A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems

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

doi:10.1109/tpel.2019.2911594

Cited by 270 publications

(96 citation statements)

References 26 publications

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“…A DT is a digital holistic emulation of a physical system or assembly using integrated simulations and service data from multiple sources across the product lifecycle. This information is continuously updated and is visualized in a variety of ways to predict current and future conditions [68] [69]. In case of the CBs, it could be possible to define a dynamic learning model based on some diagnosis signals such as CC, vibration, TC along with temperature, gas pressure of the interrupter, and number of operations as variables controlling the parameters of the dynamic aging model of the CB.…”

Section: Vibrationmentioning

confidence: 99%

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Razi-Kazemi

Niayesh

2021

IEEE Trans. Power Delivery

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High voltage circuit breakers (HVCBs) play a critical role on providing the desired reliability, and resiliency in power systems. In order to extend their lifetime and predict the failures, various maintenance policies could be applied on these critical components. Amongst these strategies, condition-based maintenance (CBM) provides a satisfactory agreement with future smart environment. This paper aims to provide an insight into the relevant developments in this subject and to explore the viable visions compatible with future research stream. Accordingly, three directions, i.e. diagnostic signals, intelligent modelling and using monitoring data in asset management have been addressed in this paper. It presents challenges dealing with real-time assessment of the diagnostic signals relating to measurements, and analyses. Subsequently, the issues associated with using artificial intelligent (AI) and Machine learning for providing intelligent algorithms have been discussed. Finally, the connection between the monitoring data and the asset management approach is investigated. The latter is looking for the subjects including remaining lifetime estimation, prioritization, and health index definitions. This paper has attempted to make a bridge from past to future research trends in the failure diagnosis of HVCBs.

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Section: Vibrationmentioning

confidence: 99%

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Razi-Kazemi

Niayesh

2021

IEEE Trans. Power Delivery

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“…The stress in the PE converters due to using WBGs is precisely measured using the RT-models. In addition, the RT-models can be utilized as digital-twins for predictive maintenance of the EVs components [22]. Moreover, the RT-models can be used to verify the reliability of the PE converters for different mission profiles, thus providing a guideline to estimate the PE components ageing factor [23].…”

Section: Necessity Of Hardware-in-the Loop Testingmentioning

confidence: 99%

“…Each topology has its own advantages and drawbacks and should be chosen based on the functionalities, requirements, efficiency, component count, controllability, compactness, cost and reliability [26]. For the high-power BEV a standard 3-phase IBC topology has been proposed in [4], [22], [26], [32]- [38], thanks to its less device current stress, simple controllability, compact design, high power-density, better efficiency and high reliability. Numerous articles have been published on the HV DC/DC converter for the EVs applications.…”

Section: E Automotive Hv Dc/dc Converter's Research Trendsmentioning

confidence: 99%

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

et al. 2020

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Automotive Original Equipment Manufacturers (OEMs) require varying levels of functionalities and model details at different phases of the electric vehicles (EV) development process, with a trade-off between accuracy and execution time. This article proposes a scalable modelling approach depending on the multi-objective targets between model functionalities, accuracy and execution time. In this article, four different fidelity levels of modelling approaches are described based on the model functionalities, accuracy and execution time. The highest error observed between the low fidelity (LoFi) map-based model and the high fidelity (HiFi) physics-based model is 5.04%; while, the simulation time of the LoFi model is ~10 4 times faster than corresponding one of the HiFi model. A detailed comparison of all characteristics between multi-fidelity models is demonstrated in this paper. Furthermore, a dSPACE SCALEXIO Hardwarein-the-Loop (HiL) testbench, equipped with a minimal latency of 18μsec, is used for real-time (RT) model implementation of the EV's HV DC/DC converter. The performance of the entire HiL setup is compared with the Model-in-the-Loop (MiL) setup and the highest RMSE is limited to 0.54 among the HiL and MiL results. Moreover, the accuracy (95.7%) of the passive component loss estimation is verified through the Finite Element Method (FEM) software model. Finally, the experimental results of a full-scale 30-kW SiC DC/DC converter prototype are presented to validate the accuracy and correlation between multi-fidelity models. It has been observed that the efficiency deviation between the hardware prototype and multi-fidelity models is less than 1.25% at full load. Furthermore, the SiC Interleaved Bidirectional Converter (IBC) prototype achieves a high efficiency of 98.4% at rated load condition. INDEX TERMS DC/DC interleaved converter, EV, efficiency, electro-thermal modelling, multi-fidelity models, optimization, scalable modelling, Hardware-in-the-loop, and wide-bandgap technology.

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“…For PV irradiance changes and load variations, the fault detection and isolation scheme was decoupled. Jain et al 21 have explained the fault diagnosis of PV source and source level power converter with digital twin approach. Error residual vector was evaluated and generated by the fault diagnosis scheme.…”

Section: A Brief Review Of Recent Research Workmentioning

confidence: 99%

Fault identification and isolation for components of photovoltaic energy storage system based on IEEE21451 standard

Jaibalaganesh

Jayaraman

Raman

et al. 2020

Int J Numerical Modelling

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Major faults arise in power converters mainly due to faulty MOSFET switches, diode and electrolyte capacitors. Converter with fault tolerant circuit improves the overall reliability of DC‐DC converter. We develop a self‐test configuration that identifies the faults in analog to digital converter (ADC), DC to DC converter and battery. IEEE 21451 standard‐based interface is implemented to develop a knowledge‐based smart controller. The centralized controller is performed to execute the maximum power point tracking (MPPT). Photovoltaic (PV) energy storage system is constructing to prove how the converter’s sensing, processing, and actuation capabilities in actual time may allow effective fault identification. The simulation result of the proposed method is executed in the MATLAB/Simulink working platform. The experimental results are implemented with field‐programmable gate array of Spartan‐6 Xilinx. The simulation as well as experimental result shows different fault identification in PV with energy storage system. Battery is provided through 12 V/4.5 Ah battery. The simulation and experimental results make ensure that the proposed method reveals its proficiency. It shows that the received results may fault identification at less than the period of one switching usually by 100 ms with suggested method.

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A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems

Cited by 270 publications

References 26 publications

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

Fault identification and isolation for components of photovoltaic energy storage system based on IEEE21451 standard

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