Optimal Power Research Articles

Multi-objective performance analysis of different SCO2 Brayton cycles on hypersonic vehicles

To provide the continuous and stable power supply of hypersonic vehicles, this study investigates four candidate cycles on the basis of three critical parameters. Specifically, the effects of cycle pressure ratio (PR), recompression split ratio (x), and cycle intermediate pressure ratio (RPR) on the regenerative, recompression, precompression, and partial cooling supercritical carbon dioxide (SCO2) Brayton cycles (BCs) are emphasized. The analysis is carried on by using an established thermodynamic simulation platform. Using PR as the working condition parameter, the influence laws of x and RPR on each cycle at common working conditions are given. The Non-dominated Solution Genetic Algorithm-II (NSGA-II) performs multi-objective optimization for different cycle types. The performance of these cycles is then compared under optimal conditions. The results show that the regenerative SCO2 BC could maintain relatively high output power while keep the structure simple, compared with other three cycles. The optimum power generation in the study range and the common operating conditions of regenerative SCO2 BC is 203.49 kW and 154.03 kW, respectively. Therefore, the regenerative SCO2 BC preferably satisfies the requirements of the hypersonic vehicles.

Microwave irradiation-induced yield enhancement of coconut shell biomass-derived graphene-like material

This paper reports a new strategy (rapid and selective microwave irradiation) to achieve an improved yield of graphene-like material derived from coconut shell biomass. The influence of various microwave irradiation (MWI) powers (80, 240, and 400 W) treatment on the crystalline structures, morphology, and electrochemical performance of the produced samples was determined and compared with the virgin-untreated specimen. The obtained samples were analyzed using varied analytical techniques. The FESEM images of the irradiated samples revealed the existence of graphene-like morphologies accompanied by some thin and transparent sheets. The sample at 80 W displayed the best quality with the highest yield, improving the carbon content, reducing the oxygen functional groups, and increasing the BET-specific surface area by as much as 1238.48 m2/g. The electrochemical properties of the sample treated at 80 W (optimum MWI power treatment) exhibited rectangular curves against scanning speeds, indicating an ideal capacitive behaviour called electric double-layer capacitance (EDLC). It is asserted that the proposed systematic and eco-friendly approach in obtaining the high-performance graphene-like material at low cost may open up sundry opportunities for practical applications, leading towards sustainable development.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

The Optimal Control Method of Test Power Supply for DC Distribution Network

Background:: The test power supply is one of the key devices in the DC distribution network, which is necessary to supply power with high quality for testing other devices' performance. The double active bridge (DAB) converter is a common circuit topology for test power supply, which has the advantages of high-frequency electrical isolation, bi-directional power flow, and high power density. For the converter, control methods, such as single-phase-shift (SPS) control, and single-current stress or single-return power optimization under extended-phase-shift (EPS) control, have their limitations, which influences efficiency. Objective:: This paper aims to propose a dual-objective optimal control method, which can effectively improve efficiency of the test power supply. Methods:: This paper addresses the limitations of SPS control, and single-current stress or singlereturn power optimization under EPS control of the DAB converter, and proposes a dual-objective optimal control method based on the idea of using objective planning in the full power range under the condition of satisfying soft switching, which effectively improves efficiency of the test power supply. Results:: With the proposed dual-objective optimal control method, the converter achieves a smaller current stress similar to that with the single-current stress optimal control, and the return power is also reduced by 29.51%. Efficiency of the test power supply reaches 86.9%, which is better than 82.5% with SPS control and 85.3% with single-current stress optimization under EPS control. The experimental results fully verify the effectiveness of the proposed control method. Conclusion:: A dual-objective optimal control method is proposed. By using the presented method, current stress and return power are both optimally designed, so the efficiency of the test power supply can be effectively improved.

Key technologies of end-side computing power network based on multi-granularity and multi-level end-side computing power scheduling

With the development of modern society, business organizations have higher and higher requirements for the efficiency of cloud computing services. In order to improve the comprehensive computing capability of cloud computing network, it is very important to optimize its end-side computing power. This research takes the Hadoop platform as the computing end-side cloud computing network structure as the research object, and designs a Hadoop end-side multi-granularity and multi-level multi-level network that integrates the Graphics processing unit (GPU) and the information transfer interface (Multi Point Interface, MPI). Hierarchical computing power optimization scheduling model and improved microservice deployment s11trategy that integrates multi-level resources. The performance verification experiment results show that the mean value of all node balance ratios of the original strategy and the improved strategy on computing resource-oriented, memory resource-oriented, and disk resource-oriented microservices are 0.13 and 0.12, 0.21 and 0.17, and 0.22 and 0.19, respectively. The value of the service instance cost in the scheme using the critical path optimization scheduling strategy is always at a low level, while the instance cost value of the native strategy is significantly higher than the former. It can be seen that the end-side computing power optimization scheduling model designed in this study can indeed play a role in improving the computing performance of the end-side computing power network.

