Fault Prognosis for Power Electronics Systems Using Adaptive Parameter Identification

Poon, Jason; Jain, Palak; Spanos, Costas J.; Panda, Sanjib Kumar; Sanders, S.R.

doi:10.1109/tia.2017.2664052

Cited by 82 publications

(37 citation statements)

References 24 publications

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“…A bank of fault identification filters (which is analogous to a library of fault templates) is designed using (14) and (15) for distinguishing between various component faults f i , as shown in Table II. Each FI filter can be described using (11), (12), (13), and (16). We design three FI filters for the proposed system which correlates to faults in inductor, capacitor and half bridge leg respectively.…”

Section: Fdi Scheme For Solar Pv Power Electronic Convertermentioning

confidence: 99%

“…Luenberger observers and Kalman filters), parity space methods, and parameter estimation [5]. These FDI schemes have been investigated in the literature before [4], [6]- [11] for dc-dc converters. However, most of the earlier works are fault-specific, for instance in [6], [7] only switch faults are investigated using signal processing techniques for dc-dc converters.…”

Section: Introductionmentioning

confidence: 99%

“…Few works have focused on a generalized model-based methodologies for fault prognosis and diagnosis which are converter topology-and fault-agnostic [4], [10], [11]. However, the FDI schemes based on parity space methods in [4], [10] lack design for robustness, and are limited in identifying faults (for instance, the fault signatures used to identify different faults are indistinguishable).…”

Section: Introductionmentioning

confidence: 99%

“…However, the FDI schemes based on parity space methods in [4], [10] lack design for robustness, and are limited in identifying faults (for instance, the fault signatures used to identify different faults are indistinguishable). Similarly, the FDI scheme based on parameter estimation in [11] is promising for passive component degradation, however it does not account for failures in switching components.…”

Section: Introductionmentioning

confidence: 99%

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A luenberger observer-based fault detection and identification scheme for photovoltaic DC-DC converters

Jain

Poon

et al. 2017

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

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This paper presents the design, analysis, and experimental validation of a fault detection and identification (FDI) scheme for dc-dc power electronic converters. The FDI scheme includes two new classes of fault filters: (1) a linear-switched fault detection (FD) filter and (2) a bank of linear-switched fault identification (FI) filters. Both the FD filter and the bank of FI filters have a structure similar to that of Luenberger observer. The FD filter detects a fault event and the bank of FI filters identify a faulted converter component, for instance, failure in switching or passive components. We present simulation and experimental results for a prototype 2 kW solar photovoltaic (PV) boost dc-dc converter system to demonstrate the efficacy of the proposed FDI scheme. The experimental results demonstrate accurate fault detection and identification for a collection of catastrophic component faults in PV dc-dc power converters.

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Section: Fdi Scheme For Solar Pv Power Electronic Convertermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A luenberger observer-based fault detection and identification scheme for photovoltaic DC-DC converters

Jain

Poon

et al. 2017

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

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“…Apart from IGBT open-circuit fault, sensor fault is also one of the main concerns in this thesis. Generally, there are four common types of sensor faults according to the sensor measurement output: stuck fault, drift fault, gain fault, and noise fault, which are expressed mathematically in (2-16) - (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19).…”

Section: Sensor Fault Analysismentioning

confidence: 99%

Data-driven fault diagnosis in the converter system

Xia¹

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Firstly, the author would like to extend sincere appreciation for his supervisor, Asst. Prof. Xu Yan, for his patient advice, guidance and support. Enlightened by inspired communication with Prof. Xu, the novel idea can be generated in this research work. Besides, Prof. Xu's rigorous attitude of research has a great influence on the author, reshaping the author's learning and living styles. Moreover, the author truly appreciates Dr. Gou Bin, Research Fellow of EEE, who has provided considerable instruction and help, when author confronts difficulty. Those valuable advices solved a great deal of author's problems throughout the research work. Finally, yet importantly, the author would like to show his gratitude for family, which gives the author mental and financial support, as solid backing. In addition, the author also wants to thank his friends, who share pleasant time with each other.

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Robust fault detection and adaptive parameter identification for DC‐DC converters via switched systems

LiJian

Pan

SuQingyu

2020

Adaptive Control & Signal

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In this paper, the problem of fault detection and identification for DC-DC converters is presented. First, switched systems model and fault model are analyzed based the switched characteristics of the DC-DC converters, taking the DC-DC buck converter as an example. According to the switched Lyapunov function technique, a fault detection observer and a bank of linear switched fault identification observers are designed for the switched systems. Next, the fault detection observer detects the fault based on the residual produced by the observer output and actual output. After the fault is detected, fault identification observers are activated. The location of fault is identified by comparing the residual evaluation functions. Meanwhile, the adaptive parameter identification is achieved by choosing an appropriate adaptive law. Finally, in order to show the feasibility of the fault detection and identification, the simulation results are given in this article.

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Fault Prognosis for Power Electronics Systems Using Adaptive Parameter Identification

Cited by 82 publications

References 24 publications

A luenberger observer-based fault detection and identification scheme for photovoltaic DC-DC converters

A luenberger observer-based fault detection and identification scheme for photovoltaic DC-DC converters

Data-driven fault diagnosis in the converter system

Robust fault detection and adaptive parameter identification for DC‐DC converters via switched systems

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