Output Power Of System Research Articles

Thermoelectric Energy Harvesting for Exhaust Waste Heat Recovery: A System Design.

Thermal energy harvesting for high-speed moving objects is particularly promising in providing an efficient and sustainable energy source to enhance operational capabilities and endurance. Thermoelectric (TE) technology, by exploiting temperature gradients between a heat source and ambient temperature, can provide a continuous power supply to such systems, reducing the reliance on conventional batteries and extending operation times. However, the integrated thermoelectric generator (TEG) system design research is far behind materials development. In this study, both experimental and numerical studies of TEG systems are designed and conducted to recover thermal energy. An integrated proof-of-concept platform is developed to simulate an exhaust gas emission system and demonstrate the designed TEG system performance. Triangular plate-fin heat exchangers are designed to collect heat from exhaust pipelines such as engine exhaust gas. While longitudinal trapezoidal fin cylindrical heatsink is designed for dissipating heat via forced convection, particularly for high-speed moving objects, the heatsink design is optimized using finite element analysis (FEA) simulations to maximize the temperature gradient and electrical output power. As a result, under a temperature gradient (ΔT) of 190 °C with commercial bismuth telluride-based TE modules, a maximum system output power of 40 W is achieved experimentally. Furthermore, a computational simulation is conducted to showcase the feasibility of the current system design under high-speed moving vehicle conditions. This study provides an advanced TEG system design bridging the TE device and integrated system transition and represents a potential advancement in the pursuit of high-speed moving objects such as autonomous and longer-lasting aerial platforms.

Adaptive Disturbance Rejection and Power Smoothing Control for Offshore Hydraulic Wind Turbines Based on Pitch and Motor Tilt Angles

This paper investigates an adaptive disturbance rejection control (ADRC) strategy for dual-variable power smoothing for hydraulic wind turbine systems deployed in marine environments. Initially, fluctuations in wind speed induce variations in the output torque and rotational speed of the wind turbine; this study examines the interaction between these two variables and subsequently decouples them. An innovative dual-variable anti-disturbance control strategy is proposed, which independently regulates the pitch angle of the rotor and the swing angle of the variable motor to mitigate fluctuations in both speed and torque, thereby achieving a smoother system output power. The simulation results obtained through MATLAB/Simulink (Version R2022a) indicate that employing the proposed control strategy leads to an 8.31% reduction in power generation compared to optimal power tracking strategies while enhancing output power stability by 56.67%. Furthermore, the effective smoothing of power fluctuations is accomplished without necessitating energy storage devices. Finally, the effectiveness of the power smooth output control strategy proposed in this paper was verified based on a semi-physical simulation experimental platform for a 30 kW hydraulic wind turbine. The control method proposed in this paper provides a theoretical basis for the promotion and application of hydraulic wind turbines with stable power output.

Optimizing tilt angle of PV modules for different locations using isotropic and anisotropic models to maximize power output

The optimal integration of Photovoltaic (PV) systems into an electric grid is dependent upon the total output power of the PV system. To optimize the output power of a PV system, the modules must be positioned at an optimal tilt angle (OTA) to maximize the absorption of solar radiations. This research focused on a mathematical model to optimize incident solar radiation. The proposed model is used to determine the OTA and evaluate its impact on the optimum configuration and power output capacity using MATLAB for six cities located in different temperature zones across Pakistan. The isotropic and anisotropic models have been used to calculate the total solar radiations (HT) on a sloped surface. During the summer season, all four selected models present similar findings in terms of the monthly average daily solar radiations on the tilted surface. During the winter season, the anisotropic models performed better than the isotropic models. The anisotropic model achieved a 14.82% energy increase in January and a 0.16% increase in June compared to the isotropic model. We present monthly and annual OTA calculated from the anisotropic model. The OTA has been determined for the HT across slope values ranging from 0° to 90° with a 1° resolution. The monthly OTA using anisotropic model for the Faisalabad, Lahore, Multan, RYK, Islamabad and Karachi ranges from 7° to 54°, 7° to 53°, 6° to 52°, 5° to 52°, 10° to 58° and 1° to 50° and the annual OTA for cities has been calculated to be 30.5°, 30.25°, 29.33°, 28.66°, 33.34° and 25.5° respectively. Utilizing the OTA calculated from the selected model, a PV array with a rated power of 52.200 kW has been used to analyze the system performance. The annual average output power at the monthly OTA results in gains of 8.83%, 9.40%, 9.78%, 9.77%, 9.82%, and 9.91% compared to the annual OTA. This research study is particularly beneficial for researchers and the industry in deploying PV systems across different climatic zones of Pakistan, intending to maximize output power while minimizing energy costs.

