Output Power Research Articles

Cr3+-doped Na2CaSn2Ge3O12 garnet showing broadband near-infrared emission at 820 nm with zero thermal quenching and near-unity quantum yield

The continually advancing near-infrared (NIR) phosphor-converted light-emitting diode (pc-LED) hold pivotal significance in optical imaging and NIR spectroscopy. Cr3+-doped garnet broadband NIR phosphors have attracted considerable attention owing to their high optical performance enabled by the rigid structure. Nonetheless, the emission wavelength is severely restricted, and longer wavelength emissions usually result in inferior quantum efficiency and poor luminescence stability. In this work, the concurrent reconstruction of dodecahedral and octahedral sites by introducing [Na+-Sn4+]C unit in the framework of Na2CaSn2Ge3O12 garnet contributes to the Cr3+ broadband NIR emission spanning 700–1200 nm, with a peak at 820 nm. Utilizing a charge compensation strategy via Ta5+ codoping largely boosts the internal quantum efficiency (IQE) of Na2CaSn2Ge3O12:Cr3+ from 93.06 % up to 95.04 %. Trap-mediated energy compensation is proposed to achieve a record-breaking zero-thermal-quenching behavior (99.39 %@423 K) of Cr3+-doped garnet with such a longer wavelength emission. The encapsulated prototype broadband NIR pc–LED gives rise to an overwhelming NIR output power of 590.9 mW@1300 mA and a photoconversion efficiency (PCE) of 32.62 % at 30 mA, rendering exceptional performance in night vision, detection, and NIR imaging applications.

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.

Thermodynamic and economic analysis of ocean thermal energy conversion system using zeotropic mixtures

Zeotropic mixtures offer a promising strategy for enhancing the thermodynamic efficiency and economic feasibility of ocean thermal energy conversion (OTEC) systems. This study investigates two binary mixtures containing R32: R32/R125 and R32/R134a. Through the development of comprehensive thermodynamic and economic models, the research examines the impact of mass fraction and evaporation temperature on the efficiency and cost-effectiveness of the OTEC system. The results indicate that, especially at high evaporation temperatures, the R32/R134a mixture—characterized by significant temperature glide—substantially increases the total energy production capacity of the OTEC system. Compared to pure R32, the OTEC with R32/R134a (mass fraction of R32 is 0.55) has a net output power increase of 9.87kW and a reduction in LCOE of about 61.4 %. In addition, the advantages of R32/R125 mixtures over pure working fluids are not significant due to the small glide temperature. Ultimately, this investigation enhances the overall performance of OTEC systems, thereby supporting sustainable energy solutions for island communities.

Performance evaluation of different photovoltaic array configurations under partial shading

In Partial Shading Conditions (PSCs), Photo Voltaic (PV) systems often experience notable output power and efficiency reductions due to weather variations. This study is dedicated to determining the most effective PV array configuration under partial shading. Various configurations, including Series Parallel (SP), Total Cross Tied (TCT), Bridge Linked (BL), Honey Comb (HC), Double Tied (DT), and hybrid connections, are simulated and evaluated under PSCs. Nine shading patterns, such as vertical, horizontal, centre-wise, upper triangular, cross-wise, expansive, arbitrary, L-shaped, and diagonal, are examined using a 6 × 6 array of PV Configuration. Performance analysis is based on parameters such as open circuit voltage (Voc), short circuit current (Isc), Global Maximum Power Point (GMPP), maximum voltage (Vm), maximum current (Im), Fill Factor (FF), Mismatch Loss (ML)/Power Loss (PL), and efficiency (η). Additionally, hybrid configurations like Alternate- Total Cross Tied -Bridge Linked (ALT-TCT-BL), Alternate -Total Cross Tied -Double Tied (ALT-TCT-DT), and Alternate -Total Cross Tied -Triple Tied (ALT-TCT-TT) are investigated and compared with existing configurations. Hybrid configurations with fewer cross-ties are recommended to simplify circuit complexity. MATLAB/Simulink software is employed for simulation, using a Sunpower SPR-E18-295-COM panel. Comparative analysis confirms that hybrid configurations exhibit higher efficiency for various shading patterns than existing configurations, equal or at par with the TCT connection. For eight connections and nine shading scenarios along with zero (no) shading conditions, eighty distinct combinations have been analysed, and on comparative analysis with contemporary work, the optimal output is obtained from the connections considered under PSCs. For all the shading patterns considered, the proposed work fetches maximum efficiency compared to the contemporary work, and it ranges between 13.61 % and 17.84 % for different shading patterns. The maximum value of efficiencies for horizontal, vertical, diagonal, centre-wise, expansive, arbitrary, upper triangular and L-shaped shading patterns is 13.77 %, 17.24 %, 17.84 %, 15.66 %, 13.61 %, 17.21 %,17.35 % and 15.11 % respectively. The hybrid configuration achieves higher efficiency than current methods for all shading patterns and configurations, with improvements in efficiency ranging from 2.1 % to 42.22 % across all cases.

