Fault indicator

About: Fault indicator is a research topic. Over the lifetime, 10057 publications have been published within this topic receiving 143482 citations. The topic is also known as: FCI & power line fault indicator.

A Systematic Stochastic Petri Net Based Methodology for Transformer Fault Diagnosis and Repair Actions

Abstract: Transformer fault diagnosis and repair is a complex task that includes many possible types of faults and demands special trained personnel. Moreover, the minimization of the time needed for transformer fault diagnosis and repair is an important task for electric utilities, especially in cases where the continuity of supply is crucial. In this paper, Stochastic Petri Nets are used for the simulation of the fault diagnosis process of oil-immersed transformers and the definition of the actions followed to repair the transformer. Transformer fault detection is realized using an integrated safety detector, in case of sealed type transformer that is completely filled with oil, while a Buchholz relay and an oil thermometer are used, in case of transformer with conservator tank. Simulation results for the most common types of transformer faults (overloading, oil leakage, short-circuit and insulation failure) are presented. The proposed Stochastic Petri Net based methodology provides a systematical determination of the sequence of fault diagnosis and repair actions and aims at identifying the transformer fault and estimating the duration for transformer repair.

Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis

Abstract: The development of electrical control system faults can lead to increased mechanical component degradation, severe reduction of asset performance, and a direct increase in annual maintenance costs. This paper presents a highly accurate data driven classification system for the diagnosis of electrical control system faults, in particular, wind turbine pitch faults. Early diagnosis of these faults can enable operators to move from traditional corrective or time based maintenance policy towards a predictive maintenance strategy, whilst simultaneously mitigating risks and requiring no further capital expenditure. Our approach provides transparent, human-readable rules for maintenance operators which have been validated by an independent domain expert. Data from 8 wind turbines was collected every 10 minutes over a period of 28 months with 10 attributes utilised to diagnose pitch faults. Three fault classes are identified: “no pitch fault”, “potential pitch fault” and “pitch fault established”. Of the turbines, 4 are used to train the system with a further 4 for validation. Repeated random sub-sampling of the majority fault class was used to reduce computational overheads whilst retaining information content and balancing the training and validation sets. A classification accuracy of 85.50% was achieved with 14 human readable rules generated via the RIPPER inductive rule learner. Of these rules, 11 were described as “useful and intuitive” by an independent domain-expert. An expert system was developed utilising the model along with domain knowledge, resulting in a pitch fault diagnostic accuracy of 87.05% along with a 42.12% reduction in pitch fault alarms.

Unsynchronized fault location based on the negative-sequence voltage magnitude for double-circuit transmission lines

Abstract: This paper describes a new approach to fault location for double-circuit transmission lines based on only the voltage data of both ends of the faulted circuit. The ratio between the magnitudes of negative-sequence voltages measured at both ends of the faulted circuit is utilized to estimate the fault location. Since only the magnitudes are used, the data of both ends are not required to be synchronized, which removes any concern about data synchronization. Moreover, since only the voltage data are required, the errors caused by current transformers can be avoided. The proposed method can effectively locate the single-phase-to-ground, double-phase-to-ground, and phase-to-phase faults disregarding the fault resistance and prefault conditions and without any need for fault classification and phase selection. Unlike the iterative methods, the proposed method is fully analytical and does not cause much computing burden to the line relays. The accuracy and practicality of the proposed method make it an attractive function to implement in numerical relays.

Fault Current Management Using Inverter-Based Distributed Generators in Smart Grids

Abstract: This paper presents a novel fault current management (FCM) technique for radial distribution systems with embedded inverter-based distributed generators (IB-DGs). At the point of connection to a power system, many distributed generators (DGs) require power electronic (PE) interfaces, which are normally idle during faults. The proposed FCM method employs these PE interfaces for control of the fault current. For this purpose, operation of IB-DGs is modified to FCM mode at the moment of fault and new current references are applied. Of the two controllable parameters of the IB-DG output current-current magnitude and current phase angle-the current phase angle is chosen as the means of controlling the fault current magnitude. The reference current phase angle is calculated based on the relation between the fault current elements and their phase angles. As a result of this novel operation, IB-DGs with larger capacity can be connected at different locations of the system without affecting the fault current magnitude. Also, implementing this technique in smart grids is economically proven, since the asset of power system which have been designed for normal operation are employed to manage the fault current magnitude. Moreover, possibilities of synchronization problems are reduced by keeping IB-DGs connected to the system at all the time. The evaluation of the proposed FCM technique using the standard IEEE 33-bus distribution system demonstrates the effectiveness of the proposed method for managing the fault current magnitude.