Enhanced dynamic modeling of regenerative CO2 transcritical power cycles: Comparative analysis of Pham-corrected and conventional turbine models

Turbine modeling plays a critical role in assessing system power output and performance in CO₂ transcritical power cycle (CTPC) systems, particularly under dynamic operating conditions. Conventional turbine models often fail to provide accurate predictions when inlet parameters deviate from the design point, leading to discrepancies in system behavior. This paper introduces a novel methodology by integrating a Pham-based corrected turbine performance map into a regenerative CTPC dynamic system. This approach enhances turbine modeling accuracy compared to traditional models, such as the uncorrected turbine performance map, nozzle model, and empirical model. The proposed Pham-corrected model method improves system response stability, particularly in terms of inlet pressure and CO₂ mass flow rate under heat source temperature reductions, offering more reliable performance predictions. In contrast, the empirical model shows substantial deviations in net power output (32 kW or 4.0 %) and thermal efficiency (1.36 %), leading to longer system stabilization times. The Pham-corrected and uncorrected turbine performance map models exhibit similar responses under cooling water mass flow rate reductions but diverge in turbine inlet pressure trends compared to the nozzle and empirical models. Additionally, the uncorrected turbine performance map model shows significant deviations in net power (25.6 kW or 3.17 %) and thermal efficiency (0.5 %) under a 10 % reduction in pump speed. This study highlights the importance of correcting turbine inlet parameters and addressing prediction deviations in conventional models. The Pham-corrected turbine model improves reliability, performance prediction, and design optimization, especially under fluctuating heat sources. With its potential to enhance system stability and efficiency, this methodology offers significant prospects for future CTPC applications, including waste heat recovery and renewable energy integration.

Robust Generalized Super‐Twisting Sliding Mode Control Design for Optimal Performance in Three‐Phase Grid‐Connected Photovoltaic Systems

ABSTRACTThis article introduces a third‐order super‐twisting sliding mode control (Gen‐STSMC) algorithm designed for the secure operation of a grid‐connected photovoltaic (PV) system. The improved method changes the traditional super‐twisting sliding mode control (STSMC) technique by incorporating both a linear term and a higher power term to reduce convergence time and chattering effects. This strategy generates the optimal control signals to regulate the output power of the PV system by compensating the state‐dependent uncertainties using the linear and growing term. The controller's stability analysis is conducted to demonstrate its rapid convergence compared to PI and conventional STSMC scheme. A comparative simulation study against PI and STSMC is conducted to showcase the controller's rapid convergence and robust performance in various grid‐connected PV system scenarios. Experimental tests are also performed to validate the controller's effectiveness in maintaining stable power generation in the presence of environmental fluctuations and DC link voltage variations.

Cylindrical near-field solar thermophotovoltaic system with multilayer absorber/emitter structures: Integrated solar radiation absorption and cooling energy consumption

Near-field radiative heat transfer enhances the intensity of the thermal radiation significantly through evanescent waves, while the combination of selective emitters can effectively improve the output power and system efficiency of solar thermophotovoltaic systems. By calculating the polariton dispersion relation between different layers and combining the energy transmission coefficient of the emitter at different layers, the mechanism by which the emitter enhances near-field radiative heat transfer was analyzed. Concurrently, a comprehensive consideration was given to the conversion of solar radiation to thermal energy, the transformation of thermal radiation into electrical energy, and the impact of the circulating water-cooling system on performance. The analysis indicates that with concentration ratio of > 70 and operating temperatures ranging from 900 K to 1200 K, the output power of near-field solar thermophotovoltaic system can achieve a range of 8905 W/m2 to 52875 W/m2, and the system efficiency can be stably maintained above 20 %.

