Particle Swarm Method Research Articles

Wing Shape Optimization Using FFD and Twist Parameterization

Optimization of wing shapes for aerodynamic performance is presented using a combination of particle swarm method and surrogate models. The wing shape deformations are parameterized using free form deformation together with wing twist. The developed strategy is applied to the lift-constrained drag minimization of Onera M6 wing.

An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks

With many distribution networks adopting photovoltaic (PV) generation systems in their networks, there is a significant risk of over-voltages, reverse power flow, line congestion, and increased harmonics. Therefore, there is a need to estimate the amount of PV that can be injected into the distribution network without pushing the network towards these threats. The largest amount of PV a distribution system can accommodate is the PV hosting capacity (PVHC). The paper proposes an efficient method for estimating the PVHC of distribution networks that combines particle swarm optimization (PSO) and the gradient descent algorithm (GD). PSO has a powerful exploration of the solution space but poor exploitation of the local search. On the other hand, GD has great exploitation of local search to obtain local optima but needs better global search capabilities. The proposed method aims to harness the advantages of both PSO and GD while alleviating the ills of each. The numerical case studies show that the proposed method is more efficient, stable, and superior to the other meta-heuristic approaches.

Optimizing generating unit maintenance with the league championship method: A reliability-based approach

The electrical power industry has experienced an unprecedented pace of digital transformation as a prevailing economic trend in recent years. This shift towards digitalization has resulted in an increasing interest in the collection of real-time equipment condition data, which provides opportunities for implementing sensor-driven condition-based repair. As a result, there is a growing need for the development of generator maintenance scheduling to consider probabilistic equipment behavior, which requires significant computational efforts. To address this issue, the research proposes the use of a meta-heuristic league championship method (LCM) for generator maintenance scheduling, considering random generation profiles based on generation adequacy criteria. The experimental part of the study compares this approach and its modifications to widely used meta-heuristics, such as differential evolution and particle swarm methods. The identification and demonstration of optimal method settings for the generation maintenance scheduling problem are presented. Subsequently, it is illustrated that employing random league scheduling expedience can reduce the variance of objective function values in resulting plans by over three times, with values of 0.632 MWh and 0.205 MWh for conventional and proposed techniques respectively. In addition, three approaches are compared to assess generation adequacy corresponding to different schedules. The study emphasizes the efficacy of employing the LCM approach in scheduling generator maintenance. Specifically, it showcases that among all the methods examined, the LCM approach exhibits the lowest variance in objective function values, with values of 38.81 and 39.90 MWh for LCM and its closest rival, the modified particle swarm method (MPSM), respectively.

Construction Cost Control and Management Based on Mapping Model and BIM

Abstract At present, the information management of highway construction is still at different levels. Poor information coordination ability. Lack of adequate information technology support. BIM technology is used to realize cooperation and information integration among various disciplines. According to the characteristics of highway construction, this paper constructs the decomposition code of the BIM model by the particle swarm method. This model can better reflect the whole process of highway project cost control characteristics. The corresponding relationship between the “three-level list” and the BIM code is constructed. This paper makes a comprehensive analysis and discussion on the mapping between it and the “item list” in the three types of lists. Thus, BIM can be efficiently connected with the three-level list in the construction of bridge projects. It can better adapt to engineering construction and other engineering management requirements.

Superstructure design and optimization on closed Brayton cycle system of fluoride-salt-cooled high-temperature advanced reactor

To exploit the advantages of Fluoride-Salt-cooled high-Temperature Advanced Reactors (FuSTAR) such as high temperature, inherent safety, compact structure, and modularization, an energy conversion system based on a closed Brayton cycle was adopted, and the superstructure design was optimized to attain the best parameters and configuration. To enhance the convergence rate of a single computation, the simultaneous equation method was employed, while the particle swarm method was utilized to prevent local optimality. Irreversible analysis and sensitivity analysis were performed to evaluate the system's performance. The findings demonstrate that thermal efficiency can be significantly enhanced by incorporating reheating into the system. Furthermore, for air and Ar, the recompression intercooling configuration proved effective in reducing the heat transfer temperature difference and enhancing thermal efficiency. For CO2 and Xe, the incorporation of reheating, recompression, and intercooling significantly improved thermal efficiencies beyond 53%, indicating their immense potential for further development. Introducing reheating into the Xe cycle led to a substantial increase in both thermal efficiency and the area of the temperature-entropy graph. The irreversible analysis revealed that for CO2 and SF6, the predominant loss occurred in the cooler and recuperator. In contrast, for the Xe cycle, all kinds of irreversible losses were evenly distributed across each device. Sensitivity analysis indicated that the thermal efficiency of near-critical fluids was more sensitive to the lowest pressure than that of far-critical fluids. Both the near-critical split ratio and the far-critical minimum pressure had a significant impact on the mass flow rate. The design results of this method can guide the selection of the optimal power cycle system configuration for FuSTAR.

