Aiming at the negative vibration effect caused by the increased unsprung mass of the hub motor-air suspension (HM-AS) vehicle and the unbalanced magnetic force (UMF) of the hub motor, a linear quadratic regulator (LQR) air suspension control strategy based on a genetic algorithm is proposed. Considering the coupling effect of road surface excitation and UMF, the model of the HM-AS vehicle is established. Then, according to optimal control theory and the genetic algorithm, the LQR controller is designed to suppress the vibration of the HM-AS vehicle, and the weight matrix of the LQR controller is optimized through the genetic algorithm. The simulation results show that the proposed LQR control strategy effectively improves the ride comfort and motor safety of the HM-AS vehicle.

The stator coreless axial flux permanent magnet (AFPM) motor with a compact structure, low torque ripple, and high efficiency is particularly suitable as a motor for electric propulsion systems. However, it still requires great effort to design an AFPM motor with higher torque density and lower torque ripple. In this paper, a stator coreless multidisc AFPM (SCM-AFPM) motor with a three-rotor and two-stator topology is proposed. To reduce rotor mass and increase torque density, the proposed SCM-AFPM motor adopts the hybrid permanent magnets (PMs) array with Halbach PMs in the two-terminal rotor and the conventional PMs array in the middle rotor. In addition, a multi-objective optimization model combining response surface method (RSM) and genetic algorithm (GA) is proposed and applied to the proposed SCM-AFPM motor. With the help of the three-dimensional finite-element analysis (3-D FEA), it is found that the torque ripple of the optimized SCM-AFPM motor is 4.73%, while it is 6.21% for t... [more]

The optimal allocation of protective devices is a serious issue in an electrical power system; in order to reduce the possibility of faults, the protection devices should be optimally placed. The paper presents a continuous genetic algorithm (CGA) for the optimal allocation of directional relays for the efficient energy minimization in a radial distribution system (DG). The algorithm is flexible to use for the changes and improvements in the optimal location for a DG unit and can optimize the energy consumption in the radial distribution system. The proposed algorithm has been implemented on IEEE 33 and 69-bus system using MATLAB (R2014b, MathWorks). Low energy consumption is a common design objective in an energy-constrained distribution system. Engineers, power utilities, and network operators can profit from the proposed methodology to enhance the use of DG in distribution networks.

We have established a simulation platform for the machine−electro-hydraulic coupling system of the transmission system and the control system to study the root causes of the problems of large shifting impact and slow change of the machine tool transmission system. The dynamic analysis of the gear shift work of the gearbox was carried out, and the main factors affecting its shift instability were studied. With the impact and sliding power as the optimization goals, the shift quality is optimized based on the multi-objective genetic algorithm. Through the shift experiment, it was found that the power interruption phenomenon during the shift process was eliminated after optimization, and the quality of the shift was improved. Simulated planetary row wheel gear meshing force was found in the same gear, and the second planetary row gear meshing force was the largest among the planetary rows. The stress of the node near the top of the tooth is greater than the stress of the node near the nod... [more]

This paper presents a novel linear hybrid excited flux switching permanent magnet machine (LHEFSPMM) with a crooked tooth modular stator. Conventional stators are made up of a pure iron core, which results in high manufacturing costs and increased iron core losses. Using a modular stator lowers the iron volume by up to 18% compared to a conventional stator, which minimizes the core losses and reduces the machine’s overall cost. A crooked angle is introduced to improve the flux linkage between the stator pole and the mover slot. Ferrite magnets are used with parallel magnetization to reduce the cost of the machine. Two-dimensional FEA is performed to analyze and evaluate various performance parameters of the proposed machine. Geometric optimization is used to optimize the split ratio (S.R) and winding slot area (Slotarea). Genetic algorithm (GA) is applied and is used to optimize stator tooth width (STW), space between the modules (SS), crooked angle (α), and starting angle (θ). The pro... [more]

The wireless power transmission (WPT) of an autonomous underwater vehicle (AUV) tends to have non-negligible eddy current loss with increasing frequency or coil current due to the conductivity of seawater. In this paper, the inductor-capacitor-capacitor and parallel (LCC-P) topology and the magnetic coupler with an H-shaped receiver structure are chosen to achieve a compact system on the receiving side. The conditions for constant current output of the LCC-P topology are analyzed based on the cascaded circuit analysis method. The traditional parameter design method does not consider the influence of eddy current loss on the system circuit model, by introducing the equivalent eddy current loss resistance at both the transmitting side and receiving side, a modified circuit model of the WPT system in the seawater condition was obtained. Afterward, a nonlinear programming model with the optimal efficiency of the constant current mode as the objective function is established, and the geneti... [more]

Magnetic gears (MGs) have gained increasing attention due to their sound performance in high torque density and low friction loss. Aiming to maximize the torque density, topology design has been a popular issue in recent years. However, studies on the optimization comparisons of a general MG topology pattern are very limited. This paper proposes a Taguchi-method-based optimization method for a general MG topology pattern, which can cover most of the common types of radially magnetized concentric-surface-mounted MGs (RMCSM-MGs). The Taguchi method is introduced to evaluate the influence of each parameter in MGs. Moreover, the parameter value range is re-examined based on the sensitivity analysis results. The genetic algorithm (GA) method is adopted to optimize the topology pattern in the study.

