Thesis Abstract

Abstract
Power system protection is a crucial aspect of ensuring the stability and reliability of electrical networks, particularly in high voltage systems such as those operating at 132kV. Traditional protection schemes involve the use of relays and circuit breakers to detect faults and isolate faulty sections of the network. However, with the increasing complexity and interconnectivity of modern power systems, there is a growing need for more advanced protection techniques that can respond quickly and accurately to a wide range of fault conditions. In this research, a novel approach for power system protection in high voltage power systems at 132kV is proposed. The approach integrates advanced fault detection algorithms with intelligent decision-making capabilities to enhance the speed and accuracy of fault identification and isolation. The system utilizes data from sensors distributed throughout the network to continuously monitor the system's operating conditions and detect any abnormalities that may indicate a fault. One key aspect of the proposed approach is the use of machine learning algorithms to analyze the sensor data and identify patterns associated with different types of faults. By training the algorithms on historical data and continuously updating them with real-time information, the system is able to improve its fault detection capabilities over time and adapt to changing network conditions. Another important feature of the proposed approach is the use of intelligent decision-making algorithms to determine the appropriate response to detected faults. These algorithms take into account factors such as the location and severity of the fault, as well as the network topology and operating constraints, to quickly isolate the faulty section of the network while minimizing the impact on the rest of the system. To validate the effectiveness of the proposed approach, extensive simulations are conducted using a detailed model of a 132kV power system. The results demonstrate that the novel protection approach is able to detect and isolate faults faster and more accurately than traditional protection schemes, thereby improving the overall reliability and stability of the power system. Overall, the research presents a promising new approach for power system protection in high voltage systems, offering enhanced fault detection and isolation capabilities through the integration of advanced fault detection and intelligent decision-making algorithms.

Thesis Overview

In this thesis, a novel approach for the protection of transmission lines which utilizes only coefficient energy for both detection and classification is proposed. The fault current signals generated by workspace on MATLAB simulation model have been analyzed using Daubechie-4 (d4) mother wavelet at 7th level decomposition with the help of Wavelet Toolbox embedded in MATLAB. A case study of 132kV, 160km transmission line has been used to test the novel approach. The value of the coefficient energy of the current signals gives the indication of fault and no-fault conditions. The energy of the three phase current signal (A,B,C) at 7th level decomposition were calculated as 0.1559×10-5,   0.1328 x10-5, 0.1737 x10-5 (for normal condition), 6.4200 x10-5, 1.7730 x10-5, 1.6660 x10-5 (for A-G fault), 667.1000 x10-5, 700.9000 x10-5, 0.7860 x10-5 (for AB-G fault), 677.8000 x10-5, 689.9000 x10-5, 0.1740 x10-5(for A-B fault), 885.6000 x10-5, 898.3000 x10-5, 832.7000 x10-5(for ABC fault). Also, the coefficient energy ratios were calculated to help classify the faults. The total ratio of the coefficient energies of the three phases were found to be approximately 3.4819 (for normal condition), 5.9177 (for A-G fault), 1741.4580 (AB-G fault), 7861.3448 (for A-B fault), 3.1423 (for ABC fault). Like the coefficient energy, the ratio was found to be increasing as the severity of the fault increases, except for L-L-L fault. Hence, both coefficient energy and ratio were employed in fault classification. With the approach presented in this work, ten classes of fault (A-G, B-G, C-G, A-B, B-C, A-C, AB-G, BC-G, AC-G & ABC) could be correctly identified and classified within fault duration of 0.085 seconds. The results therefore, demonstrate the proposed approach to be fast and reliable.