国网重庆市电力公司

With the popularization of new types of electrical equipment,the load of the power system has become increasingly complex,and the nonlinear characteristics have become more prominent.However,the traditional def-inition of three-phase apparent power is not unified and cannot truly reflect the energy utilization rate,which cannot meet the requirements of most power consumption conditions.Based on the core essence of apparent power,this paper starts from the equivalent process and engineering approximation of apparent power in a single-phase power system,elaborates on the equivalent principle,calculation method and practical significance of effective apparent power,and extends it to a three-phase power system.At the same time,a three-phase intelligent apparent power meter supporting arithmetic,vector and equivalent algorithms has been developed based on the effective apparent power theory.Its measurement accuracy and engineering applicability have been verified through a verification de-vice,a traceability circuit and an LED nonlinear load test.The results show that the measurement uncertainty of the apparent energy of this electricity meter does not exceed 0.08%,which can provide a reliable basis for energy effi-ciency assessment and power quality management in smart grids.

The presence of harmonic and inter-harmonic components in grid voltage and current signals produces energy that variably impacts different electrical devices,making it imperative to meter these components separately.This paper begins by categorizing the electrical energy generated from grid voltage and current signals into four dis-tinct types,including fundamental,harmonic,inter-harmonic,and cross terms.Subsequently,the interpolation factor of the measurement matrix,constructed using the Dirichlet kernel,is adjusted to enhance the resolution of voltage and current spectra obtained through fast Fourier transform(FFT).Furthermore,a Bayesian compressive sensing(BCS)reconstruction algorithm is employed to estimate the frequency,amplitude,and initial phase angle of all different frequency components present in the grid voltage and current signals.This is followed by a time-do-main reconstruction of each frequency component,leading to the calculation of the four types of partitioned electri-cal energies.Simulation tests are conducted to validate the performance.The results demonstrate that,in scenarios including inter-harmonic components,the proposed algorithm enhances spectral resolution without prior knowledge of the number of frequency components in voltage and current,thereby providing higher measurement accuracy and improved applicability compared to conventional methods commonly used in the field.

Objective The goal of digital image adversarial examples is to add subtle,nearly imperceptible noise to an image to deceive computer vision systems into making incorrect decisions.Most existing adversarial example generation methods aim to maximize the loss function,pushing the image away from the true label.These methods typically involve multiple searches across the input space to find possible adversarial perturbations.However,this approach often leads to overfitting to the source model,reducing the transferability of adversarial examples,making them minimally effective in attacking other models.Furthermore,performing iterative searches across the entire input space increases the computa-tional cost of generating adversarial samples.To address the above issues,we propose a novel adversarial example genera-tion method based on a generative architecture,which selectively interferes with the frequency components that are crucial for the decision-making process of the model.This method significantly improves the transferability of adversarial attacks.Once the generator is trained,it can rapidly generate adversarial examples by simply feeding in the input,thus enhancing the efficiency of adversarial example generation.Our method not only addresses the challenges of transferability and overfit-ting but also reduces the computational cost by eliminating the need for exhaustive searches in the input space,enabling fast and effective adversarial attacks.Method Specifically,our method begins by feeding the input into a generator archi-tecture,which produces two outputs:Branch 1 generates the initial adversarial noise,and Output 2 provides the initial mask information.The purpose of the initial mask is to retain the feature information that is beneficial for classification in the original image.After obtaining the initial adversarial noise,we add it to the original sample to create the preliminary adversarial example.The next step involves applying a discrete cosine transform(DCT)to the adversarial example,fol-lowed by multiplying the DCT-transformed adversarial sample with the mask information to isolate the crucial frequency components that affect the decision-making process.Afterward,an inverse DCT(IDCT)is applied to recover the image in the spatial domain.This processed adversarial image is then fed into an image classifier,and the generator is optimized by maximizing the loss function,which drives the adversarial example to produce the desired misclassification results.The incorporation of the mask ensures that only the most influential frequency components,which directly influence the model's classification,are preserved.This approach not only guides the generator to focus on the critical parts of the image but also enhances the transferability of the adversarial example to different models,given that the manipulated frequency compo-nents are less likely to be overfitted to a specific model.By strategically targeting and preserving important frequency infor-mation,our method improves the effectiveness and efficiency of generating adversarial examples.Result Our method out-performs baseline attack methods in terms of attack effectiveness when applied to convolutional neural networks,vision Transformer models,and adversarially trained models.We also employed our approach to attack Google's commercial API,and the results demonstrated that our method effectively compromised their classifier,causing it to produce incorrect detection outcomes.To further understand the underlying reasons for the success of our method,we conducted a heatmap visualization analysis.This analysis revealed that our method successfully shifted the model's attention away from the fore-ground and toward the background,thereby manipulating the model's decision-making process.Furthermore,leveraging the generative architecture,we used the trained generator to generate adversarial examples on the Animal dataset and the Microsoft common objects in context dataset.These adversarial samples were then applied to attack the cross-dataset image classification tasks and the downstream object detection tasks.The experimental results indicate that our method maintains its attack effectiveness in cross-dataset image classification tasks and downstream tasks,highlighting its transferability and versatility.Hence,our attack method is not only effective for image classification models but also exhibits strong attack capabilities for more complex tasks such as object detection.These results validate the broad applicability and robustness of our adversarial attack strategy across different types of computer vision models and tasks.Conclusion The generator architecture proposed in this paper targets frequency components that are beneficial for classification decisions,signifi-cantly improving the transferability of adversarial attacks.

