Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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 KemalEccentricity 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.Article Citation - WoS: 10Citation - Scopus: 10FEA Based Fast Topology Optimization Method for Switched Reluctance Machines(Springer, 2022-01-04) Tekgun, Didem; Tekgun, Burak; Alan, IrfanIn this paper, a finite element analysis (FEA) based fast optimization method to optimize a lightweight in-wheel switched reluctance machine is presented. This method speeds up the switched reluctance machine optimization procedure by running the FEA simulations with single-phase constant current excitations for half electrical cycle and estimating the machine performance metrics using the gathered FEA data. Hence, the machine`s dynamic performance estimation process takes shorter for each design candidate. The optimization algorithm employs designs of experiments (DOE), response surface (RS) analysis method, and differential evolution algorithm (DE). Here, the DOE method is used to reduce the search space by narrowing down the upper and lower boundaries of each design variable based on the RS analysis. Although this process does not guarantee getting the Pareto front, it places the search space close to the actual one. Hence, the multi-objective DE optimization finds the Pareto optimal solution set without requiring a large number of iterations as well as a large number of candidate designs for each iteration. The method is applied to a 24/16 SRM that is intended to be used in a lightweight race car as a hub motor. Six dimensionless geometric variables are optimized to satisfy three objective functions, namely torque ripple, motor mass, and copper loss. While the conventional DE takes at least 3000 candidate designs, the proposed method considers only 559 designs to reach a similar Pareto front. It is observed that the proposed method takes about 6 h 30 min compared to the conventional method that takes 32 h 50 min using the same computer. Therefore, the computation time is reduced almost five times with the proposed method.