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Wind energy conversion systems have become a key technology to harvest wind energy worldwide. In permanent magnet synchronous generator-based wind turbine systems, the rotor position is needed for variable speed control and it uses an encoder or a speed sensor. However, these sensors lead to some obstacles, such as additional weight and cost, increased noise, complexity and reliability issues. For these reasons, the development of new sensorless control methods has become critically important for wind turbine generators. This paper aims to develop a new sensorless and adaptive control method for a surface-mounted permanent magnet synchronous generator. The proposed method includes a new model reference adaptive system, which is used to estimate the rotor position and speed as an observer. Adaptive control is implemented in the pulse-width modulated current source converter. In the conventional model reference adaptive system, the proportional-integral controller is used in the adaptation mechanism. Moreover, the proportional-integral controller is generally tuned by the trial and error method, which is tedious and inaccurate. In contrast, the proposed method is based on model predictive control which eliminates the use of speed and position sensors and also improves the performance of model reference adaptive control systems. In this paper, the proposed predictive controller is modelled in MATLAB/SIMULINK and validated experimentally on a 6-kW wind turbine generator. Test results prove the effectiveness of the control strategy in terms of energy efficiency and dynamical adaptation to the wind turbine operational conditions. The experimental results also show that the control method has good dynamic response to parameter variations and external disturbances. Therefore, the developed technique will help increase the uptake of permanent magnet synchronous generators and model predictive control methods in the wind power industry.

In this study a system constituted by seven double skin facades (DSF), three equipped with venetian blinds and four not equipped with venetian blinds, applied in a virtual chamber, is developed. The project will be carried out in winter conditions, using a numerical model, in transient conditions, and based on energy and mass balance linear integral equations. The energy balance linear integral equations are used to calculate the air temperature inside the DSF and the virtual chamber, the temperature on the venetian blind, the temperature on the inner and outer glass, and the temperature distribution in the surrounding structure of the DSF and virtual chamber. These equations consider the convection, conduction, and radiation phenomena. The heat transfer by convection is calculated by natural, forced, and mixed convection, with dimensionless coefficients. In the radiative exchanges, the incident solar radiation, the absorbed solar radiation, and the transmitted solar radiation are considered. The mass balance linear integral equations are used to calculate the water mass concentration and the contaminants mass concentration. These equations consider the convection and the diffusion phenomena. In this numerical work seven cases studies and three occupation levels are simulated. In each case the influence of the ventilation airflow and the occupation level is analyzed. The total number of thermal and indoor air quality uncomfortable hours are used to evaluate the DSF performance. In accordance with the obtained results, in general, the indoor air quality is acceptable, however, when the number of occupants in the virtual chamber increases, the Predicted Mean Vote index value increases. When the airflow rate increases the total of Uncomfortable Hours decreases and, after a certain value of the airflow rate, it increases. The airflow rate associated with the minimum value of total Uncomfortable Hours increases when the number of occupants increases. The energy production decreases when the airflow increases and the production of energy is higher in DSF with venetian blinds system than in DSF without venetian blinds system.

A new bi-directional circuit breaker is presented for medium-voltage dc (MVDC) systems. The Y-source impedance network topology is used to implement the breaker. The current transfer function is derived to show the frequency response and the breaker operation with the high frequencies. Mathematical analysis is achieved with different conditions of coupling among the breaker inductors. The minimum level of the magnetic coupling is determined, which is represented by the null condition. The effect of the turns-ratio on this condition is investigated as well. The breaker is designed with two types of fault conductance slope rates. The Y-source breaker is simulated, and the results verify the breaker operation during the fault condition and the load change. The results also demonstrate the effect of the coupling level on the minimum values of the source current when the fault occurs. Based on the expected fault type in the MVDC systems, the proposed breaker is developed to interrupt the overcurrent due to any of these fault types. A protection scheme is proposed for a 12-bus, two-level micro-grid, where the Y-source breakers are used in the bi-directional zones. The results verify the ability of the breaker to conduct and interrupt the current in both directions of the power flow.

This paper proposed a test bench device to emulate or simulate the electrical impulses of a wave energy converter (WEC). The objective of the study is to reconstruct under laboratory conditions the dynamics of a WEC in the form of an emulator to assess the performance, which, in this case, is the output power. The designed emulator device is programmable, which makes it possible to create under laboratory conditions the operating mode of the wave generator, identical to how the wave generator would work under real sea conditions. Any control algorithm can be executed in the designed emulator. In order to test the performance of the constructed WEC emulator, an experiment was conducted to test its power output against that of a real point-absorber WEC. The results indicate that, although the power output for that of the real WEC was higher than the WEC emulator, the emulator performed perfectly well. The relatively low power output of the emulator was because of the type of algorithm that was written for the emulator, therefore increasing the speed of the motor in the algorithm (code) would result in higher output for the proposed WEC emulator. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

The problem of localization of nodes of a wireless sensor network placed in different physical media (anchor nodes above ground and sensor nodes underground) is addressed in this article. We use time of arrival of signals transmitted between neighboring sensor nodes and between satellite nodes and sensor nodes as the ranging measurement. The localization problem is formulated as a parameter estimation of the joint distribution of the time of arrival values. The probability distribution of the time of arrival of a signal is derived based on rigorous statistical analysis and its parameters are expressed in terms of the location coordinates of the sensor nodes. Maximum likelihood estimates of the nodes’ location coordinates as parameters of the joint distribution of the various time of arrival variables in the network are computed. Sensitivity analysis to study the variation in the estimates with respect to error in measured soil complex permittivity and magnetic permeability is presented to validate the model and methodology.

Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

Discrete feeding of forest seeds is an urgent task for accurate detection of seeds during grading and reducing the seeding rate. The study used Pinus sylvestris L. seeds from cones collected in a natural stand of the Lisinsky educational-and-experimental forestry farm, Leningrad region, Russia, in 2017. Well-known methods for determining the properties of bulk materials were used, the results were processed by mathematical statistics using the MS Excel program. The physical and mechanical properties of uncalibrated seeds as a bulk material were determined. The angle of repose of Pinus sylvestris L. seeds, determined using a hollow cylinder by calculation, is 24.92&deg, The angle of repose of Pinus sylvestris L. seeds, determined using a hollow cylinder and a goniometer, is 32.7&deg, The significant discrepancy (~8&deg, ) in the values obtained by these methods is explained by the seeds shape. The flowability of Pinus sylvestris L. seeds corresponds to an excellent (free) category. The study will enable correct seed treatment protocols for sustainable forest management. However, some questions remained unresolved: how to orient a single seed in the right direction? How does seed-size calibration affect the angle of repose?

In measurement systems operating under various disturbances the probabilistic characteristics of measurement noises are usually known approximately. To improve the observation accuracy, a new approach to the Kalman&rsquo, s filter adaptation is proposed. In this approach, the Covariance Matrix of Measurement Noises (CMMN) is estimated by accurate measurements detected irregularly by the mobile object observation system (from radiofrequency identifiers, etalon reference, fixed points etc.). The problem of adaptive estimation of the observer&rsquo, s noises covariance matrix in the Kalman filter is solved analytically for two cases: mutual noises correlation, and its absence. The numerical example for adaptive filtration of complexing navigation system parameters of a mobile object using irregular accurate measurements is given to illustrate the effectiveness of the proposed algorithm. Coordinate estimating errors have changed in comparison with the traditional scheme from 100 m to 2 m in latitude, and from 200 m to 1.5 m in longitude.

Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature. Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020 grant 01/SAICT/2018. Antonio Ruano acknowledges the support of Fundação para a Ciência e Tecnologia, through IDMEC, under LAETA, grant UIDB/50022/2020. info:eu-repo/semantics/publishedVersion

Thermal power plants in different fields are regularly adapted to the state-of-the-art emissions standards, applying &ldquo, The Best Available Techniques Reference&rdquo, Since 2016 in the power plant area new, more stringent limits for power plant units with a thermal output of more than 300 MW operated with black coal are valid. Usually, in order to reach the new limits e.g., for NOX emissions, downstream reduction processes (Selective Non-Catalytic Reduction, SNCR or Selective Catalytic Reduction) are applied, which use of operating resources (essentially ammonia water) thereby increase. By the means of an experimentally validated process, by which pulverized fuel is fed by oscillation through a swirl burner into a pilot combustion chamber with a thermal output of 2.5 MW, nitrogen oxides can be reduced without further activities, for instance from 450 mg/mN3 in non-oscillation operation mode (0 Hz) to 280 mg/mN3 in oscillation operation mode (3.5 Hz), normalized to an O2&ndash, content of 6% each. These findings were patented in EP3084300. Particularly promising are the experiments which utilize oscillation of a large portion of the burn out air instead of the fuel in order to minimize the fatigue of the pulverized fuel oscillator, amongst others. Thereby, the nitrogen conversion rate, which describes the ratio of NOX to fuel nitrogen, including thermal NOX can be reduced from 26% for non-oscillation operation mode down to 16%. The present findings show that fuel oscillation alone is not sufficient to achieve nitrogen oxides concentrations below the legislative values. Therefore, a combination of different primary (and secondary) measures is required. This paper presents the experimental results for oscillating coal-dust firing. Furthermore, an expert model based on a multivariate regression is developed to evaluate the experimental results.

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The most profound requirements of fifth-generation (5G) technology implementations are the architecture design and the radio base station technology to capably run applications such as device-to-device, machine-to machine and internet of things at a reduced latency. Owing to these requirements, the implementation of 5G technology is very expensive to mobile network operators (MNO). In this study we modified the existing 4G network to form a distributed wireless network architecture (DWNA), the picocell and distributed antenna system were modified to support the enabling technology of 5G technology were a multi-edge computer (MEC), software-defined networking (SDN), massive multiple input multiple output (MIMO), ultra-dense network (UDN), Network Functions Virtualization (NFV) and device-to-device (D2D) communication at a reduced cost of ownership, improved coverage and capacity. We present a mathematical model for operational expenditure, capital expenditure and total cost of ownership (TCO) for the DWNA. A mathematical model for DWNA capacity and throughput was presented. Result shows that it is very economical for MNO to rent the space of the tower infrastructure from tower companies. The sensitivity analysis also shows a significant reduction in TCO for both the modified picocell and modified distributed antenna systems.

This paper aims to highlight the potential of solar water heater installations in Morocco. The project involves the comparison of active and passive solutions for energy efficiency in buildings. To this end, a numerical simulation model of solar water heater installations is created under TRNsys. Three hot water demand scenarios (Low, Standard, and High) were taken into account for the six climatic zones defined in the Moroccan thermal regulation of constructions. The same software (TRNsys) is used to model a pilot building consisting of 16 flats. Energy efficiency actions have been applied to the building envelope (insulation and glazing) and simulations are made for the six areas. The simulation results comparing energy and financial savings show the influence of subsidized gas prices on solar water heaters&rsquo, relevance despite significant energy savings. This work proves that solar water heaters will be a primary obligation for Morocco, taking into account changes in butane gas prices.

The present inquiry studies the influence of mass transfer in magnetohydrodynamics (MHD) upper-convected Maxwell (UCM) fluid flow on a stretchable, porous subsurface. The governing partial differential equations for the flow problem are reformed to ordinary differential equations through similarity transformations. The numerical outcomes for the arising non-linear boundary value problem are determined by implementing the successive linearization method (SLM) via Matlab software. The accuracy of the SLM is confirmed through known methods, and convergence analysis is also presented. The graphical behavior for all the parametric quantities in the governing equations across the velocity and concentration magnitudes, as well as the skin friction and Sherwood number, is presented and debated in detail. A comparability inquiry of the novel proposed technique, along with the preceding explored literature, is also provided. It is expected that the current achieved results will furnish fruitful knowledge in industrious utilities and correlate with the prevailing literature.

