Parameter Improved Particle Swarm Optimization Based Direct-Current Vector Control Strategy for Solar PV System

Nammalvar, P.; Ramkumar, S.

doi:10.4316/aece.2018.01013

Cited by 17 publications

(6 citation statements)

References 19 publications

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“…At every iteration, each particle moves in the direction of the best solution discovered so far in the swarm. Keeping this interaction, the particle continues searching for a better solution than the previous one and moves toward it, thereby exploring the region thoroughly [ 41 , 42 ]. The position and velocity of the i th particle of the swarm in the search space vector are represented as X i = [ X i 1 , X i 2 ,…., X iD ] and V i = [ V i 1 , V i 2 ,….., V iD ], respectively [ 43 ].…”

Section: Pi-based Pso Algorithmmentioning

confidence: 99%

Particle swarm optimization algorithm-based PI inverter controller for a grid-connected PV system

et al. 2020

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The lack of control in voltage overshoot, transient response, and steady state error are major issues that are frequently encountered in a grid-connected photovoltaic (PV) system, resulting in poor power quality performance and damages to the overall power system. This paper presents the performance of a control strategy for an inverter in a three-phase grid-connected PV system. The system consists of a PV panel, a boost converter, a DC link, an inverter, and a resistor-inductor (RL) filter and is connected to the utility grid through a voltage source inverter. The main objective of the proposed strategy is to improve the power quality performance of the three-phase grid-connected inverter system by optimising the proportional-integral (PI) controller. Such a strategy aims to reduce the DC link input voltage fluctuation, decrease the harmonics, and stabilise the output current, voltage, frequency, and power flow. The particle swarm optimisation (PSO) technique was implemented to tune the PI controller parameters by minimising the error of the voltage regulator and current controller schemes in the inverter system. The system model and control strategies were implemented using MATLAB/Simulink environment (Version 2020A) Simscape-Power system toolbox. Results show that the proposed strategy outperformed other reported research works with total harmonic distortion (THD) at a grid voltage and current of 0.29% and 2.72%, respectively, and a transient response time of 0.1853s. Compared to conventional systems, the PI controller with PSO-based optimization provides less voltage overshoot by 11.1% while reducing the time to reach equilibrium state by 32.6%. The consideration of additional input parameters and the optimization of input parameters were identified to be the two main factors that contribute to the significant improvements in power quality control. Therefore, the proposed strategy effectively enhances the power quality of the utility grid, and such an enhancement contributes to the efficient and smooth integration of the PV system.

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Section: Pi-based Pso Algorithmmentioning

confidence: 99%

Particle swarm optimization algorithm-based PI inverter controller for a grid-connected PV system

et al. 2020

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“…However, there are limitations to the training process where there are local minimums and large errors 33,38,39 . The PSO algorithm is derived from a simulation of bird predation behavior [40][41][42] . The fitness is used to measure the pros and cons of the particles and the optimal solution of the neural network is evaluated.…”

Section: Pso-bp Network Weightsmentioning

confidence: 99%

Photosynthetic rate prediction model of newborn leaves verified by core fluorescence parameters

Zhang

et al. 2020

Sci Rep

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Due to the imperfect development of the photosynthetic apparatus of the newborn leaves of the canopy, the photosynthesis ability is insufficient, and the photosynthesis intensity is not only related to the external environmental factors, but also significantly related to the internal mechanism characteristics of the leaves. Light suppression and even light destruction are likely to occur when there is too much external light. Therefore, focus on the newborn leaves of the canopy, the accurate construction of photosynthetic rate prediction model based on environmental factor analysis and fluorescence mechanism characteristic analysis has become a key problem to be solved in facility agriculture. According to the above problems, a photosynthetic rate prediction model of newborn leaves in canopy of cucumber was proposed. The multi-factorial experiment was designed to obtain the multi-slice large-sample data of photosynthetic and fluorescence of newborn leaves. The correlation analysis method was used to obtain the main environmental impact factors as model inputs, and core chlorophyll fluorescence parameters was used for auxiliary verification. The best modeling method PSO-BP neural network was used to construct the newborn leaf photosynthetic rate prediction model. The validation results show that the net photosynthetic rate under different environmental factors of cucumber canopy leaves can be accurately predicted. The coefficient of determination between the measured values and the predicted values of photosynthetic rate was 0.9947 and the root mean square error was 0.8787. Meanwhile, combined with the core fluorescence parameters to assist the verification, it was found that the fluorescence parameters can accurately characterize crop photosynthesis. Therefore, this study is of great significance for improving the precision of light environment regulation for new leaf of facility crops.

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“…The use of optimization algorithms as alternative methods for tuning PID controllers has been a recent topic of research in electric machines control. New optimization techniques are proposed, for instance, the Imperialist Competitive Algorithm (ICA) [18], evolutionary algorithm [19], Genetic Algorithm (GA) [20][21], BAT algorithm [22] ,Particle Swarm Optimization (PSO) [23][24][25][26], and Ant Colony Optimization (ACO) algorithm [27], Harmony Search (HS) [28], hybrid GA [29][30], adaptive Cuckoo Search algorithm (CS) [31].…”

Section: Introductionmentioning

confidence: 99%

Direct torque control of dual star induction motor using a fuzzy-PSO hybrid approach

Boukhalfa

Belkacem

Chikhi

et al. 2020

ACI

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This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

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Parameter Improved Particle Swarm Optimization Based Direct-Current Vector Control Strategy for Solar PV System

Cited by 17 publications

References 19 publications

Particle swarm optimization algorithm-based PI inverter controller for a grid-connected PV system

Particle swarm optimization algorithm-based PI inverter controller for a grid-connected PV system

Photosynthetic rate prediction model of newborn leaves verified by core fluorescence parameters

Direct torque control of dual star induction motor using a fuzzy-PSO hybrid approach

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