Power Grid Corporation (India)

This study presents wind tunnel experiments on an adjustable-tilt solar photovoltaic (PV) system, covering wind directions from 0° to 180° and tilt angles from 5° to 60°, with time histories of column base shear force recorded under 65 working conditions. The Gaussianity of the shear force time histories is assessed using skewness (Csk) in the range of −0.15 to 0.15 and kurtosis (Cku) greater than 3.2, which explicitly indicates a non-Gaussian distribution. Results show that base shear forces tend to follow a Gaussian distribution at lower tilt angles, while higher angles lead to non-Gaussian behavior due to organized vortex shedding. Three extreme value analysis methods are compared, with the generalized Pareto distribution (GPD) method demonstrating the highest accuracy and the smallest estimation deviation. The GPD-based maximum shear forces not only align closely with observed values but also satisfy structural wind resistance requirements. Compared with average values, the extreme shear force generally shows higher magnitudes under the same conditions. These findings highlight the importance of accounting for extreme loading effects in structural design and offer practical guidance for the wind-resistant design of adjustable-tilt PV systems.

Addressing the challenge of coordinated dispatch between wind/solar and thermal power in new energy grids, this research proposes a thermal power unit output prediction method based on a Transformer-BiLSTM hybrid deep learning model. First, a simulated annealing algorithm optimizes the output configuration of solar thermal power plants to mitigate fluctuations in wind and solar combined generation. An ant colony-greedy algorithm is then integrated to determine the optimal dispatch data for thermal power units, constructing a high-quality training dataset under physical constraints. In the model design, a bidirectional long short-term memory network captures short-term temporal features, while the Transformer’s multi-head self-attention mechanism models long-term dependencies. The model innovatively incorporates the learnable positional encoding to enhance temporal awareness. Experimental results demonstrate accurate predictions, with the power constraint mechanism effectively correcting over-limit forecasts. This ensures 98.7% of predictions during low-load periods comply with unit technical specifications. Compared to existing methods, this model avoids data limitations and manual feature engineering bottlenecks through the end-to-end wind–solar–thermal mapping, providing a high-precision solution for dispatch decisions in renewable-dominated grids.

In RISC-V processor design, the Store-Forward processing architecture is used to coordinate data storage and loading operations to avoid collisions. However, as the demand for multi-core and parallel tasks increases, traditional methods face high transmission latency and low bandwidth utilization problems, affecting the overall system performance. Therefore, we propose a multi-channel priority scheduling mechanism, which dynamically adjusts the priorities of multiple data streams to optimize resource allocation, reduce congestion, and improve transmission efficiency. A RISC-V test platform is built based on the Gem5 simulator, and the performance of standard scheduling and multi-channel priority scheduling under data-intensive workloads is compared. The experimental results show that after adopting the new mechanism, the average transmission delay is reduced from 60 nanoseconds to 48 nanoseconds, which is a reduction of 20%. At the same time, the peak throughput increased from 5GB per second to 6.5 GB, an increase of 30%. In 1000 random data transmission tasks, the scheduling mechanism reduces the processing completion time to 80 milliseconds, which reduces the waiting time by 20% compared to the benchmark of 100 milliseconds. These data indicate that the mechanism effectively mitigates the congestion problem and optimizes resource utilization. Through multi-channel priority scheduling, the transmission efficiency of the Store-Forward processing structure is significantly improved in the RISC-V environment, providing a feasible solution for high-performance computing applications.

Existing ferroresonance fault identification methods often suffer from high misclassification rates, strong threshold dependency, and insufficient noise resistance. To bridge this gap, we propose a novel ferroresonance fault recognition method based on the Markov transition field (MTF) and three-branch Gaussian clustering (TBGC). Firstly, a symplectic geometric algorithm is employed to denoise the resonance feature signal, extract effective dominant modes, and reshape the series. Secondly, the reshaped feature series is converted into a Pixel matrix image employing the MTF. Subsequently, the gray-level co-occurrence matrix (GLCM) is utilized to extract the two-dimensional texture features of MTF images corresponding to different resonance types and construct corresponding TBGC models. Finally, the overvoltage sequence to be recognized is input into the TBGC model after feature extraction, and accurate discrimination of ferroresonance types is achieved based on cosine similarity. The analysis of fault recording data indicates that this method achieves 100% discrimination accuracy in eight test cases, surpassing the comparative method (maximum accuracy of 62.5%) by 37.5%, thereby validating its effectiveness and accuracy in ferroresonance identification.

