This paper focuses on the load frequency control (LFC) for a multi-area interconnected microgrid power system with the introduction of communication networks, a robust sliding mode control strategy based on the adaptive event-triggered mechanism is proposed against the frequency deviation caused by power unbalance or time delays. First, a three-area power system attached to renewable energies and energy storage is considered, the corresponding LFC model is established. Second, the networked control is introduced into the LFC scheme, and the adaptive trigger mechanism which can adaptively adjust the event-triggered threshold is designed to improve the data transmission efficiency, the LFC scheme with network induced delays is formulated. Third, the Luenberger observer is designed to estimate the state errors and to facilitate the design of the sliding surface, the overall closed-loop system asymptotically stable and robust performance are analyzed by solving linear matrix inequalities. Then the control law is proved that the system state trajectory can be driven into the designed sliding surface in a limited time. Finally, some simulation experiments are given, and the results show that the proposed method is effective and has excellent robust performance. INDEX TERMS Microgrid power system, load frequency control, event-triggered control, robust performance, sliding mode observer.

Abstract:In this paper, the secondary load frequency controller of the power systems with renewable energies is investigated by taking into account internal parameter perturbations and stochastic disturbances induced by the integration of renewable energies, and the power unbalance caused between the supply side and demand side. For this, the µ-synthesis robust approach based on structure singular value is researched to design the load frequency controller. In the proposed control scheme, in order to improve the power system stability, an ultracapacitor is introduced to the system to rapidly respond to any power changes. Firstly, the load frequency control model with uncertainties is established, and then, the robust controller is designed based on µ-synthesis theory. Furthermore, a novel method using integrated system performance indexes is proposed to select the weighting function during controller design process, and solved by a differential evolution algorithm. Finally, the controller robust stability and robust performance are verified via the calculation results, and the system dynamic performance is tested via numerical simulation. The results show the proposed method greatly improved the load frequency stability of a micro-grid power system.

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In machine learning-based transient stability assessment (TSA) problems, the characteristics of the selected features have a significant impact on the performance of classifiers. Due to the high dimensionality of TSA problems, redundancies usually exist in the original feature space, which will deteriorate the performance of classification. To effectively eliminate redundancies and obtain the optimal feature set, a new feature reduction method based on neighborhood rough set and discernibility matrix is proposed in this paper. First, 32 features are selected to structure the initial feature set based on system principle. An evaluation index based on neighborhood rough set theory is used to characterize the separability of classification problems in the specified feature space. By constructing the discernibility matrix of input features, a feature selection strategy is designed to find the optimal feature set. Finally, comparative experiments based on the proposed feature reduction method and several common feature reduction techniques used in TSA are applied to the New England 39 bus system and Australian simplified 14 generators system. The experimental results illustrate the effectiveness of the proposed feature reduction method.

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