Zhiyi Pang

Lightning strikes pose an increasing threat to wind turbine blades, whose insulating GFRP laminates and polyurethane coatings accumulate surface charge with no dissipation pathway. Polyaniline-grafted polyurethane (PANI-PU) copolymers are being explored as conductive coating materials that dissipate charge under high electric fields. This research used density functional theory to compare three graft copolymers differing only in the diisocyanate hard segment, HDI, HMDI and IPDI, under a static field up to 205.7 kV/mm along the backbone with all other conditions identical. The response was analysed by Bader charge partitioning, differential charge density, orbital energetics and surface potential. HMDI-PANI transfers the largest net charge, 0.75 e, keeps its PU hydrogens near neutral at 0.10 e, spreads charge most diffusely with an index of 122.6, and shows the largest gap narrowing, 0.92 eV. HDI-PANI polarizes twelve free C-H bonds, raises its polarizability by 4.0 a.u. while its dipole falls by 0.25 D, and narrows its gap from 2.19 to 1.66 eV. IPDI-PANI reorients its methyl groups, giving the largest polarizability increase, 9.0 a.u., and dipole enhancement, 1.29 D, without a matching gain in transfer, 0.68 e. These results identify the dicyclohexylmethane hard segment as the architecture maximizing transfer efficiency, providing a molecular-level design basis for conductive graft copolymer coatings.

Polyurethane (PU) coatings provide mechanical robustness and weather resistance for wind turbine blades but suffer from inherent electrical insulation, which promotes surface charge accumulation and increases lightning strike vulnerability. Rendering PU electrically conductive is therefore a promising strategy to mitigate this risk. In this study, we employ density functional theory (DFT) calculations at the B3LYP/6-31 G(d,p) level to investigate PU hard segments (MDI-BDO) grafted with three conductive polymers: polyaniline (PANI), polypyrrole (PPy), and pyrrole-3-carboxylic acid-functionalized polythiophene (PTh). The isocyanate (–NCO) group is identified as an efficient ‘electron trigger’ that initiates a cascade from covalent bonding to electronic state redistribution. Grafting substantially modifies the electronic structure: the HOMO-LUMO gap narrows from 5.28 eV for pure MDI-BDO to 2.24 eV for MDIBDO-g-PANI, compared with 4.12 eV and 4.11 eV for the PPy and PTh systems, respectively. The PANI-grafted system achieves the strongest interfacial binding of 146.4 kJ mol ⁻¹ , the highest polarizability of 1306.6 a.u., and the largest vibrational entropy of 549.8 cal·mol ⁻¹ ·K ⁻¹ . The characteristic −NCO IR stretching mode at approximately 2448 cm ⁻¹ disappears in all grafted systems, confirming covalent bond formation, while Raman spectra reveal signatures consistent with narrow-gap semiconducting behavior. Among the three systems, MDIBDO-g-PANI exhibits the optimal combination of strong interfacial binding, narrow energy gap, and continuous density of states distribution. The present theoretical predictions establish a structure-activity relationship linking interfacial chemistry, electronic structure, and charge dissipation capability, providing a computational foundation for the rational design of conductive PU-based coatings with potential applications in lightning protection.

To improve the accuracy and stability of new energy vehicle (NEV) sales forecasting in China, this study develops a hybrid forecasting framework integrating the Informer model, autoregressive integrated moving average (ARIMA), and the Boomerang Aerodynamic Ellipse Optimization (BAEO) algorithm. Monthly data from January 2016 to December 2023 covering 31 provincial-level administrative regions in China (excluding Hong Kong, Macao, and Taiwan) were collected from authoritative statistical sources. A multidimensional feature system was established by incorporating factors related to charging infrastructure, transportation demand, market development, and environmental conditions. Data preprocessing techniques, including Min–Max normalization, lagged variables, rolling statistical features, and seasonal sine–cosine encoding, were applied to capture temporal dependencies and periodic patterns. The BAEO algorithm was employed to optimize the key hyperparameters of the Informer model, while the ARIMA model was introduced to correct linear patterns in forecasting residuals. The proposed BAEO–Informer–ARIMA framework was evaluated against seasonal autoregressive integrated moving average (SARIMA), Prophet, extreme gradient boosting (XGBoost), long short-term memory (LSTM), gated recurrent unit (GRU), Transformer, and Informer models under the same chronological evaluation strategy. Results show that the proposed framework achieved superior forecasting performance, with a coefficient of determination (R²) of 0.9544, mean absolute error (MAE) of 24,068, root mean square error (RMSE) of 26,822, and mean absolute percentage error (MAPE) of 3.39%. Furthermore, uncertainty analysis based on rolling-validation forecast errors was conducted to establish a 90% confidence interval for future projections. Forecast results for 2024–2030 reveal sustained NEV sales growth with gradually decreasing growth rates and persistent seasonal variations. This study provides quantitative insights for NEV market planning, charging infrastructure deployment, and low-carbon policy formulation.

