TITLE:
An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique
AUTHORS:
M. Gopila, I. Gnanambal
KEYWORDS:
Power Transformer, Inrush, Internal Fault, Hyperbolic S-Transform, Bacteria Foraging Optimization
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.8,
June
15,
2016
ABSTRACT: Power transformers in
transmission network are utilized for increasing or decreasing the voltage
level. Power Transformers fail to connect directly to the consumers that result
in the less load fluctuations.Power
transformer operation under any abnormal condition decreases the lifetime of
the transformer.Power
Transformer protection from inrush and internal fault is critical issue in
power system because the obstacle lies in the precise and swift distinction
between them. Due to the limitation of heterogeneous resources, occurrence of
fault poses severe problem. Providing an efficient mechanism to differentiate
between faults (i.e.inrush
and internal) is the key for efficient information flow. In this paper, the
task of detecting inrush and internal fault in power transformers is formulated
as an optimization problem which is solved by using Hyperbolic S-Transform
Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency-
based Hyperbolic S-Transform detects the faults at much earlier stage and
therefore minimizes the computation cost by applying Cosine Hyperbolic
S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been
proposed and has demonstrated the capability of identifying the maximum number
of faults covered with minimum test cases and therefore improving the fault
detection efficiency in a wise manner. The HS-TBFO technique is evaluated and
validated in various simulation test cases to detect inrush and internal fault
in a significant manner. This HS-TBFO technique is investigated based on three
phase power transformer embedded in a power system fed from both ends. Results
have confirmed that the HS-TBFO technique is capable of categorizing the inrush
and internal faults by identifying maximum number of faults with minimum
computation cost as compared to the state-of-the-art works.