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

Load forecasting based on multi-core learning Support Vector Machine (SVM)

Abstract The development of smart grids requires enhanced data integration, robust risk assessment, and dynamic response optimization. In this paper, a multi-core learning Support Vector Machine (SVM) model is presented to improve the accuracy and efficiency of load and photovoltaic output forecasting. The model leverages kernel function optimization and parallel computing frameworks to handle large-scale data efficiently. Additionally, a comprehensive risk assessment system is developed to quantify risks such as overvoltage, undervoltage, line overload, and load loss in distribution networks. An adaptive genetic algorithm-based risk control model is also proposed, optimized in two stages—day-ahead and intra-day—to achieve minimal comprehensive risk through real-time adjustments in distributed power output and electric vehicle charging strategies. Furthermore, an integrated virtual synchronous control online verification method for source-network-load-storage is introduced, enhancing system response speed and control accuracy. These innovations collectively provide a solid theoretical foundation and technical support for the efficient and safe operation of smart grids, addressing the increasing demands of modern energy systems.

Analysing a grid-forming storage hub for an offshore platform cluster supplied by wind energy

Abstract This paper performs an analysis of the electrification of an of-the-grid offshore oil and gas cluster of six platforms by means of a wind farm combined with a hydrogen energy hub. The latter, whose model is made publicly available, contains a grid-forming battery system and grid-following electrolysis and fuel cells units. State-of-the-art aerodynamic simulations are used for representing the wind farm power output, which encompasses wake losses and correlated wind fuctuations between turbines arising from farm-scale turbulence. One of the offshore platforms is represented by a detailed publicly available model named LEOGO. The other five platforms are represented as aggregated mixes of constant power and constant impedance loads. The computer simulations presented in the paper provide valuable insights into furthering the design and optimization of the hub concept. These insights include, among others, sizing of batteries depending on maximum ramp rates of fuel cells and electrolysers, identifying and mitigating instabilities induced by interactions among power electronic converters, and evaluating the consequences for the converters when those contribute with voltage support at the hub.

Modelling of wind and solar power output uncertainty in power systems based on historical data: A characterisation through deterministic parameters

Modelling of wind and solar power output uncertainty in power systems based on historical data: A characterisation through deterministic parameters

Controller influence on the fatigue of a floating wind turbine and load case impact assessment

Abstract The assessment of fatigue in Floating Offshore Wind Turbines (FOWT) is a complex and widely debated topic within the industry. The dynamic behaviour of FOWT is heavily influenced by sea and wind conditions, which can significantly challenge the achievement of structural integrity and performance objectives. Additionally, the interaction of blade aerodynamics and control strategies further complicates this assessment. This study evaluates the impact of different control strategies and blade formulations on both the expected real performance and the estimated fatigue of FOWT. While control strategies can influence both the expected real performance and fatigue estimations, the blade formulation primarily affects the accuracy of performance estimations without altering the actual expected performance. However, the blade formulation can significantly modify the fatigue estimation of the FOWT. Damage Equivalent Loads (DEL) calculation is a commonly used method for assessing fatigue. However, while individual DEL calculations are useful, they do not show the contribution of each load case to the overall fatigue, since they do not consider the probability of occurrence. Therefore, this paper proposes a new method of normalising DEL, allowing for comparison of loading cases and identifying operational areas that pose greater fatigue risks. The findings underscore the substantial impact that different control strategies and blade designs can have on fatigue and overall performance. Essentially, there is a trade-off between maximising power output and minimising fatigue damage.