Short-term power load forecasting is fundamental to the secure operation and optimal dispatch of modern power systems. This study proposes an Improved Pied Kingfisher Optimization–Temporal Convolutional Network–Bidirectional Gated Recurrent Unit (IPKO-TCN-BiGRU) model to address the challenges of strong non-stationarity, high randomness, and multi-factor coupling in load time series. The model employs a multi-scale TCN for simultaneous extraction of local and global temporal features, a BiGRU enhanced with an Improved Self-Attention (ISA) mechanism for bidirectional dependency modeling, and an Autoregressive (AR) module combined with an election mechanism to jointly capture linear and nonlinear load components. The Improved Pied Kingfisher Optimization (IPKO) algorithm—incorporating SPM chaotic initialization, a planetary optimization strategy, and adaptive t-distribution perturbation—is applied to globally optimize key hyperparameters, demonstrating superior convergence accuracy and global search capability over the original PKO and other benchmark optimizers. To ensure evaluation integrity, dataset splitting precedes all normalization operations, with StandardScaler fitted exclusively on the training set and applied to the test set without leakage. Validation is conducted on two benchmark datasets: a U.S. residential electricity load dataset (hourly, 2012, 13-dimensional features including HVAC and lighting systems) and a China Electrical Engineering Mathematical Modeling Competition dataset (15 min intervals, three years, enriched with five meteorological variables). The U.S. dataset exhibits a clear annual double-peak seasonal pattern, while the Chinese dataset shows strong intraday fluctuations significantly coupled with temperature and humidity, both posing substantial forecasting challenges. On the U.S. dataset, the proposed model achieves MAE = 0.0190 kW, RMSE = 0.0301 kW, MAPE = 1.7673%, and R² = 0.9947; on the China dataset, MAE = 79.8125 MW, RMSE = 109.4154 MW, MAPE = 1.1124%, and R² = 0.9955. The proposed model consistently outperforms six mainstream baseline models—including Transformer, Autoformer, and FEDformer—reducing RMSE by up to 34.4% and 18.9% on the two datasets, respectively, while maintaining a compact architecture of 15.2 MB and 74.6–78.9 MFLOPs. Ablation experiments confirm the significant and synergistic contribution of each module, and the direct comparison between PKO-TCN-BiGRU and IPKO-TCN-BiGRU validates that the algorithmic improvements translate into measurable forecasting gains beyond benchmark function optimization. The proposed model is most suitable for ultra-short-term to short-term single-step-ahead forecasting within a horizon of 15 min to 24 h, with an inference latency of 2.3–2.7 ms per sample, fully meeting the real-time requirements of practical power dispatching systems.

Benzyltoluene insulating oil is widely utilized as the insulating medium for power capacitors due to its superior dielectric properties. To improve its dielectric and moisture resistance performance, this study investigates the synergistic modification mechanism of nano-SiO2-doped benzyltoluene insulating oil via molecular dynamics simulation. The models of pure benzyltoluene and SiO2-doped systems are constructed, and the dielectric constant, electrical strength, free volume fraction, and water diffusion behavior of the benzyltoluene and SiO2-doped systems are analyzed under thermal and electric field conditions. The results indicate that, in the electric heating operating environment, nano-SiO2 reduces the relative dielectric constant of the system by approximately 10.4% via the interface effect and suppresses space charge polarization. Additionally, its rigid structure decreases the free volume fraction, constrains the thermal motion of molecular chains, and consequently enhances the electric strength by approximately 10.5%. Simultaneously, the hydroxyl groups on the particle surfaces form hydrogen bond networks with water molecules, reducing the diffusion coefficient and inhibiting water droplet migration and partial discharge. This study elucidates the multi-scale coupling mechanism among nano-particles, micro-water, and electric fields, providing a theoretical foundation for designing high-stability composite insulating oils.

Characterized by its exceptional electrical, physical, and chemical properties, 1-phenyl-1-xylylethane (PXE) insulating oil finds extensive application in the realm of power capacitor insulation. In this study, molecular simulation is employed to investigate the reactivity of PXE insulating oil molecules and the impact of temperature on water diffusion behavior in PXE insulating oil, as well as its solubility. The findings demonstrate a higher propensity for hydrogen atoms in nucleophilic and electrophilic positions within PXE insulating oil molecules to interact with water molecules. The inclusion of a temperature field enhances the Brownian motion of water molecules and improves their diffusion ability within the oil. Furthermore, the temperature field diminishes the interaction force between water molecules and the oil medium. Under the influence of this temperature field, there is an increase in the free volume fraction of PXE insulating oil, leading to a weakening effect on hydrogen bonds between oxygen and hydrogen atoms within PXE insulating oil. Additionally, with increasing temperature, there is an elevation in moisture solubility within insulating oil, resulting in a transition from a suspended state to a dissolved state.