A multi-terminal high voltage DC (MTDC) grid, is an optimum and cost-effective transmission network to minimize the energy crisis worldwide largely. However, the core demand for a fast DC protection scheme with an extraordinary strict fault clearance time of a few milliseconds, is a key research gap in this network, holding back its development and scalability. To bridge this gap, this paper proposes a novel protection scheme for a meshed MTDC grid. The main goals of the scheme include accurate discrimination of a faulty line, rapid fault detection, fault location estimation, significant fault current reduction, and fully selective isolation of only the faulted line, while continuing normal power flow in the healthy grid zones. Reliability of the scheme for long and extra-long-distance power transmission is increased by aiding the differential protection and Type-D traveling wave (TW)-based algorithms utilizing the distributed optical current sensing technology with the other auxiliary methods and backup plans. These auxiliary methods include independent discrete wavelet transform (DWT), current derivative polarity principles with a minimum sample (short time) window, overcurrent relays, and AC circuit breakers (ACCBs). A faulty segment of a transmission line is accurately discriminated from the healthy ones by measuring a series of multi-point differential currents on it. A faulty line at a particular DC node is accurately discriminated using the differential protection by measuring the current flowing into or out of each line from each side at every node to obtain the algebraic sum. The current sum of a real-time local transient data and a delayed remote data is compared to a preset threshold level. DC fault current is significantly reduced below the breakable levels by coordinating bidirectional hybrid DC circuit breakers (HDCCBs) with the active and passive fault current limiters (FCLs) and the half bridge-VSC-based modular multilevel converters (MMCs). The proposed concepts are successfully verified by the simulation results under a variety of fault scenarios and are found to be accurate.

Polymer based composite high voltage insulators (HVIs) due to their hydrophobic surfaces have consistently outperformed the traditional ceramic insulators in highly contaminating coastal-desert environments. However, the harsh environment causes polymer surfaces of these HVIs undergo rapid ageing leading to permanent loss of hydrophobicity. In this paper, we have presented three samples of 380 kV composite HVIs with a service life of over ten years near west coast of the Kingdom of Saudi Arabia. Material investigation using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy revealed substantial surface deterioration and change in the composition. Hydrophobicity investigations revealed that surface of the HVIs have become hydrophilic leading to accumulation of continuous films of water. Electrical withstand strength was evaluated using steam fog test showing significant decrease from the rated values. The distribution of electric field around an insulator is a key design criterion for HVIs. The consequences of loss of hydrophobicity on the electric field distribution across insulator surface is explained analytically and through numerical simulations based on Finite Element Method (FEM). The pristine and field aged HVIs are modelled and were covered with realistic water distributions observed during hydrophobicity tests. It has been observed that the intensity of electric field on field aged insulator surpasses the corona inception threshold at certain areas of the insulator, resulting in the initiation of corona discharge on the insulator’s surface. This corona inception further causes surface deterioration and accelerated ageing in composite HVI.

Growing urbanization coupled with increased power demands have led to an increasing use of mixed power transmission lines with sections of overhead lines (OHL) and the underground cables. Due to differences in surge impedance of cables and OHL, voltage surges experience reflections and refractions at their boundaries which make the transient behavior of mixed high voltage direct current (HVDC) line quite peculiar. Lightning strikes on overhead sections of lines induce voltage surges that travel along OHL and enter the cable section. Lightning overvoltage can cause OHL insulators to flashover and stress cable insulation or cause its permanent breakdown. In this paper we simulated a fast front model of a mixed HVDC transmission line using an electromagnetic transient (EMT) simulation program (PSCAD) to analyze its transient behavior under a lightning strike. The leader progression model (LPM) has been used to predict the dielectric performance of OHL insulators. It has been shown that transition towers adjacent to the cable section are much less vulnerable to flashover than subsequent towers. The length of a riser section (connecting OHL and cable) and tower footing impedance have shown to significantly influence the flashover performance of OHL insulators. In addition, the length of cable segments and sheath grounding impedance have been found to influence cable overvoltage. This study can be used to evaluate the insulation coordination and overvoltage protection requirements for a mixed HVDC transmission line.

