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The electrical distribution system is crucial for the utility grid to transmit power from generators to consumers. Considering the intricate structure of distribution systems and their significant role in power networks, establishing a robust fault classification and location scheme is vital. Due to ageing, distribution systems are often prone to faults from factors like poor operational conditions and wear and tear. The line faulting rate and the pertinent restoration epochs influence the frequency and duration of power disruptions. Thus, precisely locating the fault section is essential to minimize power restoration timeframes. This paper presents a hybrid fault classification and location technique in a combined continuous overhead and underground distribution line. A simulation of the hybrid model was designed in Simulink for an 11 kV combined continuous overhead and underground electrical distribution line, considering short circuit faults as they are the most predominant and cause massive damage in distributed systems. The proposed technique first classifies the fault using Discrete Wavelet Transforms (DWT) and Multi-layer Perceptron-Artificial Neural Networks (MLP-ANN). Next, the impedance and Adaptive Neuro-Fuzzy System (ANFIS) based technique is employed for fault location. At a sample rate of 50 kHz, the DWT was applied to current signals and the coefficients used for ANN training, while phase impedance values were used as input to the ANFIS for training. The simulation results showed accuracy of 96.6% for fault classification and 99.17% for fault location. The developed models can significantly enhance fault location for speedier outage resolution by promptly repairing the affected distribution lines.

Transformer performance and efficiency can be enhanced by effectively address the properties of its insulation system. The power transformer insulation system weakens as a result of operational thermal stresses brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial metric that must be maintained below the prescribed limit while power transformers are operating so as to maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging effect of insulation depends on transitions in hot spot temperatures. Due to the non‐linear nature of the conventional mathematical models used to determine these temperatures, and complexity of thermal phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique. The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oil-filled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132 kV power transformers under dynamic operating regimes. The ANFIS model results were validated against field measured values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation. The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and 0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct determination of HST can help asset managers in thermal analysis trending of the in-service transformers, helping them to make proper loading recommendations for safeguarding the asset.