Deregulation in power industry accelerates competition among various power companies. These companies are trying to gain maximum profit with minimum maintenance cost. This could be achieved by developing a condition-based monitoring product for the assessment of health of substation and service transformers. This study presents online condition monitoring system (OCMS) for transformers which is useful to replace reactive and preventive maintenance of transformers by predictive maintenance. Proposed OCMS is cost effective, online and accurate tool and it has several features like proposing corrective actions for the benefit of power utilities. Proposed system results are evaluated by comparing with the results obtained from utility model. Proposed OCMS applies only to transformers without abnormal faults such as detected by dissolved gas analysis. It is therefore very different from the other health index algorithms present in the market for condition monitoring of transformers, including in case of abnormal faults.
Summary
This paper presents a smart online condition monitoring system for distribution transformer monitoring and condition analysis. The system comprises of intelligent electronic device capable of receiving input from transformer through sensors. The processing unit calculates the health index (HI) of transformer based on weightage associated with healthy sensor input optimized by genetic algorithm. Use of communication system enables to initiate action based on command received from the substation. Thus, active control of the system is possible. In extreme cases like overload, overvoltage, excessive oil temperature, and low oil level, the algorithm can issue a local trip command. The experimentation is performed on 15‐kVA, 400/400 volts custom made transformer for different operating conditions. Health indices are computed by conventional numerical and genetic algorithm methods, which offer improved weighing approach than the former. The proposed system offers development of smart transformer for applications in the growing distribution grid.
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