The issue of unintentional islanding in grid interconnection still remains a challenge in grid-connected, Distributed Generation System (DGS). This study discusses the general overview of popular islanding detection methods. Because of the various Distributed Generation (DG) types, their sizes connected to the distribution networks, and, due to the concern associated with out-of-phase reclosing, anti-islanding continues to be an issue, where no clear solution exists. The passive islanding detection technique is the simplest method to detect the islanding condition which compares the existing parameters of the system having some threshold values. This study first presents an auto-ground approach, which is based on the application of three-phase, short-circuit to the islanded distribution system just to reclose and re-energize the system. After that, the data mining-decision tree algorithm is implemented on a typical distribution system with multiple DGs. The results from both of the techniques have been accomplished and verified by determining the Non-Detection Zone (NDZ), which satisfies the IEEE standards of 2 s execution time. From the analysis, it is concluded that the decision tree approach is effective and highly accurate to detect the islanding state in DGs. These simulations in detail compare the old and new methods, clearly highlighting the progress in the field of islanding detection.

Flooding in the Kathmandu Valley has intensified in recent years due to rapid urbanization, unregulated land-use change, and insufficient drainage infrastructure. Existing flood hazard assessments are often based on low-resolution datasets and lack proper field validation. This study presents an integrated flood modeling framework that combines Unmanned Aerial Vehicle (UAV)-derived Digital Elevation Models (DEMs), field-based flood measurements, and hydrological simulations to assess urban flood hazards in the Bagmati-Nakkhu confluence, Nepal. High-resolution UAV-derived DEM and field survey data, including flood marks and high-water levels, were used as the foundation for the analysis. Hydrological modeling was conducted using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) to estimate the peak discharges of the Nakkhu River (2000–2024), which were then used to derive design flows for return periods of 5 to 150 years using the Gumbel distribution. These flows were used as boundary condition inputs for the Hydrologic Engineering Center—River Analysis System (HEC-RAS) to simulate flood depth and inundation extent under different scenarios. Flood extents for the 27 September 2024 event were derived from Sentinel-2 imagery and validated against surveyed flood marks. Additionally, land use/land cover (LULC) mapping based on UAV data was used to support flood impact analysis. The results show that flood depths ranged from approximately 0.5 m to 2.8 m, with inundation areas increasing by 35–50% under extreme rainfall. Model validation demonstrated strong agreement with simulated results, with deviations generally within ±0.3–0.5 m. Scenario analysis further indicates that urban expansion significantly increases runoff and flood extent, particularly in low-lying areas near the river confluence. Socio-economic exposure analysis for the 27 September 2024 event indicates that approximately 2569 residents (56.4% of the study zone population) and 4.011 km (77.42%) of the local road network were exposed to inundation. Overall, the results demonstrate that integrating high-resolution UAV data, field observations, and hydrological modeling greatly improves the accuracy and reliability of flood hazard assessments in data-scarce urban environments.

Nepal is equipped with one of the world’s largest hydropower potentials, estimated to be over 83,000 MW, yet less than 3% has been utilised to date. Nepal relies on hydropower projects for rural electrification. In such contexts, protecting water conveyance infrastructure against hydraulic transients is critical for operational safety and longevity. Surge tanks remain the industry-standard mitigation against water hammer in hydropower systems; however, experimental assessment of surge tank behaviour through manual testing remains rare in the literature. This paper presents an experimental assessment of surge tank performance under transient conditions induced by manual valve closure, using a laboratory system built at the WEIR Laboratory of Kathmandu University. The system comprises a 4-inch (101.6 mm) diameter headrace tunnel of 11.305 m length, a 6-inch (152.4 mm) diameter open surge tank, and a 3-inch (76.2 mm) diameter penstock. Three valve closure protocols (<1 s, 1 s, and 3 s) are used to induce transient waves of varying amplitude. Because closure is performed manually, exact closure-law repeatability cannot be guaranteed; the resulting variability is quantified and discussed. Water level oscillations in the surge tank are recorded using manual measurement techniques and characterised in terms of maximum upsurge, maximum downsurge, mass oscillation period, and decay time. Tested parameters are compared against analytical predictions. The experimental results provide an empirically assessed dataset useful for informing numerical models for hydropower systems, with the caveat that agreement between simplified analytical predictions and observations is mixed: first-cycle upsurge magnitude agrees well, whereas the oscillation period prediction differs from experiment by nearly a factor of two.