSON] et al. [10] proposed a control structure composed of the PI controller, adaptive feedforward compensator for the wind speed, and adaptive gain compensator for tower damping. This control structure was based on a Pareto optimization, multi-objective genetic algorithms and multi-criteria decision-making (MCDM) methods, and its performance was evaluated and compared with a classic baseline PI controller. [PERSON] et al. [11] reviewed the state-of-the-art approaches of wind power forecasting utilizing physical, statistical (time series and artificial neural networks), and hybrid methods. A guideline was provided for wind power forecasting, allowing the wind turbine/farm operators to identify the most appropriate predictive methods. [PERSON] et al [12] assessed input parameters such as wind-inflow conditions, turbine structural, and aerodynamic properties, and an elementary effects sensitivity analysis was performed using the National Renewable Energy Laboratory 5 MW base-line wind turbine under normal turbine operation conditions. They explained that inboard lift distribution, blade-twist distribution, and blade mass imbalance are the most important secondary parameters. Since aerodynamic imbalance loads due to individual pitch movement of the blade could adversely affect the entire offshore wind turbine, research on this subject is becoming more and more important [13]. Therefore, there are needs for continuous and systematic research on characteristics of ultimate loads and allowable pitch angle regions due to individual pitch movement when aerodynamic imbalance loads occur. In this study, a NREL- 5 MW offshore wind turbine was modeled using GH-BladedTM with a jacket-type substructure for the integrated load analysis under the aerodynamic imbalance of individual pitch movement. Here, the study scope was focused on 11 m/s, 14 m/s, 17 m/s, 20 m/s, 22 m/s, and 24 m/s of wind speed for pitch control. For aerodynamic imbalance load analysis, ultimate load analysis was performed by changing the individual pitch angle of the blade at 2\({}^{\circ}\) intervals from 0\({}^{\circ}\) to 30\({}^{\circ}\) under the steady state wind speed. Then, the maximum force and moment of the stationary hub were converted into resultant loads for quantitative load analysis. Allowable Pitch Angle (APA) region for each wind speed was confirmed by comparing ultimate loads and aerodynamic imbalance loads analyzed in the CPC state. Additionally, the APA region was modeled using the artificial neural network (ANN) to identify the possible integration with the IPC strategy.