A frequency response analysis (FRA) is a well-known technique for evaluating the mechanical stability of a power transformer’s active part components. FRA’s measuring practices have been industrialised and are codified in IEEE and IEC standards. However, because there is no valid coding in the standard, the interpretation of FRA data is still far from being a widely acknowledged and authoritative approach. This study proposes an innovative fault segmentation and localisation technique based on FRA data. The algorithm is based on regression analysis to estimate the repeatability and relationship between the FRA fingerprint and the latest measured data. Initially, the measuring frequency is discretised into three regions to narrow the location of the fault; the regression model of the fingerprint and current FRA data are then evaluated. As a benchmark, two statistical indicators are the employed benchmark against the proposed method. Finally, the proposed scheme identifies and characterises various transformer conditions, such as healthy windings, axial and radial winding deformations, core deformation and electrical faults. The database used in this study consists of FRA measurements from 70 mineral-oil-immersed power transformers of different designs, ratings and manufacturers that were physically inspected for various faults and comparable frequency regions. The results achieved corroborate the efficacy of the proposed regression analysis fault recognition algorithm (RAFRA) model for transformer fault diagnosis using FRA. Further recommendations are made to address the reproducibility concerns induced by multiple FRA testing conditions.

The design of a compliant, safe, and reliable substation earth grid is not a straightforward process; in most cases, it requires some additional measures to be taken due to various constraints that differ from environment to environment, such as soil resistivity, a high fault level, a limited surface area, construction budget, etc. The IEEE Std 80-2013 proposes various refinement methods that can be applied to address different situations. For this study, the current limiting method, current diversion method, and touch and step voltage increment method were applied using the Electrical Transient Analysis Program (ETAP). A power system was designed, where a fault current generated by the supply transformers and back-fed by the power system’s motors was exported to the earth grid. Using this fault current, various simulations were conducted to assess the performance of the earth grid. The analysis results show that the application of the current limiting method using neutral earthing resistors has a great impact on the design of the earth grid as this method significantly reduces the fault current injected into the earth grid. Furthermore, by applying the current diversion method, the amount of fault current injected into the earth grid is reduced by a fair amount, which improves the performance of the earth grid. Lastly, increasing the tolerable limits of touch and step voltages by reducing the fault clearance times significantly improves the compliance of the earth grid as the clearance time is reduced. From this study, it is therefore concluded that, by implementing the refinement methods depending on the design requirements and feasibility of the application, one can improve the compliance state of an earth grid.

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.