The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural network (CNN) is used as the base classifier. A model training method based on contrastive learning is introduced, aiming to increase the spatial distance between positive and negative samples in the mapping space. This approach effectively improves the accuracy of the model in recognizing unbalanced samples. In the online phase, when real data with different distribution characteristics from the offline data are encountered, an active transfer strategy is employed to update the model. New system samples are obtained through instance transfer from the original system, and an active sampling strategy considering uncertainty is designed to continuously select high-value samples from the new system for labeling. The model parameters are then updated by fine-tuning. This approach drastically reduces the cost of updating while improving the model’s adaptability. Experiments on the IEEE39-node system verify the effectiveness of the proposed method.

This paper studies the containment control problem of linear multi-agent systems (MASs) subject to external disturbances, where the communication graph is a directed graph with the followers being undirected connections. In order to save communication costs and energy consumption, a distributed disturbance observer-based event-triggered controller is employed based on the relative outputs of neighboring followers. Compared with conventional controllers, our observer-based controller utilizes the relative outputs of neighboring followers at the same triggered instant. Furthermore, it is shown that Zeno behavior can be avoided. Finally, the validity of our proposed methodology is demonstrated by a simulation example.

In order to improve the reliability of power supply, the sophisticated design of the structure of electric cable network has become an important issue for modern urban distribution networks. In this paper, an electric cable path planning model based on heuristic optimization algorithm considering mixed-land scenario is proposed. Firstly, based on different land samples, the kernel density estimation (KDE) and the analytic hierarchy process (AHP) are used to estimate the construction cost of each unit grid, in order to construct the objective function of comprehensive investment for electric cable loop network. Then, the ant colony optimization (ACO) was improved in pheromone concentration, factor increment and search direction to accelerate the solving speed, and the cable path planning result with minimum construction cost is obtained. Finally, the feeder’s tie line of the cable loop network is planned by the genetic algorithm (GA) to achieve the minimum operating cost. In the case analysis, compared with the traditional method, not only the subjective factors in the process of investment estimation can be avoided, but also the speed of model solving and the quality of the optimal solution are improved.

More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods. The traditional model-driven methods have clear physical mechanisms and reliable evaluation results but the calculation process is time-consuming, while the data-driven methods have the strong fitting ability and fast calculation speed but the evaluation results lack interpretation. Therefore, it is a future development trend of transient stability assessment methods to combine these two kinds of methods. In this paper, the rate of change of the kinetic energy method is used to calculate the transient stability in the model-driven stage, and the support vector machine and extreme learning machine with different internal principles are respectively used to predict the transient stability in the data-driven stage. In order to quantify the credibility level of the data-driven methods, the credibility index of the output results is proposed. Then the switching function controlling whether the rate of change of the kinetic energy method is activated or not is established based on this index. Thus, a new parallel integrated model-driven and data-driven online transient stability assessment method is proposed. The accuracy, efficiency, and adaptability of the proposed method are verified by numerical examples.

This paper addresses issues in islanded DC microgrids, including voltage deviation, inaccurate current sharing, and high communication burden, by proposing a distributed secondary control strategy that integrates a dynamic consensus algorithm with an adaptive event-triggered mechanism. Within a hierarchical control framework, the secondary layer employs an improved dynamic consensus algorithm to estimate the average voltage and proportional current through information exchange among neighboring nodes. Corresponding voltage and current compensations are designed to mitigate voltage droop and ensure accurate proportional sharing of load currents. In this study, a 100 V power supply is stepped down to 47.4 V following primary control. Then, by employing the secondary controller with the proposed algorithm, the voltage is precisely restored to the desired value of 48 V. To further reduce the communication burden, a dynamic event-triggered condition is intended for the output current of each power source, enabling communication and control updates only when the state changes significantly. This approach substantially reduces redundant data transmission and the frequency of controller actions. The positions of the triggering points under the action of the event trigger are also illustrated in the corresponding figures in the following sections. The positions of the triggering points under the action of the event trigger are illustrated in the corresponding figures in the following sections. While communication is accomplished, the voltage remains stable at 48 V. Furthermore, the currents of each distributed unit are stabilized around 6.4 A, satisfying the 1:1:1 current-sharing setting. The asymptotic stability of the closed-loop system is proven based on Lyapunov theory, and Zeno behavior is effectively avoided. Simulation results demonstrate that the proposed strategy achieves rapid voltage restoration and high-precision current sharing under scenarios such as load transients and plug-and-play operations while significantly reducing communication frequency and enhancing system economy and reliability.

As a typical high-emission process industry, cement manufacturing features strong non-linearity, time-varying delays, and nonstationary dynamics, with clinker calcination dominating energy costs and carbon emissions. This paper proposes a receding-horizon MPC coordinated optimization framework for reducing coal and electricity consumption during calcination. First, ReliefF screening extracts key energy-related state variables from high-dimensional DCS data under process-mechanism constraints. VMD and sliding windows are then used to construct multiscale dynamic features describing nonstationary and delayed process responses. An HHO-ELM model predicts future energy-consumption trajectories, while a GWO-based upper-layer optimizer searches for energy-efficient reference trajectories under operating constraints. A data-identified discrete-time plant-response model serves as the internal dynamic model of the lower-layer MPC, which calculates constrained control moves for tracking these references. The framework is evaluated using archived industrial DCS data and an offline receding-horizon closed-loop simulation. VMD-HHO-ELM yields the lowest prediction errors among the compared predictors, and GWO shows favorable convergence for economic reference generation. The simulated MPC results indicate coordinated reductions in coal and electricity consumption within the observed operating region.