Power equalization and optimization of photovoltaic module based on forward-flyback converter

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes.

Formation of a potential barrier by a ZnSXSe1−X electron-blocking layer to improve the efficiency of CdSe0.3S0.7/CdSe quantum dot sensitized solar cells

We designed and fabricated multilayer quantum dots sensitized solar cells (QDSCs) with TiO2 NPs/CdSe0.3S0.7/CdSe/ZnSXSe1−X, X = S/S + Se = 0, 0.7, 0.8, 0.9, and 1 photoelectrodes to achieve optimum power conversion efficiency (PCE). In the corresponding structure, incoming light is absorbed by the CdSe0.3S0.7 and CdSe quantum dots (QDs), while the ZnSXSe1−X QDs act as an electron blocking layer or passivation layer, forming an energy barrier that restricts electron mobility from the light absorbers to the polysulfide electrolyte. The beneficial effect of the alloyed ZnSXSe1−X layer is the adjustability of its nanoparticle size/bandgap energy proportional to variations in the concentration of anionic precursors X, according to optical results. The PCE value of the reference cell with the TiO2 NPs/CdSe0.3S0.7/CdSe photoanode is 4.15 %. Following the coating of ZnSe, ZnS0.7Se0.3, ZnS0.8Se0.2, ZnS0.9Se0.1, and ZnS QDs via two successive ionic layer adsorption and reaction (SILAR) cycles (2Sc) on the CdSe0.3S0.7/CdSe co-sensitized photoelectrode increasing stability and improving the PCE value to 4.9 %, 5.40 %, 5.70, 5.25 %, and 5.05 %, respectively. Next, by modifying the thickness of the champion cell's passivation layer, the QDSCs with TiO2 NPs/CdSe0.3S0.7/CdSe/ZnS0.8Se0.2 (3Sc) photoelectrode exhibit maximum photovoltaic performance parameters of short circuit current density (JSC) = 26.25 mA/cm2, open circuit voltage (VOC) = 560 mV, fill factor (FF) = 0.42 % and PCE = 6.15 %. This improvement in performance is the result of decreased internal energy loss and recombination of charge carriers at the interface between the electrolyte and photoelectrode, which increases electron collection efficiency and electron injection efficiency in the visible region according to the external and internal quantum efficiency curves.

Controlling chaos using edge computing hardware

Machine learning provides a data-driven approach for creating a digital twin of a system – a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often, the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection. Here, we show that a nonlinear controller based on next-generation reservoir computing can tackle a difficult control problem: controlling a chaotic system to an arbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devices. Furthermore, the model only requires 25.0 ±7.0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 7.0$$\\end{document} nJ per evaluation, well below other algorithms, even without systematic power optimization. Our work represents the first step in deploying efficient machine learning algorithms to the computing “edge.”

Municipal sewage sludge dewatering performance enhancement by ultrasonic cavitation and advanced oxidation: A case study.

The number of published literature on the effect of ultrasonic cavitation and advanced oxidation pretreatment on the dewatering performance of anaerobically digested sludge is very limited. This study aims at determining the optimum operating conditions of large-scale filtering centrifuges in wastewater treatment plants. The optimum dose of hydrogen peroxide, ultrasonic power, ultrasonic duration, ultrasonic pulse and particle size distribution for improved dewatering performance were determined in this study. In addition, shear stress-shear rate and viscosity-shear rate rheograms were developed to show the rheological flow properties for varying ultrasonic power and treatment duration. Optimum sonication power, time, pulse and amplitude were determined to be 14 W, 1 min, 55/5 and 20%, respectively. At a pH of 6.8, the optimum concentration of hydrogen peroxide was found to be 43.5 g/L. The optimum hydrogen peroxide dose in the combined conditioning experiments was determined to be 500 mg/L at a pH of 3. Under these optimum conditions, capillary suction time was reduced significantly by 71.1%. This study helps to reduce polymer consumption and provides the optimum pretreatment and dewatering operating conditions, and better monitoring and control in the dewatering unit has significant impact in the overall economy of wastewater treatment plants.