In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for various applications. Apparently, a nonthreatening, but slow-burning issue of the electric power systems is the low-frequency oscillations (LFO), which, if not dealt with appropriately and on time, could result in complete network failure. This paper addresses the role of a prominent ML family member, particle swarm optimization (PSO) tuned adaptive neuro-fuzzy inference system (ANFIS) for real-time enhancement of LFO damping in electric power system networks. It adopts and models two power system networks where in the first network, the synchronous machine is equipped with only a power system stabilizer (PSS), and in the other, the PSS of the synchronous machine is coordinated with the unified power flow controller (UPFC), a second-generation flexible alternating current transmission system (FACTS) device. Then, it develops the proposed ML approach to enhance LFO damping for both adopted networks based on the customary practices of statistical judgment. The performance measuring metrics of power system stability, including the minimum damping ratio (MDR), eigenvalue, and time-domain simulation, were used to analyze the developed approach. Moreover, the paper presents a comparative analysis and discussion with the referenced works&rsquo, achieved results to conclude the proposed PSO-ANFIS technique&rsquo, s ability to enhance power system stability in real-time by damping out the unwanted LFO.

Nowadays, by increasing energy demand and considering the importance of environmental issues in recent decades, the use of renewable energies is expanding. Among renewable energies, wind power and its technology are growing and evolving more rapidly. Resource assessment in Iran has revealed the significant potential of wind energy around the country. To further develop wind energy in the country and create large-scale wind power plants, the consideration of distributed power generation using small wind turbines for applications in agricultural and residential use is needed. Conventional small wind turbines and small wind lens turbines have been developed in recent years. In this research project, a small wind lens turbine is designed. The advantages of this turbine are an increased production capacity and reduced cut-in speed and noise pollution. In this study, a lens (or shroud) is added to a small turbine, and the maximized annual energy production (AEP) and minimization of the levelized cost of energy (LCOE) are modeled. We applied the NSGA-II algorithm for optimization to find the best answer. The input parameters in the objective function of the AEP are cut-in, cut-out, rated speeds, scale factor, and shape factor. Additionally, the input parameters in the objective function of the LCOE are the power production, initial capital cost, annual operating expenses, and balance of energy. The results indicate that installing a wind lens turbine in Kish Island led to an LCOE decrease of 56% on average, and we can see an 83% increase in the AEP. In the Firoozkooh area, an average reduction of 59% in the LCOE and 74% increase in the AEP for a wind lens turbine is observed.

This paper presents the effect of grinding wheel contact stiffness on the plunge grinding cycle. First, it proposes a novel model of the generalized plunge grinding system. The model is applicable to all plunge grinding operations including cylindrical, centerless, shoe-centerless, internal, and shoe-internal grinding. The analysis of the model explicitly describes transient behaviors during the ramp infeed and the spark-out in the plunge grinding cycle. Clarification is provided regarding the premise that the system stiffness is composed of machine stiffness and wheel contact stiffness, and these stiffnesses significantly affect productivity and grinding accuracy. The elastic deflection of the grinding wheel is accurately measured and formulas for representing the deflection nature under various contact loads are derived. The deflection model allows us to find the non-linear contact stiffness with respect to the normal load. The contact stiffnesses of four kinds of grinding wheels with different grades and bond materials are presented. Both cylindrical grinding and centerless grinding tests are carried out, and it is experimentally revealed that the time constant at ramp infeed and spark-out is significantly prolonged by reducing the grinding force. It is verified that a simulation of the grinding tests using the proposed model can accurately predict critical parameters like forces and machine deflection during plunge grinding operations. Finally, this paper provides a guideline for grinding cycle design in order to achieve the required productivity and grinding accuracy.

The world is targeting fully renewable power generation by the middle of the century. Distributed generation is the way to increase the penetration level of renewable energies. This paper presents load frequency control of a hybrid tidal, wind, and wave microgrid to feed an isolated island. This research is a step towards 100% renewable energy communities in remote seas/oceans islands. The wave and tidal generation systems model are presented. The study presents load frequency control through three supplementary control strategies: conventional integrators, fractional order integrator, and non-linear fractional order integrator. All the controllers of the microgrid are designed by using a novel black widow optimization technique. The applied technique is compared to other existing state-of-the-art algorithms. The results show that the black widow non-linear fractional integrator has a better performance over other strategies. Coordination between the unloaded tidal system and blade pitch control of both wind and tidal systems are adopted in the microgrid to utilize the available reserve power for the frequency support. Simulation and optimization studies are performed using the MATLAB/SIMULINK 2017a software application.

Vast expansion in consumption is leading to natural resource scarcity and global warming. The integrated management of natural resources, such as food, energy, food (FEW) as one of the most important aspects has been proposed as a solution to meet these challenges. The FEW nexus is a world-wide solution for simultaneously assessing the development and implementation of various approaches focusing on energy, water and food security, sufficiency. This approach is intended to foster sustainable development and improve the quality of life of communities while preserving the natural, human and social capital, address the long-term sustainability challenges and protecting all-natural resources. This paper tries to review some recent research on this topic. For this purpose, first, we describe some facts about demand growth and exponential consumption in these three areas, with emphasis on presented statistics. Then, the most critical research published in this field is reviewed, considering that it took a decade or so before that the original idea was introduced. The most important policymakers of this emerging concept, including committees and conferences, and finally significant challenges and opportunities to the implementation along with future insights, are addressed.