Performance investigation of linear Fresnel concentrating photovoltaic systems with mini-channel heat sink using R141b/R245fa mixture enhanced by electric field

Reinforcement of heat transfer efficiency by electrohydrodynamics is a low-energy, high-efficiency technology. In this paper, we utilize the property of electrohydrodynamics to enhance heat transfer within a linear Fresnel concentrator photovoltaic (PV) system, with the anticipation of boosting the overall performance of the PV system in the process. The cooling fluid used in this work is R141b/R245fa mixture. In this paper, the output voltage, output current and inlet and outlet temperatures of the cooling fluid are tested in an outdoor environment. This paper analyzes the output power, system energy and environmental impact of a linear Fresnel PV system. The results show that the output power of the linear Fresnel PV system under the action of electric field (Applied voltage = 200 V) can reach a maximum of 11.45W and the surface average temperature of the solar cell can be maintained between 31 °C and 34 °C. When the applied voltage is 0 V, the average surface temperature of the solar cell fluctuates between 30 °C and 50 °C. Through the aforementioned study, it has been observed that the incorporation of the electric field effect can facilitate stable and efficient operation of the linear Fresnel photovoltaic system.

Improving Solar PV System Performance with DC-DC Converters with MPPT Technique in Real-Time Simulation

A Photo Voltaic (PV) system turns Solar Energy (SE) into electricity that needs efficient converter topology. One significant obstacle associated with solar systems is the fluctuation in energy production from solar PV modules due to meteorological factors like temperature and irradiance. The advancement of Maximum Power Point Tracking (MPPT) algorithms has enhanced the effectiveness of solar panels. Implementing an effective operational control approach may optimize the performance of PV panels, while utilizing proper semiconductor materials can maximize the efficiency of converters. This work introduces a MPPT method based on Perturb and Observe (P&O) algorithm. The PV system performs with optimal efficiency when it is near the IV curve, where it produces the highest amount of electricity due to its non-linear voltage-current characteristics. The power output is determined by the degree of irradiance and the temperature of the cells, resulting in the maximum achievable power output. The conditions vary according on the season, time of day, and environmental factors. Rapid variations in light can occur due to factors like clouds. Thus, it is imperative to precisely monitor the maximum power point (MPP) in all settings to optimize power generation. This study proposes a method to optimize the power output of a PV system by integrating MPPT with a DC-DC energy converter. This approach ensures that the PV generator operates at its maximum power level, irrespective of external factors such as sunshine intensity and temperature. MPPT algorithms exhibit variations in their implementation and performance, with a range of MPPT control algorithms being accessible. The P&O algorithms are widely used due to their ability to regulate the MPP of PV panels. The method is validated in real-time using Typhoon Hill Simulation.

Hybrid Energy (Thermoelectric Generator-Archimedes Screw Turbine) Study and Experiment as a Green Energy Generator Based on the Internet of Things (IoT)

The heat energy from the hot water source of Mount Sinabung can be used as a source of electrical energy before being channeled as a source of hot water baths. The hot water flow has a fairly high temperature and a flow rate that can be converted into a source of electricity generation using a Micro Hydro Power Plant (PLTMH) and Thermoelectric Generator (TEG). This data collection was simulated using a heat source designed in a reservoir and a cold water flow that is channeled into the PLTMH-TEG system space as a source of temperature delta. This paper aims to study the TEG series TEG1-199-1.4-0.5 and the Archimedes screw Turbine (PLTMH) as a Hybrid Generator (Green Energy). Data analysis was carried out to calculate the system power output, battery charging time, and efficiency of the TEG and PLTMH. Data analysis in this study applies the Internet of Things (IoT). Test data shows that the maximum output parameter of the PLTMH during testing, obtained a maximum voltage of 20.42 Vdc. The maximum current is 759.75 mA and the maximum water discharge is 2.31 m3/s. In the TEG system, the power generated by the TEG is 20.64 watts at a temperature difference of 70.5˚C. It is concluded that the higher the amount of discharge flowing into the Archimedes turbine system and the temperature difference absorbed by the TEG, the greater the power that will be generated and vice versa.