Springback Control in Complex Sheet-Metal Forming Based on Advanced High-Strength Steel

Sheet-metal forming is one of the most important manufacturing processes in the automotive industry. This study proposes a multiobjective optimisation scheme that controls both sheet-metal formability and springback. First, the mechanical properties of DP590 steel were characterised to obtain the mechanical parameters and forming limit diagram (FLD) of DP590. Then, the FLD, thinning rate, and average springback were selected as forming quality evaluation indices. Response surface tests were then conducted for different process parameters for the A-pillar side-stiffener drawing process to analyse the DP590 steel’s formability and springback. The optimal process parameters for the drawing process were obtained using a multiobjective optimisation algorithm based on an improved particle swarm method. Finally, a springback compensation scheme was proposed based on the results of the multiprocess simulation. The scheme was applied experimentally to the A-pillar side-stiffener drawing process, and the formability and springback compensation performances verified that the scheme successfully and efficiently eliminated springback and rupture in formed DP590 steel.

Hybrid Optimization Approach for Improved Efficiency in Permanent Magnet Brushless DC Motor Design: Combining Cauchy Particle Swarm & Moth Flame Methods

This article proposes a novel design strategy for a PMBLDC motor through hybrid optimization, combining Cauchy Particle Swarm Optimization and Moth Flame Optimization approaches to find the optimal set of design parameters that yield the highest efficiency. The motivation is to improve the efficiency of PMBLDC motors, which are widely used in electric cars and renewable energy systems that place a premium on minimal power usage. The proposed work implements the hybrid optimization technique performs conventional single optimization strategies in terms of efficiency improvement. This study contributes in identifying the optimal values for the various design parameters of PMBLDC motors using the hybrid CPSO-MFO approach, which converged to the optimal design parameters. The performance of the PMBLDC motor was evaluated using the optimal design parameters, with an overall power loss of 49.1 W, and an efficiency of 97.88%. The study’s results provide valuable insights for design engineers into the optimal geometrical parameters of PMBLDC motors to achieve higher efficiency and lower power loss. The proposed novel hybrid optimization approach, which is faster and more efficient than conventional single optimization approaches, making it a useful tool for designing high-performance PMBLDC motors.

An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm

This work proposes a new, efficient, economically, and environmentally viable approach for developing cutting-edge energy systems and assisting the anticipated global green transition with maximal renewable integration. The cogeneration of hydrogen and power is driven by biomass, which in turn drives the vanadium chloride cycle and the proton exchange membrane fuel cells. A cooling absorption unit is powered by waste heat recovered using a passive energy improvement technique to improve performance and cut costs. Energy, exergy, exergo-economic, exergo-environmental impacts, and CO2 emission rate of the suggested renewable-based model are analyzed using an engineering equation solver tool. Parametric analysis is also used to assess the impact of key operational factors on main performance indicators. With machine learning, a particle swarm method is implemented in MATLAB to find the optimal operating state with high precision and low computing cost. The results show the importance of multi-objective optimization by pointing out a conflicting change in the performance metrics from different angles by picking up the biomass moisture content and fuel cell current density. According to the optimization results, an acceptable total cost, environmental damage effectiveness, and exergy efficiency of 5 $/h, 0.86, and 55% are achieved through the integration of particle swarm optimizer and artificial neural network method. The results further reveal that the gasification temperature is not sensitive; however, changing the fuel cell utilization factor significantly impacts the system's performance from all sides. Finally, the chord diagram of the irreversibility rate indicates that the fuel cell and gasifier have the highest destruction of 6.4 kW and 2.6 kW under the optimum condition, owing to mixing and chemical reactions. As for the environmental aspect, by optimizing the system, the system's CO2 emission are greatly reduced.