A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing

Abstract In recent years, an immense amount of distributed photovoltaics are integrated into low-voltage distribution network, producing a substantial volume of operational data. The centralized cloud data center cannot process massive amounts of data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low-voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enables local data processing to improve the forecasting service’s real-time reliability. In this regard, this paper proposes a distributed photovoltaic short-term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which removes redundant parts of the model, resulting in a compact and efficient forecasting model. By validating real-world datasets, the results demonstrate that the model presented in this article has a smaller size and higher forecasting accuracy than other state-of-the-art forecasting models.

Extreme weather impact on carbon-neutral power system operation schemes: A case study of 2060 Sichuan Province

The impact of increasingly frequent and intense climate change events on power supply and consumption has become a concern for many countries. This study evaluated the impact of extreme weather on the power system economy and security by conducting an empirical case study of the Sichuan power network to address uncertainty attributed to different extreme weather events under carbon neutrality scenarios. First, our paper proposed a CNPSSO model of four seasonal typical days by jointly considering the regional hourly disparities of load curves and renewable power output curves. Then, the model creatively introduced the effects of extreme weather on the power supply and demand curves based on various scenario analyses. The most cost-effective output strategy using various generation technologies was provided based on optimization models. Finally, Sichuan Province, a hydropower-dominated province was selected as an empirical case study. The results indicate that extreme weather significantly affects the hourly operational and dispatching schemes of Sichuan's power system. Compared to the "Normal weather scenario”, hydropower's share plummets from 83.3 % to 46.1 %, while pumped storage surges 7.1 billion kWh in summer in extremely hot and dry weather. Imported electricity rises by 24.4 %, 30.5 % and 24.8 % in dry scenario, extremely hot and dry scenario, and extremely cold scenario, respectively. Furthermore, extreme weather increases carbon emissions (mainly from thermal power and imported electricity) and costs. Specifically, when juxtaposed against normal weather, extreme drought, extreme heat drought and extremely cold lead to respective increases in carbon emissions by 32.9 %, 33.5 %, and 12.3 %. Regarding the levelized cost of electricity (LCOE), "Extremely dry scenario”, "Extremely hot and dry scenario”, and "Extremely cold scenario” drive up the LCOE by 7.2 %, 9.2 %, and 4.2 %, respectively.

An active two-stage class-J power amplifier design for smart grid’s 5G wireless networks

<span>The wireless communication networks in the smart grid’s advanced metering infrastructure (AMI) applications need 5G technology to support large data transmission efficiently. As the 5G wireless communication network’s overall bandwidth (BW) and efficiency depend on its power amplifier (PA), in this work, a two-stage class-J power amplifier’s design methodology that operates at 3.5 GHz centre frequency by utilizing the CGH40010F model gallium nitride (GaN) transistor is presented. The proposed design methodology involves proper designing of input, output, and interstage matching networks to achieve class-J operation with improved power gain over desired BW using the advanced design system (ADS) electronic design automation (EDA) tool and estimating its integration feasibility through active element-based design approach using the Mentor Graphics EDA tool. The proposed PA provides 54% drain efficiency (D.E), 53% power added efficiency (PAE) with a small signal gain of 27 dB at 3.5 GHz and 41 dBm power output with 21 dB of improved power gain across a BW of around 400 MHz using 28 V power supply into 50 Ω load. By replacing the two-stage PA's passive elements with active elements, its layout size is estimated to be (15.5×29.2) μm2 . The results of the proposed PA exhibit its integration feasibility and suitability for the smart grid’s 5G wireless networks.</span>

Massage prior to exercise delays the onset of the physical working capacity at the fatigue threshold (PWCFT).

