The wind turbine simulation system serves as a crucial experimental and testing tool for the study of wind power generation technology. An inertia compensation stabilization strategy suitable for variable communication delays is proposed in this paper to address the oscillatory instability caused by variable communication delays in the wind turbine simulation system. The strategy is based on the current model of the wind turbine simulation system, which considers fixed communication delays. This method improves the original high-order filter structure, ensuring the stability of the wind turbine simulation system within a certain range of delay deviations. Firstly, the paper derives the analytical expression for the stability conditions of the system under variable communication delays based on the wind turbine simulation system model. Subsequently, a zero-pole placement method is employed to design the inertia compensation stabilization strategy suitable for variable communication delays. The stability of the wind turbine simulation system is analyzed from both the transfer function and time-domain response perspectives. Finally, a comparative analysis of the simulation effects between the existing strategy and the proposed strategy is conducted through experiments on a 15 kW wind turbine simulation system. The results demonstrate that the proposed strategy effectively suppresses the instability of the wind turbine simulation system under different delay deviation multiples, maintaining system stability and achieving favorable simulation outcomes under variable communication delays.
To address the issue of decreased reliability in power grid fault detection and state estimation based on synchronized measurement data, caused by network attack threats such as false data injection resulting from the exposure of vulnerable nodes in power systems, a model-free synchronized phasor data source authentication method based on one-dimensional time series visualization is proposed. Firstly, to improve the authentication speed, the piecewise aggregate approximation algorithm is adopted to reduce the dimensionality of the original grid frequency time series, lowering computational complexity while retaining characteristic frequency information. Secondly, to preserve the absolute temporal relationship of the time series, the Gramian angular difference field method is employed to transform the one-dimensional grid frequency time series into a two-dimensional visualization. Then, to capture grid frequency features at different scales, dilated convolution is applied to obtain a large receptive field while ensuring authentication speed. Finally, validation experiments are conducted using measured multi-point synchronized phasor measurement data. Comparative analysis with different authentication methods verifies the effectiveness and feasibility of the proposed approach. Experimental results demonstrate that the model-free authentication method based on a visualized dilated convolutional neural network achieves a test accuracy of 94.76%, and can effectively authenticate synchronized phasor data sources at various locations.
Under "dual carbon" action plan, which aims to achieve the largest reduction in carbon emission intensity globally, rural energy, as an important component of China energy system, urgently requires the establishment of a diversified, green, and low-carbon energy structure. Given the inherent advantages of hydrogen energy storage, including its environmental sustainability, high energy density, and long-duration storage capabilities, it exhibits a natural synergy in addressing the issues of significant pollution in rural energy consumption, low overall efficiency, and inadequate supply reliability. A capacity configuration model for a combined electricity-thermal multi-energy system with hydrogen storage in rural household is proposed. Initially, an energy system architecture centered on photovoltaic and hydrogen storage is established, based on the rural solar resource endowment and the seasonal electricity and heating demand of households. Subsequently, a bi-level optimization model is developed with the objectives of minimizing the system investment payback period and maximizing annual operational profits. The capacity configuration is solved using a linearly decreasing inertia weight particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed electricity-thermal multi-energy system with hydrogen energy storage in rural contexts is expected to recover costs within six years, with the payback period reduced by approximately 11 a compared to conventional energy storage systems. Sensitivity analysis indicates that factors such as increases in hydrogen sale price and proportion, decrease in equipment costs, improvement in energy conversion efficiency, and economies of scale all contribute to enhancing system economics. The findings suggest that distributed rural household energy systems with hydrogen energy storage, when considering both combined heat and power generation and the benefits of energy sales, offer substantial economic feasibility and provide an effective solution for the green and low-carbon transition of rural energy structures.
Aiming at the problems that traditional neural networks have long prediction time and are difficult to update autonomously in load forecasting, which cannot meet the requirements of smart grids, a digital twin based seasonal autoregressive integrated moving average (SARIMA) model is proposed. Through distrust feature analysis, the load predict digital twin (LP-DT) model is replaced by an improved particle swarm optimization-hybrid kernel extreme learning machine (IPSO-HKELM) algorithm. Firstly, comprehensive similarity analysis is conducted on points with poor prediction performance in historical data, and the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is adopted to obtain the distrust point set and distrust features. Then, on the basis of the distrust feature point set, the IPSO algorithm is used to optimize hyperparameters such as kernel weights in the hybrid kernel extreme learning machine, and load forecasting is performed after training with historical distrust points. Finally, case analysis and comparison with traditional models are carried out, with the goodness-of-fit coefficient, root mean square error, mean absolute error and mean absolute percentage error as evaluation indicators. The proposed model achieves the values of 0.983, 53.87 kW, 40.55 kW and 0.74% respectively, which are significantly better than those of other models. This verifies the effectiveness and superiority of the proposed method in short-term load forecasting, and provides a reference for the design of load forecasting systems in smart grids.
Currently, excellent performance has been demonstrated by machine learning-based fault diagnosis models in detecting latent faults within high-voltage switchgear. However, primary focus is placed on common latent faults by most diagnostic models, leaving less detectable faults such as excessive grease coating, abnormal contact ablation, and floating potential discharge of metal parts insufficiently addressed. To resolve this limitation, decomposition component analysis is integrated in this study to establish a fault diagnosis model for high-voltage switchgear, which is developed through weighted principal component analysis (WPCA) and a support vector machine (SVM) optimized by an adaptive sparrow search algorithm (ASSA). Training is conducted using latent fault sample data collected from in-service high-voltage switchgear, with model validation being performed through three evaluation dimensions: feature extraction methods, classifier performance, and optimization algorithms. Regarding feature extraction, superior performance is exhibited by the proposed model through effective reduction of feature overlap across different faults and enhancement of recognition accuracy. In classifier evaluation, an average accuracy of 93.70% is achieved, accompanied by excellent outcomes in mean Kappa coefficient, precision, and recall metrics, indicating consistently stable diagnostic performance across all fault categories. For optimization effectiveness, strong global optimization capability and accelerated convergence speed are demonstrated. The validation results confirm that robust diagnostic performance is delivered by the proposed model, enabling precise detection of excessive grease coating, abnormal contact ablation, and floating potential discharge. Significant enhancement is brought to the latent fault diagnosis capability based on decomposition component analysis for high-voltage switchgear through this research.