For footwear insoles, high rebound performance is required in some instances such as for running, while softness for comfort is of higher importance during normal walking and standing to minimize high stress. Hence, materials with rebound performance in some scenarios and softness for other scenarios are desired. In this paper, we investigate rebound performance and hardness of composites made of a shear-thickening material and elastic foam. First, a hydrogel type of shear-thickening material (Slime) is characterized to investigate the influence of water content. After that, two particular shear-thickening hydrogels with better rebound performance (but not outstanding in the shear-thickening effect) are selected and integrated into the elastic foam to produce a composite insole. It is found that, as compared with the commercial elastic insole and commercial shear-thickening insole, softer and superior rebound performance can be achieved simultaneously only if the right shear-thickening material is used in the composite. Published version

This paper reviews the energy storage participation for ancillary services in a microgrid (MG) system. The MG is used as a basic empowering solution to combine renewable generators and storage systems distributed to assist several demands proficiently. However, because of unforeseen and sporadic features of renewable energy, innovative tasks rise for the consistent process of MGs. Power management in MGs that contain renewable energy sources (RES) can be improved by energy storage. The energy storage systems (ESSs) have several merits, such as supply and demand balancing, smoothing of RES power generation, enhancing power quality and reliability, and facilitating the ancillary services like voltage and frequency regulation in MG operation. The integration of ESS technology has become a solution to the challenges the power distribution networks face in achieving improved performance. By simplifying a smooth and robust energy balance within the MG, storage devices match energy generation to consumption. MG, and its multidisciplinary portrait of current MG drivers, tasks, real-world applications, and upcoming views are elucidated in this paper.

The aim of this paper is to address the gap in the academic literature towards the development of methodological approaches to the industrial ecosystem sustainability assessment. This study was focused on the industrial ecosystems formed based on an entropy model and implementing the principles of complex systems. This article systematizes the problem of applying the ecosystem approach to cross-industry interaction. A contribution to the literature was achieved by providing a systemic perspective on the sustainable industrial process. In this paper, we develop the methodological foundations to improve the understanding of integration processes&rsquo, influence on the industrial ecosystem potential. For a relevant analysis of industrial ecosystem potential, the existing patterns of system functioning were taken into account, including entropy equilibrium and the Harrington model. We illustrate our assumptions with an empirical case study of the National University of Science and Technology (NUST) &ldquo, MISIS&rdquo, ecosystem&mdash, &ldquo, Green technologies for resource conservation&rdquo, (Russia), with an assessment of ecosystem sustainability through the actors&rsquo, collaboration level. The propositions arising from this analysis provide information to help academics, policymakers, government, and individual enterprises with a more adequate understanding of the practical mechanisms and tools that help trigger the self-organization and sustainability of the industrial ecosystems.

Data security of present-day power systems, such as the electricity market, has spurred global interest in both industry and academia. The electricity market can either be regulated (state-controlled entrance, policies, and pricing) or deregulated (open for competitors). While the security threats in a deregulated electricity market are commonly known and have been investigated for years, those in a regulated market still have scope for extensive research. Our current work focuses on exploring the data security of the regulated electricity market, and the regulated New Zealand Electricity Market (NZEM) has been considered for this research. Although the chances of cyberattacks on state-controlled regulated electricity market are relatively less, different layers of the current SCADA systems do pose some threats. In this context, we propose a decentralized Ethereum Blockchain-based end-to-end security prototype for a regulated electricity market such as the NZEM. This prototype aims to enhance data security between the different layers of the current SCADA systems. The detailed operation process and features of this prototype are presented in this work. The proposed prototype has prospects of offering improved data security solutions for the regulated electricity market.

The boiling process is utterly fundamental to the design and safety of water-cooled fission reactors. Both boiling water reactors and pressurised water reactors use boiling under high-pressure subcooled liquid flow conditions to achieve high surface heat fluxes required for their operation. Liquid water is an excellent coolant, which is why water-cooled reactors can have such small sizes and high-power densities, yet also have relatively low component temperatures. Steam is in contrast a very poor coolant. A good understanding of how liquid water coolant turns into steam is correspondingly vital. This need is particularly pressing because heat transfer by water when it is only partially steam (‘nucleate boiling’ regime) is particularly effective, providing a great incentive to operate a plant in this regime. Computational modelling of boiling, using computational fluid dynamics (CFD) simulation at the ‘component scale’ typical of nuclear subchannel analysis and at the scale of the single bubbles, is a core activity of current nuclear thermal hydraulics research. This paper gives an overview of recent literature on computational modelling of boiling. The knowledge and capabilities embodied in the surveyed literature entail theoretical, experimental and modelling work, and enabled the scientific community to improve its current understanding of the fundamental heat transfer phenomena in boiling fluids and to develop more accurate tools for the prediction of two-phase cooling in nuclear systems. Data and insights gathered on the fundamental heat transfer processes associated with the behaviour of single bubbles enabled us to develop and apply more capable modelling tools for engineering simulation and to obtain reliable estimates of the heat transfer rates associated with the growth and departure of steam bubbles from heated surfaces. While results so far are promising, much work is still needed in terms of development of fundamental understanding of the physical processes and application of improved modelling capabilities to industrially relevant flows.

In this proposed research, clear prospects of a real life marketing scenario, by analyzing a price discount policy and variable demand, are derived. The proposed study presents a production model along with time-dependent and selling price related demand for decaying items. Items deteriorate over time, therefore, considering deterioration in this model makes it more acceptable to the present marketing situation. The concept of delay-in-payments is utilized in this inventory system. In this research, a retailer buys some products, enjoys constant credit-period offers which are provided by the supplier. This model depicts a price discount strategy which is based on purchasing cost to attract more consumers in any business industry. By using this strategy, any manufacturer or business may gain more profit in comparison to methods suggested by earlier literature. The average profit function of the inventory system is maximized analytically and also finds the selling-price per unit and duration of the inventory cycle optimally. A numerical example, along with a case study and their graphical representations, are incorporated to verify the optimality of this research very clearly. The findings of this research have maximized the average profit function more than the existing literature.

Human action recognition has turned into one of the most attractive and demanding fields of research in computer vision and pattern recognition for facilitating easy, smart, and comfortable ways of human-machine interaction. With the witnessing of massive improvements to research in recent years, several methods have been suggested for the discrimination of different types of human actions using color, depth, inertial, and skeleton information. Despite having several action identification methods using different modalities, classifying human actions using skeleton joints information in 3-dimensional space is still a challenging problem. In this paper, we conceive an efficacious method for action recognition using 3D skeleton data. First, large-scale 3D skeleton joints information was analyzed and accomplished some meaningful pre-processing. Then, a simple straight-forward deep convolutional neural network (DCNN) was designed for the classification of the desired actions in order to evaluate the effectiveness and embonpoint of the proposed system. We also conducted prior DCNN models such as ResNet18 and MobileNetV2, which outperform existing systems using human skeleton joints information.

