Philip Wing‐Tat Pong

Model-based, signal analysis-based, and data-driven methods are commonly employed for fault detection in lithium-ion batteries. However, these approaches are often limited by their reliance on accurate parameter identification, threshold-setting issues, and the need for large-scale, high-quality data. In recent years, there has been increasing interest in active measurement techniques utilizing sensors for the fault diagnosis of lithium-ion batteries. Among these, magnetic-field sensing presents a contactless solution for fault diagnosis. This paper proposes an innovative intelligent magnetic-field sensing technique based on Tunnel Magnetoresistive (TMR) sensors and magnetic-field image recognition for the effective and low-cost fault diagnosis of lithium-ion batteries. First, the electrochemical and magnetic characteristics of lithium-ion batteries are investigated to establish the underlying principles of magnetic field variation and to model the battery. A TMR sensor is employed to measure the magnetic field of lithium-ion batteries under various conditions, and magnetic field distribution patterns and image features are subsequently identified. A contactless fault diagnosis system is then developed using the convolutional neural network algorithm and a Raspberry Pi 5. The proposed system is demonstrated to reliably diagnose the healthy, aging, anomalies, and swelling of batteries, offering a promising solution for production quality inspection and recycling of lithium batteries.

As power systems evolve with the integration of renewable energy sources and the implementation of smart grid technologies, there is an increasing need for flexible and scalable modeling approaches capable of accurately capturing the complex dynamics of modern grids. To meet this need, various methods, such as the sparse identification of nonlinear dynamics and deep symbolic regression, have been developed to identify dynamical systems directly from data. In this study, we examine the application of a converter-based resource as a replacement for a traditional generator within a lossless transmission line linked to an infinite bus system. This setup is used to generate synthetic data in grid-following control mode, enabling the evaluation of these methods in effectively capturing system dynamics.

The magnetic-flux-based condition monitoring techniques are becoming increasingly popular due to their advantages, such as non-invasiveness, low costs, and ease of sensor installation. However, developing diagnostics and prognostics-based condition monitoring systems becomes challenging as magnetic signals-based wind time series exhibit non-linear and non-stationary characteristics due to being subjected to diverse combination of dynamic system behavior. The present data-driven feature extraction techniques, as well as knowledge-based and parameter estimator models, fall short in effectively quantifying these characteristics due to increased signal complexity and computation cost. Additionally, methods like the short-time Fourier transform that assumes stationarity of signals and has a fixed window size and discrete wavelet transform that has pre-defined wavelet bases have proven suboptimal for analyzing non-linear and non-stationary magnetic flux signals due time-frequency resolution tradeoffs. This research bridges the gap by investigating a new methodology of feature extraction to adeptly process the non-linear and non-stationary behavior in magnetic signals-based wind time series. This methodology is unique in nature since it utilizes magnetic signature-based fault condition indicators derived from the wind generator of a drive train model that accounts for the electromagnetic coupling. Additionally, the signal processing technique employed here considers non-linearity and non-stationarity that arises from wind characteristics, gearbox dynamics, and grid conditions and its effects on the magnetic flux density of the wind generator. Notably, this methodology offers new insights on motivation, applications, and significance of magnetic-flux-based condition monitoring techniques and highlights its potential as a critical tool for non-invasive fault detection on multiple components of a wind turbine.

Medium-voltage (MV) power converts have various intended applications in renewable energy resources integration, power substations, and electrical mobility. The high power and high switching frequency of MV power converters have necessitated the development of a contactless current sensing technique based on magnetoresistive (MR) sensors to provide galvanic isolation, wide bandwidth, and minimum stray inductive on the current path. The prevailing solution involves placing the MR sensor under a straight printed circuit board (PCB) trace. However, this setup suffers reduced sensitivity due to sensor installation errors and inaccurate measurement owing to the nearby interfering conductors. To address these two issues, a double circular PCB trace is proposed to replace the traditional straight PCB trace. In the proposal, two MR sensors are encircled by a circular PCB trace separately. The interference impact is mitigated by subtracting the differential output from the sensors. Additionally, the double circular trace (DC trace) is optimally designed to enhance the position tolerance of sensor installation by providing a uniform magnetic field within the sensing area. The experiment measurement under a 2 mm position error shows that the proposed method improves the sensitivity from 0.1736 V/A in the conventional straight trace to 0.5902 V/A. Moreover, the sensitivity remains consistent even in the presence of interfering conductors.

