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The volatility and uncertainty introduced by increasingly integrated renewable energy pose challenges to the reliable and stable operation of the power system. To mitigate the operation risks, a two-stage optimal preventive control model that incorporates transient stability constraints and considers uncertainties from multiple resources is proposed. First, the uncertainties of different re-sources are modeled, with which the non-sequential Monte Carlo sampling method is used to correspondingly generate the scenarios. Thereafter, a two-stage control model that balances operational safety and economy and realizes preventive control and emergency control is built. The operation schedule from the preventive control stage aims to minimize the transient stability probability and operation costs. If any faults destabilize the system, the emergency control stage will be activated immediately to help the system recover stability with minimal control costs. To expedite the solving of the two-stage model, a multi-objective particle swarm algorithm based on entropy-TOPSIS is proposed. Finally, the effectiveness of the proposed model and solving algorithm are validated with the modified IEEE118 node system.

A fuzzy radial basis inference network with grouped signal feature embedding (GFE-FINN) classification model is proposed for multi-source time-varying signal fusion analysis and feature knowledge embedding, which multi-channel signals are divided into several groups according to the sources, attribute, features and sensitivity of signals. Each pattern class of the grouped signal sample set is divided into several pattern subclasses which are more similar features according to the grouping index, and typical feature samples are extracted to implicitly express the category features knowledge of the grouped signal. A fuzzy radial basis process neuron (FRBPN) is defined, which is used as parametric membership functions, and the typical feature signal samples of the grouped pattern subclass are used as the kernel centers of FRBPN to realize the embedding of the diverse feature knowledge. Through the kernel transformation in FRBPN, the input signals of each group are fuzzified respectively. Fuzzy multiplication operation is used to realize the information synthesis based on the membership degree of grouped pattern subclasses and establish fuzzy reasoning and classification rules. The proposed method can realize the feature fusion based on fuzzy membership degree and the semantic representation based on fuzzy rules hierarchically. Through the learning of the sample set, fuzzy membership function, reasoning and classification rules are established adaptively. A comprehensive learning algorithm was given. An experiment was conducted using 4-groups 12-lead long ECG signals in diagnosis of difficult heart disease. The correct recognition rate reaches 87.95%, and the performance evaluation index and generalization ability are significantly improved. INDEX TERMSMulti-source time-varying signal classification, signal grouping, feature knowledge embedding, parametric membership function, fuzzy reasoning rules