Some of the most promising distributed recycling and additive manufacturing (DRAM) technical systems use fused particle fabrication (FPF) or fused granular fabrication (FGF), where compression screws force post-consumer waste plastic through a heated nozzle for direct 3D printing. To assist the technical evolution of these systems, this study provided the details of an invention for a low-cost, easily replicable open-source grinding machine for compression screw manufacturing. The system itself can be largely fabricated using FPF/FGF following the self-replicating rapid prototyper (RepRap) methodology. This grinding machine can be made from a cordless cut-off grinder and &lt, $155 in parts. The new invention is demonstrated to be able to cut custom screws with variable (i) channel depths, (ii) screw diameters, (iii) screw lengths, (iv) pitches, (v) abrasive disk thicknesses, (vi) handedness of the screws, (vii) and materials (three types of steel tested: 1045 steel, 1144 steel, and 416 stainless steel). The results show that the device is more than capable of replicating commercial screws as well as providing makers with a much greater flexibility to make custom screws. This invention enables the DRAM toolchain to become even more self-sufficient, which assists the goals of the circular economy.

In the present scenario for the development of the unmanned aerial vehicle (UAV), artificial intelligence plays an important role in path planning and obstacle detection. Due to different environments, it is always a task to achieve the proper moment for achieving the target goal while avoiding obstacles with minimum human interference. To achieve the goal with the avoidance of obstacles, individual optimization techniques with metaheuristic algorithms such as fuzzy, particle swarm optimization (PSO), etc. were implemented in various configurations. However, the optimal solution was not attained. Thus, in order to achieve an optimal solution, a hybrid model was developed by using the firefly algorithm and the fuzzy algorithm, establishing multiple features of the individual controller. The path and time optimization were achieved by a standalone controller and a hybrid firefly&ndash, fuzzy controller in different conditions, whereby the results of the controller were validated by simulation and experimental results, highlighting the advantages of the hybrid controller over the single controller.

Metal additive manufacturing (AM) has gained much attention in recent years due to its advantages including geometric freedom and design complexity, appropriate for a wide range of potential industrial applications. However, conventional metal AM methods have high-cost barriers due to the initial cost of the capital equipment, support, and maintenance, etc. This study presents a low-cost metal material extrusion technology as a prospective alternative to the production of metallic parts in additive manufacturing. The filaments used consist of copper, bronze, stainless steel, high carbon iron, and aluminum powders in a polylactic acid matrix. Using the proposed fabrication technology, test specimens were built by extruding metal/polymer composite filaments, which were then sintered in an open-air furnace to produce solid metallic parts. In this research, the mechanical and thermal properties of the built parts are examined using tensile tests, thermogravimetric, thermomechanical and microstructural analysis.

Recurring and increasing large-scale wildfires across the globe (e.g., Southern Europe, California, Australia), as a result of worsening climate conditions with record temperatures, drought, and strong winds, present a challenge to mankind. Early fire detection is crucial for a quick reaction and effective firefighting operations, minimizing the risk to human lives as well as the destruction of assets, infrastructures, forests, and wildlife. Usually, ground firefighting relies on human intervention and dangerous exposition to high temperatures and radiation levels, proving the need for mechanisms and techniques to remotely or autonomously detect and combat fire. This paper proposes an autonomous firefighting system built with a motorized water turret, narrow beam far infrared (FIR) sensors, and a micro-controller running novel algorithms and techniques. Experimental field results validated the technical approach, indicating that when a small fire front is within the field of view of the FIR sensor and within the range of the water jet, it is possible to provide an early alarm and even autonomously extinguish or delay the approaching fire front, increasing the chance for evacuation.

This paper synthesizes the design of an infeed cylindrical grinding system into a total system composed of the grinding mechanism and the grinding machine characteristics. The causalities between the grinding parameters and the machine structures are discussed, and the infeed grinding processes are analyzed as outputs that represent responses to the inputs. These relationships are integrated into a block diagram with closed-loop feedback. A novel model exhibiting practical parameters such as grinding speed, infeed rate and MRR (Material Removal Rate) is proposed. The analysis of the grinding system derived a critical factor, the &ldquo, grinding time contact,&rdquo, which governs the transient behaviors of process parameters such as forces and machine deflection. The process parameters during the infeed cycle including spark-out grinding were investigated, and the formulas required for the cycle design are presented. Furthermore, to improve accuracy and productivity, the features of the cycle design are described and procedures for controlling size error and roundness are discussed. Finally, the model was verified with infeed grinding tests applied to both the chuck-type cylindrical and centerless grinding methods.

Owing to the expensive applications of nanoparticles in engineering sciences, an admirable attention has been intended by researchers on this topic in recent years. The utilization of nanoparticles as asource of energy is intended much attention of investigators in recent decade. This novel attempt investigates the thermal properties of Casson nanofluid containing microorganisms induced by an oscillatory moving surface. The fundamental features of heat and mass phenomenon are inspected by utilizing the temperature-dependent viscosity. Buongiorno&rsquo, s mathematical model is used to report the famous Brownian motion and thermophoretic diffusion consequences. The flow problem characterizes the partial differential equations for which analytical solution has been computed with a convincible accuracy. The insight physical features are inspected with help of various curves. The physical significances of flow parameters is studied via various graphs.

