Search results for "Power engineering"
showing 10 items of 126 documents
Modeling Stator Winding Inter-Turn Short Circuit Faults in PMSMs including Cross Effects
2020
Author's accepted manuscript. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper presents a detailed analysis of stator winding inter-turn Short Circuit (ITSC) faults, taking the cross effects in the three phases of a permanent magnet synchronous motor (PMSM) into account by considering insulation degradation resistances. A PMSM with series coils in eac…
A Novel Fault-Tolerant Routing Algorithm for Mesh-of-Tree Based Network-on-Chips
2019
Use of bus architecture based communication with increasing processing elements in System-on-Chip (SoC) leads to severe degradation of performance and speed of the system. This bottleneck is overcome with the introduction of Network-on-Chips (NoCs). NoCs assist in communication between cores on a single chip using router based packet switching technique. Due to miniaturization, NoCs like every Integrated circuit is prone to different kinds of faults which can be transient, intermittent or permanent. A fault in any one component of such a crucial network can degrade performance leaving other components non-usable. This paper presents a novel Fault-Tolerant routing Algorithm for Mesh-of-Tree …
Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network
2019
Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density es…
Autonomous Bearing Fault Diagnosis Method based on Envelope Spectrum
2017
Abstract Rolling element bearings are one of the fundamental components of a machine, and their failure is the most frequent cause of machine breakdown. Monitoring the bearing condition is vital to preventing unexpected shutdowns and improving their maintenance planning. Specifically, the bearing vibration can be measured and analyzed to diagnose bearing faults. Accurate fault diagnosis can be achieved by analyzing the envelope spectrum of a narrowband filtered vibration signal. The optimal narrow-band is centered at the resonance frequency of the bearing. However, how to determine the optimal narrow-band is a challenge. Several methods aim to identify the optimal narrow-band, but they are …
Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning
2018
Pitch systems are among the wind turbine components with most frequent failures. This article presents a multicomponent fault detection for induction motors and planetary gearboxes of the electric pitch drives using only the three-phase motor line currents. A deep autoencoder is used to extract features from the extended Park's vector modulus of the motor three-phase currents and a support vector machine to classify faults. The methodology is validated in a laboratory setup of a scaled pitch drive, with four commonly occurring faults, namely, the motor stator turns fault, broken rotor bars fault, planetary gearbox bearing fault and planet gear faults, under varying load and speed conditions.
Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks
2018
Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is propos…
Current fault signatures of Voltage Source Inverters in different reference frames
2016
This paper considers different current patterns used to identify the correct fault signatures in Voltage Source Inverters (VSI). At the beginning, the Authors consider the currents patterns from which a simple or a double fault can be encompassed both in the case of controllable device only or with its free wheeling companion diode. After the discussion of diagnosis algorithm suitable for electrical drives and principally based on a persistent near zero current condition current in the natural phase reference frame, the stationary reference frame is then considered as a tool to identify both the faulted phase as the device or various combination of faulted devices. On the contrary, the Auth…
Multi-band identification for enhancing bearing fault detection in variable speed conditions
2020
Abstract Rolling element bearings are crucial components in rotating machinery, and avoiding unexpected breakdowns using fault detection methods is an increased demand in industry today. Variable speed conditions render a challenge for vibration-based fault diagnosis due to the non-stationary impact frequency. Computed order tracking transforms the vibration signal from time domain to the shaft-angle domain, allowing order analysis with the envelope spectrum. To enhance fault detection, the bearing resonance frequency region is isolated in the raw signal prior to order tracking. Identification of this region is not trivial but may be estimated using kurtosis-based methods reported in the li…
Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks
2019
Abstract The power curve of a wind turbine describes the generated power versus instantaneous wind speed. Assessing wind turbine performance under laboratory ideal conditions will always tend to be optimistic and rarely reflects how the turbine actually behaves in a real situation. Occasionally, some aerogenerators produce significantly different from nominal power curve, causing economic losses to the promoters of the investment. Our research aims to model actual wind turbine power curve and its variation from nominal power curve. The study was carried out in three different phases starting from wind speed and related power production data of a Senvion MM92 aero-generator with a rated powe…
Analysis of a Fast Reserve Unit Behaviour with Additional Modular Synthetic Inertia Control
2021
The paper presents the results of a theoretical study on the behaviour of a battery storage system operated as a Fast Reserve Unit and equipped with additional synthetic inertia control. The Fast Reserve Unit is assumed connected to the transmission system of Sicily, operated as an isolated grid in order to show more clearly the effect of the Fast Reserve Unit intervention during a power imbalance. The unit is controlled also to provide synthetic inertia with a conventional control scheme and with a new scheme proposed by the authors and named “Modular Synthetic Inertia”. The latter has been conceived for offering a modular response as a function of the Rate of Change of Frequency, to avoid…