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PublicationA comparative study on DG placement using marine predator and Osprey algorithms to enhance loss reduction index in the distribution system(Iran University of Science and Technology, 2025-06)The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.
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PublicationEffect of TiO₂/eggshell composite using sol gel method photoanode for dye-sensitized solar cell applications (DSSC) and comparison using k-nearest neighbors method(Elsevier, 2025-04)This study investigated the impact of TiO₂/eggshell (TE) composite with different ratios via sol gel method and used for the development of photoanodes in DSSC. The impact of eggshell incorporation into TiO₂ on its structural, optical properties, electrochemical properties and photovoltaic performance were investigated. The absorption spectrum revealed a reduction in the energy band gap as eggshell concentration increased, leading to an enhancement in the DSSC properties. Addition of eggshell enhances the electrochemical properties of the photoanodes. The EIS results confirm that eggshell incorporation can lower the charge transfer resistance and enhanced the efficiency to 2.95 % using natural dye sensitizer for TE 3:10. In this research also, integration with machine learning was conducted using k-Nearest Neighbors (kNN) to predict the highest efficiency based on various samples at EIS analysis. The k-Nearest Neighbors (kNN) algorithm was employed to identify the sample with the highest efficiency, showing that DSSCs with TE 3:10 exhibited the highest efficiency, with a prediction accuracy of 90 %. To validate the kNN results, manual measurements were performed, and the findings presented in Nyquist plots confirmed that kNN predictions are equally reliable as manual measurements for efficiency estimation.
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PublicationA review of analysis of partial discharge measurements using coupling capacitor in rotating machine(Iran University of Science and Technology, 2025-06)Partial discharge (PD) is a critical phenomenon in electrical systems, particularly in high-voltage (HV) equipment like transformers, cables, switchgear, and rotating machines. In rotating machines such as generators and motors, PD is a significant concern as it leads to insulation degradation, potentially resulting in catastrophic failure. Effective and reliable diagnostic techniques are essential for detecting and analyzing PD to ensure the operational safety and longevity of such equipment. Various PD detection methods have been developed, including coupling capacitor (CC), high-frequency current transformer (HFCT), and ultra-high frequency (UHF) techniques, each offering unique advantages in assessing the condition of HV electrical systems. Among these, coupling capacitors have gained significant attention due to their ability to improve the accuracy, sensitivity, and efficiency of PD detection in rotating machines. This study focuses on the advancements in coupling capacitor-based techniques and their critical role in enhancing PD diagnostics for monitoring and maintaining high-voltage rotating machinery.
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PublicationArtificial Neural Network (ANN) backpropagation for forecasting 100% renewable energy in North Sumatera(Kauno Technologijos Universitetas, 2025-03)Growing environmental awareness and the increasing need to reduce reliance on fossil fuels have driven the development of renewable energy (RE) technologies, such as micro-hydro, photovoltaics, biomass, geothermal, and biogas. However, the utilisation of RE in North Sumatera remains limited compared with fossil fuels, highlighting the need for optimisation strategies to accelerate the transition towards 100% RE. This study develops a predictive model using the artificial neural network (ANN) backpropagation algorithm to maximise RE contributions, minimise dependence on fossil fuels, and forecast the timeline for a full transition to RE. Data from Perusahaan Listrik Negara (North Sumatera), Independent Power Producers (IPP), and palm oil mills were used to model a hybrid generation system incorporating micro-hydro, hydropower, geothermal, biomass, biogas, and solar energy. Simulations were carried out using the firefly algorithm (FA) and particle swarm optimisation (PSO), with optimisation assessed through the renewable energy contribution ratio (RECR). The results indicate that FA outperforms PSO in meeting RE targets, with an average RECR of −51.514 for FA compared with −911.054 for PSO. Predictions using ANN backpropagation suggest that the transition to 100% RE could be realised by 2064 (FA) and 2065 (PSO). This research offers valuable insights for accelerating the transition to sustainable energy, enhancing energy resilience, and reducing environmental impacts.
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PublicationChemical and surface modification in graphene oxide for optimum CO₂ gas sensing performance(IOP Publishing, 2025-04)The level of CO₂ gas sensing is very crucial for applications such as medical and air quality monitoring. The conventional metal oxide-based CO₂ sensors are sensitive but they need additional excitation like high temperature to be operated at room temperature. In this study, the effect of reduction time on the surface functional groups of the graphene-based sensing layer is investigated to achieve high performance of CO₂ gas sensors to be operated at room temperature. Five reduction times (20, 30, 40, 50, 60 min) are examined to synthesize reduced graphene oxide (rGO) from GO precursor material using green reducing agent ascorbic acid. The structural and morphological properties of rGO-based ArGO samples are investigated using FTIR, Raman, and SEM characterization techniques exhibiting the layered, wrinkled structure with apparent folds on the ArGO thin film surface. The highest and the lowest number of oxygen functional groups are shown by the ArGO20 and ArGO60 thin films, respectively. The electrical characterization presents the highest sheet resistance of 786 KΩ sq−1 and the lowest sheet resistance of 103 KΩ sq−1 for ArGO20 and ArGO60 thin films, respectively. Five sensors are fabricated following the reduction time to detect the CO₂ gas at room temperature. Among them, the ArGO40 sensor demonstrated optimum sensing response towards CO₂ gas with high sensitivity, repeatability, selectivity, and long-term stability, revealing that the reduction time of 40 min is optimum to synthesize functionalized graphene sensing material for CO₂ detection.