This paper presents an edge computing-oriented islanding detection paradigm using multi-support vector machines (SVM) and differential entropy. The proposed methodology can detect islanding events and locate which microgrid was disconnected in a distribution system with microgrid cluster. The proposed method employs multi-SVMs in the cloud to detect and locate potential islanding events in power distribution systems. The accuracy and reliability of the results of SVMs are enhanced by introducing differential entropy for quantitative evaluation. Additionally, the method decomposes the SVM inference process at the edge, enabling edge devices with low computational power and limited resources to support data-driven algorithms without compromising accuracy and speed. An extensive study is performed on the modified IEEE123 distribution system established in real-time digital simulation. Simulation results elucidate that the proposed method can accurately and promptly detect system islanding operations with zero non-detection zone. Comparison with other methods illustrates the superiority of the proposed method in terms of discrimination accuracy, detection time, and reliability.

This paper proposes a dynamic-tracking damping controller (DTDC) and designs an implementation scheme to alleviate the sub-synchronous control interactions (SSCI) in series-compensated networks integrated with doubly-fed induction generator (DFIG)-based wind farms. The DTDC can track sub-synchronous oscillatory current components under various operating conditions, providing high damping performance for DFIG wind turbines at a broader range of sub-synchronous frequencies. This is accomplished by designing an extra extended state observer (ESO)-based compensated loop for the rotor-side converter of DFIG, which enables the extraction and compensation of oscillatory current components at the sub-synchronous frequencies. In addition, the implementation scheme for the damping controller is designed based on the dissipated energy criterion, which can identify the weak damping wind farm and determine the number of wind turbines required to be installed. The proposed DTDC's effectiveness and implementation scheme are validated using the modified IEEE first benchmark model and a wind farm model in North China, respectively. The control hardware-in-loop (CHIL) experiments results demonstrate that the DTDC outperforms traditional approaches with superior damping and robustness under varying wind speeds, compensation levels, and the number of online wind turbines.

This paper proposes an active and reactive power injection strategy based on the amplitude-phase-locked-loop ( APLL ) to improve the transient performance of wind power generation (WPG) systems during fault ride through (FRT). The dynamic characteristics of the APLL can autonomously reflect the required active and reactive power of grid. The dynamic signals ( df/dt and Δ V , dV/dt ) of APLL are used to produce active and reactive power references of WPGs, respectively. Based on the proposed strategy, wind turbines (WTs) can spontaneously provide dynamic active and reactive power support for the grid without control switching from the normal condition to the FRT condition. Moreover, the spontaneous active and reactive powers support based on the APLL is able to alleviate the transient over-voltage and reduce the power-angle swing to enhance the transient performance at sending and receiving areas. Simulations based on a 2-area power system are implemented to verify the effectiveness of the proposed strategy.

The physical mechanism of leader formation and development is not well understood. In this study, we present experimental and simulation results obtained with a 10 m long air gap discharge. A 10 m outdoor discharge experiment is carried out to obtain the current, voltage, and optical image during the leader discharge process. Four different impulse voltages were applied to the rod‐plane gap. The measured current is used as an input for a plasma model, then the temperature and electric field could be calculated. The simulation results show that the temperature of the streamer stem during the dark period may exceed 2000 K. In addition, the critical charge required for leader initiation can be as low as 0.27 μC for a 10 m air gap. The channel temperature is relatively stable in the process of leader development, which is maintained at about 4500 K. The electron density is about 0.5–3 × 10 ²⁰ m ⁻³ , and the discharge channel conductivity fluctuates in the range of 1–10 S/m for the leader current between 1 and 2 A. A long dark period is tended to be associated with a higher injected charge by the first streamer. It is inferred that the voltage increments during the dark period play an important role in promoting streamer‐to‐leader transition, except for temperature and the injected charge.

The streamer discharge physical process can be described by the coupled Poisson’s equation and the convection diffusion equation. It is a multi-physical field problem involving electromagnetism and hydrodynamics. We propose a streamer discharge model based on physics-informed neural networks (PINN) to improve the computational efficiency regarding the classical approach. The Poisson’s equation and the convection-diffusion equation are trained to generate sufficient data and construct a deep operator neural network (DeepONet). The performance of the PINN, in terms of accuracy, is analyzed by applying it to different datasets (electron density and potential distribution) and comparing to a reference solution (spatial evolution of electrons). The simulation results show that the neural network has high accuracy in learning two types of equations.

The higher proportion of distributed photovoltaic and lower fossil energy integrated into the power network brings huge challenges in power supply reliability and planning. The distributed photovoltaic planning method based on big data is proposed. According to the impact of stochastic photovoltaics and loads on reliability planning, the probability model of distributed photovoltaic and load is analyzed, and the dynamic capacity–load ratios are presented based on big data. The multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source–load scenario matching model is proposed based on big data. According to the big data scenario of source–load, the reliability indexes and dynamic capacity–load ratio may be obtained. Finally, the IEEE 33-bus system is used as an example, and the results show that distributed photovoltaic planning methods based on big data can improve photovoltaic utilization and power supply reliability.