Optimisation of a converging-diverging nozzle for the wet-to-dry expansion of the siloxane MM

Wet-to-dry expansion within the nozzle guide vane of an ORC turbine has been proposed as a means to improve the power output of ORC systems for waste-heat recovery (<250 °C). However, given the rapid fluid acceleration in the stator, the phases can develop significant velocity and temperature disparity due to density difference and finite rate of interphase heat transfer. Since these factors can significantly affect the phase-change process, wet-to-dry nozzle design techniques must account for non-equilibrium effects. The first part of this paper aims to further verify a previously developed quasi-1D inviscid non-equilibrium nozzle design tool by comparing it to non-equilibrium CFD simulations, which, unlike the design model, account for lateral flow variations, viscous and turbulence effects, along with secondary momentum forces. Within the CFD model, the interphase mass, momentum, and energy exchange models have been updated using correlations better tailored to evaporating droplet flows and a corrected drag equation. Moreover, the definition of the vapour mass fraction has been modified, while a simplified droplet breakup model has been used to predict the droplet size. The results from the CFD simulations indicate that the outlet vapour mass fraction is approximately 10 to 15% lower than that predicted by the quasi-1D tool. However, the overall flow behaviour and phase-change pattern were in satisfactory agreement, justifying the use of the design tool for 1D optimisation. As such, the quasi-1D tool is coupled to a gradient-based optimiser to optimise the nozzle pressure profile and enhance evaporation of siloxane MM for expansions with an inlet pressure ranging from 450 to 650 kPa, and inlet vapour quality of 0.3. CFD simulations of the optimised geometries indicate an increase of 3.3 to 5.7% in the outlet vapour mass fraction, which was raised from 84.9, 87.7 and 90.5% to 88.2, 93.4 and 95.7% for 450, 550 and 650 kPa inlet pressures respectively. However, a more abrupt expansion in the optimised nozzles resulted in the development of a shock and led to deterioration in nozzle efficiency compared to the baseline nozzles. Finally, a CFD-based shape optimisation was conducted, which demonstrated that it may be difficult to further enhance the vapourisation rate. However, the optimised geometry did mitigate the effect of the oblique shock that appears in the diverging section of the nozzle, raising the expansion efficiency by around 3%.

Investigations of a micro-thermo-photovoltaic system fuelled with ammonia/hydrogen under extreme operational conditions

The present study is concerned with numerical investigations on the electrical power output, and NO emissions of an ammonia/hydrogen fuelled micro-thermo-photovoltaic (MTPV) system under extreme operational conditions. For this, three critical parameters are identified and examined. They include: (1) the inlet velocity, (2) the inlet equivalence ratio, and (3) the mole blending ratio of hydrogen. Increasing the inlet velocity markedly raises the electrical power output of the MTPV system. At lower inlet velocities, a higher background temperature helps reducing NO emissions (NO emissions at 2 m/s and 200K are 5.5% higher than at 350K), whereas at higher inlet velocities, lower background temperatures are more effective in reducing NO emissions (NO emissions at 12 m/s and 350K are 6% higher than at 200K). The electrical power output of the MTPV system is maximized, when the inlet equivalence ratio is set to 0.9, yielding a total power output of 15.2W. For optimizing energy efficiencies, an inlet equivalence ratio of 0.8 is preferred, with an energy efficiency of 6.3%. Blending ammonia with hydrogen significantly increases NO emissions (NO emissions at 200K with a hydrogen blend ratio of 0.5 are 26% higher than with the blending ratio of 0.1) and provides limited improvement in the energy output of the MTPV system (energy output at 200K with a hydrogen blend ratio of 0.5 is 10% higher than that with a blending ratio of 0.1, and the energy output at 300K with a hydrogen blend ratio of 0.5 is 7% higher than that at a blending ratio of 0.1).

A dual‐excitation inductive power transfer system with decoupled transformer for high misalignment tolerance

AbstractThis study delves into the design and optimization of a 4 kW dual‐excitation inductive power transfer system designed to accommodate large misalignments. The system utilizes a bipolar‐solenoid inductive coupled transformer as its coupling mechanism. Detailed simulation and analysis of this coupling mechanism are conducted. Additionally, the system employs a control strategy aimed at optimizing the coordination between output power and efficiency. This control strategy dynamically adjusts the power of the two transmitters, aiming to enhance system efficiency to the maximum extent possible under favourable coupling conditions. Moreover, it ensures that the system's output power remains at or above the rated value even when coupling conditions deteriorate. During the experiment, the system can optimize the output power and efficiency at the same time by using a suitable control strategy when the coils are misaligned. Meanwhile, the system can achieve a minimum efficiency of 81.5% even when the misalignment distance reaches 300 mm (60% of per transmitting coil size). The system and control strategy proposed in this paper can effectively overcome the deterioration of the output characteristics of the wireless power transfer system when misalignment occurs.

