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Results for “Inter-turn Faults”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

New data-driven approach to bridging power system protection gaps with deep learning

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this paper, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A combined convolutional neural network and long short-term memory (CNN-LSTM) network is implemented to achieve data translation invariance and capture the temporal correlation of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN-LSTM--based method avoids the complicated, manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on two kinds of protection gaps: high-impedance faults and transformer inter-turn faults. Lastly, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

42 ENGINEERING↗
DOE OSTI2021

Bridging Power System Protection Gaps with Data-driven Approaches

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this research, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A convolutional neural network (CNN) based fault detection approach is implemented to achieve data translation invariance of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN–based method avoids the complicated manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on four kinds of protection gaps: high impedance faults, transformer/generator inter-turn faults, distribution system PV circuit faults, and the mis-operation situations of Zone 3 line protection relays operating under system stress. Finally, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Online Detection of Inter-Turn Winding Faults in Single-Phase Distribution Transformers Using Smart Meter Data

Turn-to-turn faults between primary windings due to insulation degradation are a major cause of distribution transformer failure, and occur due to high levels of stress such as overloading and overheating. An additional consequence of these faults is an increased voltage on the transformer secondary due to effective change in turns ratio. This paper develops a novel method for early detection of insulation degradation and subsequent inter-turn winding failure by monitoring the transformer secondary voltage. The algorithm is based on a cumulative sum (CUSUM) statistic and compares voltages on neighbouring transformers to flag degrading assets. Results obtained from simulation as well as experimental data show that smart meter measurements can be utilized to achieve very high detection accuracy while keeping costs low. Here, the paper also demonstrates the validity of the algorithm in the presence of measurement noise, residential solar power injection etc.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Condition monitoring of permanent magnet AC machines for all-electric transportation systems: State of the art

The current state of the art on emerging and efficient techniques for condition monitoring of permanent magnet (PM) alternating-current (AC) machines deployed in electric vehicle (EV) applications is presented. The discussion includes the most common and specific types of faults in PM motors, such as rotor demagnetisation and stator inter-turn faults, respectively. Fault indicators, such as voltage (v s ) and current (i s ) signals and machine signatures based on motor back electromotive force (EMF) (E B ) and magnetic flux (φ), are taken into account as a measuring quantity in diagnosing motor faults. Other signatures, including thermal analysis, acoustic noise, and vibrations, are also illustrated as some of the emerging techniques in estimating the performance of EV motors while under operations. In addition, various fault modelling methods, condition monitoring techniques, and comprehensive approaches applied in diagnosing the effect of machine faults during its incipient stages are illustrated. Since most of the fault diagnostic techniques discussed here include only machine-based quantities as fault indices/indicators, the provided solutions are therefore found to be more reliable and accurate for diagnosing the motor faults. This comprehensive review study is inclusive of the existing fault diagnostic techniques, which are currently employed in industrial and commercial practices, in addition to the new methodologies proposed by the authors. All the given condition monitoring schemes therefore seem significantly vital in estimating the state of health of PM AC machines while under operation in all-electric transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