This work presents an investigation into the use of swarm intelligence techniques for the control of the doubly fed induction generator under unbalanced grid voltages. Swarm intelligence is a concept that was introduced in the late 20th century but has since undergone constant evolution and modifications. Similarly, the doubly fed induction generator has recently come under intense investigation. Owing to the direct grid connection of the DFIG, an unbalanced grid voltage harshly impacts its output power. Established mitigation measures include the use of the dual vector and proportional−resonant control methods. This work investigates the effectiveness of utilizing swarm intelligence for the purpose of controller gain optimization. A comparison of the application of swarm intelligence to the dual vector and proportional−resonant controllers was carried out. Three swarm intelligence techniques from across the timeline were utilized including particle swarm optimization, the bat algorith... [more]

In order to improve the energy capture efficiency of an oscillating buoy wave energy converter (WEC), a buoy-shape optimization design method based on the mean annual power prediction model is proposed. According to the statistical data of long-term wave characteristics in the Chinese sea area, the optimal design space is determined. Sixty-three sample points were randomly selected in the optimized space. Based on simulation, the mean annual power corresponding to each sample point is calculated to quantitatively describe the energy capture ability. The response surface method (RSM), radial basis function neural network (RBFNN), and elliptical basis functions neural network (EBFNN) are used to establish the mean annual power prediction models, respectively. By combining the prediction model with the multi-island genetic algorithm (MIGA), the optimal solution in the design space is easily obtained. The reliability of the optimal solution is further proved by quantitative analysis about... [more]

The transverse-flux motor, which has the advantage of high torque density, has become one of the research focuses of direct-drive motors for low speed and high-torque industrial applications. This paper aimed to propose a transverse-flux flux-reversal motor with consequent-pole configuration (TF-CFRM), which has a robust rotor structure and provides high torque density by using fewer expensive NdFeB permanent magnets. Firstly, the basic structure and running work principle of the TF-CFRM are introduced. Secondly, the analytical expression of electromagnetic torque is derived from a simplified 3D-equivalent magnetic-circuit model. Then, the preliminary optimization of the basic dimension is accomplished by the finite element method to improve the torque density. At last, the electrical performances, e.g., the torque density, overload capability, and power factor of the proposed TF-CFRM are analyzed and compared with those of the transverse-flux flux-reversal motor (TF-FRM) and tradition... [more]

The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete−Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete−continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of qualit... [more]

To achieve the goal of achieving carbon-neutral by 2060, the government of China has put forward higher requirements for energy conservation and consumption reduction in the energy industry. Therefore, it is necessary to reduce energy consumption in the process of transporting oil. In this paper, an optimization model that minimizes the total energy consumption of the entire pipeline system is proposed and the squirrel search algorithm (SSA) is used to solve the optimization model. Meanwhile, to improve the performance of the SSA, two strategies are proposed. One is the adaptive inertia weight strategy, and the other is the multi-group co-evolution strategy. The adaptive inertia weight can adjust the step size of the flying squirrels according to the difference of the objective function value and multi-group co-evolution is introduced to improve population diversity. The improved SSA is named multigroup coevolution-adaptive inertia weight SSA (MASSA). A total of 20 benchmark functions... [more]

The resources of the earth are being consumed day by day with the increasing population and necessities of humankind in many areas, such as industrial applications and basic needs in houses, workplaces and transportation. As a consequence, careful usage of the energy sources and the conversed energy is of great importance in order to obtain sustainable development. Machining operations have a large percentage of all manufacturing methods in terms of depleted energy which gives them a high potential for reducing the total energy consumption. The approaches handled in the literature for the minimization of the consumed energy in the machining industry were considered in this study. While several machinability characteristics under different machining processes were investigated broadly in the context of composites and superalloys, the comparison of these systems has been given cursory attention in the current literature, specifically for cutting energy saving. The overall performance of... [more]

The reliability of the compressor system determines the gas supply safety. An important method to improve the reliability is to set up standby compressors in stations, conducted by the standby compressor or power. A lack of quantitative assessments of standby compressors often results in more spare compressors or power than actually needed, which wastes money. In this study, a reliability-based method is proposed to determine the numbers and positions of the standby compressors, which can reduce investments, and ensure reliability. Firstly, Monte Carlo method was used to calculate the compressor outage probability of the whole pipeline, respectively, through which the initial number of standby compressors was obtained. Further, the standby schemes were designed, in which the positions of the failed compressors were obtained by the Monte Carlo simulation. Moreover, the worst situation in which the compressors were shut down was used to test the standby scheme, calculating the flow relia... [more]