Decreasing the share of time for the technological training of new product creation is an actual and perspective task. The actuality of the given task always increases with the development of machine-building production. Changes which happen inside the organization of industrial production are connected to the development of consumer demand. Modern tendencies are such that there can be seen a constant lowering of batch products with an increase in the range of their production, and, consequently, a reduction in the production stage of the product life cycle. Under these conditions, it is necessary to react as quickly as possible to changes of external (for example, the arrival of new production orders, adjusting the number of already started production, etc.) and internal conditions (for example, stopping technological equipment for organizational reasons, correcting production routes for parts, etc.). Obviously, the effectiveness of the functioning of the production system will be determined primarily by the right decisions. Errors lead to large material costs and a serious loss of time, which can be fatal for an enterprise. The development goal described in the article is to build a digital model of a production system that allows&mdash, on a machine time scale and in the absence of material costs&mdash, the analysis of various control scenarios and finding correct solutions in in short time. The methodological basis for constructing such models is the use of simulation modeling with the combination of discrete event and agent modeling. The construction of a model on this principle allows its openness to accumulate information about successful decisions made and use them to solve similar problems in the future. The benefit of this approach is the ability to predict the production situation over time, rational distribution of technological resources, reducing equipment downtime, streamlining the routes of production tasks and determining the stages of acquiring the necessary materials and components. It increases the competitiveness of the enterprise and ensures its economic stability.

This study analyzes transient turbulent modeling of three-dimensional multiple dimpled fin array using large eddy simulation (LES). The Navier&ndash, Stokes equations as well as the energy equation were constructed by the finite volume method and then discretized to form algebraic equations, which were solved by semi-implicit method for pressure-linked equation (SIMPLE). The solutions of temperature and velocity were obtained by iterating computation until it converged within each step. This simulation places nine fins on the bottom surface of a channel and changes the height of the dimple (0.4, 0.8, and 1.2 mm) with three different levels of Reynolds number (Re) (3500, 5000, and 6500) to investigate the temperature and flow field without gravity in forced convection. The results indicate that the dimpled fin array can generate vortices between the convex/concave dimples and the fin base and increase the influences of the height of the dimple on the flow field around the fin array. The averaged time-mean of the Nusselt number (Nu) for the dimple height of 0.8 mm is higher than that of the no-dimple case up to 14.4%, while the averaged time-mean Nu for the dimple height of 1.2 mm is lower than that of the no-dimple case up to 11.6%.

Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm.

Demand-Side Generating Assets (DSGAs) relate to existing small diesel Gensets serving the critical loads of infrastructures in unexpected electrical network interruptions. This paper investigates the exploitation of DSGA as potential suppliers of ancillary services (AS), especially in the case of isolated or weak interconnected island power systems such as in Crete. In this regard, their impact on substations&rsquo, loading reductions and associated copper losses is presented, using a substation as a case study with a typical touristic load in Crete. DSGA&rsquo, s impact on such a significant part of reliability enhancement in distribution systems is evaluated, examining different scenarios of DSGA installed capacity. Subsequently, extensive research is carried out regarding the cost analysis for their access to the electrical grid providing AS, in combination with their operating cost analysis. The calculations on fuel consumption cost show that DSGA are comparable with the most expensive gas turbines of the Cretan power system. Finally, considering the capital cost for their required configurations and their fuel consumption cost, a proposal for their remuneration prices is made. The prices that arise are lower than the actual remuneration prices of other similar services such as Genset rentals for the case of the Cretan power system.

In this work, a structured design method, the Stylistic Design Engineering (SDE), isapplied for the construction of a new minivan car, in particular a new city car, which we will callFIAT 600 Omega. The SDE, or Stylistic Design Engineering, is a structured engineering method forcarrying out automotive design projects. The SDE method consists of six different phases: (1)Analysis of stylistic trends, (2) Sketches, (3) 2D Computer Aided Design (CAD) drawings, (4) 3DCAD models, (5) Rendering, (6) Solid stylistic model (also called style maquette). This project dealswith the external redesign of the Fiat 600 multiple, a small minivan which was very successful inthe 1950s and 1960s. SDE is a methodology consisting of various technologies and designmethodologies that will be further explained in detail, such as the Pininfarina method, the QualityFunction Deployment (QFD) method, Benchmarking (BM), and Top Flop Analysis (TPA). The workwas organized according to the different phases. Initially, the Fiat style was studied, in particularthe style of the FIAT 600 MULTI PURPOUSE VEHICLE (MPV). This step is essential to betterunderstand the characteristics of the brand and also the main characteristics carried out over thedecades. Then we moved on to the freehand sketching phase, based on what we learned in theprevious phase of the study. When a satisfactory shape was found for the new car, by analyzing anddiscarding the different proposals of the various types of style, we proceeded to the evaluation ofthe proportions and dimensions through two-dimensional drawings and finally we obtained thethree-dimensional shape of the new car thanks to 3D CAD software and rendering software. Manyadvantages in the industrial world SDE takes together with its development. In fact, until the early2000s, car design and styling was considered quite a craft activity, not a technical or scientific one,mostly based on the great capability of famous car designers and masters, just like Giugiaro, Zagato,Bertone, Pininfarina, Stephenson, Bangle, etc. Then, thanks to the industrial activity of Eng. LorenzoRamacciotti, former CEO of Pininfarina Spa and Mechanical Engineer, and also thanks to theacademic studies developed at ALMA MATER STUDIORUM University of Bologna, SDE becamethe object of attention, because it is able to systematize the car design process and reduce costs. WithSDE, a good design research or an industrial product development team can complete a car designproject, also without the presence of a mentor. Car Design Process finally becomes with SDE ascientific method, Car Design becomes with SDE an industrial method. Industrial needs are niceproducts made in a short time, SDE is structured to attend these issues. Industrial challenges followinnovation, in shape and functionality, SDE is able to recognize innovation. Industrial benefits canbe reached with SDE, ensuring beautiful aesthetic projects are realized systematically and with lowcosts.

