AUTOMATIC FAULT DETECTION AND LOCATION IN POWER TRANSMISSION LINES USING ANN ALGORITHM WITH LabVIEW
Author’s Name : Sarathkumar M | Pavithra S | Gokul V | Prabhu N
Volume 02 Issue 03 Year 2015 ISSN No: 2349-2503 Page no: 25-27
Abstract:
Transmission lines, among the other electrical power system components, suffer from unexpected failures due to various random causes. These failures interrupt the reliability of the operation of the power system. When unpredicted faults occur protective systems are required to prevent the propagation of these faults and safeguard the system against the abnormal operation resulting from them. The functions of these protective systems are to detect and classify faults as well as to determine the location of the faulty line as in the voltage and/or current line magnitudes. Then after the protective relay sends a trip signal to a circuit breaker(s) in order to disconnect (isolate) the faulty line. The features of neural networks, such as their ability to learn, generalize and parallel processing, among others, have made their applications for many systems ideal. The use of neural networks as pattern classifiers is among their most common and powerful applications. This paper presents the use of artificial neural network architecture as an alternative method for fault detection, classification and isolation in a transmission line system. The main goal is the implementation of complete scheme for distance protection of a transmission line system. In order to perform this, the distance protection task is subdivided into different neural networks for fault detection, fault identification (classification) as well as fault location in different zones. Three common faults were discussed; single phase to ground faults, double phase faults and double phase to ground faults. The result provides a reliable and an attractive alternative approach for the development of a protection relaying system for the power transmission systems.
References:
- Kezunovic, M.: ‘Smart fault location for smart grids’, IEEE Trans. Smart Grid, 2011, 2, pp. 11–22
- Ouyang, Y., He, J.L., Hu, J., et al.: ‘A current sensor based on the giant magne to resistance effect: design and potential smart grid applications’, Sensors, 2012, 12, pp. 15520-15541
- Han, J.C., Hu, J., Yang, Y., et al.: ‘A nonintrusive power supply design for self-powered sensor networks in the smart grid by scavenging energy from AC power line’, IEEE Trans. Ind. Electron., 2015, 62, pp. 4398–4407
- De La Ree, J., Centeno, V., Thorp, J.S., et al.: ‘Synchronized phasor measurement applications in power systems’, IEEE Trans. Smart Grid, 2010, 1, pp. 20–27
- Bo, Q., Jiang, F., Chen, Z., et al.: ‘Transient based protection for power transmission systems’. IEEE Power Engineering Society Winter Meeting, 2000, 2000, pp. 1832– 1837
- Yu, S.-L., Gu, J.-C.: ‘Removal of decaying DC in current and voltage signals using a modified Fourier filter algorithm’, IEEE Trans. Power Deliv., 2001, 16, pp. 372–379
- Das, B., Reddy, J.V.: ‘Fuzzy-logic-based fault classification scheme for digital distance protection’, IEEE Trans. Power Deliv., 2005, 20, pp. 609–616
- Jamehbozorg, A., Shahrtash, S.M.: ‘A decision-tree-based method for fault classification in single-circuit transmission lines’, IEEE Trans. Power Deliv., 2010, 25, pp. 2190–2196
- Jamehbozorg, A., Shahrtash, S.M.: ‘A decision tree-based method for fault classification in double-circuit transmission lines’, IEEE Trans. Power Deliv., 2010, 25, pp. 2184–2189
- Hagh, M.T., Razi, K., Taghizadeh, H.: ‘Fault classification and location of power transmission lines using artificial neural network’. 2007 Conf. Proc. IPEC, 2007,vols 1–3, pp. 1109–1114