Umer Amir Khan

Urban distribution networks in hot-climate megacities face escalating fault currents, energy losses, thermal derating, and voltage instability simultaneously, yet no prior study provides a techno-economic benchmark for high-voltage superconducting cables (HVSCC) under sustained high-ambient conditions. This paper provides that benchmark through a simulation-based technical and economic evaluation of a representative 380/132 kV network serving high-density loads over dual 15 km circuits at 40 ∘ C ambient. The fault current limiting (FCL) is modeled both by a phenomenological characteristic and by a coupled E − J electrothermal formulation, the latter confirming that the limiting and the voltage response follow from the conductor physics rather than from a prescribed curve. Relative to cross-linked polyethylene (XLPE), HVSCC achieves an 88% net system-level reduction in energy draw, a figure that includes the cryogenic refrigeration penalty, modeled explicitly through a coefficient-of-performance analysis and shown to be conservative against measured AC-loss data. Autonomous FCL approximately halves the peak fault current within milliseconds, while the quenching resistance converts the conventional voltage sag into a moderate voltage rise that holds the bus within the equipment tolerance window, improving load ride-through. Parametric analysis shows the fault limiting to be insensitive, within a few percent, to fault location, fault impedance, source strength, and inception angle, simplifying protection coordination. Hybrid network analysis reveals asymmetric resilience, in which HVSCC faults minimally disturb parallel XLPE circuits while XLPE faults severely affect parallel HVSCC circuits, with direct implications for staged deployment. Lifecycle analysis establishes a positive net present value with payback within the cable service life, across the full range of cryogenic and electricity-price assumptions. The results provide quantitative benchmarks for hot-climate metropolitan grid modernization.

Generation expansion planning is critical for the sustainable development of power systems, particularly with the increasing integration of renewable energy sources like wind power. This paper presents an innovative generation expansion model identifying the optimal strategy for constructing new wind power plants. The model determines the ideal size of wind power generation and strategically allocates wind resources across multi-area power systems to maximize their capacity credit. A novel fuzzy set approach addresses wind power’s inherent uncertainty and variability, which models wind data uncertainty through membership functions for each stochastic parameter. This method enhances the accuracy of capacity credit calculations by effectively capturing the unpredictable nature of wind power. The model uses the Effective Load Carrying Capability (ELCC) as the objective function to measure the additional load that can be reliably supported by wind generation. Additionally, integrating a compressed-air energy storage system (CAESS) is introduced as a novel solution to mitigate the intermittency of wind power, further boosting the wind power plants’ capacity credit. By incorporating an energy storage system (ESS), the model ensures greater resource availability and flexibility. The study evaluates a multi-area power network, where each area has distinct conventional generation capacity, reliability metrics, load profiles, and wind data. A three-interconnected power system case study demonstrates the model’s effectiveness in increasing the load carrying capability of intermittent renewable resources, improving system reliability, and enhancing resilience. This study provides new insights into optimizing renewable energy integration by leveraging advanced uncertainty modeling and energy storage, contributing to the long-term sustainability of power systems.

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.