Search Results (3)

The increasing penetration of renewable energy, such as photovoltaic generation, makes it essential to enhance power system dynamic performance through improved grid-forming control strategies. In the grid-forming control system, the virtual synchronous generator control (VSG) is currently widely used. However, the inertia (J) and damping (D) in the traditional VSG are fixed values, which can result in large overshoots and long adjustment times when dealing with disturbances such as load switching. To address these issues, this paper proposes an adaptive virtual synchronous generator (VSG) control strategy for grid-side inverters, which is accomplished by adaptively adjusting the VSG’s inertia and damping. Firstly, we established a photovoltaic-storage VSG grid-forming system; here, the photovoltaic power is boosted through a DC-DC converter, and the energy storage is connected to the common DC bus through a bidirectional DC-DC converter. We analyzed how J and D shape the system’s output characteristics. Based on the power-angle characteristic curve, the tanh function was introduced to design the control function, and a JD collaborative adaptive control (ACL) strategy was proposed. Finally, simulation experiments were conducted under common working conditions, such as load switching and grid-side voltage disturbance, to verify the results. From the results shown, the proposed strategy can effectively improve the response speed of the system, suppress system overshoot and oscillation, and, to a certain extent, improve the dynamic performance of the system. Full article

The increasing incorporation of renewable energy into power grids has significantly reduced system inertia and damping, posing challenges to frequency stability and power quality. To address this issue, an adaptive virtual synchronous generator (VSG) control strategy is proposed, which dynamically adjusts virtual inertia and damping in response to real-time frequency variations. Virtual inertia is modulated by an exponential function according to the frequency variation rate, while damping is regulated via a hyperbolic tangent function, enabling minor support during small disturbances and robust compensation during severe events. Control parameters are optimized using an enhanced particle swarm optimization (PSO) algorithm based on a composite performance index that accounts for frequency deviation, overshoot, settling time, and power tracking error. Simulation results in MATLAB/Simulink under step changes, load fluctuations, and single-phase faults demonstrate that the proposed method reduces the frequency deviation by over 26.15% compared to fixed-parameter and threshold-based adaptive VSG methods, effectively suppresses power overshoot, and eliminates secondary oscillations. The proposed approach significantly enhances grid transient stability and demonstrates strong potential for application in power systems with high levels of renewable energy integration. Full article

Crack detection is essential for observing structural health and guaranteeing structural safety. The manual crack and other damage detection process is time-consuming and subject to surveyors’ biased judgments. The proposed Conv2D ResNet Exponential model for wall quality detection was trained with 5000 wall images, including various imperfections such as cracks, holes, efflorescence, damp patches, and spalls. The model was trained with initial weights to form the trained layers of the base model and was integrated with Xception, VGG19, DenseNet, and ResNet convolutional neural network (CNN) models to retrieve the general high-level features. A transfer deep-learning-based approach was implemented to create a custom layer of CNN models. The base model was combined with custom layers to estimate wall quality. Xception, VGG19, DenseNet, and ResNet models were fitted with different activation layers such as softplus, softsign, tanh, selu, elu, and exponential, along with transfer learning. The performance of Conv2D was evaluated using model loss, precision, accuracy, recall, and F-score measures. The model was validated by comparing the performances of Xception, VGG19, DenseNet, ResNet, and Conv2D ResNet Exponential. The experimental results show that the Conv2D ResNet model with an exponential activation layer outperforms it with an F-score value of 0.9978 and can potentially be a viable substitute for classifying various wall defects. Full article