Control-inspired design and power optimization of an active mechanical motion rectifier based power takeoff for wave energy converters

Ocean waves have high energy density and are persistent and predictable. Yet, converting wave energy to a useable form remains challenging. A significant hurdle is the oscillatory nature of waves resulting in the alternating loads, which necessitate the use of rectification at some stage of the energy conversion. This research effort presents a novel design of active mechanical motion rectifier (AMMR) for the power takeoff (PTO), which provides enhanced controllability and better power performance when compared to passive mechanical motion rectifiers (MMR). Inspired by transistors used in synchronous electrical rectifiers, the proposed design uses controllable electromagnetic clutches in the mechanical transmission to allow active engagement-disengagement control; thus, rectifying the oscillatory motion into a unidirectional rotation for high energy conversion efficiency and allowing the generator in unidirectional rotation to control the bidirectional wave capture structure for maximizing the power output. A semi-analytical computational approach is developed to efficiently evaluate the optimal power achieved using the proposed AMMR-based PTO and active control. It is found that the AMMR-based PTO design yields a higher optimal power than the previous passive MMR design across the wave spectrum. The influences of generator inertia and reactive power are discussed. The effects of control parameters on the power output and the optimal trajectories are analyzed. Wave tank tests with the AMMR prototype demonstrated the effectiveness of AMMR based PTO design and validated the numerical analysis.

The effects of solar insolation and cloud opacity on the optimum array size for a direct-coupled solar pumping system

The widespread adoption of solar photovoltaic pumping technology requires pertinent information on the sizing and design of these systems. This research investigated how the ratio of photovoltaic array peak power to motor power consumption, solar insolation and cloud opacity affects the water yield of a direct-coupled solar PV pumping system. The specific aim was to estimate the optimum photovoltaic power to motor power consumption (OPVM) ratio for a 3 W DC pump under Jamaican weather conditions. The PVM ratio is the ratio of peak array power to nominal motor power consumption. Four experimental direct coupled solar pumping systems were set up and the water yield for each measured daily for thirty days. Each system was identical in every respect except each having a different solar array peak power. The findings show that there is a strong correlation (Pearson coefficient of −0.903) between solar insolation and the optimum photovoltaic power to motor power consumption ratio. Additionally, there was found to be a strong correlation between average cloud opacity and optimum photovoltaic power to motor power consumption ratio (Pearson coefficient of 0.899). Both correlations were shown to be statistically significant, with both yielding p-values <0.001. The findings of the correlation analysis indicate that a relatively large PVM ratio would be economical when used on a site that regularly receives relatively low insolation and heavy cloud cover. Finally, the daily optimum photovoltaic power to motor power consumption ratio ranged from approximately 2.3 to 5.0. Generally, it was observed that the OPVM ratio shifted below 2.4 on clear sunny days and above 4.0 on very cloudy days. While these conclusions may not exactly extrapolate to scales of practical application, performance metrics of larger systems are expected to bear some similarity. Being guided by these results, additional studies at these larger scales are recommended.

Dynamic numerical modeling and performance optimization of solar and wind assisted combined heat and power system coupled with battery storage and sophisticated control framework

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35 %, while the SDSE attains over 28 %, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge–discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery’s SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

Energy-saving and low-carbon leather production: AI-assisted chrome tanning process optimization

Reducing CO2 emissions is a global effort to improve industrial cost efficiency, enhance product compatibility, and fight climate change. For leather-making industry, the optimization of tanning process is crucial to fulfill the goal of low-carbon manufacturing. Artificial intelligence (AI) is an emerging technology for its ability to reduce errors, increase efficiency, and help realizing precise optimization of manufacturing processes. Herein, an AI-assisted method was first proposed to optimize chrome tanning process for energy-saving and carbon reduction. By comparing the performance of four machine learning (ML) algorithms, i.e., multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), and backpropagation-artificial neural network (BP-ANN), it was found that machine learning models are superior in identifying the optimization of process parameters (pH and time) which lead to maintain key product quality indexes in the penetration procedure and binding procedure. Out of the four ML models, the BP-ANN algorithm and SVR algorithm are proven to have good predictive ability. Thus, we systematically optimized the chrome tanning process for energy saving, with key product quality as the constraints. The optimal process parameters, namely pickling pH of 2.86, penetration time of 90 min, pH after basifying of 4.08, and binding time of 90 min, were generated by solving the optimization models of the chrome tanning process via the genetic algorithm (GA) with powerful global optimization capability. Finally, it was validated that the optimized process was highly reliable by evaluating the wet blue product performances, such as the evenness of chrome distribution, thermal stability, surface reactivity, morphology, as well as physical and mechanical properties. Notably, compared to conventional chrome tanning, the optimized process will save 8353.33 kgce of energy and reduce 87295.6 kgCO2 of CO2 emission in an average tannery with an annual production capacity of 300,000 standard sheets of upper cowhide leather. This approach can precisely optimize conventional leather-making process, providing a fesible way to achieve energy-saving and low-carbon leather production.

