Wind Farm Grid-Connection Performance Quantification and Grade Evaluation Method Based on Improved TOPSIS and IGWO Algorithms

The global energy transformation promotes the development of large-scale grid-connected wind energy. The grid-connected operation performance of wind farms is related to wind power consumption and the safe and stable operation of the power system. A combined weighting system that integrates the improved analytic hierarchy process, entropy weight method, and multiplier synthesis normalization strategy is studied and constructed, and a static quantification model of the improved approximate ideal solution ranking method is established. The purpose of this system is to achieve comprehensive quantification and dynamic timing level assessment of wind farm grid-connected performance, and provide refined decision support for power grid dispatching and wind farm operation and maintenance. At the same time, it combines the improved gray wolf optimization algorithm and the multi-scale attention-bidirectional long short-term memory network to build a sequential level evaluation model. Experimental results showed that the subjective and objective consistency coefficient of the combined weighting method reached 0.9524, the standard deviation of the closeness of the improved approximate ideal solution ranking method was 0.0926, and the coefficient of variation in the small-sample scenario was 0.138. After optimization with the improved gray wolf optimization algorithm, the model’s F1-score increased to [Formula: see text] (validation-set optimum during IGWO search), and the accuracy of the fusion model reached [Formula: see text]%. Compared with the baseline long short-term memory network model, the accuracy increased by 10.6 percentage points, and all performance indicators were better than similar comparison models. The performance quantification of the proposed method can complete the precise prediction of the grid-connected performance of the wind farm, and the hierarchical evaluation method can realize the five-level sequential assessment of the grid-connected performance, providing an effective technical method for the wind farm grid-connected performance evaluation.