The distinctive fault characteristics of battery energy storage stations(BESSs)significantly affect the reliability of conventional protection methods for transmission lines.In this paper,the three-dimensional(3D)data...The distinctive fault characteristics of battery energy storage stations(BESSs)significantly affect the reliability of conventional protection methods for transmission lines.In this paper,the three-dimensional(3D)data scattergrams are constructed using current data from both sides of the transmission line and their sum.Following a comprehensive analysis of the varying characteristics of 3D data scattergrams under different conditions,a 3D data scattergram image classification based protection method is developed.The depth-wise separable convolution is used to ensure a lightweight convolutional neural network(CNN)structure without compromising performance.In addition,a Bayesian hyperparameter optimization algorithm is used to achieve a hyperparametric search to simplify the training process.Compared with artificial neural networks and CNNs,the depth-wise separable convolution based CNN(DPCNN)achieves a higher recognition accuracy.The 3D data scattergram image classification based protection method using DPCNN can accurately separate internal faults from other disturbances and identify fault phases under different operating states and fault conditions.The proposed protection method also shows first-class tolerability against current transformer(CT)saturation and CT measurement errors.展开更多
传统CNN算法在花生荚果外观识别任务中存在内存密集型和计算密集型问题,以及其在资源受限的边缘终端上部署困难,基于此,该研究提出了一种高效的花生荚果识别模型——PPINET(peanut pod identification network),以适应嵌入式设备的资源...传统CNN算法在花生荚果外观识别任务中存在内存密集型和计算密集型问题,以及其在资源受限的边缘终端上部署困难,基于此,该研究提出了一种高效的花生荚果识别模型——PPINET(peanut pod identification network),以适应嵌入式设备的资源限制需求。该模型通过结合深度可分离卷积和倒残差结构显著降低参数量和计算量,同时保留特征提取能力,并引入MQA(multi-query attention)模块增强关键特征提取,并利用TuNAS(easy-to-tune and scalable implementation of efficient neural architecture search with weight sharing)策略优化模型结构,使其在资源受限设备上表现优异。此外,采用ResNet(residual neural network)进行知识蒸馏配合三折交叉验证训练提升精度,最终量化为RKNN格式并在瑞芯微RK3588上实现NPU加速部署。PPINET模型尺寸仅为1.85 MB,参数量为0.49 M,浮点运算数为0.30G。PPINET在花生荚果分类中表现优异,准确率达98.65%,在RK3588上推理速度达321 fps。该模型具备较高的识别准确率和快速的识别速度,能够实现花生荚果的实时精准检测。展开更多
经典AOD-Net(All in One Dehazing Network)去雾后的图像存在细节清晰度不足、明暗反差过大和画面昏暗等问题。为了解决这些图像去雾问题,提出一种在AOD-Net基础上改进的多尺度算法。改进的网络结构采用深度可分离卷积替换传统卷积方式...经典AOD-Net(All in One Dehazing Network)去雾后的图像存在细节清晰度不足、明暗反差过大和画面昏暗等问题。为了解决这些图像去雾问题,提出一种在AOD-Net基础上改进的多尺度算法。改进的网络结构采用深度可分离卷积替换传统卷积方式,减少了冗余参数量,加快了计算速度并有效地减少了模型的内存占用量,从而提高了算法去雾效率;同时采用多尺度结构在不同尺度上对雾图进行分析和处理,更好地捕捉图像的细节信息,提升了网络对图像细节的处理能力,解决了原算法去雾时存在的细节模糊问题;最后在网络结构中加入金字塔池化模块,用于整合图像不同区域的上下文信息,扩展了网络的感知范围,从而提高网络模型获取有雾图像全局信息的能力,进而改善图像色调失真、细节丢失等问题。此外,引入一个低照度增强模块,通过明确预测噪声实现去噪的目标,从而恢复曝光不足的图像。在低光去雾图像中,峰值信噪比(PSNR)和结构相似性(SSIM)指标均有显著提升,处理后的图片具有更高的整体自然度。实验结果表明:与经典AOD-Net去雾的结果相比,改进算法能够更好地恢复图像的细节和结构,使得去雾后的图像更自然,饱和度和对比度也更加平衡;在RESIDE的SOTS数据集中的室外和室内场景,相较于经典AOD-Net,改进算法的PSNR分别提升了4.5593 dB和4.0656 dB,SSIM分别提升了0.0476和0.0874。展开更多
基金supported by the Fundamental Research Funds for Central Universities(No.2024JCCXJD01).
文摘The distinctive fault characteristics of battery energy storage stations(BESSs)significantly affect the reliability of conventional protection methods for transmission lines.In this paper,the three-dimensional(3D)data scattergrams are constructed using current data from both sides of the transmission line and their sum.Following a comprehensive analysis of the varying characteristics of 3D data scattergrams under different conditions,a 3D data scattergram image classification based protection method is developed.The depth-wise separable convolution is used to ensure a lightweight convolutional neural network(CNN)structure without compromising performance.In addition,a Bayesian hyperparameter optimization algorithm is used to achieve a hyperparametric search to simplify the training process.Compared with artificial neural networks and CNNs,the depth-wise separable convolution based CNN(DPCNN)achieves a higher recognition accuracy.The 3D data scattergram image classification based protection method using DPCNN can accurately separate internal faults from other disturbances and identify fault phases under different operating states and fault conditions.The proposed protection method also shows first-class tolerability against current transformer(CT)saturation and CT measurement errors.
文摘经典AOD-Net(All in One Dehazing Network)去雾后的图像存在细节清晰度不足、明暗反差过大和画面昏暗等问题。为了解决这些图像去雾问题,提出一种在AOD-Net基础上改进的多尺度算法。改进的网络结构采用深度可分离卷积替换传统卷积方式,减少了冗余参数量,加快了计算速度并有效地减少了模型的内存占用量,从而提高了算法去雾效率;同时采用多尺度结构在不同尺度上对雾图进行分析和处理,更好地捕捉图像的细节信息,提升了网络对图像细节的处理能力,解决了原算法去雾时存在的细节模糊问题;最后在网络结构中加入金字塔池化模块,用于整合图像不同区域的上下文信息,扩展了网络的感知范围,从而提高网络模型获取有雾图像全局信息的能力,进而改善图像色调失真、细节丢失等问题。此外,引入一个低照度增强模块,通过明确预测噪声实现去噪的目标,从而恢复曝光不足的图像。在低光去雾图像中,峰值信噪比(PSNR)和结构相似性(SSIM)指标均有显著提升,处理后的图片具有更高的整体自然度。实验结果表明:与经典AOD-Net去雾的结果相比,改进算法能够更好地恢复图像的细节和结构,使得去雾后的图像更自然,饱和度和对比度也更加平衡;在RESIDE的SOTS数据集中的室外和室内场景,相较于经典AOD-Net,改进算法的PSNR分别提升了4.5593 dB和4.0656 dB,SSIM分别提升了0.0476和0.0874。
