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Item type:Publication, Renewable energy based smart grid construction using hybrid design in control system with enhancing of energy efficiency of electronic converters for power electronic in electric vehicles(Hindawi Limited, 2022-01-01)The power electronic interface is critical in matching a distributed generation (DG) unit's characteristics to grid requirements as most DG technologies rely on renewable energy. Increased adoption of electric vehicles (EV) is seen as a positive step toward minimizing air pollution as well as carbon emissions. Rapid proliferation of electric vehicles as well as charging stations has exacerbated voltage quality as well as harmonic distortion difficulties, which harm the efficiency of combined renewable energy. This research proposes novel hybrid design techniques in control systems that enhance the energy efficiency of electronic converters for power electronics. The control system enhancement has been carried out using a hybrid energy storage electric convertor, and energy efficiency is improved using a synergetic battery reference adaptive controller. A plug-in hybrid electric vehicle (PHEV)'s internal combustion engine with a small photovoltaic (PV) module is utilised to assess a proposed control method which effectively regulates electric power on-grid by draining electricity from batteries during peak hours as well as then charging them during off-peak times, lowering the load on the converter as well as allowing electric vehicles to charge faster. Experimental results show the constant acceleration case obtained battery current of 92 Amps, ultracapacitor current of 89 Amps, charging voltage of 88 V, DC load current of 85 Amps, battery SOC of 72%, and the time-varying acceleration proposed technique obtained current of 94 Amps, and ultracapacitor current of 90 Amps, charging voltage of 90 V, DC load current of 82 Amps, battery SOC of 79%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant(2025-01-30)The present research is carried out for the improvement of the availability of a hydroelectric power plant through a wear-sediment quantity correlation model for the scheduling of its preventive maintenance, the data is based on the measurement of blade thicknesses, as well as visual inspection to identify discontinuities in the water equipment, once the data has been collected, data analysis techniques can be used to evaluate the condition of the Francis turbine and determine the need for preventive maintenance under working condition. The data analysis detailed is the least squares method where the independent variables considered are power and suspended particles with their nephelometric unit of measurement of turbidity in parts per million (PPM). By means of the aforementioned analysis, it is possible to complete the results with the projection of the wear to years after the data obtained from the inspection point, and it also allows taking preventive measures before a failure occurs, which helps to reduce downtime and maintenance costs. Thus, the hydroelectric power plant under study has an annual average availability of 97.21 %, reduced by the suspension of power generation due to reservoir flushing and scheduled maintenance shutdowns. While the annual average reliability is 99.89 %, it is reduced by unscheduled failures. The result of the correlation statistical model determined the preventive maintenance for improvement conditions of 98 % of the availability in the hydroelectric power plant and is reflected in the reduction of days of no electricity generation.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessment of Hammer Energy Measurement for the Standard Penetration Test (SPT) Using Pile Driving Analyzer and Kallpa Analyzer Devices in Peru(Multidisciplinary Digital Publishing Institute (MDPI), 2025)Energy measurement in dynamic penetration tests is key to correctly interpreting test results and ensuring comparable geotechnical data. Although commercial devices are widely used, their high cost limits adoption in developing regions such as Peru, affecting the accuracy of soil evaluation in many geotechnical studies. In this context, this research presents an energy measurement system called Kallpa, which uses low-cost electronic components to digitize sensor signals during Standard Penetration Tests (SPTs). Kallpa employs high-resolution analog-to-digital converters (ADCs) with an advanced sampling frequency, processing and storing data via a Raspberry Pi 4 microcomputer. The sensors, including accelerometers and strain gauges, were calibrated and compared with the Pile Driving Analyzer (PDA) to validate their accuracy in the Kallpa system. This study involved sixteen Standard Penetration Tests (SPTs) conducted in various regions of Peru using donut hammers and two tests involving automatic hammers. The results demonstrate that the Kallpa system is comparable to other energy measurement devices on the market, such as the Dynamic Penetration Test (DPT), which provides accurate SPT energy measurements. The Kallpa Processor (Version 1.0) software was developed to perform data acquisition and calibration, analyzing approximately 500 hammer blows and comparing peak values with those of the Pile Driving Analyzer. The data collected by Kallpa’s sensors strongly agreed with the PDA data, validating the reliability of the device. The Energy Transfer Ratio (ETR) for manual hammers ranged from 43.5% to 68.4%, with an average of 58.9%, whereas automatic hammers presented ETR values between 82% and 87%. The correction of the N60 blow count allowed for the estimation of the relative density of soils evaluated at different depths and locations across Peru.12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Quad-Active-Bridge Resonant-Type Single-Stage Three-Phase AC-DC Converter: Modulation and Control for V2G Applications(Institute of Electrical and Electronics Engineers Inc., 2025)The single-stage (SS) ac–dc converter is a promising solution for bidirectional electric vehicle chargers (BEVCs) due to its smaller volume, higher efficiency, and durability compared with the two-stage ac–dc converter. Nevertheless, typically SS ac–dc structures implement highly complex modulation schemes because most of them use bipolar voltage switches. In this article, a novel modulation and control strategy is introduced for the SS quad-active-bridge resonant-type ac–dc converter, which only uses unipolar voltage switches. In the proposed modulation, grid voltages are modulated by duty ratio (DR), whereas the dc source voltage is modulated by phase-shift (PS) and DR. The PS angle takes a constant value according to grid currents amplitude. Moreover, the DR angles, used to modulate grid voltages, control the sinusoidal shape of grid currents according to proportional-integral-resonant controllers cascaded to a second-order low-pass filters and an active damping. Whereas, the DR angle, used to modulate the dc source voltage, is applied to reduce the high-frequency current, in case of reactive power transfer to the grid. Experimental results from a 1.5 kW prototype show high efficiency (96.4%), bidirectional active-reactive power transfer capability, and low harmonic distortion in the grid currents (1.1%), making it an ideal alternative for BEVCs.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementation of an Embedded Electronic Control System for a Functional Prosthetic Hand(Institute of Electrical and Electronics Engineers Inc., 2025)The advancement of electronics enables the development of more complex functional prosthetics with more capabilities and functionality. The prosthetics currently developed do not leave the laboratory environment due to their complexity or the requirement to operate in conjunction with laboratory computing equipment. Therefore, it is essential to develop prosthetics that can operate outside of the laboratory environment while maintaining maximum functionality. Consequently, an electronic control system is developed that can be embedded in a functional prosthetic hand, which is then trained in real-time through electromyography (EMG) using a classification algorithm based on machine learning. The system allows the prosthetic hand to assume six different positions and be controlled by electromyography, recognizing flexion and extension movements of the wrist. This results in a control system that can be fully embedded in the prosthetic hand, with an appropriate weight and size to maintain the aesthetics and functionality of the prosthesis.3