Jason Poon

Bio: Jason Poon is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Fault (power engineering) & Power electronics. The author has an hindex of 13, co-authored 27 publications receiving 535 citations. Previous affiliations of Jason Poon include Stanford University & Massachusetts Institute of Technology.

A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems

Abstract: Rooftop and building-integrated distributed photovoltaic (PV) systems are emerging as key technologies for smart building applications. This paper presents the design methodology, mathematical analysis, simulation study, and experimental validation of a digital twin approach for fault diagnosis. We develop a digital twin that estimates the measurable characteristic outputs of a PV energy conversion unit (PVECU) in real time. The PVECU constitutes a PV source and a source-level power converter. The fault diagnosis is performed by generating and evaluating an error residual vector, which is the difference between the estimated and measured outputs. A PV panel-level power converter prototype is built to demonstrate how the sensing, processing, and actuation capabilities of the converter can enable effective fault diagnosis in real time. The experimental results show detection and identification of ten different faults in the PVECU. The time to fault detection (FD) in the power converter and the electrical sensors is less than 290 $\mu$ s and the identification time is less than 4 ms. The time to FD and identification in the PV panel are less than 80 ms and 1.2 s, respectively. The proposed approach demonstrates higher fault sensitivity than that of existing approaches. It can diagnose a 20% drift in the electrical sensor gains and a 20% shading of a solar cell in the PV panel.

Model-Based Fault Detection and Identification for Switching Power Converters

Abstract: We present the analysis, design, and experimental validation of a model-based fault detection and identification (FDI) method for switching power converters using a model-based state estimator approach. The proposed FDI approach is general in that it can be used to detect and identify arbitrary faults in components and sensors in a broad class of switching power converters. The FDI approach is experimentally demonstrated on a nanogrid prototype with a 380-V dc distribution bus. The nanogrid consists of four different switching power converters, including a buck converter, an interleaved boost converter, a single-phase rectifier, and a three-phase inverter. We construct a library of fault signatures for possible component and sensor faults in all four converters. The FDI algorithm successfully achieves fault detection in under 400 $\mu$ s and fault identification in under 10 ms for faults in each converter. The proposed FDI approach enables a flexible and scalable solution for improving fault tolerance and awareness in power electronics systems.

Scalable DC Microgrids for Rural Electrification in Emerging Regions

Abstract: We present the design and experimental validation of a scalable dc microgrid for rural electrification in emerging regions. A salient property of the dc microgrid architecture is the distributed control of the grid voltage, which enables both instantaneous power sharing and a metric for determining the available grid power. A droop-voltage power-sharing scheme is implemented wherein the bus voltage droops in response to low supply/high demand. In addition, the architecture of the dc microgrid aims to minimize the losses associated with stored energy by distributing storage to individual households. In this way, the number of conversion steps and line losses are reduced. We calculate that the levelized cost of electricity of the proposed dc microgrid over a 15-year time horizon is $0.35/kWh. We also present the experimental results from a scaled-down experimental prototype that demonstrates the steady-state behavior, the perturbation response, and the overall efficiency of the system. Moreover, we present fault mitigation strategies for various faults that can be expected to occur in a microgrid distribution system. The experimental results demonstrate the suitability of the presented dc microgrid architecture as a technically advantageous and cost-effective method for electrifying emerging regions.

Fault Prognosis for Power Electronics Systems Using Adaptive Parameter Identification

Abstract: This paper presents the design, implementation, and experimental validation of a method for fault prognosis for power electronics systems using an adaptive parameter identification approach. The adaptive parameter identifier uses a generalized gradient descent algorithm to compute real-time estimates of system parameters (e.g., capacitance, inductance, parasitic resistance) in arbitrary switching power electronics systems. These estimates can be used to monitor the overall health of a power electronics system and to predict when faults are more likely to occur. Moreover, the estimates can be used to tune control loops that rely on the system parameter values. The parameter identification algorithm is general in that it can be applied to a broad class of systems based on switching power converters. We present a real-time experimental validation of the proposed fault prognosis method on a 3 kW solar photovoltaic interleaved boost dc–dc converter system for tracking changes in passive component values. The proposed fault prognosis method enables a flexible and scalable solution for condition monitoring and fault prediction in power electronics systems.

Fault Detection and Isolation Filters for Three-Phase AC-DC Power Electronics Systems

Abstract: This paper develops and experimentally demonstrates a new class of high-fidelity model-based fault detection and isolation filters for three-phase AC-DC power electronics systems. The structure of these filters is similar to that of a piecewise linear observer and in the absence of faults the filter residual converges to zero. On the other hand, whenever a fault occurs, by appropriately choosing the filter gain, the filter residual will exhibit certain geometric characteristics that allow the fault to be detected and, in certain cases, also isolated. Key advantages of these filters include fast detection of all possible component faults and the ability to capture slow degradation in individual components. In order to experimentally demonstrate their feasibility, the filters are implemented on an ultra-fast application-specific real-time processor. While the theoretical framework developed is general, the analysis, simulations, and experiments are focused on widely used power electronics systems implementing three-phase AC-DC converters that are used in, e.g., motor drive applications and distributed static compensators.

Linear System Theory

Abstract: Chapter 8 establishes the relationship between the input and output power spectral densities of a linear system. Limitations on results are carefully detailed and the case of oscillator noise is considered. The theory is extended to multiple input-multiple output systems.

Digital Twin: Enabling Technologies, Challenges and Open Research

Abstract: Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

Digital twin-based sustainable intelligent manufacturing: a review

Abstract: As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: areview

Abstract: Global energy challenges have driven the adoption of renewable energy sources. Usually, an intelligent energy and battery management system is deployed to harness the renewable energy sources efficiently, whilst maintaining the reliability and robustness of the power system. In recent years, the battery-supercapacitor based hybrid energy storage system (HESS) has been proposed to mitigate the impact of dynamic power exchanges on battery's lifespan. This study reviews and discusses the technological advancements and developments of battery-supercapacitor based HESS in standalone micro-grid system. The system topology and the energy management and control strategies are compared. The study also discusses the technical complexity and economic sustainability of a standalone micro-grid system. A case study of a standalone photovoltaic-based micro-grid with HESS is presented.

Fault Detection and Localization Method for Modular Multilevel Converters

Abstract: The modular multilevel converter (MMC) is attractive for medium- or high-power applications because of the advantages of its high modularity, availability, and high power quality. However, reliability is one of the most important issues for MMCs which are made of large number of power electronic submodules (SMs). This paper proposed an effective fault detection and localization method for MMCs. An MMC fault can be detected by comparing the measured state variables and the estimated state variables with a Kalman filter. The fault localization is based on the failure characteristics of the SM in the MMC. The proposed method can be implemented with less computational intensity and complexity, even in case that multiple SM faults occur in a short time interval. The proposed method is not only implemented in simulations with professional tool PSCAD/EMTDC, but also verified with a down-scale MMC prototype controlled by a real-time digital signal controller in the laboratory. The results confirm the effectiveness of the proposed method.