Functional massage (FM) is a soft tissue technique that incorporates non-end range joint movement with tissue compression to treat musculotendinous pain and dysfunction. FM has been associated with promoting neural excitability and neuromuscular efficiency. In clinical practice, FM may be used at the beginning of a treatment session to prepare fibers of the target muscle and surrounding soft tissues for functional activity. The Physical Working Capacity at the Fatigue Threshold (PWC<inf>FT</inf>) uses surface electromyography (EMG) to demarcate between non-fatiguing and fatiguing exercise. To our knowledge, no studies have examined the efficacy of FM when performed prior to an exercise session. The purpose of this study was to determine whether pre-performance FM delays the onset of neuromuscular fatigue. We hypothesized that FM would increase maximal power output as well as PWC<inf>FT</inf> compared to the control condition. Thirteen healthy college-aged men (mean±SEM: age, 24.1±0.5 years; weight, 83.0±3.2 kg; and height, 1.80±0.02 m) ranging from 22 to 28 years-old volunteered for the present study. On two occasions separated by at least 7 days, participants visited the laboratory to perform single-leg knee-extensor ergometer exercise after either receiving 7 min of FM (experimental condition) or no FM (control condition). The results indicated that the onset of neuromuscular fatigue was significantly delayed for the absolute and relative PWC<inf>FT</inf> (~80%) values compared to the control condition. There was no significant mean difference between FM and control for maximal power output. The findings of the present study indicated that FM was effective in delaying neuromuscular fatigue as assessed by PWC<inf>FT</inf>. These findings may be applicable to both athletic and clinical settings as FM prior to exercise may help to prime a target muscle before training.

Simulation of unsteady ice accretion on horizontal axis wind turbine blade sections in turbulent wind shear condition

This study presents a comprehensive simulation approach to quantify power losses in horizontal axis wind turbines under environmental icing conditions. It investigates how wind shear and turbulence affect a 2.5 MW wind turbine's performance, particularly under ice accretion. Turbulence intensity, ranging from 1% to 20%, impacts the relative flow fluctuations and angle of attack on the blade sections, influencing the aerodynamic penalty ratio. The incoming wind speed and the flow angle at various blade sections were determined using the unsteady blade element momentum method, considering vortex induction effects and Prandtl and Glauert corrections. For ice accretion analysis, a fully unsteady simulation of computational grid motion due to ice accretion was performed, along with the solution of the multiphase flow of water dispersed particles in cold air, derived from the psychrometric chart. The findings highlight the significant impact of the incoming turbulent wind fluctuations on the dispersion of the ice shape formed at sections corresponding to their radial position on the blade according to the momentary angle of attack fluctuations. The formation of ice profiles along the blade has led to a subsequent degradation in the aerodynamic efficiency of the blade sections, which is directly proportional to the escalation in turbulence intensity. This phenomenon leads to a continual reduction in the power output of the wind turbine. This research provides valuable insights into the performance of wind turbines under icing conditions in real wind fluctuations.

A minute-resolution downscaling algorithm for high-resolution global irradiance time series

Numerous studies have demonstrated the significant impact of the resolution of solar irradiation data on the outcomes of hourly production models. Accurate integration of photovoltaic (PV) systems sometimes demands a high-resolution global horizontal irradiance (GHI) time series to capture the rapid fluctuations in PV power output induced by swift irradiance changes. Most of the available databases provide data at hourly resolution, leading to a lack of accuracy in PV simulations. Those existing hourly averages of global horizontal irradiance in open sources fail to represent this volatility adequately, especially when PV systems are coupled with fast ramp rate technologies. In the present work, an easy-to-use algorithm is implemented to synthesize high-resolution GHI time series from hourly averaged and clear sky irradiance datasets. By employing Linear interpolation, a technique that helps to achieve the desired time resolution and afterward computing critical factors, the algorithm identifies periods characterized by short-term weather phenomena, thus creating a high-resolution time series that accurately represents these dynamics. Avoiding the probabilistic components and machine learning techniques conserves computational power and reduces calculation time, but this comes at the cost of reduced fidelity in reproducing the results. Improving accuracy in PV simulations is not always directly related to reproducing real phenomena, but enhancing the amount of information contained in the data is sufficient. This study’s approach enhances user-friendliness and facilitates seamless integration into existing energy modeling frameworks, aiming for representation with sub-hourly time steps. The algorithm’s effectiveness is demonstrated by applying the model to hourly averaged data to revert them to a one-minute time step, and finally comparing the synthetically produced one-minute GHI data to the original measured data. The comparative analysis between synthesized and measured data demonstrated a strong agreement, with normalized mean bias error (MBE) values ranging between 1.8% and 9.6% and normalized root mean square error (NRMSE) values between 2.7% and 16.1%, depending on weather conditions. Additionally, the coefficient of determination (R2) consistently remained above 0.64. Successful algorithm validation makes our algorithm suitable for use in meteorological datasets and locations, with similar climatic characteristics.

