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The control systems for the variable-speed wind turbine based on the Doubly Fed Induction Generator (DFIG) pose some tuning challenges. The performance and stability of DFIG wind turbines during faults in power grids are directly related to their controller settings. This work investigates how incorporating protection via a braking-Chopper controller connected to the DC link (DC Chopper) and a reactive-current injection during the PI-tuning process affects the performance of DFIG wind turbines during electrical faults. For the tuning process, the Multi-Objective-Particle-Swarm-Optimization (MOPSO) algorithm was used. Thus, two different approaches adopting this methodology were investigated, considering sequential and simultaneous tuning. The results showed that sequential tuning presented a better performance in relation to the reactive-current injection and lower amplitude deviations of the electrical quantities during and after the fault. On the other hand, simultaneous tuning reached damping of the mechanical oscillations faster and presented better performance of the protection system.

The operation of the photovoltaic (PV) system under partial shading conditions (PSC) is complicated since the output characteristic of the PV system is profoundly affected by the heterogeneous irradiance of PSC. This paper proposes a dynamic reconfiguration framework to tackle PSC in the PV array. Continuous operation of the dynamic PV array reconfiguration under cloud-induced partial shading is considered by developing an emulator of the moving cloud. In addition, the Particle Swarm Optimization and Rao algorithms are improved to obtain the optimal PV array configuration under PSC. The operation of switching is enhanced by simultaneously considering the total switching times and the operation of highly active switches. The simulation results on the 9×9 PV array demonstrate the effectiveness of the proposed framework in terms of reducing the number of local maximum power points on the power-voltage characteristic, enhancing power output, and relieving stress on the switching operation of the PV array under different PSC.

With the rapid development of agriculture in China today, the demand for agricultural machinery is rapidly increasing. A large amount of exhaust gas emissions poses a severe threat to the environment. To better promote engine fuel and air mixing, enhance engine performance, and achieve low emissions, we conducted a numerical study of the pre-chamber and nozzle design in a gasoline engine for an agricultural tractor by using the G-equation method in Converge CFD software. The relevant optimization of the three model parameters in the G-equation was performed using the improved particle swarm algorithm (PSO). The model parameters after optimization by the PSO algorithm were: a1=0.77, b1=2.0, b3=1.0. It was confirmed that the predicted engine performance was enhanced greatly with the pre-chamber system. More importantly, the results reveal that the volume and area ratios of the pre-chamber played a crucial role in the performance of the pre-chamber. Through a series of parametric studies on the pre-chamber and main chamber characteristics, we can identify the best sets of volume and area ratios based on the combustion reaction progress, the turbulent mixing profiles, and the exhaust gas emission. The turbulent maximum strength and the exhaust gas concentration of nitrogen oxides can differ by 13 and 18 times, respectively. In practical design, we recommend the optimization of the concerned metrics with the findings in the paper.

A pneumatic cylinder system is believed to be extremely nonlinear and sensitive to nonlinearities, which makes it challenging to establish precise position control of the actuator. The current research is aimed at reducing the overshoot in the response of a double-acting pneumatic actuator, namely, the IPA positioning system’s reaction time. The pneumatic system was modeled using an autoregressive with exogenous input (ARX) model structure, and the control strategy was implemented using a fuzzy fractional order proportional integral derivative (fuzzy FOPID) employing the particle swarm optimization (PSO) algorithm. This approach was used to determine the optimal controller parameters. A comparison study has been conducted to prove the advantages of utilizing a PSO fuzzy FOPID controller over PSO fuzzy PID. The controller tuning algorithm was validated and tested using a pneumatic actuator system in both simulation and real environments. From the standpoint of time-domain performance metrics, such as rising time (tr), settling time (ts), and overshoot (OS%), the PSO fuzzy FOPID controller outperforms the PSO Fuzzy PID controller in terms of dynamic performance.

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users.

This paper presents an off-axis-focused solar concentrator system consisting of 190 aspheric reflectors, where the aperture radius of each reflector is 10 cm, and vertices of all reflectors are orderly arranged in the same plane. The aspheric parameters controlling the curvature of the reflectors are determined using coordinate transformations and the particle swarm optimization (PSO) algorithm. Based on these aspheric parameters, the light distribution of focal plane was calculated by the ray tracing method. The analyses show that the designed concentrator system has a spot radius of less than 1 cm and the concentration ratio over 3300:1 is achieved using only one reflection. The design results have been verified with the optical design software Zemax.

Due to the advantages of zero-voltage-switching (ZVS) and zero-current-switching (ZCS), LCLC resonant converters are universally applied in two-stage electronic power conditioners (EPCs) of space travelling-wave tube amplifiers (TWTAs). In two-stage EPCs, as the output voltage is regulated by the first stage, the main functions of LCLC resonant converters are to boost the input voltage, provide galvanic isolation, and maintain high efficiency. However, the total power loss of an LCLC resonant converter is very challenging because of the multiple resonant components and their mutual couplings. In order to solve this problem, in this paper, a PSO (Particle Swarm Optimization)-algorithm-based, efficiency-oriented optimal design method of an LCLC resonant converter is proposed. Based on an analysis of working principles, the total power loss (with consideration of all the power losses, including the driving loss and the conduction loss of the main switches), transformer loss, and power loss of the rectifiers is calculated. After that, the total power loss is appointed as the objective function of the PSO algorithm, where the optimal circuit parameters are derived to minimize the total power loss. After the optimal circuit parameters are obtained, an LCLC resonant converter with the desired parameters is built. Finally, the proposed PSO-algorithm-based, efficiency-oriented optimal design method is validated by an example.

Offshore wind power has become an important trend in global renewable energy development. Based on a particle swarm optimization (PSO) algorithm and FAST program, a time-domain coupled calculation model for a floating wind turbine is established, and a combined optimization design method for the wind turbine’s blade is developed in this paper. The influence of waves on the power of the floating wind turbine is studied in this paper. The results show that, with the increase of wave height, the power fluctuation of the wind turbine increases and the average power of the wind turbine decreases. With the increase of wave period, the power oscillation amplitude of the wind turbine increases, and the power of the wind turbine at equilibrium position decreases. The optimal design of the offshore floating wind turbine blade under different wind speeds is carried out. The results show that the optimum effect of the blades is more obvious at low and mid-low wind speeds than at rated wind speeds. Considering the actual wind direction distribution in the sea area, the maximum power of the wind turbine can be increased by 3.8% after weighted optimization, and the chord length and the twist angle of the blade are reduced.

With the large amount of distributed generation in use, the structure of the distribution system is increasingly complex. Therefore, it is necessary to establish a method to improve load reliability. Based on the reliability model of distributed generation, this paper investigates the time sequential simulation of a wind/solar/storage combined power supply system under off-grid operation. After classifying the load by power supply region, the load weight coefficient is established, which modifies the reliability index of the load point and system. The modified expected energy not supplied (EENS) is adopted as the objective function, and the particle swarm optimization algorithm is used to solving the optimal energy scheduling for improving the load reliability. Finally, the load reliability is calculated with a hybrid method. Using the IEEE-RBTS Bus 6 system as an example, the correctness and validity of the proposed method are verified as an effective way to improve load reliability.

The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.