## 1 Introduction
### Modeling of Wind Power Curves  
Regarding the modeling of wind power curves, prevalent methodologies encompass approaches grounded in the binning method, polynomial regression, and B-spline curve fitting. Binning Method for Wind Power Curve Modeling. [PERSON] et al. introduced a modeling approach using the binning method to articulate the relationship between wind velocity and generated power, examining the variability in modeling errors across different wind speed segments [1]. Similarly, [PERSON] et al. undertook a comparative analysis among the maximum value, maximum probability, and binning methods for modeling wind power curves. They highlighted the binning method's closer alignment with real-world turbine operations based on empirical data [2]. In addressing challenges posed by deterministic turbine output models' inability to precisely characterize statistical features of wind energy production, [PERSON] et al. refined power data analysis using an enhanced binning technique and data fitting processes to mitigate data interference [3]. Likewise, to accurately quantify production losses in individual turbines, [PERSON] et al. devised the GD-BIN method (Gaussian distribution and bins), creating a wind power curve model that accurately reflects each turbine's efficiency [4]. The GPR class method necessitates the random sampling of historical data for each prediction, resulting in high computational demands and reduced prediction stability [5]. Polynomial Fitting for Wind Power Curve Modeling. Engaging with power curve data selection and fitting processes, researchers employed advanced polynomial and logistic function fitting algorithms for wind speed-power curve modeling after data refinement. Comparative evaluations of model accuracy and efficiency revealed that polynomial fitting, with its straightforward principle, rapid fitting capability, and commendable accuracy, is well-suited for practical power curve modeling tasks [6,7]. [PERSON] et al. proposed an optimized local polynomial regression technique to develop an adaptive robust model for time-variant scatter power curves, aimed at enhancing predictive accuracy [8]. Through linear least squares, [PERSON] et al. assessed various polynomial regression models, identifying those offering superior approximations for diverse wind speed distributions [9]. Recent scholarly efforts have also embraced B-spline fitting for curve modeling. Research documented in [10] applied the Iteratively Reweighted Least Absolute Shrinkage and Selection Operator (Lasso) for complex autoregressive modeling with periodic B-splines, accommodating diurnal and annual variations. Investigating four curve fitting approaches, ref. [11] advanced discussions on polynomial fitting by introducing methodologies such as local weighted polynomial fitting, cubic spline fitting, and cubic B-spline curve fitting to elevate fitting precision and adaptability. An innovative wind power curve modeling technique, employing enhanced smooth splines for wind speed-power data fitting, was outlined in [12]. This approach utilized cubic splines for data approximation and applied a roughness penalty for coefficient regularization, with cross-validation determining the optimal smoothing parameters. Addressing the modeling and optimization challenges in small-sample scenarios with complex mixed-type parameter interactions, ref. [13] proposed a novel strategy integrating Gaussian process regression with B-spline curves. This method constructed a functional parameter model through a weighted combination of B-spline-based functions and control points, followed by a genetic algorithm for model optimization. The investigations cited previously have significantly advanced the modeling of wind power curves. Nonetheless, when translating these advancements into practical settings, two primary challenges emerge. Firstly, the discrepancies between mathematical function curves and empirical wind power curves present a notable challenge.