Experimental study of photovoltaic-thermoelectric systems using thermal interface materials and natural cooling

This study investigates a novel approach to enhancing photovoltaic-thermoelectric generator systems by utilizing advanced thermal interface materials in real-world conditions. The research compares two experimental systems under natural air cooling employing different thermal interface materials: one features a pyrolytic graphite sheet, while the other uses conventional thermal grease, alongside a photovoltaic-only system for reference. An Arduino-based data logger accurately monitored key environmental and operational parameters. At peak solar irradiation, the system with the pyrolytic graphite sheet achieved a surface photovoltaic temperature of 39.01 °C, generating 4.90 W and an overall efficiency of 17.95 %. In comparison, the system with thermal grease had a surface photovoltaic temperature of 48.88 °C, generating 4.67 W with an efficiency of 16.87 %, while the photovoltaic-only system reached a surface photovoltaic temperature of 55.37 °C, producing 4.54 W and an efficiency of 16.42 %. The experimental data’s accuracy and reliability were validated against simulations from previous work, revealing error margins between 1.20 % and 3.03 %. These findings underscore the potential of pyrolytic graphite sheets as effective thermal interface materials to significantly enhance the efficiency and power output of photovoltaic-thermoelectric generator systems, offering valuable insights for optimizing renewable energy technologies.

Development of a low frequency acoustic energy harvesting system by using piezoelectric transducers mounted on a metallic plate placed inside the centre of a hollow cylinder

Abstract In this work, we are demonstrating an innovative acoustic energy harvesting system that utilizes a hollow polyvinyl dichloride cylinder with a metallic circular plate placed at the centre inside with five piezoelectric transducers mounted on it. A speaker, used as a source of acoustic waves, is fixed at the top of the cylinder by using a clamp. The cylinder, upon exposure to incident acoustic waves, induces resonance within it, generating an amplified stationary wave. The generated vibration drives the metallic plate mounted with piezoelectric transducers, which serves as a diaphragm. As a result, a pressure difference is developed across it and due to piezoelectric effect on the transducers, electricity is produced. Experimental results show that the system output voltage and power depends on various factors, including the elastic properties of the metallic plate, resonance frequency, and the distance of separation between the acoustic source and the plate. At an incident sound pressure level of 75 dB, the experimental results show that, at an acoustic resonance frequency of 310 Hz, the system yielded a maximum peak to peak voltage of 1000 mV and output power of 0.612 μW. Incorporation of a voltage doubler circuit with the harvester increases its power to 57.6 μW. The simplicity in design and cost-effectiveness of the proposed acoustic energy harvesting system renders it to be a promising avenue for energy harvesting implementations.

Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data

Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&amp;O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&amp;O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios.

Free Recharging Individual Home Appliances Batteries Using Advanced Roof Top Solar Power System in India

India is having best weather system to apply the solar power system to individual home for people’s regular day to day working devices. Sunlight The local weather has a significant impact on photovoltaic systems, with dust being the most significant factor. Dust accumulation on the surface of photovoltaic (PV) panels prevents sunlight from reaching the cells, which ultimately lowers the system's power output. Depending on the location, PV integration strategy, and size of the PV power plant, regular cleaning is necessary to prevent dust-based power losses. This publication examines the effects of dust buildup on solar systems, including radiation loss and output power operation, and establishes the ideal cleaning interval. In the current sector recharging and battery maintenance is one of the most difficult in our day to day life. This paper explains the novel technology help to analyse the battery level. The systems find the level of battery as well as automatically recharge the device using solar power system. So for this devices can recharge automatically and stop recharging after completing the battery level is full. Communication is one of the most preferable and should not avoidable. This method help to recharge the mobile devices without socketing and plugin device for our regular power supply. The proposed ARTSPS helps to easy recharging is possible while we are going to apply this novel revolution. ARTSRS Solar panels are designed to work in all weather conditions. The only factor that can affect a solar installation is snow accumulation, which can reduce production due to shading.

