ABSTRACT Accurate and efficient diagnosis of motor eccentricity faults is crucial for the reliable operation of electric machines. This requires large volumes of high‐quality data across various operating conditions and fault types. While experimental data collection is costly and time‐consuming, simulations offer a more practical alternative. However, full‐order models are often computationally prohibitive due to magnetic saturation nonlinearities and repeated simulations across different conditions. This challenge motivates the development of efficient model order reduction (MOR) techniques. To alleviate the computational cost associated with nonlinear problems, look‐up table (LUT) interpolation can be used to approximate nonlinear operators, thus avoiding convergence issues commonly encountered in hyper‐reduction methods. However, conventional LUT methods face significant memory demands, especially in multi‐parameter settings. To address these limitations, a hybrid MOR framework is proposed, based on a two‐stage approach leveraging tensor decomposition, tailored for nonlinear, multi‐parametric electromagnetic problems in motor diagnostics. The two‐stage strategy is applied to both the LUT interpolation part and the MOR process. The framework is validated on a permanent magnet synchronous motor with static eccentricity, demonstrating superior accuracy and efficiency compared to traditional MOR techniques, and showing great potential for surrogate modeling in motor fault diagnosis.

ABSTRACTThe power wireless sensor network integrates wireless communication and intelligent sensing technology to monitor the operating status of power equipment in real‐time, thereby improving the stability and safety of the power system. However, numerous sensor data flows may lead to network congestion and end‐to‐end delays. In addition, in the power system, monitoring data has different delay constraints and reliability requirements, and it is necessary to divide the flow into different priorities for transmission. Therefore, this paper proposes a scheduling algorithm for power wireless sensor networks that considers service priority. We construct a power wireless sensor network model that includes network topology, queue length and problem definition. Data flow priority and delay constraints are met by introducing queue weight factors and virtual queues. The Lyapunov optimisation method maximises the throughput of the priority classification‐based power wireless sensor network. Moreover, the queue stability of the scheduling algorithm is theoretically proved. The simulation results show that the proposed algorithm can ensure the stability of all queues and strictly meet the priority and delay constraints of various data flows in the network.

Abstract The 20 Hz low‐frequency transmission technology is a new type of AC transmission technology based on fully controlled power electronic devices, which has gradually been applied in construction around the world. However, it is difficult to develop high‐precision low‐frequency standard transformers, resulting in a blank system for tracing the values of low‐frequency transformers. Inductive voltage dividers have many advantages such as high accuracy and stability, so low‐frequency voltage ratio standards are suitable for using inductive voltage dividers as the source of traceability. This article proposes the principle of two‐stage excitation and develops a two‐stage excitation low‐frequency induction voltage divider. Based on the finite element method, a three‐dimensional model was established to simulate and optimize the internal electromagnetic field and structure. Designed a closed shielded iron core structure, which has achieved good magnetic field shielding effect; In order to reduce the impact of capacitive leakage, the proportional winding adopts a coaxial cable 10 wire parallel winding method to achieve outer equipotential shielding. Based on the reference potential method, this device is calibrated for errors. The error measurement device uses a lock‐in amplifier SR850. According to the error calibration results, the 10 stage transformation ratio error of the 1 kV two‐stage excitation low‐frequency induction voltage divider is better than 1 × 10−7.

ABSTRACTThe field of wearable sensors, particularly motion sensors, has experienced noteworthy advancements in recent years. Motion sensors have significantly assisted doctors by gathering real‐time data and transmitting information remotely, proving highly beneficial in their practices. The sensor produced in this research is designed to withstand different kinds of mechanical forces such as tension, pressure, bending, twisting and contact. This sensor consists of a combination of silicone rubber, carbon nanotubes and carbon black, an extremely flexible composite material, and its electrodes are arranged in a spiral to provide sufficient strength under varying and strong forces. In order to capture more forces through the sensor while reducing the size of the sensor and lowering production costs, a buffer layer was created on the sensor. After data collection, the forces were separated using machine learning. The sensor was subjected to various tests and showed good characteristics (21‐225 sensitivity, 1000 cycles repeatability, 8% FSO non‐linearity, 13% FSO hysteresis, etc.). Finally, we attached the manufactured sensor to various parts of the body and were thus able to detect body movements.

ABSTRACTAmong the most expensive assets in power grids, power transformers are essential for the reliability of the power supply chain and the overall stability of the grid. Due to their permanent connection to the network, this equipment is exposed to all kinds of faults and phenomena, including short‐circuit faults and overvoltages caused by lightning and switching. Hence, ongoing monitoring of the transformer's condition is essential to prevent breakdowns and damage to the transformer. Among the different condition monitoring methods, the frequency response analysis (FRA) method is sensitive to the smallest functional changes of the transformer, as it is completely related to the physics and geometry of the transformer. This method stands out as one of the most effective and efficient approaches to transformer monitoring, especially for detecting mechanical faults. However, the FRA method faces an important challenge of interpretation: the correlation between the type of fault that occurred and the way the transformer's function changes is still not well‐known, and studies in this field are ongoing. One of the most widely used methods of interpreting frequency response results is the use of numerical indices, coil modelling, transformer function estimation, and artificial intelligence algorithms. This paper introduces these methods, and their advantages and disadvantages are discussed. Then, the most widely used artificial intelligence algorithms in transformer condition monitoring are presented and compared. Finally, future research directions are anticipated.

Abstract This paper proposes an online identification method utilizing phasor measurement unit (PMU) measurements, symmetric dot pattern (SDP) graphs, and the ResNet50‐L1 M model to facilitate the rapid identification of metering relative errors among interconnected capacitor voltage transformers (CVTs) in wide‐area substations within power systems. First, PMUs measured the three‐phase voltage data from interconnected CVTs across wide‐area substations. The amplitude differences of the three‐phase voltages were converted into Euclidean distances, which were then transformed into SDP graphs, with each type of metering relative error corresponding to a distinct SDP graph. The ResNet50‐L1 M model was established by integrating an L1 norm‐based attention mechanism into the ResNet50 architecture. This model was then trained on CVT SDP graphs representing various error states to develop an online error identification system. The effectiveness of the proposed method was evaluated based on data from the “Houshou transmission line” in Ningxia province, which connects the 330 kV Niushoushan substation and the 330 kV Houqiao substation operated by the State Grid Corporation of China. With 16 different error states tested, the model achieved an identification accuracy rate of 89.39%, demonstrating a notable improvement over other methods in comparative tests.

Abstract Broadband couplers consist usually of multiple coupled lines that are each a quarter wave‐length long. Herein, in contrast to conventional methods, a short length of a microstrip line is coupled to a coaxial line. The length of the coupled microstrip line is small compared to the wavelength at the lowest operating frequency. The coupling is weak and the microstrip line is λ/8 at the highest operating frequency. The proposed structure resembles a pair of coupled lines with a linear coupling response. The coupling exhibits a slope of 6 dB/octave over multi‐octave bandwidth. The microstrip line is connected to a lumped element compensating network at the coupled port. The compensating network is a low‐pass circuit with a slope of −6 dB/octave. The power handling of the coupler is high due to the coaxial line and the weakly coupled microstrip line. Electromagnetic simulations and analytical formulations are presented. The broadband coupler handles nearly 1 kW of input power. Due to the weak coupling of the microstrip‐coaxial coupler, the lumped element circuit needs to handle <1.5 W. A high‐power coupler has been fabricated for the frequency range of 30 to 500 MHz. It exhibits a coupling response of 56 ± 0.4 dB.