Energy, Exergy and Exergo-economic analyses of supercritical CO2 cycles for the exploitation of a geothermal resource in the Italian water dominant Amiata site

Alternative powerplant layouts to commonly applied flash technology is investigated for the water dominant geothermal location of Amiata Mountain, Italy. The proposed solution avoids flashing the brine stream by a borehole pump capable of maintaining pressurized conditions, thus reducing the amount of non-condensable gases released when reaching the ground level. Therefore, the need of a gas treatment section is completely avoided. The heat recovered from the hot brine at ground level is transferred to different supercritical CO2 cycle configurations, equipped with high efficiency Printed Circuit Heat Exchangers (PCHE). The optimal 45.5% calculated exergy efficiency was achieved for the recuperative cycle with intercooling and reheat, which however didn’t show the optimal electricity costs (8.092 c€/kWh) because of the more complex heat exchangers network. The lowest cost of electricity (7.4 c€/kWh) was found for the recuperative cycle configuration. These costs are in line with the current levels of geothermal binary cycles, but almost doubled compared to the current single flash +ORC solution, due to a 40-50% reduction of power output with sCO2 cycles. On the other hand, sCO2 binary cycles could guarantee an almost zero environmental impact, which is the main reason for social concerns related to the current flash powerplants.

Investigation on the wave focusing effects and energy capture of oscillating water column array

The study of OWC array hydrodynamic performance has been a focus of attention. However, due to the complex flow and strong nonlinearity generated during its interaction with waves, the main challenge and key focus in current research is how to simulate this process both quickly and accurately. A fast-numerical model of the OWC is developed using the open-source software package OpenFOAM. The numerical model replaces the orifice plate with Forchheimer-flow and the vertical walls with an immersed boundary. Compared to the traditional numerical model, the fast-numerical model can speed up the simulation by at least 10 times. The impact of row spacing and column spacing on the array gain is systematically investigated through the simulations of two-OWCs array, four-OWCs lattice array, and five-OWCs interlaced array. The results show that a two-OWCs array can generate a wave focusing area at its rear, and the location of this area changes with row spacings. For the four-OWCs lattice array, the row spacing increasing causes the wave focusing area to move closer to the array. The proximity of the wave focusing area enhances the power output of the second row of OWCs. After increasing the row and column spacing, the reflection of waves from the second row of OWCs to the first row is decreased, resulting in a reduction in the power output of the first row of OWCs. In contrast to the four-OWC lattice array, the five-OWCs interlaced array demonstrates a lower array gain in the first row of OWCs, with the second row displaying a higher array gain.

Advanced piezoelectric fluid energy harvesters by monolithic fluid–structure–piezoelectric coupling: A full-scale finite element model

A full-scale finite element model is presented for monolithic fluid–structure interaction (FSI) simulations of thin-walled piezoelectric fluid energy harvesters (PFEHs). Unlike widely used beam/plate-based models, our model employs a solid finite element discretization to precisely represent the complex PFEH designs involving microstructured transducers and non-uniform cantilevers. These features, plus the local FSI effects, are often ignored by simplified models. We applied the Galerkin method to formulate the weak form of the mixed equation system, integrating the flow dynamics, the geometrically nonlinear cantilever, the piezoelectric components, the electrode, and the output circuit within a closed-circuit electro-mechanical coupled system. The coupling of the multiple domains is achieved through boundary-fitted discretization within a monolithic scheme, using shifted-Crank–Nicolson temporal integration. This work explored implementing piezoelectric FSI systems within the FEniCS-based TurtleFSI library, and experimented techniques such as employing penalty functions for achieving electrode components with uniform electric potentials. We investigated various advanced PFEH features, including the baseplate design, the arrangement and microstructure of the piezoelectric components, and their influence on the system's dynamic and energy output behavior. The results confirmed the model's key advantages: full-scale modeling allows the integration of complex base structures and transducer microstructures in PFEH design. Combined with monolithic FSI coupling, it offers greater versatility, supporting a wider range of fluid environments and configurations in both wind and hydropower harvesting. Additionally, the modeling strategy can be intended not only to enhance power output, but also to minimize material usage, reduce mechanical fatigue, and extend the operational lifespan of PFEH systems.

