Hyperbranched polyester is effective for enhancing molecular bond strength and improving the mechanical behavior of nanofilled polymers. This study examines the dielectric and polarization relaxation characteristics of epoxy resin composites filled with nanosilica 30 nm in diameter, which is treated by terminal carboxyl hyperbranched polyester. TEM and SEM analysis indicate that the nanosilica surface is grafted with a functional polymer layer ranging in thickness from several to tens of nanometers, and the nanosilica agglomeration in epoxy resin is remarkably inhibited. Measurements of thermally stimulated depolarization current and differential scanning calorimetry show that, deep traps with an energy of 1.09 eV are present in the nanocomposites, and the glass transition temperature (T_g) is increased by 11 °C at most at filler concentrations from 1 to 7 wt%. Moreover, the room-temperature relative permittivity and dielectric loss factor of the composites at 50 Hz are decreased by 0.22 and 1.3‰, respectively. Conductivity at 10 mHz to 1 kHz and dc conductivity are also significantly decreased when the operating temperature is below T_g. The polarization relaxation process of the nanocomposite is dominated by regional carrier migration, interfacial and dipole polarization. The relaxation frequency of dipole polarization at high temperature (>T_g) is transformed to satisfy the Vogel–Tammann–Fulcher law. This research suggests that both the dielectric and the polarization relaxation properties of the epoxy resin composites can be modified by filling hyperbranched-polyester-treated nanosilica, because it enhances the bond strength of the inorganic–organic interface and enlarges the molecular scale of the composites via cross-linking reactions.

Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

The large-scale integration of renewable energy into power systems significantly alters their operational characteristics, reducing system inertia and posing challenges to transient frequency stability. Virtual synchronous generator (VSG) technology offers a solution by enhancing system inertia and improving transient frequency stability. To quantitatively analyze the transient characteristics of VSGs and optimize their control strategies, it is crucial to develop a comprehensive transient frequency model. This paper establishes a transient frequency model for VSGs, incorporating their inertial response, primary frequency regulation, and secondary frequency regulation. A MATLAB/Simulink-based (version R2021b) simulation model is constructed to validate the analytical model. Additionally, the impact of various control parameters on VSG transient frequency is examined, providing valuable insights and guidance for maintaining system frequency stability.

Given the complex form of distribution line faults, the accuracy of fault location using traditional artificial intelligence networks needs to be further improved. Here, a combined fault location method is proposed for a 110 kV distribution line based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), mantis search algorithm (MSA), and convolutional gate recurrent unit (ConvGRU). Firstly, the study used the ICEEMDAN algorithm to decompose the signals and discard the high‐frequency signals with low correlation so as to achieve the purpose of noise cancellation. Then, the study used the root mean square error (RMSE) of the ConvGRU model training as the adaptation value, optimized the internal parameters of the model using the MSA algorithm, and obtained a combined fault locating model. By using the proposed model, the effects of the fault form and transition impedance changes on the location accuracy were analysed, and the location accuracy was compared with other artificial intelligence methods. The location accuracy index showed that the proposed model had a better convergence speed of training error than the traditional model. Also, the RMSE of the localization results was reduced by 50%, with a higher fault location accuracy.

To enhance the bond strength of the nanosized silica/epoxy interface and modify the dielectric properties of nanocomposites, a plasma-assisted surface modification method is proposed for inorganic nanofillers. A gas–liquid two-phase dielectric barrier discharge at room temperature was initially used to graft the terminal carboxyl hyperbranched polyester onto the nanosilica surface. Then, epoxy resin composites filled with 1–7 wt. % nanosilica were prepared. The effects of the plasma-treated nanosilica on the dielectric properties of the epoxy resins were analyzed. The experimental results demonstrated that a 2 nm-thick film of hyperbranched polyester (HBP) was successfully deposited onto the nanosilica surface via the HBP/plasma treatment. Compared with the pristine epoxy resins at room temperature, the permittivity constant of the proposed nanocomposites decreased from 4.2 to 3.3 and their AC breakdown strength increased by 38%. Moreover, the dielectric loss factor and DC conductivity decreased by 40.7% and 48.4%, respectively. Additionally, deep traps of 0.99–1.53 eV were observed in the nanocomposites. It can be concluded that HBP/plasma processing creates strong chemical bonds and compact nanofiller/polymer interfacial regions. In addition, HBP/plasma processing modified the dielectric properties of the nanocomposites. The results of this study indicate that HBP/plasma treatment is an effective method for the surface modification of inorganic nanofillers and for the modification of the dielectric properties of polymer nanocomposites.