Integrated Motor Drives (IMDs) have recently received extensive attention. In electric vehicles (EVs), electric propulsion aircraft, and ship propulsion systems, integrated motors have the great potential to replace traditional motors with the distinct merits of compact size, high power density, high efficiency, and high-cost effectiveness. This paper investigates and reviews integrated motor drives’ development and critical technologies. It not only reveals the research progress of the motor structure, converter, volume optimization, heat dissipation design, and weakening electromagnetic interference of integrated motor drives but also explores in detail the applications of wide-bandgap semiconductors and the integration of LCL filters. In addition, this paper also puts forward the concept of integrated motor drive integration level and establishes a corresponding quantitative method to evaluate IMDs integration level. In the future, integrated wireless motor drives will have a broad... [more]

Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. Given the importance of the thermal loads’ analysis in energy-efficiency buildings, a new optimizer method, i.e., the teaching−learning based optimization (TLBO) approach, has been developed and compared with alternative techniques in the present paper to predict the heating loads (HLs). This model is applied to the adaptive neuro−fuzzy interface system (ANFIS) in order to overcome its computational deficiencies. A literature-based dataset acquired for residential buildings is used to feed these models. According to the results, all the applied models can appropriately predict and analyze the heating load pattern. Based on the value of R2 calculated for both testing and training (0.98933, 0.98931), teaching−learning-based optimization can he... [more]

Edge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge devices are timed to swap between different states to achieve stabilized energy efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by considering the offloading and computational states for energy efficacy. The proposed scheme employs state learning for swapping the computing intervals for scheduling or offloading depending on the load. The edge devices are distributed at the time of scheduling and organized for first come, first serve for offloading features. In state learning, the reward is allocated for successful scheduling over offloading to prevent device exhaustion. The computation is therefore swapped for energy-reserved scheduling or offloading based on the previous computed reward. This cognitive management induces device allocation based on energy availability and computing time to prevent energy convergence. Co... [more]

As asymmetric interior permanent magnet synchronous motor (AIPMSM) has excellent performance but complicated topological structure, a novel high-resolution encoding and edge smoothing method is proposed for topology optimization of the asymmetric rotor of interior permanent magnet synchronous motor (IPMSM) in this study. This method aims to solve complex electromagnetic design problems with time-dependent performance through a multi-objective genetic algorithm (MOGA) integrated with a high-resolution encoding and edge smoothing method. The complex structure is represented by a high-resolution image-like matrix and then vectorized by the edge smoothing method. Therefore, the commonly used discrete binary encoded variables related to the finite element (FE) model are replaced with a vectorized topological structure and other control variables. In this sense, high-resolution matrix and edge smoothing methods are used for the first time to represent the rotor topology of AIPMSMs. Compared... [more]

Core flow distribution is closely related to the thermal−hydraulic performance and safety of reactors. For natural circulation reactors with a limited driving force, flow distribution optimization is of particular significance, which can be contrived by suitably assigning the inlet resistance of a core assembly channel in reactor design. In the present work, core flow distribution optimization during the fuel life cycle is regarded as a global optimization problem. The optimization objective is to minimize the maximal outlet temperature difference of assembly channels during the fuel life cycle, while the input variable is the inlet resistance coefficient of each assembly channel. The simulated annealing algorithm is applied to the optimization code. The results show that the maximal outlet temperature difference is significantly reduced after optimization, and the resultant core outlet temperature distribution becomes more uniformed. Further evaluation indicates that the optimal solut... [more]

Climate change is a serious challenge facing the world today. Countries are already working together to control carbon emissions and mitigate global warming. Improving energy efficiency is currently one of the main carbon reduction measures proposed by the international community. Within this context, improving energy efficiency in manufacturing systems is crucial to achieving green and low-carbon transformation. The aim of this work is to develop a new preventive maintenance strategy model. The novelty of the model is that it takes into account energy efficiency, maintenance cost, product quality, and the impact of recycling defective products on energy efficiency. Based on the relationship between preventive maintenance cost, operating energy consumption, and failure rate, the correlation coefficient is introduced to obtain the variable preventive maintenance cost and variable operating energy consumption. Then, the cost and energy efficiency models are established, respectively, and... [more]

