An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

摘要: Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Transformers fail to connect directly consumers that result in less load fluctuations. Power transformer operation under any abnormal condition decreases lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because obstacle lies in precise swift distinction between them. Due limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism differentiate between faults (i.e. inrush and internal) key information flow. In this paper, the task detecting power formulated as optimization problem which solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based S-Transform detects at much earlier stage and therefore minimizes computation cost applying Cosine Hyperbolic S-Transform. Next, Bacterial (BFO) technique has been proposed demonstrated capability identifying maximum number of covered with minimum test cases therefore improving fault detection efficiency a wise manner. HS-TBFO evaluated and validated various simulation detect fault in significant This investigated based on three phase transformer embedded fed both ends. Results have confirmed capable categorizing inrush and number minimum computation as compared state-of-the-art works.