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PublicationImproved control in single phase inverter grid-tied PV system using modified PQ theory(Tech Science Press, 2023)Grid-connected reactive-load compensation and harmonic control are becoming a central topic as photovoltaic (PV) grid-connected systems diversified. This research aims to produce a high-performance inverter with a fast dynamic response for accurate reference tracking and a low total harmonic distortion (THD) even under nonlinear load applications by improving its control scheme. The proposed system is expected to operate in both stand-alone mode and grid-connected mode. In stand-alone mode, the proposed controller supplies power to critical loads, alternatively during grid-connected mode provide excess energy to the utility. A modified variable step incremental conductance (VS-InCond) algorithm is designed to extract maximum power from PV. Whereas the proposed inverter controller is achieved by using a modified PQ theory with double-band hysteresis current controller (PQ-DBHCC) to produce a reference current based on a decomposition of a single-phase load current. The nonlinear rectifier loads often create significant distortion in the output voltage of single-phase inverters, due to excessive current harmonics in the grid. Therefore, the proposed method generates a close-loop reference current for the switching scheme, hence, minimizing the inverter voltage distortion caused by the excessive grid current harmonics. The simulation findings suggest the proposed control technique can effectively yield more than 97% of power conversion efficiency while suppressing the grid current THD by less than 2% and maintaining the unity power factor at the grid side. The efficacy of the proposed controller is simulated using MATLAB/Simulink.
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PublicationGas source localization through deep learning method based on gas distribution map database(Penerbit UTM Press, 2024)The incident of harmful gas leakage can cause severe damage to the environment and several casualties to human beings while the gas localization system plays a major role in mitigating those causalities. With the advances in artificial intelligence technology, deep learning is able to enhance the accuracy of the gas localization system to locate the gas source. This paper proposes a gas localization system that utilizes three different deep learning models namely DNN, 1DCNN, and 2DCNN to locate the gas source within the gas map. The proposed method involves generating the gas distribution map through the large gas sensor array platform in real-world indoor scenarios. Those models are then trained using the collected database which allows for accurate prediction of the gas source location. The performance of each proposed deep learning model was compared to find the best model demonstrating the highest effectiveness in identifying gas leaks. The study has shown that the 1DCNN has the highest effectiveness in predicting the gas source in the range between 0.0 m to 0.3 m with 90.3% compared to the DNN and 2DCNN models.
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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.