Research on high energy efficiency and low bit-width floating-point type data for abnormal object detection of transmission lines

Achieving a balance between accuracy and efficiency in target detection applications is an important research topic.To detect abnormal targets on power transmission lines at the power edge,this paper proposes an effective method for reducing the data bit width of the network for floating-point quantization.By performing exponent prealignment and mantissa shifting operations,this method avoids the frequent alignment operations of standard floating-point data,thereby further reducing the exponent and mantissa bit width input into the training process.This enables training low-data-bit width models with low hardware-resource consumption while maintaining accuracy.Experimental tests were conducted on a dataset of real-world images of abnormal targets on transmission lines.The results indicate that while maintaining accuracy at a basic level,the proposed method can significantly reduce the data bit width compared with single-precision data.This suggests that the proposed method has a marked ability to enhance the real-time detection of abnormal targets in transmission circuits.Furthermore,a qualitative analysis indicated that the proposed quantization method is particularly suitable for hardware architectures that integrate storage and computation and exhibit good transferability.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.