The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in diagnosing chronic diseases, such as cancer in its earlier stage, which is important and challenging. However, the high-dimensionality and noisy nature of the microarray data lead to slow performance and low cancer classification accuracy while using machine learning techniques. In this paper, a hybrid filter-genetic feature selection approach has been proposed to solve the high-dimensional microarray datasets problem which ultimately enhances the performance of cancer classification precision. First, the filter feature selection methods including information gain, information gain ratio, and Chi-squared are applied in this study to select the most significant features of cancer... [more]

To improve the hydraulic performance of a new type of dishwasher pump and solve the multi-parameter optimization problem, a genetic algorithm was introduced to optimize the special design of the twin-volute structure. Six curvature radii of the twin-volute structure were defined as the optimization parameters, and 100 groups of design samples were generated based on the Latin hypercube sampling (LHS) method. The pump head and the efficiency were taken as the optimization objectives, i.e., to improve the efficiency as much as possible while ensuring that the head would not be lower than 2 m. The important parameters were identified via sensitivity analysis, and the optimization problem was solved in detail by using the multi-objective genetic algorithm (MOGA). The results showed that the external profile of the first to the fourth section of the twin-volute structure had the most significant effect on the pump head and efficiency. The response surface method (RSM) was used to select the... [more]

This paper presents the modelling of heat and moisture transfer in a clothes-conditioning unit with the aim of improving the moisture content distribution to the clothes. A multicomponent, non-reacting, two-phase Eulerian−Eulerian model was utilised to solve the computational model. The clothes inside the conditioning unit were modeled as retangular towels (porous medium) of uniform thickness. Mass flow distribution of air and steam through the clothes was studied by systematically varying the steam nozzle angle (30° to 75°) and air inflow grill angle (45° to 105°). The simulation results were studied to identify the impact of design parameters on the mass flow distribution inside the clothes-conditioning unit. The mass flow of steam and the air−steam mixture were calculated through each towel in the forward and reverse direction. Response surface analysis was conducted to correlate the total mass flow rate and steam mass flow rate through each towel with the design variables. Moreover... [more]

Fault-tolerant control systems (FTCS) are used in safety and critical applications to improve reliability and availability for sustained operation in fault situations. These systems may be used in process facilities to reduce significant production losses caused by irregular and unplanned equipment tripping. Internal combustion (IC) engines are widely used in the process sector, and efficient air−fuel ratio (AFR) regulation in the fuel system of these engines is critical for increasing engine efficiency, conserving fuel energy, and protecting the environment. In this paper, a hybrid fault-tolerant control system has been proposed, being a combination of two parts which are known as an active fault-tolerant control system and a passive fault-tolerant control system. The active part has been designed by using the genetic algorithm-based fault detection and isolation unit. This genetic algorithm provides estimated values to an engine control unit in case of a fault in any sensor. The pass... [more]

The use of a suitable modeling technique for the optimized design of a magnetic gear is essential to simulate its electromagnetic behavior and to predict its satisfactory performance. This paper presents the design optimization of an axial flux magnetic gear (AFMG) using a two-dimensional (2D) magnetic equivalent circuit model (MEC) and a Multi-objective Genetic Algorithm (MOGA). The proposed MEC model is configured as a meshed reluctance network (RN) with permanent magnet magnetomotive force sources. The non-linearity in the ferromagnetic materials is accounted for by the MEC. The MEC model based on reluctance networks (RN) is considered to be a good compromise between accuracy and computational effort. This new model will allow a faster analysis and design for the AFMG. A multi-objective optimization is carried out to achieve an optimal volume-focused design of the AFMG for future practical applications. The performance of the optimized model is then verified by establishing flux den... [more]

The forecasting of crude oil production is essential to economic plans and decision-making in the oil and gas industry. Several techniques have been applied to forecast crude oil production. Artificial Intelligence (AI)-based techniques are promising that have been applied successfully to several sectors and are capable of being applied to different stages of oil exploration and production. However, there is still more work to be done in the oil sector. This paper proposes an optimized gradient boosting (GB) model by genetic algorithm (GA) called GA-GB for forecasting crude oil production. The proposed optimized model was applied to forecast crude oil in several countries, including the top producers and others with less production. The GA-GB model of crude oil forecasting was successfully developed, trained, and tested to provide excellent forecasting of crude oil production. The proposed GA-GB model has been applied to forecast crude oil production and has also been applied to oil pr... [more]

