• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.198-203
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• 2013

There are conditions that can be unbalanced three phase currents in a large generator by untransposed lines, unbalanced loads, unsymmetrical faults, and open phases. The unbalanced conditions can producing negative sequence components of current that induce two times frequence current in the surface of the rotor, the retaining rings, the slot wedges in the field windings. These rotor currents make the rotor rapidly overheat, so the rotor can cause substantial damage in a very short time. This paper presents the digital negative sequence relay algorithm for unbalanced protection in a generator. The proposed algorithm was tested by using collected current signals on PSCAD/EMTDC considering a hydro turbine based generator control system. It can be seen that the proposed relaying by negative sequence current is useful for detection of unbalanced state of large generator.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.49-53
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• 2008

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1818-1821
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• 2008

Several off-line diagnostic tests which include the insulation resistance(IR), polarization index(PI), low-voltage AC, and recurrent surge oscillograph(RSO) tests were performed to assess the condition of generator rotor windings. The low-voltage AC and the RSO tests were performed on the gas turbine generator rotor winding to detect shorted turns. Before intentionally applying artificial shorted faults, it was confirmed by the low voltage AC and the RSO tests that the winding was in sound condition. For simulated shorted rotor winding turns, the RSO test detected the fault in the winding. The RSO test was capable of identifying the number and pole location of the shorted turns for a number of simulated shorted coils.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.154-164
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• 2019

A rotor dynamic analysis is mandatory for stability and design optimization of submerged propellers and turbines. An accurate simulation requires a proper consideration of fluid-induced reaction forces. This paper presents a bi-directional coupling of a bond graph method solver and an unsteady vortex lattice method solver where the former is used to model the rotor dynamics of the power train and the latter is used to predict transient hydrodynamic forces. Due to solver coupling, determination of hydrodynamic coefficients is obsolete and added mass effects are considered automatically. Additionally, power grid and structural faults like grid fluctuations, eccentricity or failure could be investigated using the same model. In this research work a fast, time resolved dynamic simulation of the complete power train is conducted. As an example, the rotor dynamics of a tidal stream turbine is investigated under two inflow conditions: I - shear flow, II - shear flow + water waves.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.184-185
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• 2010

This paper deals with a ride-through technique of doubly-fed induction generator (DFIG) wind turbine systems using energy storage unit (ESU). By increasing the machine speed, some portion of the turbine power can be stored in the system inertia during grid faults. Also keeping the operation of rotor-side converter (RSC) and grid-side converter (GSC), the rotor current and DC-link voltage can be limited. The effectiveness of the proposed method is verified by simulation results for 2[MW] DFIG wind turbine system.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.11
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• pp.572-576
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• 2006

As the increasing of capacity and technology of power facilities, rotating machines such as turbine generators and water turbines are getting higher at capacity but smaller in size. Thus the monitoring and diagnosis of generators for fault detection and protection has attracted intensive interest. Most of electrical faults of rotating machines appear in their windings. In case of an after-fault in high capacity rotating machines, the recovering cost is usually very expensive and additional time is necessary for returning in a normal situation. In this paper, the magnetic flux patterns in air-gap of a generator under various fault states as well as a normal state are simulated by a conventional FEM tool. These results are successfully applied to detection and diagnosis of the short-circuit condition in rotor windings of a high capacity generator.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• Journal of Power Electronics
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• v.15 no.1
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• pp.235-245
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• 2015

This paper proposes a reactive current assignment and control strategy for a doubly-fed induction generator (DFIG) based wind-turbine generation system under generic grid voltage sag or swell conditions. The system's active and reactive power constrains during grid faults are investigated with both the grid- and rotor-side convertors (GSC and RSC) maximum ampere limits considered. To meet the latest grid codes, especially the low- and high-voltage ride-through (LVRT and HVRT) requirements, an adaptive reactive current control scheme is investigated. In addition, a torque-oscillation suppression technique is designed to reduce the mechanism stress on turbine systems caused by intensive voltage variations. Simulation and experiment studies demonstrate the feasibility and effectiveness of the proposed control scheme to enhance the fault ride-through (FRT) capability of DFIG-based wind turbines during violent changes in grid voltage.