With the increasing capacity of new energy in the power system, new energy cannot provide support for the system frequency directly. This characteristic of new energy affects the frequency stability of the power system. Therefore the control strategy of a virtual synchronous generator (VSG) is proposed to improve the frequency stability of the system. An adaptive virtual inertia control strategy based on an improved bang-bang control strategy for a micro-grid is presented. On one hand, it can make full use of the variability of virtual inertia to reduce dynamic frequency deviation. On the other hand, the steady-state interval of frequency and the steady-state inertia are set to improve the system frequency stability. Then the stability analysis of the value range of the virtual inertia is performed by the small signal model of the VSG for the micro-grid. Meanwhile, the ranges of virtual inertia and steady-state inertia are determined. Finally, Matlab/Simulink is applied to accomplish simulation experiments to compare various virtual inertia control strategies. The results indicate the effectiveness of the proposed strategy.INDEX TERMS Adaptive virtual inertia, bang-bang control strategy, micro-grid, virtual synchronous generator.
The inertia of the power system changes when the generators in the system are disturbed or tripped. However, in some systems, there is no control center or communication device falling behind. It makes real-time inertia difficult to obtain and affects the adaptive under frequency load shedding (UFLS) scheme. This paper proposes an adaptive decentralized UFLS scheme based on load information to overcome this problem. First, a calculation method is derived according to the load information after the disturbance to identify the real-time of load equivalent virtual inertia. Then, the power deficit of load is estimated and the optimal adaptive load shedding scheme is obtained. Finally, compared with conventional UFLS schemes, the proposed scheme can be performed merely by load information. Hence, the requirement of communication conditions and the dependence on the control center are reduced. The simulation is carried out on the island utilizing the IEEE39-bus system. Furthermore, the accuracy of real-time identification of load equivalent virtual inertia and the validity of the load shedding scheme are verified. INDEX TERMS Load information, power deficit, inertia calculation, frequency stability, real-time calculation.
Summary
Critical situations are difficult to predict reliably by the machine learning‐based transient stability assessment (TSA) methods. Therefore, the practicality of the data‐driven TSA is limited. A parallel TSA framework constructed by two basic predictors and a comprehensive decider (CD) is proposed to achieve fast and reliable real‐time transient stability assessment (RTSA). A cost‐sensitive method is utilized for stacked sparse auto‐encoders to establish two basic predictors with opposite evaluation biases. Then, the outputs of the two basic predictors are sent to the CD. Finally, the stability of the non‐critical cases can be judged directly, and the critical cases are suggested to be analyzed by other methods. Besides, in order to enhance the reliability of the parallel predictor, a simple data augmentation approach with Gaussian white noise is employed to expand the classification boundaries. A fault severity factor is introduced to filter basic critical samples for data augmentation to improve the performance of the proposed framework. The effect of the proposed strategy is verified on the IEEE‐39 bus system and a realistic regional system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.