The data on the collective dose reduction of a nuclear power plant’s personnel after the introduction of new dose limits by the International Commission for Radiological Protection (ICRP) (Publication 60) in 1990 are presented. The main methods of personnel irradiation reduction are formulated, which are namely: to impact on radiation parameters, to increase the distance between a radiation source and a person, and to reduce the exposure time in radiation fields. The ways to implement one of the basic principles of radiation safety, the principle of optimization, are described. The possibility of route optimization in minimizing the personnel dose costs when moving in heterogeneous radiation fields is shown. The results of the algorithm development for solving the “dosimetrist problem” using the Dijkstra algorithm and dynamic programming are presented, including determining the optimal route with visiting given points in the room and bypassing possible obstacles. An interpolation algor... [more]

An interconnected power system requires specific restrictions to be maintained for frequency, tie-line power, and the terminal voltage of synchronized generators to avoid instability. Therefore, frequency stability and voltage regulation issues are covered individually and jointly in the current research work. Initially in test system 1, automatic generation control (AGC) investigations are done on two interconnected systems with thermal plants and electric vehicles in one area and distributed generation and electric vehicles in other area. The automatic voltage regulator (AVR) problem alone is chosen for investigation in test system 2. The third test system addresses the combined AGC and AVR issues. The performance of the fractional-order tilt-integral-derivative (TID) controller is compared with that of a widely used proportional integral derivative (PID) controller in all three test systems studies. The findings demonstrate better performance of the TID controller than PID in terms... [more]

The performance of a plate heat exchanger (PHE) using water as the working fluid with zigzag flow channels was optimized in the present study. The optimal operating conditions of the PHE are explored experimentally by the Taguchi method, with effectiveness as the objective function. The results are further verified by the analysis of variance (ANOVA). In addition, the zigzag flow channel geometry is optimized by the non-dominated sorting genetic algorithm-II (NSGA-II), in which the effectiveness and pressure drop of the PHE are considered the two objective functions in the multi-objective optimization process. The experimental results show that the ratio of flow rates is the most important factor affecting the effectiveness of the PHE. The optimal operating conditions are the temperatures of 95 °C and 10 °C at the inlets of hot and cold water flows, respectively, with a cold/hot flow rate ratio of 0.25. The resultant effectiveness is 0.945. Three geometric parameters of the zigzag flow... [more]

Detailed thermodynamic, exergoeconomic, and multi-objective analysis are performed for a supercritical recompression Brayton cycle in which the advanced working medium mixture of nitrous oxide and helium (N2O−He) is utilized for power generation. The thermodynamic and exergoeconomic models are propitious based on the standard components’ mass and energy conservation, exergy balance equation, and exergy cost calculation equation. An investigation of the sensitivity parametric is considered for judging the impact of crucial decision variable parameters on the performance of the proposed Brayton cycle. The proposed cycle’s performance is evaluated by systematic analysis of the thermal efficiency (ηth), exergy efficiency (ηex), total cost rate (C.), levelized cost of electricity (LCOE), and the total heat transfer area (Atotal). Furthermore, multi-objective optimization is adopted from the viewpoint of the first and second laws of exergoeconomics to find the optimum operating parameters an... [more]

The importance of integrating distributed generation (DG) units into the distribution network (DN) recently developed. To decrease power losses (PL), this article presents a meta-heuristic population-based tangent golden flower pollination algorithm (TGFPA) as an optimization technique for selecting the ideal site for DG. Furthermore, the proposed algorithm also finds the optimal routing configuration for power flow. TGFPA requires very few tuning parameters and is comprised of a golden section and a tangent flight algorithm (TFA). Hence, it is easy to update these parameters to obtain the best values, which provide highly reliable results compared to other existing techniques. In different case studies, the TGFPA’s performance was assessed on four test bus systems: IEEE 33-bus, IEEE 69-bus, IEEE 119-bus, and Indian-52 bus. According to simulation results, TGFPA computes the optimal reconfigured DN embedded along with DG, achieving the goal of minimal power loss.