Soft Switching Technique in a Modified SEPIC Converter with MPPT using Cuckoo Search Algorithm

: MPPT refers to the process of continuously tracking and adjusting the operating point of a photovoltaic (PV) system to maximize the power output from the solar panels. The operating point at which a PV system produces the most power under a specific set of environmental circumstances is known as the maximum power point (MPP). The Maximum power point tracking (MPPT) technique is used to overcome the challenges of PV arrays with variable irradiance levels, which collects the greatest power from the PV array. Standalone PV systems, as well as gridconnected systems, can benefit from a DC-DC converter with MPPT. This study compares the standard perturbation and observation (P&amp;O) technique with the soft-switching method used in the SEPIC converter utilizing the cuckoo search (CS) algorithm using MATLAB/Simulink. Reviewing the simulation results and assessing the SEPIC converter's performance. Background: SEPIC soft-switching converter, the soft-switching technique is applied to the SEPIC converter topology. It typically uses additional components, such as inductor and capacitors, to enable soft-switching operation. These additional components help to control the voltage and current waveforms across the main switching devices, reducing switching losses. Methods: The proposed method uses a soft switching technique to reduce switching losses and to improve efficiency. Soft switching is used in SEPIC (Single Ended Primary Inductor Converter) converters, a type of DC-DC converter, to enhance their performance. The projected solution further addresses the maximum power point tracking (MPPT) issue in PV systems using the Cuckoo Search (CS) method. Results: Soft switching technique is implemented in SEPIC converters to reduce these switching losses. In this system, Zero Voltage Switching (ZVS) involves turning on the switch when the voltage across it is zero. This allows the current to flow through the switch without creating any significant switching losses. CS algorithm can track the MPP quickly and accurately, it exhibits less prone to oscillations, easily monitor the MPP under a variety of weather circumstances. However, it exhibits negligible oscillation in a steady state, which results in significant power savings. Conclusion: The modified SEPIC converter with a soft-switching MPPT cuckoo algorithm is used with a solar-powered system. A prototype comprising a solar panel, a SEPIC converter, a driver, and a controller circuit was created. A soft-switching SEPIC converter with an MPPT cuckoo algorithm for the PV system is implemented, as are the converter operation, converter analysis, and theoretical analysis, and a controller circuit is analysed. The PV system, MPPT controller with tracking algorithm, and PMSM load were used in the system simulation. A 60-watt SEPIC converter prototype is built, and the outcomes are also tested experimentally.

Theoretical analysis of organic Rankine cycle for maximum power generation in optimization operation conditions

The global critical issue in energy scarcity should be appropriately solved to realize a sustainable society. Effective use of Rankine cycle is one possible way since it provides most of worldwide electricity production. In this paper, theoretical analysis model of organic working fluids R717, R134a, R1234yf, R290, R245fa and R1233zd in Rankine cycle for maximum power generation in optimization operation using low-temperature heat sources are proposed and studied for development next generation green and zero-carbon energy generation system to promote the race to zero. Results show that temperatures of warm and cold water at inlet, mass flow rate of the warm water and performance of the evaporator play a key role to obtain the theoretical optimization operation conditions for maximum power generation. In the case of same initial conditions of temperatures of warm water (85 Celsius degree) and cold water (15 Celsius degree) at inlet, mass flow rate of the warm water (10kg/s) and performance of the evaporator (100kW/K), R717 has the best performance in terms of the maximum power output 56.0kW with thermal efficiency of 8.6%, and the next is the R1233zd (54.4kW, 8.3%), R245fa (54.0kW, 8.2%), R134a (52.8kW, 7.9%), R290 (52.7kW, 7.9%), and R1234yf (51.7kW, 7.7%). Here, it should be noticed that other optimization conditions are almost the same (mass flow rate of the cold water 9.1∼9.2kg/s; performance of the condenser 91∼92kW/K) to get their maximum power output of ORC. In addition, it also known that low-GWP R1233zd (GWP: 1) can deserve the best option to replace R245fa (GWP: 950) and R1234yf (GWP: 4) also can replace r134a (GWP: 1430) since their optimization operation conditions are almost same.