Jason Poon

Model-Based Fault Detection and Identification for Switching Power Converters

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

Scalable DC Microgrids for Rural Electrification in Emerging Regions

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A scalable dc microgrid architecture for rural electrification in emerging regions

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.

Real-time model-based fault diagnosis for switching power converters

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

High-fidelity real-time hardware-in-the-loop emulation of PMSM inverter drives

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 diagnosis via PV panel-integrated power electronics

We present the design and experimental validation of a scalable dc microgrid architecture for rural electrification. The microgrid design has been driven by field data collected from Kenya and India. The salient features of the microgrid are distributed voltage control and distributed storage, which enable developed world grid cost parity. In this paper, we calculate that the levelized cost of electricity (LCOE) for the proposed dc microgrid system will be less than

Lossless voltage regulation and control of the resonant switched-capacitor DC-DC converter

0.40 per kW-hr. We also present experimental results from a locally installed dc microgrid prototype that demonstrate the steady state behavior, the perturbation response, and the overall efficiency of the 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.

Validation of Frequency- and Time-domain Fidelity of an Ultra-low Latency Hardware-in-the-Loop (HIL) Emulator

We present the analysis, design, and experimental implementation of a fault diagnosis method for switching power converters using a model-based estimator approach. The fault diagnosis method enables efficient detection and identification of component and sensor faults, and is implemented on the same computation platform as the control system. The model-based estimator operates in parallel with the switching power converter, and generates an error residual vector that can be used to detect and identify particular component or sensor faults. This paper presents an experimental demonstration for a 1.2 kW rack-level uninterruptable power supply (UPS) dc-dc converter for data center applications. Simulation and experimental results demonstrate fault detection and identification for various component and sensor faults in the converter. Moreover, we show that the proposed fault diagnosis design and analysis methods are applicable to a broad class of converter topologies and fault types.

Analysis and design of an adaptive parameter estimator for power electronics circuits

We describe the design, implementation, and experimental validation of an ultra-fast real-time hardware-in-the-loop emulation of a permanent magnet synchronous machine (PMSM) inverter drive. The power electronics converter and machine are modeled using a flexible piecewise linear state space approach and are simulated in hard real-time with 1 μs time step, which enables high-fidelity modeling of converter switching dynamics, including dead time and fully rectifying mode. We validate the fidelity of the real-time PMSM drive emulation by making real-time comparisons with a reference hardware model of a PMSM drive (scaled down system rated at 2.7 A and 200 V). Additionally, we experimentally demonstrate the capability of the hardware-in-the-loop PMSM drive emulation under various operating scenarios, including steady state, transient, and fault conditions.

FailSafe: A generalized methodology for converter fault detection, identification, and remediation in nanogrids

This paper presents the design, analysis, and implementation of a fault diagnosis method for photovoltaic (PV) energy conversion systems. We present a model-based state estimation approach for detecting and identifying three types of faults - (1) converter input faults (e.g. faults in a PV panel), (2) converter component faults (e.g. switch faults or passive component degradation), and (3) sensor faults (e.g. voltage and current sensors) for PV panel-integrated power electronics systems. The state estimator model includes a dynamic model of the PV source and a linear-switched model of the switching power converter. The estimated state values are compared with measured values from the physical power stage, which generates an error residual vector. This residual is used to detect and identify faults in either the PV source or the switching power converter. The estimator, fault detection and identification logic, and the PV converter control system (including PWM generation and maximum power point tracking) are implemented entirely on a single all-programmable system-on-chip (SoC) device, which includes an FPGA and ARM core. We present simulation and experimental results for a prototype 2 kW PV energy conversion system to demonstrate the efficacy of the proposed fault diagnosis and control platform. The experimental results demonstrate successful fault detection within 2 ms and precise fault identification within 31 ms for a collection of input, component, and sensor faults.