This paper presents a topology of a multi-port phase shift converter for integration with a photovoltaic (PV) system, a wind turbine generator, and a battery to supply a grid-connected domestic load. The main advantage of the topology is using a multi-winding high-frequency transformer (MWHFT) to integrate the input and outputs of the system. In contrast with the case of using a common electrical bus, using MWHFT presents several advantages, such as isolating the converter ports, the easier matching of different voltage levels of the converter ports by adjusting turns ratio, simpler power flow control between the converter ports by applying leading or lagging phase shift angles, and eliminating the leakage current flow through the PV panels from the utility grid. A detailed analysis of the proposed topology and the control system is presented. A numerical simulation of the proposed system is carried out to confirm the system operation and control technique. The experimental tests have been conducted on a prototype converter to approve the proposed system performance.

Fluctuations in fossil fuel prices significantly affect the economies of countries, especially oil-importing countries, hence these countries are thoroughly investigating the increase in the utilization of renewable energy resources as it is abundant and locally available in all the countries despite challenges. Renewable energy systems (RES) such as solar and wind systems offer suitable alternatives for fossil fuels and could ensure the energy security of countries in a feasible way. Zimbabwe is one of the African countries that import a significant portion of its energy needs which endanger the energy security of the country. Several studies in the literature discussed the feasibility of different standalone and hybrid RES either with or without energy storage systems to either maximize the technical feasibility or the economic feasibility, however, none of the studies considered maximizing both feasibilities at the same time. Therefore, we present a techno-economic comparison of standalone wind and solar photovoltaic (PV) in addition to hybrid PV/wind systems based on maximizing the RES fraction with levelized cost of electricity (LCOE) being less than or equal to the local grid tariff where Gwanda, Zimbabwe, is the case study. The methodology suggested in this study could increase the utilization of renewable energy resources feasibly and at the same time increase the energy security of the country by decreasing dependency on imported energy. The results indicate that the PV/wind hybrid system does not only have the best economic benefits represented by the net present value (NPV) and the payback period (PBP), but also the best technical performance, where the maximum feasible size of the hybrid system-2 MW wind and 1 MW PV-has RES fraction of 65.07%, LCOE of 0.1 USD/kWh, PBP of 3.94 years, internal rate of return of 14.04% and NPV of 3.06 &times, 106 USD. Having similar systems for different cities in Zimbabwe will decrease the energy bill significantly and contribute toward the energy security of the country.

In a growing number of battery-driven applications the need of removing any position and speed transducer is taking over due to space, cost and mechanical reliability constraints, further than making the installation easier as requiring less wiring. This paper presents the development of a sensorless algorithm capable of running an Interior Permanent Magnet Synchronous Machine (IPMSM), assuring constant torque production in the whole speed range, form standstill to high speeds. This is achieved with an hybrid method: at standstill and very low speeds the saliency of the IPM is exploited through an High Frequency Signal Injection (HFSI), which assures a robust estimation of the rotor position. At medium to high speeds an advanced V-I estimator is adopted in order to enhance the motor performances. The developed algorithm comes out of being highly scalable as it requires very little tuning, resulting in a multi-purpose application which can be employed with any motor size.

This paper presents the results of an experimental study on a two-phase ejector. The main objective is to assess the effects of the nozzle&rsquo, s divergent and the throat diameter on performance under various working conditions. Under the same conditions, ejector operation with a convergent nozzle, results in higher critical primary mass flow rate and lower critical pressure than with a convergent-divergent nozzle version. Experiments show as well that the flow expansion is higher in the convergent-divergent nozzle. The throat diameter turns out to have an important impact only on the amount of the critical mass flow rate. The nozzle geometry has no impact on its optimal position in the ejector. Globally, the ejector with the convergent-divergent nozzle provides a higher entrainment ratio, due to a reduced primary mass flow rate and an increased secondary flow induction. Tests also show that the ejector with a lower throat diameter provides a higher entrainment ratio, due to better suction with less primary flow. Unlike the convergent-divergent nozzle, the convergent nozzle permits an entrainment ratio almost insensitive to a wide range of primary inlet sub-cooling levels. Primary and secondary mass flow rates increase proportionally with the subcooling level and result in a quasi-constant entrainment ratio.

The mathematical modeling presented in this work concerns the conveyor-belt dryer with the tangential flow of air with respect to food. This dryer, if operating in co-current, has the advantage of well preserving the organoleptic and nutritional qualities of the dried product. In fact, it has a low air temperature in the final stretch where the product has low moisture content and is therefore more temperature sensitive. It is a bulkier dryer than the continuous through-circulation conveyor dryer with a perforated belt. The latter is therefore more frequently used and has received greater study attention from researchers and designers of the industry. With the aim to propose guidelines for a rational design of the conveyor-belt dryer with tangential flow, a mathematical model was developed here through the differentiation of the drying rate equation followed by its integration performed along the dryer belt. Consequently, and with the assumption that the final moisture content XF of the product is higher than the critical moisture content XC, the relationships between the intensive quantities (temperatures, humidity and enthalpies), the extensive quantities (air and product flow rates) and the dimensional ones (length and width of the belt), were obtained. Finally, on the basis of these relationships, the rules for an optimized design for XF &gt, XC were obtained and experimentally evaluated.

This paper proposes a novel adaptive controller based on digital twin (DT) by integrating software-in-loop (SIL) and hardware-in-loop (HIL). This work aims to reduce the difference between the SIL controller and its physical controller counterpart using the DT concept. To highlight the applicability of the suggested methodology, the regulation control of a horizontal variable speed wind turbine (WT) is considered for the design and assessment purposes. In the presented digital twin framework, the active disturbance rejection controller (ADRC) is implemented for the pitch angle control of the WT plant in both SIL and HIL environments. The design of the ADRC controllers in the DT framework is accomplished by adopting deep deterministic policy gradient (DDPG) in two stages: ( i ) by employing a fitness evaluation of wind speed error, the internal coefficients of HIL controller are adjusted based on DDPG for the regulation of WT plant, and ( ii ) the difference between the rotor speed waveforms in HIL and SIL are reduced by DDPG to obtain a similar output behavior of the system in these environments. Some examinations based on DT are conducted to validate the effectiveness, high dynamic performance, robustness and adaptability of the suggested method in comparison to the prevalent state-of-the-art techniques. The suggested controller is seen to be significantly more efficient especially in the compensation of high aerodynamic variations, unknown uncertainties and also mechanical stresses on the plant drive train.

