Showing 6 results for Fault Detection
F. Bagheri, H. Khaloozadeh, K. Abbaszadeh,
Volume 3, Issue 3 (7-2007)
Abstract
This paper presents a parametric low differential order model, suitable for
mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman
filter is proposed for recursively estimating the states and parameters of continuous–time
model with discrete measurements for fault detection ends. Typical motor faults as interturn
short circuit and increased winding resistance are taken into account. The models are
validated against winding function induction motor modeling which is well known in
machine modeling field. The validation shows very good agreement between proposed
method simulations and winding function method, for short-turn stator fault detection.
M. J. Abbasi, H. Yaghobi,
Volume 12, Issue 4 (12-2016)
Abstract
The doubly fed induction generator (DFIG) is one of the most popular technologies used in wind power systems. With the growing use of DFIGs and increasing power system dependence on them in recent years, protecting of these generators against internal faults is more considered. Loss of excitation (LOE) event is among the most frequent failures in electric generators. However, LOE detection studies heretofore were usually confined to synchronous generators. Common LOE detection methods are based on impedance trajectory which makes the system slow and also prone to interpret a stable power swing (SPS) as a LOE fault. This paper suggests a new method to detect the LOE based on the measured variables from the DFIG terminal. In this combined method for LOE detection, the rate of change of both the terminal voltage and the output reactive power are utilized and for SPS detection, the fast Fourier transform (FFT) analysis of the output instantaneous active power has been used. The performance of the proposed method was evaluated using Matlab/Simulink interface for various power capacities and operating conditions. The results proved the method's quickness, simplicity and security.
M. Dodangeh, N. Ghaffarzadeh,
Volume 16, Issue 2 (6-2020)
Abstract
In this paper, a new fast and accurate method for fault detection, location, and classification on multi-terminal DC (MTDC) distribution networks connected to renewable energy and energy storages presented. MTDC networks develop due to some issues such as DC resources and loads expanding, and try to the power quality increasing. It is important to recognize the fault type and location in order to continue service and prevent further damages. In this method, a circuit kit is connected to the network. Fault detection is performed with the measurement of the current of the connected kits and the traveling-waves of the derivative of the fault current and applying to a mathematical morphology filter, in the Fault time. The type and location of faults determinate using circuit equations and current calculations. DC series and ground arc faults are considered as DC distribution network disturbances. The presented method was tested in an MTDC network with many faults. The results illustrate the validity of the proposed method. The main advantages of the proposed fault location and classification strategy are higher accuracy and speed than conventional approaches. This method robustly operates to changing in sampling frequency, fault resistance, and works very well in high impedance fault.
Das P. Chennamsetty, Sravana K. Bali,
Volume 19, Issue 2 (6-2023)
Abstract
Symmetrical nature of mean of electrical signals during normal operating conditions is used in the fault detection task for dependable, robust, and simple fault detector implementation is presented in this work. Every fourth cycle of the instantaneous current signal, the mean is computed and carried into the next cycle to discover nonlinearities in the signal. A fault detection task is completed using a comparison of two sub cycle means, and the same concept is extended to faulty phase classification. Under various fault and system operating situations, the suggested technique is assessed for regular faults, remote end faults, high resistive faults, and high impedance arcing faults. This paper's extensive case studies illustrate the suggested scheme's simplicity, computational flexibility, speed, and reliability. The suggested approach yields 100% consistent results in 4-8 msec detection time.
S. Prasad Tiwari,
Volume 19, Issue 3 (9-2023)
Abstract
In spite of the numerous benefits over the traditional power distribution system, protection of the microgrid is a challenging and complex task. The varying fault resistances due to dissimilar grounding conditions can affect the performance of the protection scheme. Under such conditions, the magnitude of the fault current can vary from lower to higher level. In addition to the above, the dissimilar magnitude of fault current during grid connected and islanded mode demands a protection scheme that can easily discriminate the mode of operation. The magnitude of fault current in grid-connected and islanded modes needs a robust protection scheme. In this regard, an ensemble of subspace kNN based robust protection scheme has been proposed to detect the faulty conditions of the microgrid. The tasks of the mode detection, fault detection/classification as well as faulty line identification has been carried out in the proposed work. In the proposed protection scheme, discrete wavelet transform (DWT) has been used for processing of the data. After recording the voltage and current signals at bus-1, the protection scheme has been validated. The validation of the protection scheme in Section 6 reveals that the protection scheme is efficiently working.
Shankarshan Prasad Tiwari,
Volume 20, Issue 1 (3-2024)
Abstract
In recent years, due to the widespread applications of DC power-based appliances, the researchers attention to the adoption of DC microgrids are continuously increasing. Nevertheless, protection of the DC microgrid is still a major challenge due to a number of protection issues, such as pole-to-ground and pole-to-pole faults, absence of a zero crossing signal, magnitude of the fault current during grid-connected and islanded mode, bidirectional behaviour of converters, and failure of the converters due to enormous electrical stress in the converter switches which are integrated in the microgrid. Failure of the converter switches can interrupt the charging of the electrical vehicles in the charging stations which can affect transportation facilities. In addition to the above mentioned issues protection of the DC microgrid is more challenging when fault parameters are varying due to dissimilar grounding conditions and varying operational dynamics of the renewable sources of energy. Motivated by the above challenges a support vector machine and ensemble of k-nearest neighbor based protection scheme has been proposed in this paper to accurately detect and classify faults under both of the modes of operation. Results in the section 5 indicate that performance of the protection scheme is greater as compared to other algorithms.
