Fault section estimation in electric power systems using an optimization immune algorithm

Application of artificial bee colony-based optimization for fault section estimation in power systems

“…To validate the effectiveness of the proposed method, it has been applied to examine the fault section estimation problem through a 10-section system [12], a 28-section system [11], a real system in Taipower and a practical large power system [16]. For all of these tests, control parameters of the colony size, the number of nectar-gathering times and the maximum number of allowed iterations are set to be 50, 5 and 1000, respectively.…”

“…A number of three cases are simulated by using the proposed method with comparisons to an artificial immune system algorithm (AISA) [16]. These test cases involve multiple faults and device failure.…”

“…A.1. It consists of 99 protective relays, 55 circuit breakers, 14 busbars, 12 transformers, and 15 transmission lines [16]. In the system, the busbar is protected by differential relays of DP 1 -DP 12 , and the transformer is protected by DP 13 -DP 24 and overcurrent relays OR 1 -OR 21 .…”

“…The fault section estimation problem is to find the most probable fault section based on the reported alarm information [16,17]. From aforementioned discussions, mathematical terms of protective device are derived as follows: …”

“…The biological intelligence based clustering concepts such as genetic-based algorithms [11][12][13][14][15] and swarm intelligence algorithms [16,17] were both applied to estimate the fault section with great appreciation. Among these techniques, the swarm intelligence usually embeds with characteristics of positive feedback, negative feedback, randomness, and synergy to develop a powerful and efficient mechanism [18][19][20][21].…”