Improved Equivalent Method for Large-Scale Wind Farms Using Incremental Clustering and Key Parameters Optimization

Abstract

Large-scale wind farms (WFs) generally consist of hundreds of wind turbines (WTs), and the WFs simulation model construction would be complex and even impossible if we develop each individual WT in detail. Therefore, the WFs equivalent simulation model with required accuracy is essential to be developed to explore the WFs operation characteristics. This article proposes an equivalent method for large-scale WFs using incremental clustering and key parameters optimization. Firstly, to acquire more comprehensive and distinguishable representations of WTs operation characteristics, the time series of WT active power, reactive power, voltage and current are selected as the multi-view clustering indicator (CI). Then, considering the computer memory pressures encountered by traditional clustering algorithms in dealing with large-scale WFs, a novel clustering algorithm namely multi-view incremental transfer fuzzy C-means (MVIT-FCM) is proposed, and this algorithm can process the WTs clustering problems without requiring to consider the scale of the WFs. Finally, to further increase the equivalent accuracy of the WFs equivalent simulation model, key parameters in the equivalent model are found using Sobol’ criterion and then optimized using the designed Q-learning based non-dominated sorting genetic algorithm II (NSGA-II). To verify the effectiveness of the proposed method, the modified WFs system in China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent accuracy of the proposed model is higher when comparing with other models. Also, the proposed model has the advantage of processing the WFs equivalent problems with any scales.