The through-feed method in centerless grinding allows manufacturers to produce cylindrical parts at much higher levels of productivity than can be achieved with in-feed grinding, so it has been extensively employed in industry. However, its rounding mechanism is not yet well understood due to the complexity of the through-feed process. This paper presents the fundamental parameters, such as material removal rates, forces, and so on in the through-feed grinding, and analyses on the grinding system with feedback loops, including regenerative functions and the machine dynamic functions. Further, the characteristic roots of the system, representing the number of waves and the growth rates of the harmonics in roundness, are identified at each grinding position from entry to exit. To evaluate the grinding process stability, a rounding stability index (RSI) was proposed. It was demonstrated that the analytical tool modeled in this paper can identify the optimum operational conditions by the RSI for achieving desired grinding productivity and accuracy. Finally, the model is verified with grinding tests, and the nm-order roundness obtained by the tests is shown.

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks, GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

Growing energy demand is causing a significant decrease in the world&rsquo, s hydrocarbon stock in addition to the pollution of our ecosystem. Based on this observation, the search for alternative sorts of energy to fossil fuels is being increasingly explored and exploited. Wind energy is experiencing a very important development, and it offers a very profitable opportunity for exploitation since the wind is always available and inexhaustible. Several technical solutions exist to exploit wind energy, such as floating vertical axis wind turbines (F-VAWTs), which provide an attractive and cost-effective solution for exploiting higher resources of offshore wind in deep water areas. Recently, the use of the Darrieus vertical axis wind turbine (VAWT) offshore has attracted increased interest because it offers significant advantages over horizontal axis wind turbines (HAWTs). In this context, this article presents a new concept of floating Darrieus-type straight-bladed turbine with three-stage rotors. A double-multiple stream tube (DMST) model is used for aerodynamic simulations to examine several critical parameters, including, solidity turbine, number of blades, rotor radius, aspect ratio, wind velocity, and rotor height. This study also allows to identify a low solidity turbine (&sigma, = 0.3), offering the best aerodynamic performance, while a two-bladed design is recommended. Moreover, the results also indicate the interest of a variable radius rotor, as well as the variation of the height as a function of the wind speed on the aerodynamic efficiency.

Nowadays, the new concept of urban living is required as a path to struggle with urbanization challenges in order to maintain the world and make cities better settings for living through creating high quality of life and liveability. To achieve such areas, developing sustainability, urban planning based on Information Technology, Information Communication Technology infrastructure, and innovative management play important roles. So, authors try to find out new concepts of urban life concerned with these indicators through sustainability, innovation, ubiquitous, and smartness to create a sustainable and modern world through smart cities. Based on authors&rsquo, researches, Blue-Green infrastructure based on environmentally friendly, green strategies, sustainable water management, and ubiquitous services focusing on digitalization and high technologies are required to make a modern world. Fundamentally, innovation management in technology, business and marketing has important roles in designing such areas by keeping up with growing demands and low resources of energies. In this research, i-Sustainability Plus is introduced as a theory to create Blue-Green Ubiquitous cities as modern sustainable and liveable urban areas. Such areas could make the world a better place for living through sustainable development and improving quality of human life.

Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

Most offshore wind farms built thus far are based on waters below 30 m deep, either using big diameter steel monopiles or a gravity base. Now, offshore windfarms are starting to be installed in deeper waters and the use of these structures—used for oil and gas like jackets and tripods—is becoming more competitive. Setting aside these calls for direct or fixed foundations, and thinking of water depths beyond 50 m, there is a completely new line of investigation focused on the usage of floating structures; TLP (tension leg platform), Spar (large deep craft cylindrical floating caisson), and semisubmersible are the most studied. We analyze these in detail at the end of this document. Nevertheless, it is foreseen that we must still wait sometime before these solutions, based on floating structures, can become truth from a commercial point of view, due to the higher cost, rather than direct or fixed foundations. In addition, it is more likely that some technical modifications in the wind turbines will have to be implemented to improve their function. Regarding wind farm connections to grid, it can be found from traditional designs such as radial, star or ring. On the other hand, for wind generator modeling, classifications can be established, modeling the wind turbine and modeling the wind farm. Finally, for the wind generator control, the main strategies are: passive stall, active stall, and pitch control; and when it is based on wind generation zone: fixed speed and variable speed. Lastly, the trend is to use strategies based on synchronous machines, as the permanent magnet synchronous generator (PMSG) and the wound rotor synchronous generator (WRSG).

Wave and tidal energies are some of the most prominent potential sources of renewable energy. Presently, these energy sources are not being utilized to their maximum extent. In this paper, we present a new conversion mechanism with an innovative electrical energy converter design that enables the use of wave energy to its maximum potential. The conventional wave energy converter comprises two stages of conversion (kinetic to mechanical and mechanical to electrical), imposing transformation loss that reduces the overall system efficiency. Additionally, the architecture and operational norms are dependent on the availability of shoreline areas, and the convertor is not suitable for all ocean weather conditions. To solve these problems, we have developed a wave energy conversion system that integrates the two stages of power with the minimum number of moving parts. This results in significant reduction of transformation losses that otherwise occur in the process. This paper presents an innovative idea of designing a DC generator that reduces the hierarchy of power conversion levels involved to improve the efficiency. The back and forth motion of the machine means it operates in a two-quadrant generation mode. The machine was constructed as a square box model with windings placed on both the top and bottom stator plates, and the rotor consisted of a field winding placed between these plates with two axes of operation. The electromagnetic field (EMF) induced in the stator plates is due to the resulting flux cutting, which is generated by a rolling object (rotor) in between them. A finite element analysis (FEA) of the machine is also listed to validate the flux linkage and operational efficiency. Additionally, a generator is fabricated to the predetermined design criteria as a proof of concept and the corresponding results are posted in the paper. Additionally, we present the material and cost limitations of this invention and outline some possible future directions.