Abstract:This paper proposes an algorithm for fault detection, faulted phase and winding identification of a three-winding power transformer based on the induced voltages in the electrical power system. The ratio of the induced voltages of the primary-secondary, primary-tertiary and secondary-tertiary windings is the same as the corresponding turns ratio during normal operating conditions, magnetic inrush, and over-excitation. It differs from the turns ratio during an internal fault. For a single phase and a three-phas… Show more
“…Unfortunately, it is a fact that the electrical and mechanical properties of the transformer oil-paper insulation system become aging gradually due to the combination stresses of the mechanical vibration, thermal, electrical, oxygen, water and other factors, in the long-term service process [4][5][6][7][8]. Historically, the applications of oil sample analysis (OSA) in terms of equilibrium relationships between insulating oil and cellulose insulation, dissolved gas analysis (DGA), dielectric loss factor (DLF) and insulation resistance (IR) have been commonly used for performing non-destructive condition monitoring of transformer insulation [9][10][11][12]. However, the OSA technique only presents limited knowledge about the aging status of transformer solid insulation.…”
Abstract:Conventional dielectric response measurement techniques, for instance, recovery voltage measurement (RVM), frequency domain spectroscopy (FDS) and polarization-depolarization current (PDC) are effective nondestructive insulation monitoring techniques for oil-impregnated power transformers. Previous studies have focused mainly on some single type of dielectric measurement method. However, the condition of oil paper insulation in transformer is affected by many factors, so it is difficult to predict the insulation status by means of a single method. In this paper, the insulation condition assessment is performed by grey relational analysis (GRA) technique after carefully investigating different dielectric response measurement data. The insulation condition sensitive parameters of samples with unknown insulation status are extracted from different dielectric response measurement data and then these are used to contrast with the standard insulation state vector models established in controlled laboratory conditions by using GRA technique for predicting insulation condition. The performance of the proposed approach is tested using both the laboratory samples and a power transformer to demonstrate that it can provide reliable and effective insulation diagnosis.
“…Unfortunately, it is a fact that the electrical and mechanical properties of the transformer oil-paper insulation system become aging gradually due to the combination stresses of the mechanical vibration, thermal, electrical, oxygen, water and other factors, in the long-term service process [4][5][6][7][8]. Historically, the applications of oil sample analysis (OSA) in terms of equilibrium relationships between insulating oil and cellulose insulation, dissolved gas analysis (DGA), dielectric loss factor (DLF) and insulation resistance (IR) have been commonly used for performing non-destructive condition monitoring of transformer insulation [9][10][11][12]. However, the OSA technique only presents limited knowledge about the aging status of transformer solid insulation.…”
Abstract:Conventional dielectric response measurement techniques, for instance, recovery voltage measurement (RVM), frequency domain spectroscopy (FDS) and polarization-depolarization current (PDC) are effective nondestructive insulation monitoring techniques for oil-impregnated power transformers. Previous studies have focused mainly on some single type of dielectric measurement method. However, the condition of oil paper insulation in transformer is affected by many factors, so it is difficult to predict the insulation status by means of a single method. In this paper, the insulation condition assessment is performed by grey relational analysis (GRA) technique after carefully investigating different dielectric response measurement data. The insulation condition sensitive parameters of samples with unknown insulation status are extracted from different dielectric response measurement data and then these are used to contrast with the standard insulation state vector models established in controlled laboratory conditions by using GRA technique for predicting insulation condition. The performance of the proposed approach is tested using both the laboratory samples and a power transformer to demonstrate that it can provide reliable and effective insulation diagnosis.
“…Protection systems are essential to ensure the safe and efficient operation of any electrical installation [5,6]. Also, the proper management of these systems is an important factor to take into account in the profitability of these facilities [7].…”
Abstract:The location of ground faults in railway electric lines in 2 × 5 kV railway power supply systems is a difficult task. In both 1 × 25 kV and transmission power systems it is common practice to use distance protection relays to clear ground faults and localize their positions. However, in the particular case of this 2 × 25 kV system, due to the widespread use of autotransformers, the relation between the distance and the impedance seen by the distance protection relays is not linear and therefore the location is not accurate enough. This paper presents a simple and economical method to identify the subsection between autotransformers and the conductor (catenary or feeder) where the ground fault is happening. This method is based on the comparison of the angle between the current and the voltage of the positive terminal in each autotransformer. Consequently, after the identification of the subsection and the conductor with the ground defect, only the subsection where the ground fault is present will be quickly removed from service, with the minimum effect on rail traffic. This method has been validated through computer simulations and laboratory tests with positive results.
“…Diagnosis of transformer winding faults is usually carried out by testing [3,4] and then simulation [5,6]. Use of finite-element simulation software to simulate the structure of the core [7] and winding, or other real-time electromagnetic field distribution to determine the state of the winding, can determine whether the transformer is functioning normally [8].…”
Abstract:Monitoring of winding faults is the most important item used to determine the maintenance status of a transformer. Commonly used methods for winding-fault diagnosis require the transformer to exit operation before testing and an external exciting signal, whether the transformer is malfunctioning or not. However, if an overvoltage signal can be regarded as a broadband excitation source for fault diagnosis, then the interference caused by signal injection can be eliminated without the need for additional pulse or impulse signals. In this paper, a tapped transformer is designed and test platforms are built to compare winding diagnoses using the impulse wave and sweep frequency response analysis methods by recording voltage responses on both the high-and low-voltage sides and calculating the respective transfer functions. Based on comparison of statistical indicators, it is found that the sensitivities of both methods are similar for detecting conditions of winding-ground and winding-interlayer short circuits. It is concluded that it is feasible to use a transient overvoltage monitoring system for winding-fault diagnosis.
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