Fault (power engineering)

About: Fault (power engineering) is a research topic. Over the lifetime, 119772 publications have been published within this topic receiving 981679 citations.

Micro-grid autonomous operation during and subsequent to islanding process

Abstract: This paper investigates (i) preplanned switching events and (ii) fault events that lead to islanding of a distribution subsystem and formation of a micro-grid. The micro-grid includes two distributed generation (DG) units. One unit is a conventional rotating synchronous machine and the other is interfaced through a power electronic converter. The interface converter of the latter unit is equipped with independent real and reactive power control to minimize islanding transients and maintain both angle stability and voltage quality within the micro-grid. The studies are performed based on a digital computer simulation approach using the PSCAD/EMTDC software package. The studies show that an appropriate control strategy for the power electronically interfaced DG unit can ensure stability of the micro-grid and maintain voltage quality at designated buses, even during islanding transients. This paper concludes that presence of an electronically-interfaced DG unit makes the concept of micro-grid a technically viable option for further investigations.

System structure for software fault tolerance

Abstract: The paper presents, and discusses the rationale behind, a method for structuring complex computing systems by the use of what we term “recovery blocks”, “conversations” and “fault-tolerant interfaces”. The aim is to facilitate the provision of dependable error detection and recovery facilities which can cope with errors caused by residual design inadequacies, particularly in the system software, rather than merely the occasional malfunctioning of hardware components.

System structure for software fault tolerance

Abstract: This paper presents and discusses the rationale behind a method for structuring complex computing systems by the use of what we term "recovery blocks," "conversations," and "fault" tolerant interfa...

Ridethrough of wind turbines with doubly-fed induction generator during a voltage dip

Abstract: In this paper, a solution is described that makes it possible for wind turbines using doubly-fed induction generators to stay connected to the grid during grid faults. The key of the solution is to limit the high current in the rotor in order to protect the converter and to provide a bypass for this current via a set of resistors that are connected to the rotor windings. With these resistors, it is possible to ride through grid faults without disconnecting the turbine from the grid. Because the generator and converter stay connected, the synchronism of operation remains established during and after the fault and normal operation can be continued immediately after the fault has been cleared. An additional feature is that reactive power can be supplied to the grid during long dips in order to facilitate voltage restoration. A control strategy has been developed that takes care of the transition back to normal operation. Without special control action, large transients would occur.

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

Abstract: Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.