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Title: A Method for Open-Circuit Faults Detecting, Identifying, and Isolating in Cascaded H-Bridge Multilevel Inverters
The increasing importance of power electronic converters in supplying electrical energy to utility grids places a higher priority to detect and protect against fault conditions. Fault detection and isolation are particularly important for inverters that provide black-start recovery for microgrids since these converters provide the energy source for restoration after a power outage. This paper presents a new fault detection and location method for Cascaded H-Bridge (CHB) multilevel inverters. The new fault detection method is based on monitoring the output voltage of each cell and output current directions along with each switch’s state. By monitoring each cell’s output voltage and current direction, the faulty cell can be detected and isolated. After the faulty cell is detected, the faulty switch can be located by comparing the current direction with the switching states. This technique is implemented with Level-Shifted Pulse Width Modulation (LS-PWM) in order to maintain acceptable total harmonic distortion (THD) levels at the converter. The proposed method can be implemented for a CHB with any number of cells, can operate with nonlinear loads, and offers very fast detection times. Simulation and experimental results verify the performance of this method.  more » « less
Award ID(s):
1747757
NSF-PAR ID:
10084322
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. 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