Discriminating Between Loss of Excitation and Power Swings in Synchronous Generator Based on ANN
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Discriminating Between Loss of Excitation and Power Swings in Synchronous Generator Based on ANN Zeina A. Barakat1 · Ammar A. Hajjar1 · Tarek Kherbek1 · Hassan Haes Alhelou1,2 Received: 14 September 2018 / Revised: 3 December 2018 / Accepted: 7 March 2019 © Brazilian Society for Automatics--SBA 2019
Abstract This paper presents a newly designed scheme based on neural networks to detect loss of excitation (LOE) in synchronous generators. The proposed scheme uses more accurate mechanism and needs fewer parameters in order to achieve fast and reliable detection of LOE. Furthermore, being able to discriminate between LOE and stable power swings is a major concern to enhance the performance of traditional LOE protection. Therefore, the designed network is trained to discriminate between both cases clearly. For training and testing the proposed neural network, MATLAB program has been used for simulation. In addition, by using comparison analysis between the designed network and the previous ones and the traditional MHO relay, the results ensure that the proposed scheme has more secure and fast characters in detecting and discriminating LOE. Keywords Synchronous generator · Loss of excitation · Power swing · Protection · Neural networks · Artificial neural network · Stable power swings List of Symbols Xs The system impedance Xd The synchronous reactance of the d-axis Xd′ The transient reactance of the d-axis Xd′′ The sub-transient reactance of the d-axis Xq′ The transient reactance of the q-axis Xq′′ The sub-transient reactance of the q-axis Tdo′ The d-axis transient open-circuit time constant Tdo′′ The d-axis sub-transient open-circuit time constant Tqo′ The q-axis transient open-circuit time constant I Generator’s current V Generator’s voltage Abbreviations AI Artificial intelligence ANFIS Adaptive neuro-fuzzy inference system ANN Artificial neural network FACTS Flexible AC transmission systems LOE Loss of excitation * Hassan Haes Alhelou [email protected]; [email protected] 1
Electrical Power Engineering Department, Faculty of Mechanical and Electrical Engineering, Tishreen University, Latakia City, Syria
Department of Electrical and Computer Engineering (ECE), Isfahan University of Technology, Isfahan 84156‑83111, Iran
2
NN Neural network PMU Phasor measurement unit SG Synchronous generator SPS Stable power swing SVM Support vector machine S Apparent power
1 Introduction Loss of excitation (LOE) is a protection function which detects the failure of excitation system in synchronous machines. Determining LOE conditions is distinctly considered as one of the most important studied issues to improve the LOE protection. Hence, there are many different conditions that cause maloperations. Stable power swings (SPSs) are the most popular conditions. Therefore, improving the sensitivity of protection is considered as a priority in such studies. LOE is a common fault with synchronous generators (SG) in modern power systems (Shi et al. 2012). It may be caused by different reaso
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