With increase of practical power system complexity, power system online stability assessment and control is more and more important. Application of the traditional model-driven methods is always limited by contradiction between accuracy and efficiency, while data-driven methods demonstrate strong abilities for the online decision-making support with advancement of various data mining techniques. Instead of direct application of data-driven methods in the power system, this paper first discusses feasible integration approaches for the model-driven and data-driven methods based on the existing achievements, and then, proposes to integrate both methods for the power system online frequency stability assessment and control. The integrated method consists of frequency dynamics prediction and load shedding procedure. In frequency dynamics prediction procedure, integration of system frequency response (SFR) model and the extreme learning machine (ELM)-based learning model is applied, where basic physical causality is kept in the SFR model and ELM is used to fit and correct error of the SFR. The ELM also plays a part in load shedding prediction model construction by digging out mapping relationship from samples. Finally, the proposed prediction and control scheme for the frequency stability is verified by simulations on WSCC 9-bus, New England 39-bus, and NPCC 140-bus system. Results show that the reliability, time efficiency, and accuracy are enhanced with the proposed method.

This paper proposes a graph genetic dynamic minimum spanning tree (DMST) optimization method for the layout designing of large-scale offshore wind farm collector system to minimize total lifecycle cost, considering both current carrying capacity and cable crossing avoiding (CCA) constraints. In this method, DMST is used to generate an initial feasible solution, ensuring a high-quality and reasonable solution at the first. Then Graph Genetic Algorithm (GGA) is applied to optimize these feasible solutions. A partitioning approach is employed to effectively reduce the complexity of the problem. This method can quickly find solutions with low total lifecycle costs while ensuring the feasibility and quality of the solution. Meanwhile, the proposed method is compared with DMST and particle swarm optimization (PSO) based algorithm, and its effectiveness and efficiency are validated through case studies of offshore wind farm projects, especially for large-scale wind farms.

Ensuring frequency stability in microgrids (MGs) is becoming increasingly challenging due to the openness of networked control systems, the volatility of renewable energy sources, and the growing threat of hybrid cyber-physical attacks. This paper presents a delay-tolerant resilient model predictive control (MPC) approach for load frequency stability in MGs. First, a comprehensive MG load frequency control (LFC) model is developed, explicitly accounting for communication time delays. A control strategy that combines resilient MPC with delay compensation is proposed to enhance system resilience and dynamic performance. To address hybrid cyber-physical attacks, a resilient MPC-based output feedback controller is designed based on a hybrid $H_{2}/H_{\infty }$ performance index, in which the $H_{2}$ criterion is optimized online under a guaranteed $H_{\infty }$ performance bound, ensuring robust frequency regulation. Feasibility and safety of the controller are guaranteed by reformulating the non-convex constraints via cone complementary linearization, and solving the controller using linear matrix inequality techniques. Stability and feasibility analyses of the algorithm are also provided. Finally, a series of comparative simulations under various attack scenarios and perturbation conditions are conducted to validate the robustness of the proposed method and demonstrate its effectiveness in enhancing the resilience of the MG system.

Inertia is the measure of a power system’s ability to resist power interference. The accurate estimation and prediction of inertia are crucial for the safe operation of the power system. To obtain the accurate power system inertia provided by generators, this paper proposes an estimation method considering the influence of frequency and voltage characteristics on the power deficit during transients. Specifically, the traditional swing equations-based inertia estimation model is improved by embedding linearized frequency and voltage factors. On this basis, the snake optimization algorithm is utilized to identify the power system inertia constant due to its strong global search ability and fast convergence speed. Finally, the proposed inertia estimation method is validated in four test systems, and the results show the effectiveness of the proposed method.