Microgrids, or distributed systems of local energy generation, transmission, and demand, are now technologically and operationally capable of providing power to communities, especially in rural and peri-urban regions of developing nations. The reliability of the system, the cost of power generation, and the operating environmental impact are the major issues when designing and evaluating the performance of an off-grid hybrid renewable energy microgrid (HREM). This paper presents an integrated method for optimal sizing and operation of an HREM for rural agricultural communities in the Southern Philippines composed of run-of-the-river hydropower, photovoltaics (PV), diesel generator, and a battery energy storage system (BESS) using multi-objective particle swarm optimization (MOPSO) and a proposed multi-case power management strategy. The three conflicting objective functions that were simultaneously minimized were: loss of power supply probability (LPSP), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions, subject to several constraints. The optimization generated 200 non-dominated or Pareto optimal alternative solutions, 4 of which were selected as solutions of interest. Based on the results, the optimal sizes of the main components for the reliable operation of the system are 100 panels with a rating of 0.25 kW for PV, 100 kWh for BESS, and 13 kW for the diesel generator, with corresponding LCOE, LPSP, and GHG emission values of 0.1795 USD/kWh, 0.05%, and 7874 kg, respectively, for 1 year. The effectiveness of the proposed HREM design was also analyzed, and the study yielded plenty of useful findings that could aid the electrification of the area.

This study proposes an integrated framework for assessing the suitability of renewable energy systems, including wind, solar, hydro and hybrid wind–solar and hydro–solar, in the southern Philippines. The framework employs a combination of the Fuzzy-Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) techniques to evaluate various socio-environmental and techno-economic factors. Several suitability indices were developed and used in the analysis. The criteria used in the analysis are based on a comprehensive literature review and input from experts in renewable energy and micro-grid technology. The results indicate that energy production is the most important factor and the total suitable areas for hybrid wind–solar and hybrid hydro–solar systems are 126.60 and 629.02 square kilometers, respectively. This research provides valuable insights for decision-makers and potential investors in the renewable energy sector in the study area.

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids.

Vegetation-related high-impedance faults (VHIFs) are difficult to detect because their low and intermittent currents often evade conventional protection and phasor-based monitoring. These events arise on overhead feeders when conductors contact vegetation, producing weak, sporadic arcs whose diagnostic signatures concentrate in the high-frequency (HF) band. This study develops a waveform-derived severity mapping and placement framework that converts HF arcing indicators into bus-level risk indices and then into a candidate set of diagnostic test points. Using the staged Vegetation High-Impedance Fault (VeHIF) dataset, wavelet- and spectral-based descriptors are classified with an RBF-SVM to generate VHIF severity maps, achieving 96.1% out-of-fold accuracy with near-symmetric performance across risk classes. Test-point placement is formulated as a bi-objective problem that maximizes normalized severity coverage while minimizing adjacency-based redundancy. NSGA-II is used to explore the coverage–redundancy trade-off and identify a Pareto knee solution, while a tuned Binary Particle Swarm Optimization (BPSO) stage reproduces this compromise as a single deployable layout. MOEA/D serves as a decomposition-based benchmark to assess algorithmic stability. On the IEEE 14-bus system, the selected knee configuration achieves 80.32% VHIF-severity coverage with a redundancy ratio of 0.400 using eight test points. The BPSO stage reproduces the same objective balance with significantly lower runtime, supporting rapid scenario re-evaluation. The framework provides a reproducible pathway from HF waveform evidence to risk-informed bus selection. It is implemented as OPTIVE HIF, a web-based platform that operationalizes classification, spatial severity mapping, and optimization for the IEEE-14 demonstration. These results illustrate how HF-driven severity modeling can support structured, risk-aware planning for VHIF monitoring in distribution networks.

This study presents the design, embedded implementation, and screening-level experimental terrain-performance evaluation of an RHex-inspired hexapod robot using a fixed encoder-assisted alternating-tripod state-machine gait. The work aims to provide an experimentally grounded assessment of how terrain properties and practical leg-thickness variation influence the locomotion of a fabricated low-complexity legged platform. A 2 × 2 × 2 full-factorial screening design was adopted to evaluate terrain roughness, terrain compliance, and leg thickness. Four terrain conditions were tested: concrete, rocky terrain, foam mats, and grass, corresponding to smooth–rigid, rough–rigid, smooth–soft, and rough–soft surfaces, respectively. Each treatment combination was evaluated in two replicate runs using final forward displacement, lateral displacement, absolute displacement, and peak current as the response variables. The full-factorial analysis showed that terrain roughness had the clearest significant effect on forward displacement and absolute displacement, while terrain compliance significantly affected absolute displacement and showed observable trends in lateral displacement and peak current. Leg thickness did not produce a statistically significant main effect within the tested 2.5 mm and 5.0 mm configurations, fixed gait, and terrain set. The findings indicate that, for this platform and experimental scope, terrain roughness and compliance affected locomotion more strongly than the tested morphology variation. The study contributes a reproducible baseline workflow for terrain-performance evaluation in low-complexity legged robots and identifies directions for future work involving stronger replication, quantified terrain characterization, improved energy measurement, and closed-loop terrain-adaptive control.