Reliability evaluation of wind power systems by integrating granularity-related latin hypercube sampling with LSTM-based prediction

Abstract

When evaluating the reliability of a wind power system, it is imperative to undertake differentiated sampling and meticulously predict extensive datasets. Existing studies frequently constrain raw data within narrowly defined parameter spaces to enhance their statistical significance. However, such an approach may inadvertently engender overly optimistic reliability evaluations, neglecting rare yet crucial failure scenarios. Consequently, this oversight potentially underestimates systemic risks and undermines robustness. To date, the dichotomy between high data acquisition rates and the intrinsic characteristics of collected data remains inadequately addressed. Concurrently, an urgent requirement persists for developing precise data distribution models capable of comprehensively assessing wind power system reliability. In response, Long Short-Term Memory (LSTM) models are employed to bridge this research gap, enabling predictions of wind power generation through analyses of data at varying granularities. Subsequently, an Improved Latin Hypercube Sampling (ILHS) methodology is implemented to partition sampling intervals, integrating seamlessly with the Monte Carlo (MC) method for wind power data sampling. This reliability assessment model fully exploits the flexibility of the proposed sampling technique, enhancing the precision of sample probability distributions, interval segmentation, and data stratification. Empirical evidence demonstrates that the proposed algorithm exhibits superior predictive accuracy and enhanced statistical efficacy relative to conventional methodologies. Thus, it offers a robust and efficacious solution for assessing the reliability of wind power integration. This study evaluates the practical reliability of a local wind power integration system in Southwest China. Additionally, methods for discerning vulnerabilities are systematically applied to fortify critical power buses and augment overall system reliability.