Overhead lines are important parts of distribution network at present. Assessment and prediction of their health status is of great significance to ensure the reliable operation of distribution network. This paper presents a method for assessing and predicting the health status of overhead lines based on health index (HI). First, the key features set of overhead lines including ontological and non-ontological factors is determined. Then, a calculation method for evaluating HI based on analytic hierarchy process (AHP) and technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. Subsequently, the health assessment results of the actual 110 kV and 10 kV overhead lines located in China show that the proposed method could reflects the actual situation of overhead lines well. Finally, based on the proposed method, this paper takes two areas of China, Beijing and Nanjing, which have different climatic conditions, as research objects, and studies the influence of natural factors on the health status of overhead lines respectively. Based on the analysis results, the health statuses of the overhead lines in the two regions are predicted. The research results of this paper can provide guidance for the operation and maintenance of overhead lines.

Wireless sensor networks (WSNs) are widely used in industrial applications. However, many of them have limited lifetimes, which has been a considerable constraint on their widespread use. As a typical application of WSNs, distributed measurement of the electric field under high-voltage direct-current (HVDC) transmission lines also suffers from this issue. This paper first introduces the composition of the electric-field measurement system (EFMS) and its working principle. Considering the actual power supply of the system, this paper mainly introduces the composition of the wireless sensor node (WSND) and analyzes the power consumption and potential working state transformation of the WSND, together with a comprehensive study on parameters affecting the power consumption of the wireless communication unit. Moreover, an energy-efficient scheduling approach is proposed after specially designing a working sequence and the study on system parameters. The proposed approach is verified by experiments on not only the experimental line of the national HVDC test base, but also a commercial operation HVDC transmission line with the challenge of long endurance, which is considered in this paper with a new strategy. The results show that the proposed method can greatly extend the lifetime of the WSND.

A heightened demand for improved printed circuit board (PCB) fault detection arises with the increasing integration and enhanced functionality of PCBs. Traditional visible light image analysis demonstrates accuracy and safety advantages in nonintrusive detection. However, it exhibits limitations in detecting obscured faults or lack of visible defects. The study proposes a fault detection framework based on infrared thermal imaging, aiming to enhance the practicality and engineering efficiency of PCB fault detection. This article introduces an infrared-thermography-based framework for anomaly detection and fault classification of PCBs. The framework encompasses preprocessing infrared thermal images, extraction of multimodal feature vectors (MMFVs), density-based anomaly detection (DAD), and fault classification based on deviation matrix clustering. The framework extracts multimodal features from the residual temperature scalar fields and residual temperature gradient vector fields, using multiscale detection of global and local images for fault classification. Experimental validation on a two-phase drive (TPD) circuit illustrates a significant enhancement in PCB anomaly detection and fault classification accuracy compared with existing feature extraction techniques. This research provides an innovative and practical PCB manufacturing and maintenance tool, effectively elevating anomaly detection accuracy and operational feasibility in engineering practices.

The electric field is an essential electromagnetic environmental parameter in designing and constructing high-voltage direct-current (HVDC) transmission lines. In this paper, a differential structure is proposed to improve the performance of the field mill sensor. Two groups of vanes with the same characteristics generate induced currents with opposite phases, and the difference operation calculates the ground electric field. We calculated the charges and induced currents and optimized the number of vanes of the sensing electrode using the simulation model. Then we designed the I/V circuit and amplifier based on the differential structure and conducted circuit simulation and sensor calibration experiments. Compared with the single input electric field sensor, the noise of the sensor with the differential structure is significantly reduced, and the sensitivity reaches 0.085 V/(kV/m). The results indicate that the field mill sensor with improved differential structure enhances the anti-interference ability and sensitivity.

A wireless sensor network-based distributed measurement system is designed for collecting and monitoring the electric field under the high voltage direct current (HVDC) transmission lines. The proposed system architecture is composed of a group of wireless nodes connected with electric field sensors and a base station. The electric field sensor based on Gauss's law is elaborated and developed. For the design of wireless node, the ARM microprocessor and Zigbee radio frequency module are employed. The proposed system has been used in China's state grid HVDC test base and the in situ power transmission projects. Based on the experimental results, the proposed measurement system demonstrates that it can adapt to the complex electromagnetic environment under the transmission lines and can accomplish the accurate, flexible, and stable demands of the electric field measurement.

With the increase of space activities, the safety detection of equipment in supercold environment becomes more and more important. However, the safety detection of equipment in liquid hydrogen environment is an unsolved worldwide problem. In this article, we present a new method to detect acoustic emission (AE) signals in liquid hydrogen environment, which can be used to confirm the safety and reliability of the equipment working in liquid hydrogen. First, the principle of mandrel-based fiber-optic ring (MBFOR) AE sensor and the corresponding demodulation method are introduced. Second, the effects of physical parameters, such as the length of the sensing fiber, the diameter and material of the mandrel, and the number of fiber winding layers on the sensor's sensitivity, are studied by experiment. Finally, the AE source localization accuracy test of the FOR AE detection system at low temperatures and the adaptability test of the proposed method in −252.8 °C liquid hydrogen are carried out. The results show that the proposed MBFOR sensor and the multichannel AE detection system can complete the defects detection of aerospace pressure vessel in liquid hydrogen. Certainly, it can be used for safety detection for other spacecraft equipment in harsh environments.