Overspeed is more likely to occur in the process of load rejection or large disturbances for nuclear steam turbines due to the large parameter range and low steam parameters, as well as the power of the low-pressure cylinder accounting for a high proportion of the total power. It is of great significance to study the overspeed characteristics of nuclear power plants (NPPs) to ensure the safe and stable operation of the unit and power grid. According to the characteristics of NPPs, the overspeed protection model and the super-acceleration protection model were established, which were added to the speed-governing system model. The response characteristics of the reactor, thermal system, steam turbine and speed-governing system in the process of load rejection or large disturbances of the power grid were analyzed and simulated. The results were compared using the simulation software personal computer transient analyzer (PCTRAN). The simulation results showed that quickly closing both the high and medium pressure regulating valves could effectively realize frequency control when load rejection or a large grid disturbance occurred. The over-acceleration protection cooperates with the super-acceleration protection to avoid the repeated opening/closing of the valves due to overspeed protection. This could effectively reduce the impact of large disturbances on the reactor, thermal system, and turbine.

The asymmetric faults often cause the power grid current imbalance and power grid oscillation, which brings great instability risk to the power grid. To address this problem, this paper presented a modeling and parameter optimization method of grid-connected photovoltaic (PV) systems, considering the low voltage ride-through (LVRT) control. The harmonics of the grid current under asymmetric faults were analyzed based on the negative-sequence voltage feedforward control method. The notch filter was added to the voltage loop to filter out the harmonic components of the DC bus voltage and reduce the harmonic contents of the given grid current value. The proportional resonant (PR) controller was added to the current loop. The combination of these two components could reduce the 3rd, 5th, and 7th harmonics of the grid current and the output power fluctuation. Then, the parameters of the inverter controller were identified by the adaptive differential evolution (ADE) algorithm based on the sensitivity analysis. The effectiveness of the proposed method was compared with two other strategies under the asymmetric grid faults. The suppression of DC bus voltage fluctuation, power fluctuation, and low-order harmonics of the grid current all had better results, ensuring the safe and stable operation of the PV plant under grid faults.

Short-term bus load forecasting in distribution networks faces severe challenges of non-stationarity, high-frequency disturbances, and multi-scale coupling arising from renewable integration and emerging loads such as centralized EV charging. Conventional statistical and deep learning approaches often exhibit instability under abrupt fluctuations, whereas decomposition-based frameworks risk redundancy and information leakage. This study develops a hybrid forecasting framework that integrates variational mode decomposition (VMD), locally weighted scatterplot smoothing (LOWESS), and a multi-channel differential bidirectional long short-term memory network (Δ-BiLSTM). VMD decomposes the bus load sequence into intrinsic mode functions (IMFs), residuals are adaptively smoothed using LOWESS, and effective channels are selected through correlation-based redundancy control. The Δ-target learning strategy enhances the modeling of ramping dynamics and abrupt transitions, while Bayesian optimization and time-sequenced validation ensure reproducibility and stable training. Case studies on coastal-grid bus load data demonstrate substantial improvements in accuracy. In single-step forecasting, RMSE is reduced by 65.5% relative to ARIMA, and R² remains above 0.98 for horizons h = 1–3, with slower error growth than LSTM, RNN, and SVM. Segment-wise analysis further shows that, for $h=1$, the RMSE on the fluctuation, stable, and peak segments is reduced by 69.4%, 62.5%, and 62.4%, respectively, compared with ARIMA. The proposed Δ-BiLSTM exhibits compact error distributions and narrow interquartile ranges, confirming its robustness under peak-load and highly volatile conditions. Furthermore, the framework’s design ensures both transparency and reliable training, contributing to its robustness and practical applicability. Overall, the VMD–LOWESS–Δ-BiLSTM framework achieves superior accuracy, calibration, and robustness in complex, noisy, and non-stationary environments. Its interpretable structure and reproducible training protocol make it a reliable and practical solution for short-term bus load forecasting in modern distribution networks.