The absence of electricity is among the gravest problems preventing a nation’s development. Hybrid renewable energy systems (HRES) play a vital role to reducing this issue. The major goal of this study is to use the non-dominated sorting genetic algorithm (NSGA)-II and hybrid optimization of multiple energy resources (HOMER) Pro Software to reduce the net present cost (NPC), cost of energy (COE), and CO2 emissions of proposed power system. Five cases have been considered to understand the optimal HRES system for Kutubdia Island in Bangladesh and analyzed the technical viability and economic potential of this system. To demonstrate the efficacy of the suggested strategy, the best case outcomes from the two approaches are compared. The study’s optimal solution is also subjected to a sensitivity analysis to take into account fluctuations in the annual wind speed, solar radiation, and fuel costs. According to the data, the optimized PV/Wind/Battery/DG system (USD 711,943) has a lower NPC t... [more]

The stereometry of the working units of mining machines is optimized at the design stage, in terms of selected criteria based on computer simulations of the mining process. The recovered bodies of cutting heads or drums used in manufacturing are regenerated in the overhaul process. Ensuring that their dimensions comply with the nominal ones is labor-intensive and raises production costs. However, deviations of these components from the nominal shape can make it difficult to position the pick holders (which can cause collisions) or make welding them impossible (which results from too large a distance between the pick holders’ base and the side surface of the working unit). This applies especially to robotic technologies. By utilizing automatic (online) measurements of the distribution of the actual distances of the pick holders’ bases from the side surface of the working unit (taken during their positioning using a robot), it is possible to correct their positions without changing the s... [more]

Although the proportional integral derivative (PID) is a well-known control technique applied to many applications, it has performance limitations compared to nonlinear controllers. GAPID (Gaussian Adaptive PID) is a non-linear adaptive control technique that achieves considerably better performance by using optimization techniques to determine its nine parameters instead of deterministic methods. GAPID represents a multimodal problem, which opens up the possibility of having several distinct near-optimal solutions, which is a complex task to solve. The objective of this article is to examine the behavior of many optimization algorithms in solving this problem. Then, 10 variations of bio-inspired metaheuristic strategies based on Genetic Algorithms (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are selected to optimize the GAPID control of a Buck DC−DC converter. The computational results reveal that, in general, the variants implemented for PSO and DE present... [more]

With the gradual increase in load demand due to population and economic growth, integrating renewable energy sources (RES) into the grid represents a solution for meeting load demand. However, integrating RES might change the power system type from radial to non-radial, where the current can flow forward and backward. Consequently, power system analysis methods must be updated. The impact on power systems includes changes in the load flow affecting the voltage level, equipment sizing, operating modes, and power system protection. Conventional power system protection methods must be updated, as RES integration will change the power flow results and the short circuit levels in the power system. With an RES contribution to short circuit, existing settings might experience missed coordination which will result in unnecessary tripping. This paper considers the impact of integrating renewable energy sources into power system protection on overcurrent time delay settings. A new method to upgr... [more]

The calorific value of coal gangue is a critical index for coal waste recycling and the energy industry. To establish an accurate and efficient calorific value forecasting model, a method based on hybrid kernel function−support vector regression and genetic algorithms is presented in this paper. Firstly, key features of coal gangue gathered from major coal mines are measured and used to build a sample set. Then, the forecasting performance of single kernel function-based models is established, and linear kernel and Gaussian kernel functions are chosen according to forecasting results. Next, a hybrid kernel combined with the two kernel functions mentioned above is used to establish a calorific value forecasting model. In addition, a genetic algorithm is introduced to optimize critical parameters of SVR and the adjustable weight. Finally, the forecasting model based on hybrid kernel function−support vector regression and genetic algorithms is built to predict the calorific value of new c... [more]

Disruptive events, such as the winter storm of 2021 that left 40 million people in the U.S. without power, have revealed the potential danger of societal dependence on centralized energy sources. Localized energy grids (called microgrids (MGs)) can help add energy reliability and independence by using distributed generators (DGs) with photovoltaic (PV) energy sources and energy storage systems (ESSs). Such MGs can independently energize critical energy demand nodes (DNs) when isolated from the primary grid with renewable energy. The optimal sizes and assignments of PVDG/ESS units to the DNs during outages are crucial to increasing energy reliability. However, finding an optimal configuration−energy management strategy is difficult due to the investment costs, complexity of assignments, potential capacities, and uncertainties in the PV system output. In this research, we developed a simulation framework, augmented by genetic algorithms (GAs), to optimize costs and fulfill energy demands... [more]

