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The prediction of fault type in transformers at an early stage is an important aspect for power system reliability. Checking the transformer status begins with the dissolved gas analysis (DGA) test. Rogers' four-ratio and IEC 60599 standard methods are commonly DGA-based techniques that used to detect the transformer faults. These methods are very easy to implement, but their diagnostic accuracies are very poor. The target of this work is to build a new PSO-FS optimization platform identifying the optimal limits of gas ratios and corresponding rules for each fault type to enhance the diagnostic accuracies of Rogers' four-ratio and IEC 60599 code methods. In the proposed platform, PSO optimization technique specifies the gas ratio limits and corresponding rules where the fuzzy system identifies the fault types. Based on the results of the optimization process, the new codes that used to diagnose the fault types were developed. The performance of the proposed approach investigated using 481 collected datasets. The overall accuracies of the Rogers' four-ratio and IEC code models enhanced from 47.19 and 55.09 to 85.65 and 85.03, respectively. However, when datasets due to mixtures of faults (samples with undetermined or misidentified faults) are removed from the calculations, the accuracies of the PSO-FS methods are not significantly better than when using existing DGA methods. [ABSTRACT FROM AUTHOR]

This paper focuses on a Smart Fault Diagnostic Approach (SFDA) based on the integration among the output results of recognized dissolved gas analysis (DGA) techniques. These techniques are Dornenburg method, Electro-technical Commission standard (IEC) Code, the Central Electricity Generating Board (CEGB) Code based on Rogers' four ratios, Rogers method given in IEEE-C57 standard, and the Duval triangle. The artificial neural networks (ANN) model is constructed to monitor the transformer fault conditions and trained for each technique individually. The fault decision of each ANN model supplies the proposed integrated SFDA. The integration between these DGA approaches not only improves the fault condition monitoring of the transformers but also overcomes the individual weakness and the differences between the above methods. Toward a better diagnostic scheme, a new SFDA is developed based on the integration of the most three appropriate DGA methods. Further gas concentrations have been considered as raw data (California State University Sacramento (CSUS) as an example) to enhance the proposed SFDA performance. Comparison of each DGA concept with respect to the proposed one is reported, where the results provide evidences of the efficacy of the proposed SFDA. [ABSTRACT FROM PUBLISHER]