High-frequency power converters are gaining increasing attention and popularity due to the high power density and high efficiency demand in electric vehicles. In this condition, high-frequency current sensing accuracy is essential. However, existing current sensing methods cannot meet the needs in terms of low power loss, small size, being contactless, or large bandwidth. This paper proposes a new current sensing technique with bandwidth enhancement. First, a skin effect model for commonly used rectangular conductors is investigated. Then, variations of out-of-plane and in-plane magnetic fields caused by skin effect are analysed theoretically. It reveals that measuring out-of-plane magnetic fields shows a larger bandwidth than measuring in-plane magnetic fields. Subsequently, a circle trace for current sensing is proposed. The corresponding simulation results illustrate necessary considerations in a circle trace-based PCB design. Then, the current responses of a proposed circle trace and a conventional straight trace are compared in experiments. In experiments, the step current response has an average improvement of 48%, and the measurement bandwidth is extended from 10 kHz to 200 kHz using the circle trace method. These experimental results confirm that the circle trace shows better performances. Thus, the proposed method is validated for application in high-frequency current sensing.

A guided wireless electric vehicle (EV) charging strategy was proposed to effectively mitigate the issue of misalignment, which could result in decreased charging efficiency in wireless charging systems. Typically, sensor data fusion and mapping-based techniques are employed to establish the relationship between the sensor output and coil position. However, the implementation of these techniques necessitates sensor arrays or imposes a substantial computational burden. To address these challenges, an alternative methodology arises through the estimation of coil coordinates based on magnetic field components. In this proposal, the ratio of in-plane magnetic field components (denoted as By/Bx) is employed for the determination of coil coordinates. The proposed method is operationalized with the integration of merely four dual-axis anisotropic magnetoresistive (AMR) sensors affixed at the receiver coil side as point field detectors. This streamlined configuration facilitates the derivation of a concise formula for the numerical positioning of the coil. Following the adjustment of the receiver coil contingent upon estimated coil coordinates, a wireless charging stage was triggered on the transmitter coil side. The trigger signal was generated by the relay’s action, obviating the necessity for supplementary communication devices. A laboratory-scale wireless EV charging prototype was developed and tested. The proposed method possesses the merits of being accurate, computationally efficient, and communication-free. There were 187 samples demonstrated in the testing range of 20×16 cm. The positioning errors were found to be less than 1 cm which validated the effectiveness of the proposed method.

Community microgrids are applicable universally because they can decentralise grid-connected areas and electrify remote areas. Moreover, they can provide energy security by improving the reliability and resiliency of electric distribution while diminishing the infrastructure costs of preserving or constructing electric power generation, transmission, and distribution assets. Using renewable energy sources (RESs) maybe incentivised through the government and utilities, which can help make such projects economically viable. A model has been developed to calculate the costs and benefits of building a standalone community microgrid in various cities in the United States, powered by wind turbines and photovoltaic (PV) panels where the battery is the backup. Using the System Advisor Model, the sizing of the microgrid system was optimised. This microgrid community's capital, operation, and maintenance costs were calculated using various per unit costs obtained from previous research. Real-estate data were collected from websites to calculate the average price of land and houses. An automated tool was created to calculate the developer's profit and house price subsidy for building such communities. The result demonstrated that several states in the USA could be profitable for developers to build and homebuyers to be a member of this microgrid community. Additionally, this microgrid community has the objective of real/near power supply during disaster and emergency events (e.g., coastal/riverine flooding, tsunamis, earthquakes, wildfires, possible damage, and terrorist attacks). The benefit is not only the direct power generation cost but also the advantage of keeping some critical services operational during catastrophic events that cause electricity blackouts

Wind turbine drive trains consist of mechanical components such as the gearbox, the bearings and the rotating shaft, and an electrical component, the electric generator. These components are electromagnetically and electromechanically coupled with each other and have continuously rotating parts, making them more susceptible to faults. Unfortunately, these components are individually modeled while performing finite element analysis (FEA), and the coupling effects are neglected, yielding approximations that are not acceptable for, e.g., modal analysis. This paper addresses the issue and presents a FEA model of the complete wind turbine drive train subsystem. Here, a unique co-simulation platform was developed to integrate and analyze the mechanical and electrical components of the drive train. The case studies performed here report the existence of electromagnetic coupling among the components of the drive train by simulating various faults such as broken gearbox tooth and demagnetization of permanent magnets and studying their influence on the magnetic flux of the electric generator. The paper offers new insights on motivation, applications as well as the importance of FEA modeling of the entire wind turbine drive train, and how it could be critical to non-destructive evaluations and fault detections on the wind turbines.