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.
Highlights
Power transformers are considered as the most critical and expensive component in the power substations
Computation cost (CC) measures the cost involved in computing the inrush and internal faults
A comparison of the results shows that Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique has more performance improvement using Gaussian Frequency-based Hyperbolic S-Transform that measures Cosine Hyperbolic S-Transform, more suitable for the commercial systems with higher input points
Summary
Power transformers are considered as the most critical and expensive component in the power substations. Power transformer operation under any abnormal condition such as inrush or internal faults results in the compromise of the life of the transformer. Adequate protection has to be provided for identifying and detecting the inrush and internal faults in power transformer at an earlier stage. Many research works have been conducted in the discrimination of inrush and internal faults by search researchers. Detection and Location of High Impedance Faults (DL-HIF) [1] was performed using power line communication devices resulted in the ability to effectively identify and detect HIFs. The DL-HIF method applied on multiconductor overhead distribution networks to detect the fault rate in an efficient manner. Phase Angle Difference-based Inrush Restraint (PAD-IR) [2] distinguished the inrush current from internal fault current by applying phase shift between the two currents. The relaying decision remains unaffected resulting in the efficient discrimination between the two currents
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