In the context of the current smart grid, there are numerous typical low power factor load scenarios. The feature differences among different scenarios are small, and the structure of power load data is complex. As a result, classifying low-power electrical loads has always been a difficult problem in practical research. It is necessary to develop advanced models to improve the accuracy and efficiency of classification. This paper combines cluster analysis and classifier recognition, and attempts to conduct analysis and implementation from the combination of power load curve cluster analysis based on the t-SNE algorithm and improved K-means, and load pattern recognition based on the support vector machine classifier. The t-SNE algorithm can not only reflect the local sensitivity of the original data but also retain its global structural features, and can be effectively applied to load data with a low power factor. The improved K-means uses the elbow criterion to determine the number of clusters K. Selects the initial center points using a method based on the density and dissimilarity attributes of the data set, which can effectively improve the computational efficiency, accuracy, and cluster stability. The SVM classifier can fully utilize the clustering results and features. Once the classifier is trained, it can quickly perform intelligent classification and recognition on new unknown load data, thus improving efficiency. This paper evaluates the effectiveness and stability of the clustering effect of the model from validity indicators such as SC, CHI, and DBI, and all obtain good results. Moreover, the classification accuracy of the SVM classifier on the test set reaches 100%.

Wind power DC collection system can effectively solve the harmonic resonance problems of AC collection system, and the series-parallel topology can effectively reduce the system cost compared with other structures, so it is very important to study the stable operation control of series-parallel wind power DC collection system. Based on the coupling characteristics of series port voltages, the rotor and energy storage coordinated control strategy (RES-CCS) is proposed to solve the overvoltage problem caused by wind speed fluctuation during steady operation of the wind turbine. Firstly, the operating characteristics of the series-parallel wind power DC collection system and the shortcomings of the existing overvoltage control strategy are analyzed. Then, the series port voltage limit is studied. The RES-CCS method is used to clamp the series port voltage within the limit value, and the energy storage capacity of the required super capacitor is designed. Finally, a series parallel wind power DC collection system model is built in the PSCAD/EMTDC simulation platform, and RES-CCS is simulated and verified. The results show that the overvoltage control method proposed in this paper can reduce the abandonment loss and the energy storage capacity of the wind turbine, and improve the wind energy utilization rate and operation economy of the wind turbine.