This paper inquiries into the impact of photovoltaic source intermittency on behaviour of existing distribution networks. Real PV power output profile are added into detailed simulations conducted on 33-bus IEEE radial distribution system. Load flow study was conducted in evaluating steady-state operational conditions before integration of PV Systems. Subsequently, PV power sources was introduced to a networks, and the effect of Photo Voltaic power outputs intermittency were examined over 24-hour time durations. Result indicate that PV integration improves overall voltage profiles by an average of 5% and reduces power losses by 10%. Additionally, active power productions from traditional source decreases by 15% due to PV integration. However, the intermittent nature of PV lead to significant fluctuations in daily bus voltage profiles of up to 8% and modify the power production plan by 20% and also that BiLSTM outperforms MLP and SVM across all metrics, with RMSE of 7.536, MAE of 4.369, MAPE of 15.87, and PCC of 0.961. In comparison, MLP has RMSE of 12.181, MAE of 7.526, MAPE of 34.71, and PCC of 0.914, while SVM shows RMSE of 14.923, MAE of 11.549, MAPE of 38.96, and an unusually high PCC of 9.928. Thus, BiLSTM exhibits the lowest error rates and highest correlation, indicating its superior predictive performance. To mitigate these issues, this study can assist planning operators in adjusting regulation programs and daily power generation plans and electrical power systems deregulation, conducting numerous thematic simulations of this nature can provide comprehensive insights into system behavior in the presence of intermittent power sources, thereby guiding planners in deciding on protection and defense actions. To enhance the prediction of PV power output intermittency, Bi-directional Long Short-Term Memory (Bi-LSTM) network can be used. Bi-LSTM models have shown promising results in capturing temporal dependencies and can be used to forecast PV power output profiles with higher accuracy, thereby aiding in better understanding and managing the impact of PV intermittency on distribution networks.

With the continuous expansion of global power systems, cable terminations—serving as critical nodes connecting cables, power equipment, and overhead lines—are vital for grid stability. This study investigates the physico-chemical coupling mechanisms of surface contamination on cable terminations and develops an efficient, non-destructive laser cleaning technology to mitigate pollution flashover risks. A theoretical model of dust particle adsorption under electro-thermal-humidity multifield coupling was constructed, analyzing the effects of Van der Waals, capillary, and electrostatic forces on the stability of the contamination layer. Subsequently, nanosecond pulsed fiber laser experiments and simulations were conducted to evaluate the influence of laser power, scanning speed, and surface moisture on cleaning efficiency and substrate damage thresholds. Results indicate that a laser power of 90–100 W and a scanning speed of approximately 1000 mm/s effectively remove hard scales dominated by SiO₂ and Al₂O3 while restricting the substrate temperature rise to avoid porcelain glaze cracking or peeling. This work clarifies the microscopic laws of contamination-insulator coupling and provides a parameter optimization scheme for the maintenance of online power equipment. It holds significant theoretical and engineering value for enhancing the anti-pollution flashover capabilities and intelligent operation of modern power grids.

The development of efficient and cost-effective electrocatalysts for the oxygen evolution reaction (OER) is crucial for advancing green hydrogen production via water electrolysis. While perovskite oxides represent promising non-noble metal catalysts, their OER performance is often limited by poor conductivity and insufficient active site exposure. In this work, we report a rapid and versatile plasma engineering strategy to significantly enhance the OER activity of perovskite oxides LaMO3 (M = Fe, Co, Ni). The modified catalyst (V-LaFeO3) exhibits increased specific surface area, abundant oxygen vacancies, and improved charge transfer capability. As a result, V-LaFeO3 achieves a low overpotential of 332mV at a current density of 10mA cm- 2, outperforming both pristine LaFeO3 and commercial RuO2. The universality of this approach is further demonstrated with LaCoO3 and LaNiO3 oxides, which also show enhanced OER performance after plasma treatment. This study highlights plasma engineering as a general and efficient strategy for designing high-performance perovskite-based electrocatalysts for sustainable energy applications.