This paper presents a comprehensive experimental validation of machine learning for contamination classification of polluted high voltage insulators using leakage current. A meticulous dataset of leakage current for porcelain insulators with varying pollution levels was developed under controlled laboratory conditions. Critical parameters of temperature and varying humidity were also included in the dataset to reflect the impact of environmental conditions and bring the dataset close to real world scenarios. The dataset generated was preprocessed and critical features were extracted from time, frequency, and time-frequency domains. Four distinct machine learning models, encompassing decision trees and neural networks, were trained and evaluated on this dataset. The Bayesian optimization technique was used to optimize the parameters of Machine Learning Models. The models demonstrated exceptional performance, with accuracies consistently exceeding 98 %. Notably, the decision tree-based models exhibited significantly faster training and optimization times compared to their neural network counterparts. This study underscores the effectiveness of machine learning in improving the reliability of insulator maintenance and monitoring systems, paving the way for more robust predictive maintenance strategies.

The long-term reliability of high-voltage polymeric insulators is critically affected by surfacedegradation processes driven by environmental and electrical stressors. In our prior work, the role of hydrophobicity loss in intensifying surface electric field stress was established using finite element modeling. This study extends that investigation by experimentally assessing the severity and spatial variability of environmental pollution—specifically, soluble and non-soluble deposits—on field-aged 380-kV composite insulators that had been in service for over a decade. Using standardized procedures defined in IEC 60507, the Equivalent Salt Deposit Density (ESDD) and Non-Soluble Deposit Density (NSDD) were measured on individual sheds along the insulator profile to capture localized contamination patterns. Results reveal strong non-uniformity in pollution distribution, with significantly higher contamination levels on lower sheds due to gravitational runoff and environmental exposure asymmetry. Finite Element Method simulations were performed using COMSOL Multiphysics to evaluate the surface electric field distribution on degraded insulators. Material properties and boundary conditions were defined to reflect field-aged conditions and shed-level pollution gradients. The electric field maps showed intensified surface stress in regions with combined effects of hydrophobicity loss and high ESDD/NSDD values, particularly near the lower sheds. Microscopic observations further confirmed contaminant accumulation and physical erosion in these high-risk regions. The findings offer a comprehensive degradation model that integrates field-contamination data with electric field intensification mechanisms. This study provides actionable insights for predictive maintenance, condition assessment, and improved insulation design under polluted service environments.

High-voltage insulators play a critical role in ensuring the reliability of power transmission systems by preventing flashover under severe environmental conditions. Traditional monitoring techniques rely on visual inspection or static classification schemes, which often fail to capture the progressive nature of surface discharge activity leading to flashover. This article presents a novel machine learning framework that addresses this limitation by classifying leakage current signals into five distinct operational stages: negligible leakage current, leakage current starting, leading to flashover, preflashover, and flashover. This multistage classification approach enables more accurate early warning of impending flashover by identifying subtle changes in leakage current behavior that precede catastrophic insulation failure. Controlled contamination experiments were conducted using porcelain insulators under varying environmental stressors and leakage current data was systematically acquired, segmented, and labeled based on amplitude variations, harmonic distortion, and dry band arcing characteristics. The proposed model, based on inception modules with residual connections, effectively captures multiscale temporal patterns in leakage current signals. Furthermore, posttraining quantization was applied to compress the model for edge deployment, achieving a 91.4% reduction in model size and a 90% decrease in inference time with negligible accuracy loss. Comparative evaluation against conventional neural networks and state-of-the-art ML architectures demonstrated the superior classification accuracy, robustness, and computational efficiency of the proposed framework. This architecture not only facilitates early detection of flashover stages but also enables low-latency, low-power deployment on resource-constrained devices, such as embedded systems, in remote substations.