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

Abstract

Detection of transformer faults avoids the transformer's undesirable loss from service and ensures utility service continuity. Diagnosis of transformer faults is determined using dissolved gas analysis (DGA). Several traditional DGA techniques, such as IEC code 60599, Rogers' ratio method, Dornenburg method, Key gas method, and Duval triangle method, but these DGA techniques suffer from poor diagnosis transformer faults. Therefore, more research was used to diagnose transformer fault and diagnostic accuracy by combining traditional DGA techniques with artificial intelligence and optimization techniques. In this paper, a proposed Adaptive Dynamic Polar Rose Guided Whale Optimization algorithm (AD-PRS-Guided WOA) improves the classification techniques' parameters that were used to enhance the transformer diagnostic accuracy. The results showed that the proposed AD-PRS-Guided WOA provides high diagnostic accuracy of transformer faults as 97.1%, which is higher than other DGA techniques in the literature. The statistical analysis based on different tests, including ANOVA and Wilcoxon's rank-sum, confirms the algorithm's accuracy.

Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm

“…The WOA algorithm shows its advantages for different problems in the area of optimization. WOA is considered in the literature as one of the most effective optimization algorithms [20], [37]. However, it might suffer from a low capability of exploration.…”

“…The following equation can be used in the WOA algorithm for the purpose of updating agents' positions. The term Guided WOA, in this work, indicates a modified version of the original WOA algorithm [37]. In Guided WOA, the drawback of the original WOA is alleviated by updating the search strategy through one agent.…”

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

“…In this section, the proposed ASCA algorithm is evaluated in compared with PSO [42], [43], WOA [44], [45], GA [46],GWO [47], SSA [48], HHO [32], [49], HGSCADE [50], HMSCACSA [51], MPA [52], ChOA [53], and SMA [54] algorithms. For a fair comparison, the proposed ASCA algorithm and the compared algorithms begin in the experiment with the same number of agents (population) with same size and are applied to the same objective function using same number of iteration, dimensions, and boundaries.…”

“…A dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon) is used for the experiments. The proposed ASCA algorithm is evaluated in compared with Particle Swarm Optimizer (PSO) [42], [43], Whale Optimization Algorithm (WOA) [44], [45], Genetic Algorithm (GA) [46], Grey Wolf Optimizer (GWO) [47], Squirrel search algorithm (SSA) [48], Harris Hawks Optimization (HHO) [32], [49], Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE) [50], Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) [51], Marine Predators Algorithm (MPA) [52], Chimp Optimization Algorithm (ChOA) [53], and Slime Mould Algorithm (SMA) [54]. Major contributions of our work are as follow:…”

Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

“…In this case, normal processes include plotting learning curves and evaluating bias and variance values. In most cases, the designer's intuition plays a big influence in improving a model's performance [20][21][22][23][24].…”