Optimal scheduling of active distribution network based on RBF-stochastic response surface method

As the high proportion of distributed generation (DG) and diversification of loads increase, the distribution network faces increasingly greater impact. To address the high-dimensional stochastic problems arising from the multi-dimensional uncertainty of power source and load, this paper proposes a two-stage optimization scheduling scheme for AC distribution networks based on RBF-stochastic response surface method (RBF-SRSM). This scheme takes into account economy, environmental protection and safety, and consists of day-ahead and intra-day stages. The day-ahead decision scheduling is based on an optimal low-carbon economy operation plan, while the intra-day reactive power optimization is achieved by converting nonlinear integral indicators into discrete linear indicators through probabilistic power flow calculation and the first moment derivation principle inspired by SRSM. The mixed integer second-order cone programming (MISOCP) method is used to solve the dispatching plan, and the results suggest that the proposed method is more efficient than the SRSM.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

Fresnel lenses and auto tracking to increase solar panel output power

This solar panel, which is equipped with microcontroller-based auto tracking, is also equipped with a Fresnel lens to obtain more optimal power because the Fresnel lens has the property of bending or refracting light rays that pass through it so that it can focus sunlight. The Fresnel lens focuses sunlight radiation onto the solar panels. To determine the magnitude of the influence of Fresnel lenses on increasing the power produced by solar panels, two solar panels of the same size (11×11) cm were moved by auto tracking using the same axis. One solar panel is not fitted with a Fresnel lens, and the other is fitted with a Fresnel lens measuring 30×30 cm, where the distance between the Fresnel lens and the solar panel is 5 cm. Measurements of the output power of both solar panels were carried out simultaneously, namely from 08.00 to 18.00 WIB (West Indonesia time). The power output of both solar panels was measured in 30-minute intervals, resulting in 21 measurements. Solar panels using Fresnel lenses produce an output power that is 105.306% more significant than that of those without Fresnel lenses.

Joint Optimization of Trajectory and Jamming Power for Multiple UAV-Aided Proactive Eavesdropping

Joint Optimization of Trajectory and Jamming Power for Multiple UAV-Aided Proactive Eavesdropping

Current-sensorless model predictive control for DAB converter based on back-flow power optimization

To solve problems such as high back-flow power, low system efficiency and poor dynamic properties of dual-active-full-bridge DC-DC converters in energy routers under traditional single-phase-shift modulation, the present paper introduces a current-sensorless-based model predictive control method combining back-flow power optimization based on Extended-Phase-Shift (EPS) modulation strategy. The transmission power model of the DAB converter is analyzed, taking into account the extended phase shift and the optimal shift comparison combination is found by the Lagrange multiplier method with the return power as the objective function. Then, the cost function is constructed to track the output voltage to achieve model predictive control without a current sensor. Finally, the accuracy and efficiency of the proposed strategy are verified by simulation. The results demonstrate the efficacy of this approach in mitigating system back-flow power and enhancing dynamic performance.

Predictive analysis of concrete slump using a stochastic search-consolidated neural network

Attaining a dependable measurement of concrete slump is crucial as it is a valuable indication of concrete workability. On the other hand, complexities associated with costly traditional approaches have driven engineers to use indirect efficient models such as metaheuristic-based machine learning for approximating the slump. While the literature shows promising application of some metaheuristic techniques for this purpose, the large variety of these algorithms calls for evaluating the most capable ones to keep the solution updated. Stochastic fractal search (SFS) is one of the most powerful optimization algorithms in the literature that has not received appropriate attention in analyzing concrete mechanical parameters. In the present research, a multi-layer perceptron neural network (NN-MLP), is enhanced using the SFS. The proposed SFS-NN-MLP model aims to predict the slump based on the amount of ingredients in the mixture, as well as the curing age of specimens. Accuracy assessment revealed that the proposed model can deal with the assigned task with excellent accuracy. It indicates that the SFS could properly tune the parameters required for training the NN-MLP, and consequently, the trained network could reliably calculate the slump of specimens that were not analyzed before. For comparative validation, the SFS was replaced with two similar optimizers, namely elephant herding optimization algorithm (EHO) and slime mould algorithm (SMA). Based on the calculated mean square errors of 5.6526, 6.1129, and 7.3561 along with mean absolute errors of 4.6657, 5.0078, and 6.3066, as well as the percentage-Pearson correlation coefficients of 78.06 %, 73.95 %, and 58.11 %, respectively for the SFS-NN-MLP, EHO–NN–MLP, and SMA–NN–MLP, it was shown that the SFS-NN-MLP is the most accurate predictor. Hereupon, the SFS-NN-MLP model is recommended to be effectively used for obtaining a cost-efficient approximation of concrete slump in real-world projects.