Real-time fully digital control scheme for pulse coherent beam combining.

A fully digital control scheme for non-polarization-maintaining (non-PM) nanosecond pulse coherent beam combining (CBC) is proposed, where digital locking of optical coherence by single-detector electronic-frequency tagging (LOCSET) for active phase control and stochastic parallel gradient descent (SPGD) for active polarization control is proposed. The fully digital control scheme is integrated on a real-time field-programmable gate array (FPGA) empowered hardware platform and then experimentally validated in a four-channel all-fiber non-fully polarization-maintaining nanosecond pulse CBC system. Consequently, the system can be fully locked in 9.5 ms, and the polarization extinction ratio (PER) of the combined beam is 21.5 dB with a CBC efficiency of 95.3%. The fully digital control scheme integrates the advantages of digital LOCSET and multi-channel active polarization control, enhancing the channel scalability and the potential output power of the non-PM pulse CBC system.

A study of automatic output power control methods for stand-alone photovoltaic power generation systems

Abstract The conventional power generation system’s output power automatic control structure is generally unidirectional, and the control coverage is small, leading to the increase in the random error obtained in the final test. Therefore, the design and research of this article are proposed. According to the current control demand, the generation system shall be stabilized first, and the power compensation shall be calculated. The multi-step mode shall be adopted to improve the control coverage and design the multi-step automatic control structure. According to this, it is constructed, and the automatic control processing is realized using jump balance. The test results show that compared with the traditional DFIG-DRWT system power output automatic control method and the traditional three-phase asynchronous generation system power output automatic control method, the random error finally obtained by the designed independent photovoltaic power station output power automatic control method is relatively effective. This indicates that the application effect and coverage of the designed power automatic control method are high, that controllability has been significantly improved, and that control flexibility has also been strengthened.

Photovoltaic Maximum Power Point Tracking Technology Based on Power Prediction Algorithm Combined with Variable Step Length Disturbance Observation Method

Under partial shading conditions, the system operating sequence of photovoltaic cells does not fall on a single characteristic curve, and the traditional maximum power point tracking (MPPT) control algorithm is prone to causing the system output power to oscillate around the maximum power point. In the case of multiple peaks, the traditional MPPT algorithm is prone to being trapped in a local optimum solution. This paper proposes an MPPT control algorithm based on the WOA-RF prediction algorithm combined with a variable step disturbance observation method. By establishing a photovoltaic system simulation model, the improved algorithm is verified through simulation. The improved MPPT control algorithm can adaptively adjust the step size under different conditions to ensure that the system can quickly and accurately track the maximum power point. At the same time, by optimizing the algorithm logic and parameter settings, it can effectively avoid problems such as local optimum solutions and instability in the system, and improve the system convergence speed and tracking accuracy.

Investigation on battery-less voltage of piezoelectric V-shape cantilever beam energy harvester using FEA method for pacemaker

AbstractThis paper presents an investigation on a battery-less voltage of Piezoelectric (PZT) V-shape cantilever beam Energy Harvester (EH) using human body vibration. The frequency ranges are walking (0–5 Hz), running (6–10 Hz) and motions (11–15 Hz) for human movement. Pacemaker devices typically require a lower resonant frequency with higher voltage which is powered by batteries. The battery has a limited duration during its working process and the battery is difficult to replace in the human body. To address the aforementioned issue, a V-shape cantilever beam EH has been developed as a solution to overcome these limitations. The cantilever beam was designed in COMSOL Multiphysics software 5.5 version using the Finite Element Analysis (FEA) method for experimental investigations followed by three categories of frequency ranges of the human body. The simulation results showed that the generated battery-less higher voltage was 269 mV (AC) at the resonant frequency of 14.37 Hz in the motion range of 11–15 Hz. Later, an Ultra Low Power (ULP) electronic circuits will be designed and simulated in the LTSPICE software to convert and boost-up from 269 mV (AC) to DC voltage attained. The estimated output power of the energy harvester system can be powered up (4.7 µW) for modern pacemaker applications.