- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2019 IEEE International Ultrasonics Symposium (IUS) - Glasgow, United Kingdom (2019.10.6-2019.10.9)] 2019 IEEE International Ultrasonics Symposium (IUS) - Integrated Ultrasound and Photoacoustic Imaging for Effective Endovenous Laser Ablation: A Characterization Study
摘要： A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural network (ANN) is used to model and optimize the chemical composition of a steel bar. The novel contribution of a TPSO is the use of a Taguchi method mechanism to exploit better solutions in the search space through iterations, the use of the conventional non-linear PSO to increase convergence speed, and the use of random movement for particle diversity. The exploration and exploitation capability of the TPSO were confirmed by performance comparisons with other PSO-based algorithms in solving high-dimensional global numerical optimization problems. Experiments in this paper showed that the TPSO provides higher computational efficiency and higher robustness when solving problems involving seven non-linear benchmark functions, including three unimodal functions, one multimodal functions, two rotated functions, and one shifted functions. The results for the computational experiments show that the TPSO outperforms other PSO-based algorithms reported in the literature. Finally, the results obtained by a TPSO-based ANN model of the chemical composition of the steel bar were consistent with the actual data. That is, the proposed TPSO with three-layer feedforward ANN can be used in practical applications.
关键词： yield point,feedforward artificial neural network,tensile strength,particle swarm optimization,chemical composition of steel bar,Taguchi method
更新于2025-09-19 17:13:59
-
Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach
摘要： This paper presents an experimental study carried out on Nimonic 263 alloy sheets to determine the optimal combination of laser cutting control factors (assisted gas pressure, beam focus position, laser power, and cutting speed), with respect to multiple characteristics of the cut area. With the aim of designing laser cutting parameters that satisfy the specific specifications of multiple responses, an advanced multiresponse optimization methodology was used. After the processing of experimental data to develop the process measure using statistical methods, the functional relationship between cutting parameters and the process measure was determined by artificial neural networks (ANNs). Using the trained ANN model, particle swarm optimization (PSO) was employed to find the optimal values of laser cutting parameters. Since the effectiveness of PSO could be affected by its parameter tuning, the settings of PSO algorithm-specific parameters were analyzed in detail. The optimal laser cutting parameters proposed by PSO were implemented in the validation run, showing the superior cut characteristics produced by the optimized parameters and proving the efficacy of the suggested approach in practice. In particular, it is demonstrated that the quality of the Nimonic 263 cut area and the microstructure were significantly improved, as well as the mechanical characteristics.
关键词： artificial neural networks (ANNs),microhardness,laser cutting,microstructural characterization,Nimonic 263,parameters optimization,particle swarm optimization,simulated annealing (SA),surface roughness
更新于2025-09-16 10:30:52
-
A Novel Plant Propagation-Based Cascaded Fractional Order PI Controller for Optimal Operation of Grid-Connected Single-Stage Three-Phase Solar Photovoltaic System
摘要： Grid-connected photovoltaic (PV) inverters are gaining attention all over the world. The optimal controller setting is key to the successful operation of a grid-connected PV system. In this paper, a novel plant propagation algorithm-based fractional order proportional-integrator (FOPI) controller for cascaded DC link voltage and inner current control of a grid-connected PV controller has been proposed, which outperforms particle swarm optimization-based PI and elephant herding optimization-based FOPI in terms of multicriteria-based analysis. The performance of the proposed controller also has been measured in terms of total harmonic distortion to maintain the appropriate power quality. Also, the proposed controllers were tested under various solar irradiance and voltage sag conditions to show the effectiveness and robustness of the controllers. The whole system is developed in OPAL-RT using MATLAB/Simulink and RT-LAB as a machine-in-loop (MIL) system to validate the performance in real time.