Research on the durability test method of electric driving systems based on fuzzy clustering and particle swarm algorithm

Compared to traditional fuel vehicles, the structure of pure electric vehicles (BEVs) and the actual driving behaviour of users have changed. Therefore, the original durability evaluation conditions of traditional fuel vehicles cannot fully cover the use of new energy vehicles. In the past, the determination of durability targets was mainly based on user data collection, but this work required a lot of manpower and material resources to meet the engineering requirements. In this paper, the fuzzy clustering method is used to mine the user trajectory to obtain the user-based endurance target of pure electric vehicle, and then according to the durability target, the particle swarm method is used to correlate the user behaviour and the proving ground, and the proving ground test method of electric drive system is developed. Studies have shown that user data mining methods can obtain more user information, so as to better formulate durable target close to users. The particle swarm algorithm can improve the simulation correlation accuracy and reduce the iteration time, which shortens the simulation iteration time by more than 80% compared with polynomials. The test acceleration ratio of 7:1 in relation to user behaviour with the proving ground. During the durability test of the electric drive system of pure electric vehicles, it is found that the test specification can well reflect the user’s motor operation during actual driving.

Optimal planning of a micro‐grid containing tidal barrage equipped to the hydro‐pumps

AbstractDue to the advantages of micro‐grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro‐grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy‐based generation units especially tidal barrages is high, and to generate the cost‐effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro‐grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro‐pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro‐grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution.

Selecting the PID Controller Settings for an Automatic Generation Control Device in a Power System

The integration of renewable generation facilities into integrated power systems entails new challenges for maintaining a reliable and safe electric power infrastructure. It is necessary to expand the use of automatic generation control devices to ensure the ability to control the modes of power systems with a significant share of renewable energy sources. The generation control devices can be used not only for frequency control, but also for maintaining the inter-area wheeling in interconnected power systems. The article suggests application of the particle swarm method, which is one of evolutionary optimization methods, to find optimal parameter settings for proportional-integral-differentiating (PID) controllers of the automatic generation control device. The mathematical description of electric power system elements is given, and a structure of automatic generation control is given. The tuning parameters of the PID controllers of the automatic generation control device based on the particle swarm method, as well as some of classical optimization methods, have been determined. The results of tuning PID controllers obtained by different methods in the form of frequency variation characteristics in power system parts, as well as the torques of an equivalent steam turbine in response to power fluctuations in one of the power system parts are compared.

Market competition oriented air-rail ticket fare optimization

In the business of intermodal passenger transport, fare optimization of intermodal products has significant effects on corporate revenue and passenger travel convenience. This study takes the competitive relationship between high-speed rail (HSR) and airlines as well as carrier connectivity as the starting point, analyzes the advantages and disadvantages of different carriers in the different markets, and researches the optimization of fares. The stochastic user equilibrium model based on elastic demand is used to establish a bi-level programming model for the optimization of fares; the upper and lower models are solved using the particle swarm algorithm and method of successive averages, respectively. The results suggest that airlines are willing to cooperate with the HSR sector and improve the connectivity between aviation and HSR, and a reasonable pricing strategy is more likely to motivate cooperation between aviation and HSR.

Optimal Power Flow For Non-Smooth Cost Function Using Particle Swarm Optimization On 150 Kv System

Optimal power flow by considering the non-smooth cost curve using the meta-heuristic algorithm method, namely particle swarm optimization (PSO) in the 150 kV Sulselrabar electrical system. In this study, the PSO algorithm was used to optimize optimal power flow so that the cheapest generation price was obtained with a non-smooth cost curve and still considered the limitations of similarity and inequality. In this study, the PSO algorithm was used to optimize optimal power flow so that the cheapest generation price was obtained with a non-smooth cost curve and still considered the limitations of similarity and inequality . From the results of generation optimization using the Particle Swarm method, it produces the cheapest generation costs from other methods, namely Rp. 93,498,916.1,- / hour to generate power of 270.14 MW with losses of 25.73 MW. The Particle Swarm Optimization (PSO) method is able to reduce the cost of generating the Sulselrabar system by Rp. 34,382,857.58 / hour or 26.89%. From the results of generation optimization using the Ant Colony method, it resulted in a total generation cost of Rp. 94670335.98 / hour to generate power of 270,309 MW with losses of 25.91 MW. The Ant Colony method is able to reduce the cost of generating the Sulselrabar system by Rp. 33,211,437.70 / hour or 25.98%. From the results of generation optimization using the lagrange method, it resulted in a total generation cost of Rp. 117,121,631.08 / hour to generate power of 339.4 MW with losses of 25,016 MW . The lagrange method is able to reduce the cost of generating the Sulselrabar system by Rp. 10,760,142.60 / hour or 8.41%. The artificial intelligence method based on Particle Swarm Optimization (PSO) can well perform optimization of Optimal Power Flow, from the results of the analysis obtained the cheapest generation cost compared to the comparison method, Lagrange Method and Ant Colony artificial intelligence method .

Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model.

Continuous grasping force estimation based on electromyography (EMG) signals, is very useful in practical applications including prosthetic control and human force observation. However, implementing the practical grasping force estimation usually considers a trade-off between the computational precision and resources. Specifically, the estimation based on the Huxley-type muscle model reaches detailed approximation of physiological process at a cost of larger computational resources for solving nonlinear partial differential equations while the counterpart with a traditional Hill-type muscle model. In this article, we achieve the grasping force estimation based on a reduced Huxley-type musculoskeletal model with high accuracy yet low time delay. Leveraging on a balanced truncation method, we further reduce the dimensionality of the spectral method solution in the Huxley-type musculoskeletal model for the model simplification. In addition, we introduce the Kalman filter method to process the EMG signals obtained by an armband, yielding better real-time performances and accuracy compared to the signal treatment using the traditional EMG filter method. Moreover, we also implement an effective identification of the model parameters using a particle swarm method. Finally, we trained the model on the first day and made grasping force estimation experiments involved with three participants over the course of a month. We envision that this effective and practical method would further improve the practical applications in the field of grasping force estimation.

A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method

In recent years, with the vigorous construction of 5G networks, the high-density deployment, low delay, and high bandwidth of 5G network systems have enabled high-precision positioning services. By integrating the BeiDou Navigation Satellite System (BDS) and 5G systems, more reliable positioning services can be provided, and BDS–5G signal integrated positioning has become a new research hotspot. However, BDS–5G signal fusion positioning faces the problems of how to build an effective fusion positioning model between heterogeneous systems and the high complexity of multiobjective function positioning solutions. Therefore, this paper constructs a TOA/TDOA fusion positioning model of BDS–5G signals and introduces the multiobjective particle swarm optimization positioning solution method (MOPSO) to realize the reliable positioning of BDS and 5G signal system fusion (MOPSO-TOA/TDOA). The experimental results show that compared with the traditional BDS–5G signal fusion positioning algorithm based on a Kalman filter, the positioning accuracy of the BDS–5G signal joint solution system based on a multiobjective particle fusion algorithm is 24.8% higher than that of the Kalman filter in terms of positioning convergence time, 18.9% higher in terms of system positioning accuracy, and 50–80% higher than that of the BDS positioning system alone, and the reliable positioning ability in complex environments is effectively enhanced.

Using dual mutation particle swarm method to optimize the variable cross-section of a thermoelectric generator based on a comprehensive thermodynamic model

The shape design for a thermoelectric generator (TEG) plays an important role in its performance. In this paper, a hyperbolic function was introduced to design a variable cross-section TEG module to optimize the configuration for maximum power generation and efficiency was sought. A comprehensive thermodynamic model was applied to establish the governing equations for the newly designed TEG module. The mutation particle swarm optimization (MPSO) method was invoked to solve the thermodynamic model. The thermodynamic model's output results include the temperatures at both ends of the TE element, thus making it possible to evaluate the actual performance of the TEG module. The results indicate that both the power generation and efficiency of the hyperbolic TEG are superior to those obtained based on a traditional design. The studies also disclosed that the hyperbolic structure can increase the thermal resistance of the TE couple making it possible to enlarge the temperature difference. This is the main mechanism to improve the performance of a hyperbolic TEG. Besides, the four non-dimensional parameters (shape parameter ( β ), area ratio ( μ ), temperature ratio ( θ ), and resistance ratio ( r x )) related to the geometric structure and working conditions have notable effects on the TEG performance. It is thus worthwhile optimizing the TEG power generation and efficiency in the variable searching space of these parameters. However, differing from the traditional optimization, it is necessary to solve the governing equations in every iteration when searching for an optimal configuration based on the comprehensive model. In order to overcome the challenge, the Dual-MPSO algorithm was used in this research. • MPSO is effective in solving the control equations for a comprehensive TEG model. • Hyperbolic varying TE element is better than constant cross-section. • The effect of the temperature ratio on a TEG's performance is non-monotonic. • The cross-sectional area affects the temperature difference of TE elements. • The hyperbolic TEG performance can be optimized through the Dual-MPSO method.

