In this paper, a type of multi-agent system is adopted in the practical project with time delay, uncertainties, and linear feedback. In addition, sliding mode control is used to ensure the robust stability of the system since it is insensitive to parameter change and interference. For the system in three different conditions, namely fixed structure, every agent being only influenced by single time delay and each agent being affected by multiple time delay, the corresponding sliding surface and the control law are improved. The reaction factors are proven by Lyapunov functions and the linear matrix inequality approach is taken to guarantee the robust stability of the sliding surface. To prove the effectiveness of the conclusion, experiments on each condition are conducted. Besides, in the last part, a simple application is applied and proved to be effective.

The beetle antennae search (BAS) algorithm is an outstanding representative of swarm intelligence algorithms. However, the BAS algorithm still suffers from the deficiency of not being able to handle high-dimensional variables. A quantum-based beetle swarm optimization algorithm (QBSO) is proposed herein to address this deficiency. In order to maintain population diversity and improve the avoidance of falling into local optimal solutions, a novel quantum representation-based position updating strategy is designed. The current best solution is regarded as a linear superposition of two probabilistic states: positive and deceptive. An increase in or reset of the probability of the positive state is performed through a quantum rotation gate to maintain the local and global search ability. Finally, a variable search step strategy is adopted to speed up the ability of the convergence. The QBSO algorithm is verified against several swarm intelligence optimization algorithms, and the results show that the QBSO algorithm still has satisfactory performance at a very small population size.

To enhance wind farm frequency regulation in renewable-dominant power systems, this paper proposes a bi-level hybrid framework integrating deep learning and model predictive control (MPC) by retaining the critical wake propagation delay while neglecting higher-order turbulence effects. The upper layer employs a synthetic inertial intelligent control strategy based on contractive autoencoder (CAE) and deep neural network (DNN). Particle swarm optimization (PSO) obtains optimal synthetic inertial parameters for dataset construction, CAE extracts features from multi-dimensional inputs, and DNN outputs optimal coefficients to determine the total power deficit the wind farm needs to supply. The lower layer uses a nonlinear model predictive control (NMPC) strategy with the discretized rotor motion equation as the prediction model and optimization under constraints to allocate the total power deficit to each turbine. MATLAB/Simulink case studies show that, compared with fixed-coefficient synthetic inertial control, the proposed framework raises the frequency nadir by 0.01–0.02 Hz, shortens the settling time by over 200 s under 2–4% load disturbances, and maintains rotor speed within the safe range. This work significantly enhances the wind farm’s frequency regulation performance, contributing to power system and energy sustainability.

This paper is concerned with the nonfragile state estimation for a kind of delayed fractional-order neural network under the event-triggered mechanism (ETM). To reduce the bandwidth occupation of the communication network, the ETM is employed in the sensor-to-estimator channel. Moreover, in order to reflect the reality, the transmission delay is taken into account in the model establishment. Sufficient criteria are supplied to make sure that the augmented system is asymptotically stable by using the fractional-order Lyapunov indirect approach and the linear matrix inequality method. In the end, the theoretical result is shown by means of two numerical examples.

With the continuous and large-scale development of renewable energy, there is a prominent decrease in the level of inertia in new power systems. This decrease leads to the weakening of the system’s capability to provide inertia support and frequency regulation during disturbance events. The wind turbines (WT), as the main representatives of renewable energy generation, should be more efficiently involved in the power system frequency regulation dynamics. However, optimal frequency regulation is difficult to achieve through the combined inertial control strategy of wind turbines because it greatly depends on control parameters and fluctuates in different scenarios. To cope with disturbance efficiently and quickly in different scenarios and obtain the optimal frequency regulation results, this paper presents an improved combined inertial intelligent control strategy of WT based on contractive autoencoder (CAE) and deep neural network (DNN). This method obtains the optimal parameters for combined inertial control using the particle swarm optimization (PSO) algorithm, then effectively extracts features from actual data using CAE followed by building a network model to predict the optimal combined inertial control parameters online. To verify and test the proposed method, it is applied in the IEEE 9-bus test system. The simulation results show that the method can obtain optimal control parameters with a faster computational time, good prediction accuracy, and generalization capability.

Filtering or state estimation plays an important role in the cyber-physical systems (CPSs). This study aims to solve the data-driven non-fragile filtering problem for the cyber-physical system. Randomly occurring gain variations (ROGVs) are considered so as to account for the parameter fluctuations occurring during the filter implementation. The data-driven communication mechanism is utilized to reduce the measurement transmission frequency and save energy for the CPSs. Therefore, a unified H∞ filtering framework that combines the data-driven communication mechanism and the non-fragility of filters is constructed. Based on this unified framework, the influence of the simultaneous presence of networked-induced packet dropouts, quantization, randomly occurring nonlinearities (RONs) and randomly occurring parameter uncertainties (ROUs) in the CPS is investigated. A modified dropouts model is proposed under the data-driven communication mechanism. By utilizing stochastic analysis and Lyapunov functional theory, sufficient conditions guaranteeing the filtering performance are derived. The H∞ filter is obtained through the proposed algorithm. Last, a simulation is given to demonstrate the filtering method for CPS in this paper.