关键词： DC link voltage controller,inner current controller,elephant herding optimization,plant propagation algorithm,grid-connected PV,particle swarm optimization,fractional order PI controller
更新于2025-09-16 10:30:52
-
[IEEE 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) - Chengdu, China (2019.5.25-2019.5.28)] 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) - ABC-SVM and PSO-RF Model for Photovoltaic Forecasting Based on Big Data
摘要： Prediction of photovoltaic output is of great significance to the stable operation of microgrid system. Firstly, the artificial bee colony based support mechine (ABC-SVM) method is used to train historical meteorological data and photovoltaic output data, which can divide the weather condition into four categories. Secondly, tens of thousands of data are selected under four types of meteorological conditions, and each group of data is trained by particle swarm optimization based random forest (PSO-RF) model. After training, the four different PSO-RF model with different parameters can be obtained for the photovoltaic forecasting individually. Finally, we collect weather information and photovoltaic data from a microgrid station in Yangjiang Guangdong province to test our combined model. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional RF methods.
关键词： random forest,support vector machine,particle swarm optimization,artificial bee colony
更新于2025-09-16 10:30:52
-
[IEEE 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Chengdu, China (2019.5.21-2019.5.24)] 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - An optimal operation method for cascade hydro-photovoltaic-pumped storage hybrid generation system
摘要： Since photovoltaic power stations and cascade hydropower stations have complementary characteristics, while pumped storage power stations have energy storage and rapid regulation characteristics, it is of great significant to combine cascade hydropower, photovoltaic, pumped storage to increase the absorption of photovoltaic. To improve the stability of the system operation and the economic benefits, this paper proposes a cascade hydro-photovoltaic-pumped storage hybrid generation system. Considering the actual conditions such as uncertainty of photovoltaic output, the reservoir capacity of pumped storage power station and real time electricity price, an objective function and related constraint conditions are established. In addition, a particle swarm optimization algorithm with linear decreasing of inertia weight is used to find the optimum results of the model. Finally, actual power station parameters are selected for simulation test and the results show that the hybrid generation system has good economic benefits and water-light complementary ability, which guarantee the system to operate more safely and economically.
关键词： cascade hydropower-PV-pumped storage hybrid generation,particle swarm optimization,optimal scheduling
更新于2025-09-16 10:30:52
-
A Novel Charging and Discharging Algorithm of Plug-in Hybrid Electric Vehicles Considering Vehicle-to-Grid and Photovoltaic Generation
摘要： Considering, the high penetration of plug-in electric vehicles (PHEVs), the charging and discharging of PHEVs may lead to technical problems on electricity distribution networks. Therefore, the management of PHEV charging and discharging needs to be addressed to coordinate the time of PHEVs so as to be charged or discharged. This paper presents a management control method called the charging and discharging control algorithm (CDCA) to determine when and which of the PHEVs can be activated to consume power from the grid or supply power back to grid through the vehicle-to-grid technology. The proposed control algorithm considers fast charging scenario and photovoltaic generation during peak load to mitigate the impact of the vehicles. One of the important parameters considered in the CDCA is the PHEV battery state of charge (SOC). To predict the PHEV battery SOC, a particle swarm optimization-based artificial neural network is developed. Results show that the proposed CDCA gives better performance as compared to the uncoordinated charging method of vehicles in terms of maintaining the bus voltage profile during fast charging.
关键词： state of charge,artificial neural network,particle swarm optimization,plug-in hybrid electric vehicle,charging and discharging control algorithm
更新于2025-09-16 10:30:52
-
[IEEE 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) - Bucharest, Romania (2019.9.29-2019.10.2)] 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) - Multi-layer Reactive Power Control of Solar Photovoltaic Systems in MV Distribution Network
摘要： The growing penetration of photovoltaic (PV) systems in distribution networks causes various power quality issues e.g. voltage rise, network losses. To deal with the overvoltage, inverter-based reactive power contribution is one of the most commonly proposed approaches. However, using standard strategies with fixed droop parameters limits the control effectiveness. Moreover, much reactive power absorbed by PVs around noon might increase the network loss, overload the substation consequently, especially in the network with long distribution feeders. In this paper, a combined centralized and local control method is proposed with the suggestion of multi-layer structure. Droop parameters of local control are updated every 15 min by centralized control to minimize reactive power flow through substation while keeping voltages in permitted levels. By avoiding overloading substation, more PV generation can be added to the feeder.