Wind energy is a powerful resource contributing to the decarbonization of the electric grid. However, wind power penetration introduces uncertainty about the availability of wind energy. This article addresses the complementarity of remote offshore wind sites in Brazil, demonstrating that strategic distribution of wind farms can significantly reduce the seasonality and the risk of periods without generation and reduce dependence on fossil sources. Field observations, atmospheric reanalysis, and simplified optimization methods are combined to demonstrate generation improvement considering regions under environmental licensing and areas not yet considered for offshore development. Aggregated power results demonstrate that with the relocation of wind turbines, a 68% reduction of the grid seasonal variability is possible, with a penalty of only 9% of the generated energy. This is accomplished through optimization and the inclusion of the northern region, which presents negative correlation... [more]

Driven by the goals of carbon neutral and carbon peak, coal power units need increased flexibility in peak shaving to accommodate intermittent renewables, especially for a region with a large proportion of combined heat and power (CHP) units in China. In this study, the data-mining-based method is proposed for revealing and utilizing the heat−power coupling mechanism of CHP units, which can be used to solve the mentioned issues. Specifically, extraction-condensing (EC) units, high-back-pressure (HBP) units and low-pressure turbine zero power output (LZPO) units are introduced into the proposed dispatch model for maximizing renewable energy accommodation. The operation schemes and the feasible minimum output power of the CHP system under one certain heat load are obtained via the genetic algorithm. Results show that the CHP system is capable of reducing its output power by 18.7% to 41.7% in the heating season, compared with the actual operation data. Furthermore, the influence of multi-... [more]

With the growth of electric vehicles’ (EVs) deployment as a substitute for internal combustion engine vehicles, the impact of this kind of load on the distribution grid cannot be neglected. An in-depth study needs to be performed on a regional basis to investigate the impacts of electric vehicle (EV) charging on the grid for each country’s grid configuration and specifications, in order to be able to reduce them. In this work, we built a case study of a charging infrastructure of a Tunisian workplace parking lot, by combining different measured data and simulations using OpenDSS and Matlab. The first objective was to analyze the integration impacts on the Tunisian low-voltage (LV) grid including phase unbalance, voltage drop, harmonics, and power losses. We found that 10 metric tons of carbon dioxide (MtCO2) in yearly emissions were caused by power losses, and 50% of these emissions came from harmonic losses, which can be avoided by active and passive filtering. The second objective wa... [more]

Reducing energy consumption while providing a high-quality environment for building occupants has become an important target worthy of consideration in the pre-design stage. A reasonable design can achieve both better performance and energy conservation. Parametric design tools show potential to integrate performance simulation and control elements into the early design stage. The large number of design scheme iterations, however, increases the computational load and simulation time, hampering the search for optimized solutions. This paper proposes an integration of parametric design and optimization methods with performance simulation, machine learning, and algorithmic generation. Architectural schemes were modeled parametrically, and numerous iterations were generated systematically and imported into neural networks. Generative Adversarial Networks (GANs) were used to predict environmental performance based on the simulation results. Then, multi-object optimization can be achieved th... [more]

In an effort to address the load adjustment time in the thermal and electrical load distribution of thermal power plant units, we propose an optimal load distribution method based on load prediction among multiple units in thermal power plants. The proposed method utilizes optimization by attention to fine-tune a deep convolutional long-short-term memory network (CNN-LSTM-A) model for accurately predicting the heat supply load of two 30 MW extraction back pressure units. First, the inherent relationship between the heat supply load and thermal power plant unit parameters is qualitatively analyzed, and the influencing factors of the power load are screened based on a data-driven analysis. Then, a mathematical model for load distribution optimization is established by analyzing and fitting the unit’s energy consumption characteristic curves on the boiler and turbine sides. Subsequently, by using a randomly chosen operating point as an example, a genetic algorithm is used to optimize the... [more]

The development of local energy communities observed in the last years requires the reorganization of energy consumption and production. In these newly considered energy systems, the commercial and technical decision processes should be decentralized in order to reduce their maintenance costs. This will be allowed by the progressive spreading of IoT systems capable of interacting with distributed energy resources, giving local sources the ability to be optimally coordinated in terms of network and energy management. In this context, this paper presents a decentralized controlling architecture that performs a wide spectrum of power system optimization procedures oriented to the local market management. The controller framework is based on a decentralized genetic algorithm. The manuscript describes the structure of the tool and its validation, considering an automated distributed resource scheduling for local energy markets. The simulation platform permits implementing the blockchain-bas... [more]