The successful supply of an economy with coal fuel, for a country that carries out its large-scale extraction and import, is a complex production and logistics problem. Violations of the usual supply scheme in conditions of crises in the energy markets, international conflicts, etc., lead to the problem of simultaneous restructuring of the entire supply scheme. This requires changes in the directions and capacities of domestic production and imports. In this article, the above problem is solved by the economic and mathematical model of production type. The developed model includes subsystems of domestic production and import supply. The results of modeling economy supply with thermal coal for different values of demand are given. The model was used to determine the amounts of coal production for Ukraine with the structure of the coal industry of 2021 and under the condition of anthracite consumers’ transformation to the high volatile coal. Simulations have shown that eliminating the us... [more]

Due to rising fossil fuel costs, electricity tariffs are also increasing. This is motivating users to install nanogrid systems to reduce their electricity bills using solar power. However, the two main constraints for a solar system installation are the initial financial investment cost and the availability of space for the installation of solar panels. Achieving greater electricity savings requires more panels and a larger energy storage system (ESS). However, a larger ESS also increases the electricity bill and reduces the available solar power due to higher charging power requirements. The increase in solar power leads to the need for more space for solar panel installation. Therefore, achieving the maximum electricity savings for a consumer unit requires an optimized number of solar panels and ESS size within the available financial budget and the available physical space. Thus, this study presents a fiscal- and space-constrained mixed-integer linear programming-based nanogrid syst... [more]

Nowadays, photovoltaic (PV) systems are responsible for over 994 TWH of the worldwide energy supply, which highlights their relevance and also explains why so much research has arisen to enhance their implementation; among this research, different optimization techniques have been widely studied to maximize the energy harvested under different environmental conditions (maximum power point tracking) and to optimize the efficiency of the required power electronics for the implementation of MPPT algorithms. On the one hand, an earthquake optimization algorithm (EA) was introduced as a multi-objective optimization tool for DC−DC converter design, mostly to overcome component shortages by optimal replacement, but it had never been tested (until now) for PV applications. On the other hand, the original EA was also taken as inspiration for a promising EA-based MPPT, which presumably enabled a solution with simple parametric calibration and improved dynamic behavior; yet prior to this research... [more]

To solve the problems of the low energy efficiency and slow penetration rate of drilling, we took the geological data of adjacent wells, real-time logging data, and downhole engineering parameters as inputs; the mechanical specific energy and unit footage cost as multi-objective optimization functions; and the machine pump equipment limit as the constraint condition. A constrained Bayesian optimization algorithm model was established for the optimization solution, and drilling parameters such as weight-of-bit, revolutions per minute, and flowrate were optimized in real time. Through a comparison with NSGA-II, random search, and other optimization algorithms, and the application results of example wells, we show that the established Bayesian optimization algorithm has a good optimization effect while maintaining timeliness. It is suitable for real-time optimization of drilling parameters, can aid a driller in identifying the drilling rate and potential tapping area, and provides a decis... [more]

A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network’s lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Jaya Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effecti... [more]

The efficiency of permanent magnet synchronous hub motors (PMSHM) used in light electric vehicles (EVs) is lower than that used in commercial EVs. Therefore, in this study a high-efficiency radial-flux outer-rotor PMSHM was designed for light EVs. The high-efficiency motor will contribute to the reduction of the power consumption demand from the batteries of EVs, the longer life of the batteries and the longer uninterrupted operation of the system. The optimization objectives, such as motor sizing, number of slots and poles, air gap length, material selection, stator winding structure, stator slot shape, magnet thickness, and cutting method for stator sheets were considered to ensure high efficiency and low cogging torque. In this study, three validation stages were followed; electromagnetic analyzes with FEM, analytical calculations, and finally experimental validation. First, the design parameters of the motor were determined based on the analyses results obtained using ANSYS Maxwell... [more]

Past studies of microgrids have been based on measurements of fuel consumption by generators under static loads. There is little information on the fuel efficiency of generators under time-varying loads. To help analyze the impact of time-varying loads on optimal generator operation and fuel consumption, we formulate a mixed-integer linear optimization model to plan generator and energy storage system (ESS) operation to satisfy known demands. Our model includes fuel consumption penalty terms on time-varying loads. We exercise the model on various scenarios and compare the resulting optimal fuel consumption and generator operation profiles. Our results show that the change in fuel efficiency between scenarios with the integration of ESS is minimal regardless of the imposed penalty placed on the generator. However, without the assistance of the ESS, the fuel consumption increases dramatically with the penalty imposed on the generator. The integration of an ESS improves fuel consumption b... [more]