Power transformer is the most essential and vital aspect in power system. This equipment takes an important role and also become expensive components of the electrical power generation and distribution system. However, the power transformer sometimes can fail due to several factors which can make huge cost for maintenance and also for the financial lost. Almost half of the failures have been correlated to windings and the insulating system. Degradation of the insulation system is the cause of forming turn to turn faults (TTF). Current differential protection is used to protect the power transformer from severe fault. This protection is the most common type of transformer protection, providing good fault sensitivity, selectivity, and security. However, differential protection still suffers from accidental tripping trouble during external forces such as magnetizing inrush condition and internal fault such as TTF. Precise calculation of the corresponding circuit parameters of the transformer assists in efficient control of power transformers. In certain cases, this method allows the transformer to be removed from service which is considered an impractical solution. In this study the proposed methodology called Boundary approaching Particle Swarm Optimization (BPSO) is demonstrated to diagnose the power transformer condition using parameter estimation. This method did not need to disconnect the transformer for testing and did not need to mount various sensors within a transformer. The simulation result of the proposed methodology achieved good result compare with conventional PSO and another optimization method such as Coyote Optimization Algorithm (COA). The BPSO algorithm achieved the minimum difference value between estimation to name plate parameter value in different initial value for each parameter. This proposed method also achieved the best estimation parameter value among the other optimization algorithm (PSO and COA) with smallest difference value to reference. The estimation results also shown a significant change for the parameter value under turn to turn fault condition. This change was used to identify power transformers fault.

Power transformer is the most essential and vital aspect in power system. This equipment takes an important role and also become expensive components of the electrical power generation and distribution system. However, the power transformer sometimes can fail due to several factors which can make huge cost for maintenance and also for the financial lost. Almost half of the failures have been correlated to windings and the insulating system. Degradation of the insulation system is the cause of forming turn to turn faults (TTF). Current differential protection is used to protect the power transformer from severe fault. This protection is the most common type of transformer protection, providing good fault sensitivity, selectivity, and security. However, differential protection still suffers from accidental tripping trouble during external forces such as magnetizing inrush condition and internal fault such as TTF. Precise calculation of the corresponding circuit parameters of the transformer assists in efficient control of power transformers. In certain cases, this method allows the transformer to be removed from service which is considered an impractical solution. In this study the proposed methodology called Boundary approaching Particle Swarm Optimization (BPSO) is demonstrated to diagnose the power transformer condition using parameter estimation. This method did not need to disconnect the transformer for testing and did not need to mount various sensors within a transformer. The simulation result of the proposed methodology achieved good result compare with conventional PSO and another optimization method such as Coyote Optimization Algorithm (COA). The BPSO algorithm achieved the minimum difference value between estimation to name plate parameter value in different initial value for each parameter. This proposed method also achieved the best estimation parameter value among the other optimization algorithm (PSO and COA) with smallest difference value to reference. The estimation results also shown a significant change for the parameter value under turn to turn fault condition. This change was used to identify power transformers fault.

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