The rapid development of the power Internet of Things (IoT) has greatly enhanced the level of security, quality and efficiency in energy production, energy consumption, and related fields. However, it also puts forward higher requirements for the security and privacy of data. Ciphertext-policy attribute-based encryption (CP-ABE) is considered a suitable method to solve this issue and can implement fine-grained access control. However, its internal bilinear pairing operation is too expensive, which is not suitable for power IoT with limited computing resources. Hence, in this paper, a novel CP-ABE scheme based on elliptic curve cryptography (ECC) is proposed, which replaces the bilinear pairing operation with simple scalar multiplication and outsources most of the decryption work to edge devices. In addition, time and location attributes are combined in the proposed scheme, allowing the data users to access only within the range of time and locations set by the data owners to achieve a more fine-grained access control function. Simultaneously, the scheme uses multiple authorities to manage attributes, thereby solving the performance bottleneck of having a single authority. A performance analysis demonstrates that the proposed scheme is effective and suitable for power IoT.

A magnetostrictive FeNi-coated surface acoustic wave (SAW)-based current sensor was proposed in this work. The weak remanence and hysteresis effect of the FeNi itself contributes to suppress the asymmetry in sensor response at increasing and decreasing current. The sensor response was simulated by solving the coupled electromechanical field equation in layered structure considering the magnetostrictive effect and an approach of effective dielectric constant. The effects from the aspect ratio and thickness of the FeNi film on sensor response were analyzed to determine the optimal design parameters. Differential oscillation structure was used to form the sensor, in which, the FeNi thin film was deposited along the SAW propagation of the sensor chip by using RF magnetron sputtering. The magnetostrictive effect of the FeNi coating induced by the magnetic loading generates the perturbation in SAW velocity, and corresponding oscillation frequency. High sensitivity of 10.7 KHz/A, good linearity and repeatability, lower hysteresis error of 0.97% were obtained from the developed prototype 150 MHz SAW FeNi coated current sensor.

In order to detect the status of power equipment from infrared transmission line images under the spatial positioning relationship of the transmission line equipment, such as corridor, substation equipment, and facilities, this paper presents an improved PCNN model which merges an optimized parameter setting method. In this PCNN model, the original iteration mechanism is abandoned, and instead, the thresholding model is built by the maximum similarity thresholding rule. To ensure similarity during classifying neighboring neurons into cluster centers, a local clustering strategy is used for setting the linking coefficient, thus improving the efficiency of the method to detect the power equipment in infrared transmission line images. Finally, experimental results on transmission line infrared images show that the proposed method can provide the basis for the diagnosis of power equipment, preventing the casualties and property damage caused by the thermal damage of power equipment, and effectively improving the safety risk identification and operation control ability of power grid engineering.

To address the issue of human object detection in transmission line inspection, an enhanced single-stage neural network is proposed, which is based on the improvement of the YOLOv7-tiny model. Firstly, a lighter GSConv module is utilized to optimize the original ELAN module, reducing the parameters in the network. In order to make the network less sensitive to the targets with an unconventional pose, a module based on CSPNeXt and GSConv is designed and integrated with the ELAN module to extract deep features from the targets. Moreover, a WIoU (Wise Intersection over Union) loss function is utilized to enhance the ability of the YOLOv7-tiny model to detect objects with an unconventional pose and the interference of the background. Finally, the experimental results on human targets in transmission line inspection demonstrate that the proposed network improves detection confidence and reduces missed detection. Compared to the YOLOv7-tiny model, the proposed method promotes the performance of accuracy while reducing the amount of parameters.