Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements

Abstract

The transformer fault diagnosis based on dissolved gas analysis is greatly affected by the uncertainties existing in measured data during oil sampling, handling and storage. This work aims to develop an efficient code matrix based on dissolved gas percentages for accurate fault diagnosis considering measurement uncertainties. Fuzzy system is utilized to produce the rules that map the limits of gas ratios for different fault types. Each gas percentage range is divided into three regions represented by three fuzzy memberships. The fuzzy system is then developed to relate the gas percentages to the fault type. The membership limits are then optimized by using heuristic algorithms in order to maximize the accuracy. Hybrid Grey Wolf Optimization (HGWO) algorithm is utilized to produce the diagnostic code matrix. The proposed code limits the impact of measurement uncertainties on the output fault diagnosis. Different levels of measurement uncertainties up to 20% are considered to validate the effectiveness of the new code in improving the diagnostic accuracy. The accuracies during the training and testing processes attained 97.45% and 95.45%, respectively, with the maximum uncertainty level of 20%. Moreover, randomly selected case studies representing various fault types are investigated using the proposed method. For each case study, various levels of uncertainties are imposed on the original data. The proposed method proved its easiness towards diagnosing transformer faults and robustness against measurement uncertainties.

Fractional order PID controller adaptation for PMSM drive using hybrid grey wolf optimization

“…The local best positions of individuals ( ) and global best position of the swarm ( ) are used to adjust the location of individuals using velocity ( +1 ) depending on space between locations of individuals and best positions of the gray wolf as in (27) to (29). The fittest modified positions are saved for the use in the upcoming GA loop [45].…”

Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

“…More recently, the artificial neural network (ANN) is considered the most extensively used method in the literature for not only DGA but also diverse practical applications [ 15 , 16 , 17 ]. In [ 18 ], a fuzzy logic system is combined with a metaheuristic approach that is the hybrid grey wolf optimizer which adjusts DGA considering a diagnostic way that is robust against uncertainties. It is demonstrated that fuzzy logic, metaheuristics, and ANN can provide improved performance in general engineering applications [ 19 , 20 , 21 , 22 , 23 ].…”

Adaptive Dynamic Meta-Heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults

“…The kernel principal component analysis (KPCA) is utilized to extract the features decreasing the model training time. A fuzzy system produced the rules to limit the gas ratio for transformer fault types considering three memberships for three regions of each gas percentage range [33]. The membership limits can be optimized using GWO, which developed the diagnostic code matrix.…”

“…Moreover, combining the DGA methods and artificial intelligence methods to enhance the diagnostic accuracy of traditional DGA techniques requires much work, and diagnostic accuracy is still low. Therefore, the researchers attempted to use the optimization techniques to optimize the DGA method parameters or to optimize the classification parameters for the classification techniques [1], [21], [22], [28]- [33]. The optimization techniques maximize the agreement of predicting and the actual faults to develop the highest diagnostic accuracy of the transformer faults.…”