PID-controller tuning algorithm development for a dynamical system “crane-load”

One of the main problems in the operation of cranes is the oscillation of the load on a flexible suspension. One of the ways to eliminate load oscillations on a flexible suspension is to use a proportional-integral-differential controller that generates a control signal for the crane movement. However, for it to function properly, it must be properly tuned. Standard approaches to tuning a PID controller, which is common in the practice of engineering calculations, do not allow solving this problem, and that is why it can be considered as a scientific and applied one. The research aims to develop an algorithm for tuning a proportional-integral-differential controller. For this purpose, a research issue was defined, which includes a mathematical model of the dynamic system, restrictions on the overload capacity of the crane drive and the control function, conditions for achieving the steady-state speed of the crane and eliminating pendulum oscillations of the load on a flexible suspension. Using the modified particle swarm method, ME-D-PSO, the coefficients of the proportional-integral-differential controller were determined for a wide range of values of the load mass and the length of the flexible suspension. Based on the obtained values of the coefficients, an algorithm is presented that allows calculating the values of the coefficients for any values of the cargo mass and the length of the suspension. The dynamics of the movement of the crane-load system are analyzed for the smallest and largest selected parameters and for the case obtained by applying the developed algorithm. Practical application of the developed algorithm will allow obtaining optimal values of the proportional-integral-differential controller, which in turn eliminates oscillations of the load on a flexible suspension during crane operation, which in turn increases the safety of crane operation, structural durability, and increases the crane’s productivity

Fault Diagnosis of Transformer Winding Based on VMD-SVM

Considering the nonlinear and non-stationary characteristics of the transformer vibration acceleration signal obtained from the surface of the transformer tank, the variational mode decomposition (VMD) theory is introduced. Simulation analysis shows that the VMD decomposition has obvious advantages over EMD when the needle frequency is similar to the signal. It effectively avoids two types of modal aliasing and over-decomposition, and accurately reflects the characteristics of the source signal. Aiming at the problem that the two core parameters of the support vector machine are difficult to determine, the Pareto particle swarm method is used to perform multi-objective parallel optimization of the two core parameters of the support vector machine to obtain the optimal parameters. The VMD-SVM fault diagnosis model is tested using the transformer instance fault data, and compared with the other two methods. The instance test results show that the VMD-SVM proposed in this paper has the highest diagnostic accuracy and realizes the latent fault of the power transformer winding. accurate diagnosis.

The English Teaching Mode under the Environment of Computer Technology

The development of Internet information technology has opened up new potential for the education sector, encouraging the development of innovative teaching and learning methods as well as the optimal use of available educational resources. In this environment, the online plus offline mixed teaching model emerged, which can not only get around time and space restrictions and link smoothly inside and outside the classroom but also enhance the form of teaching and learning and strengthen teacher-student relationships. However, to assure the quality of instruction, this research first examines the successful ways of developing an online plus offline hybrid teaching mode from the perspective of college English professors. Second, in this paper, the support vector machine and particle swarm method are used to build an evaluation model of the English teaching effect. The model has the potential to increase the accuracy and efficiency of English teaching impact evaluation while also meeting the standards of English teaching effect evaluation. The experimental findings show that, while evaluating the effects of college English instruction, the technique beats the comparison algorithm in terms of evaluation accuracy and algorithm time consumption.

Research on Web GUI Image Recognition System Based on Improved Convolutional Neural Network

In this paper, a MobileNet V2 convolutional neural network depending on L2 regularization method, a amended particle swarm model and Dropout method are constructed in a bid for enhancing the accuracy and speed of Web GUI image recognition for establishing Web Web GUI image recognition system of Web GUI test. Firstly, an improved MobileNet V2 convolutional neural network is constructed. Secondly, the basic models of L2 regularization method, improved particle swarm method and Dropout method are studied, the improved MobileNet V2 convolution neural network optimization algorithm is proposed, and the basic model of image processing is designed. Finally, simulation analysis is carried out on Web GUI image recognition through using the amended convolutional neural network on basis of MINIST data set as the research object. The simulation results illustrate that the amended convolutional neural network proposed in this study is more accurate and efficient in Web GUI image recognition.