The transient instability mechanism and enhanced control for Grid-Forming controlled voltage source converter (GFM-VSC) during voltage sags has been widely investigated. However, the postfault performance are overlooked in the previous studies. In this paper, the postfault transient instability and windup problem of GFM-VSC with virtual inertia is first revealed based on established piecewise nonlinear model and power angle curve. Second, the impact of key parameters on postfault transient stability and windup phenomenon and the impact mechanism is analyzed, revealing that the conventional deceleration effect enhancement method increase the postfault transient stability, but increase the risk of windup problem. Third, the torque error and q -axis voltage feedforward based hybrid synchronization method is adopted to achieve transient stability enhancement and anti-windup. Different from the conventional global deceleration effect enhancement, the adopted control method can inject extra kinetic energy during first current saturation mode and extra damping during current unsaturation state, therefore, the global deceleration effect can be enhanced and the local deceleration effect in the first current saturation period can be deteriorated. Then, the anti-windup and transient stability enhancement is achieved. Finally, the analysis and effectiveness of the designed control strategy has been verified on a 500W experimental prototype.

The current global climate is complex with an increasing frequency of extreme weather events. The randomness, variability, and intermittency of new energy sources pose significant challenges for the balance between power generation and consumption in electricity grids. This challenge is particularly pronounced during extreme weather events such as cold waves, which aggravate the challenges of power supply stability. Accurate prediction of the wind power output can reduce the need for system backup capacity, ensuring the stable operation and reliability of the power system. However, current prediction models do not effectively consider the impact of extreme weather, leading to low prediction accuracy and large deviations. Additionally, the mechanisms by which extreme weather affects wind power differ from those under normal weather conditions. Extreme weather events such as cold waves are rare, and sample data are scarce, making it difficult to establish precise prediction models. To address these issues, this study proposes a wind power output prediction correction method based on time-series generative adversarial networks. First, a time-series generative adversarial network algorithm was used to generate samples from meteorological and power data, constructing an extreme weather meteorological and power database to effectively address the issue of sample scarcity. Second, the improved particle swarm algorithm (IPSO) and Bayesian optimization (BO) algorithm were used to optimize the parameters of single-prediction algorithms such as eXtreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO). Subsequently, by combining the algorithm performance and principle of maximum diversity, the optimal algorithm combination was determined using the evaluation indicators of the prediction error and Pearson correlation coefficients to construct an extreme weather power prediction model under the stacking ensemble learning framework, overcoming the limitations of single algorithms and effectively improving the prediction accuracy. Finally, Support Vector Regression (SVR) was used to construct a power prediction correction model for similar days, correcting the predictions. Through verification using an actual cold wave case study, the proposed method demonstrated significantly improved prediction accuracy and performance compared to single algorithms and conventional prediction models.

Optimally reconfiguring an active distribution network (ADN) during power outages has been regarded as a reasonable approach to facilitate system secure operation and reliability. Nevertheless, most existing studies for the reconfiguration virtually focus on taking actions from the generationand network-side, in which the potential achievement from the demand-side is underestimated. Moreover, the phase-unbalance and voltage violation in ADNs should be restricted to avoid extreme conditions of distributed generators (DGs) that jeopardize system reliability. To bridge the gap, a new approach to reconfigure ADNs under multiple faults is proposed in this paper, incorporating a phase demand balancing (PDB) model to improve dispatch performance. The model regulates asymmetrical loads to mitigate the phase-unbalance issue from the demand-side, cooptimized with step voltage regulators (SVRs) and DG dispatching to enhance reliability and flexibility in reconfiguring ADNs. The derived optimization is a challenging mixed-integer nonconvex programming (MINCP), which is reformulated as an efficiently solvable mixed-integer second-order cone programming (MISOCP) via exact equivalence and accurate approximation techniques. Case studies based on modified IEEE benchmark systems validate the effectiveness and advantages of the proposed method.

With the increasing demand for power supply in data centers, resonant switched-capacitor converters with high power density and high efficiency have been widely studied and applied. In this article, a 3-to-1 high-performance switched-capacitor converter is proposed, and optimizing methods are investigated to improve its performance. First, a detailed analysis of its working states under different modes is conducted. Second, the state-plane analysis is conducted to obtain the relationship between its state variables and various circuit parameters. By utilizing these relationships, the circuit parameters are optimized and designed, increasing the efficiency under zero voltage switching (ZVS) mode. Third, using the proposed 3-to-1 circuit structure, a cascaded switched-capacitor converter with a step-down ratio of 6-to-1 is constructed. In order to verify the theoretical analysis, a 48-to-8 V experimental prototype is built. The experimental prototype achieves 1078 W/in ³ power density. Its peak efficiency reaches 97.4%, and full-load efficiency reaches 94.2%.

Photovoltaic (PV) power generation is considered to be a clean energy source. Solar modules suffer from nonlinear behavior, which makes the maximum power point tracking (MPPT) technique for efficient PV systems particularly important. Conventional MPPT techniques are easy to implement but require fine tuning of their fixed step size. Unlike conventional MPPT, the MPPT based on reinforcement learning (RL-MPPT) has the potential to self-learn to tune step size, which is more adaptable to changing environments. As one of the typical RL algorithms, the Q-learning algorithm can find the optimal control strategy through the learned experiences stored in a Q-table. Thus, as the cornerstone of this algorithm, the Q-table has a significant impact on control ability. In this paper, a novel Q-table of reinforcement learning is proposed to maximize tracking efficiency with improved Q-table update technology. The proposed method discards the traditional MPPT idea and makes full use of the inherent characteristics of the Q-learning algorithm such as its fast dynamic response and simple algorithm principle. By establishing six kinds of Q-tables based on the RL-MPPT method, the optimal discretized state of a photovoltaic system is found to make full use of the energy of the photovoltaic system and reduce power loss. Therefore, under the En50530 dynamic test standard, this work compares the simulation and experimental results and their tracking efficiency using six kinds of Q-table, individually.