关键词： Reactive power control,droop control,particle swarm optimization,voltage control,photovoltaic (PV) systems
更新于2025-09-16 10:30:52
-
[IEEE 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) - Tamilnadu, India (2019.4.11-2019.4.13)] 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) - Implementation of Particle Swarm Optimization for Maximum Power Absorption From Photovoltaic System Using Energy Extraction Circuit
摘要： The focus of this paper, improvements in universal rules have seen the thrust for larger use of non-conventional energy sector. To absorb the maximum power output from the solar photovoltaic (PV) panel, Particle Swarm Optimization (PSO) based Maximum Power Point tracking (MPPT) control algorithm is suggested. At a specific operating point, PV panel can harvest maximum power called Maximum Power Point (MPP) in which the solar PV panel must operate at this MPP to give maximum efficiency. In this paper, the capability of the PV panel to track the MPP using PSO under great variation of insolation has been modelled and analyzed in MATLAB/SIMULINK.
关键词： Photovoltaic (PV) panel,Particle Swarm Optimization (PSO),Maximum Power Point tracking (MPPT)
更新于2025-09-16 10:30:52
-
Optimization of Machining Parameter during the Laser Cutting of Inconel-718 Sheet Using Regression Analysis based Particle Swarm Optimization Method
摘要： This experimental work describes the utilization of a hybrid approach of regression modeling and particle swarm optimization (PSO) for optimizing the process parameters during the laser cutting of the Inconel-718 sheet. The experiments have been performed by using four machining parameters such as assist gas pressure, standoff distance, cutting speed and laser power. The kerf width and kerf taper are used as an output quality characteristic. The experiments have been performed by using well planned orthogonal array L27.The second order regression models have been developed for kerf width and kerf taper by using the experimental data. The developed second order regression models have been utilized in optimization by particle swarm optimization. The comparison of the experimental result with optimum results confirms that the individual improvement in output quality characteristics kerf width and kerf taper is approximate 10% and 57%, respectively. The overall improvement of 46% has been observed during the optimization. Finally, the effects of different process parameters on different performances have also been discussed. The parametric effect analysis shows that minimum kerf taper may be obtained at lowest values of laser power and middle values of standoff distance.
关键词： Kerftaper,Particle swarm optimization,Kerfwidth,Laser cutting,Inconel-718
更新于2025-09-16 10:30:52
-
Control Strategy of the Pumped Storage Unit to Deal with the Fluctuation of Wind and Photovoltaic Power in Microgrid
摘要： With the development and utilization of distributed energy and microgrid, distributed energy storage has become a new development trend. However, small pumped storage units have the advantages of ?exible engineering location, low investment, quick e?ect, low requirements on transmission lines, and a better solution to the peak load demand of the system. Therefore, it is more and more used in the microgrid, and it conducts joint dispatching with wind power, photovoltaic, and other clean energies. To solve the capacity problem of small pumped storage units within the microgrid, a new control strategy is proposed in this paper. Two pumped storage units are used for joint operations. Taking the smoothed combined output power of wind power, photovoltaic power, and pumped storage power as the target, and considering the limitations of transmission lines, the constraints of wind power and photovoltaic power ?elds as well as the restrictions of pumped storage power units and corresponding reservoirs are taken into account. In this paper, social particle swarm optimization (SPSO) with improved weight is used to calculate and solve the model. The e?ectiveness of the new control strategy is veri?ed.
关键词： microgrid dispatch,particle swarm optimization,pumped storage unit,control strategy,renewable energies
更新于2025-09-16 10:30:52