基于阈值调整的负荷辨识开集识别算法OA

Load identification is one of the key technologies in power system planning,operation,and management,playing a crucial role in the efficient scheduling and stable operation of smart grids.Traditional load identification methods typically rely on the closed-set assump-tion.However,in practical applications,the presence of unknown appliances makes it difficult for algorithms based on this assumption to achieve accurate recognition.To address this issue,an open-set load identification algorithm,OpenAppliance,based on threshold adjust-ment is proposed.The proposed algorithm integrates deep learning and probabilistic models,calibrating the neural network outputs to en-hance the detection capability for unknown categories while maintaining recognition accuracy for known categories.First,load data is trans-formed into an image format suitable for deep learning,and a CNN-based load identification model is constructed.Then,the OpenAppliance algorithm is applied for post-processing to adjust classification thresholds and optimize recognition results.Finally,the method is validated on the BLUED load dataset and compared with existing load identification algorithms.Experimental results demonstrate that the OpenAppli-ance algorithm enhances the generalization ability of load identification and significantly improves the accuracy and robustness of the load identification system.