WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Subject: search.filters.subject.Fault Detection

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  • Article
    Noninvasive Condition Monitoring for Eccentricity Fault Detection in Large Hydro Generators
    (TÜBİTAK Scientific & Technological Research Council Turkey, 2026-01-16) Lemeski, Atena Tazikeh; Tekgun, Didem; Keysan, Ozan; Leblebicioglu, Kemal; Gol, Murat; Leblebicioglu, Mehmet Kemal
    Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in T & uuml;rkiye. The effects of DE faults on the SPSG's magnetic equivalent circuit parameters are then examined under various fault severities. A comprehensive hydropower plant model-including the synchronous generator, governor, and excitation system-is developed in MATLAB/Simulink, with all input parameters obtained from real plant data and equivalent circuit variations extracted from FEA. After completing the modeling stage, including fault scenarios, MATLAB and Simulink are employed together to estimate key magnetic equivalent circuit parameters using a modified particle swarm optimization (MPSO) algorithm, achieving highly accurate parameter estimation. Since the hydropower system allows measurement of the three-phase output currents, parameter estimation is performed based on current variations under different fault conditions. The simulation results verify the method's ability to detect faults with high accuracy; thus, this integrated and noninvasive approach provides a robust framework for ensuring the operational reliability and longevity of large hydro generators.
  • Conference Object
    Citation - WoS: 10
    Citation - Scopus: 13
    An Observer-Based Fault Diagnosis in Battery Systems of Hybrid Vehicles
    (IEEE Computer Society help@computer.org, 2013-11) Ablay, Günyaz
    Hybrid electric vehicles (HEVs) currently use Nickel-Metal Hydride (Ni-MH) batteries which have advantages of design flexibility, superior power, environmental acceptability and recyclability, long life, wide-range operating temperature and low cost. No matter how good a battery is, a failure can always occur in a battery leading to serious inconvenience, performance deterioration and costly replacement. Thus, it is desirable to be able to detect the underlying degradation and to predict level of unsatisfactory performance. By using current, voltage and temperature measurements of Ni-MH batteries, they can be modeled so that the internal dynamics of the batteries can be estimated and state of health of the batteries can be predicted for secure and long-life operations. An observer-based fault diagnosis approach is designed to analyze the state of health of the Ni-MH battery system of HEVs in this study. Real-world input data is used to assess the efficiency of the approach in the existence of uncertainties. The possible sensor faults and unexpected parameter deviations are diagnosed efficiently with statistical evaluation of the generated residuals. © 2013 The Chamber of Turkish Electrical Engineers-Bursa. © 2020 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 16
    Citation - Scopus: 23
    Adaptive Fault Detection Scheme Using an Optimized Self-Healing Ensemble Machine Learning Algorithm
    (China Electric Power Research inst, 2021) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Li, Xiangjun; Muyeen, S. M.
    This paper proposes a new cost-efficient, adaptive, and self-healing algorithm in real time that detects faults in a short period with high accuracy, even in the situations when it is difficult to detect. Rather than using traditional machine learning (ML) algorithms or hybrid signal processing techniques, a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms. In the proposed method, the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization (PSO) weights. For this purpose, power system failures are simulated by using the PSCAD-Python co-simulation. One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information. Therefore, the proposed technique will be able to work on different systems, topologies, or data collections. The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.