Decision Tree Supported Distance relay for Fault Detection and Classification in a series compensated line Subodh Kumar Mohanty, Member IEEE, Anshudip Karn, Shobhan Banerjee, Student Member IEEE School of Electrical Engineering, KIIT Deemed to be University, Bhubaneswar, India Abstract—Fault detection and classification in a series compensating power transmission line is a big challenge for protective relays in power system. The proposed method is based on machine learning algorithm application in distance protection for fault detection and classification in power transmission network. In this proposed method one cycle post fault current data is taken into consideration for fault classification and detection using decision tree method. This technique is tested on 400 kV, 50 Hz two area system which is simulated in PSCAD platform. The post fault current data extracted from relay point fed in to decision tree algorithm in machine learning application. The data extracted from relaying point in a different condition like different distance with several fault resistance. Keywords—Fault detection, Transmission Network, Machine Learning, Decision Tree I. INTRODUCTION Power transmission line is the tie line between the generation end and consumer end. Now-a-days the increased demand of power and restriction of new building transmission line creates headache for power system engineer due to heavy transmission line loading and increased power losses. To fulfill these requirements series capacitor is mostly preferred in long transmission system day by day. By the use of series compensating line, it further supports the stability of power system, increase of power transfer capability and voltage profile improvement. But the inclusion of series capacitor in long transmission line creates huge protection problem due to several reasons. a) The current in steady state increases remarkably compare to the fault current of line to ground at boundary of the line due to series compensation. b) The used MOV (metal oxide varistor) is a non linear resistor for over voltage protection of capacitor during fault increases complexity problem for protection point of view in the series compensation arrangement. c) Current and Voltage Inversion signals d) The transmission line produced current and voltage signals containing various type of frequency components. The various frequency elements consisting non sinusoidal fundamental deteriorate as well as DC components decaying due to series capacitor and line inductance, subsynchronous frequencies having frequency elements varies about the half of the fundamental component frequency value, MOV conduction during faults for odd harmonics, elements of high frequency caused by line inductance and capacitance resonance and fundamental elements of the fault current during steady state. Therefore, traditional technique of digital distance protection, which depends on the calculation of fundamental current and voltage phasors for series compensated transmission lines are of little effectiveness. Fig 1: Current and Voltage waveform during L-G fault Symmetrical faults, even though occur less, but when occur are more dangerous than asymmetrical faults and need to be analyzed under certain circumstances. Again the current magnitude may be the same for different location of the transmission line (after and before the series compensation by TCSC/UPFC) for similar type of fault. Therefore, fault classification is a big challenging task for TCSC/UPFC connected series compensated transmission line. The impedance assessment [1] without the FACTs device is just like an normal transmission line for the fault but the impedance assessment must include the impedance for the FACTs device when the fault relates to FACTs. The relay gives a trip signal to circuit breaker when the line impedance compared with the relay setting impedance and if it is less than a predefined value in the protective region. In reference [2] for large number of features and classes in multivariable analysis generally one wants to determine defined parameters of class distributions like high dimensional distribution. This problem is avoided by using decision tree classifier for a smaller number of attributes at each internal node without degrading the presentation. References [3]-[4] explain the significance of Decision Trees in machine learning and application in Power systems. In [1], the author has compared Support Vector Machine with Decision Tree to identify fault zone in TCSC and UPFC lines, and type of fault in which Decision trees have been concluded superior to Support Vector Machine. 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020) 978-1-7281-4251-7/20/$31.00 ©2020 IEEE 1