scholarly journals Research on Adaptive Suppression of LCL Converter Resonance Grid-Connected System

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Junwei Li ◽  
Yafang Tang ◽  
Junke Li
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stability Margin ◽  
System Stability ◽  
Current Feedback ◽  
Swarm Optimization ◽  
Lcl Filter ◽  
Rbf Network ◽  
Grid Impedance ◽  
Suppression Method

LCL-type converters are widely used in grid-connected systems due to their small size and good filtering performance. However, the resonance suppression problem brought by the LCL filter cannot be ignored. The capacitive current feedback is a commonly used resonance suppression method. In applications, the grid impedance can cause LCL filter resonance. Thus, this paper presents an adaptive resonance suppression method based on the RBF network optimized by particle swarm optimization. This method optimizes the initial parameters of the RBF network through particle swarm optimization, identifies the parameters of the PI controller by RBF neural network’s own identification capability, and updates the active damping coefficient based on constraints such as stability margin, thereby realizing the LCL-type inverter to maintain the system stability when the grid impedance changes. The effectiveness of the method is verified by experiments.

scholarly journals Parameters Identification of Equivalent Model of Permanent Magnet Synchronous Generator (PMSG) Wind Farm Based on Analysis of Trajectory Sensitivity

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4607
Author(s):  
Jian Zhang ◽  
Mingjian Cui ◽  
Yigang He
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Wind Farm ◽  
Particle Swarm ◽  
System Stability ◽  
Equivalent Model ◽  
Classical Particle ◽  
Measurement Unit ◽  
Swarm Optimization ◽  
Trajectory Sensitivity

As wind farms have great influences on power system stability, it is essential to develop an adaptive as well as robust equivalent model of it. In this paper, a detailed equivalent model of PMSG wind farm and initialization method is developed. The trajectory sensitivity of parameters is analyzed. Then, the key parameters are estimated using improved Genetic Learning Particle Swarm Optimization (GLPSO) hybrid algorithm with phasor measurement unit (PMU). The description and generalization capability, stability for parameter identification of the equivalent model under wake effects, and when some wind turbines are off-line or wind speed is unknown after an event are analyzed. The maximum differences between the values of estimated parameters and their real ones are less than 10% for the proportional magnification constant of DC voltage controller Kp2 and grid side current controller Kp3. The convergence rate and global optimization performance of the improved GLPSO hybrid algorithm is 0.5 times higher than the classical particle swarm optimization algorithm (PSO) and genetic algorithm (GA).

Integrative optimization by RBF network and particle swarm optimization

2009 ◽  
Vol 92 (12) ◽  
pp. 31-42 ◽  
Author(s):  
Satoshi Kitayama ◽  
Keiichiro Yasuda ◽  
Koetsu Yamazaki
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Rbf Network

scholarly journals Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization

2019 ◽  
Vol 52 (5-6) ◽  
pp. 493-508 ◽  
Author(s):  
Alper Kerem ◽  
Ali Saygin
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Particle Swarm ◽  
System Stability ◽  
Training Process ◽  
Swarm Optimization ◽  
Hybrid Metaheuristic ◽  
Radial Movement ◽  
Artificial Neural

This paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-based incremental learning, particle swarm optimization, and radial movement optimization in the literature. The success of each model is recorded in graphs. In order to make the closest estimation and to increase the system stability, a new hybrid metaheuristic model was developed using particle swarm optimization and radial movement optimization, and the training process of artificial neural networks was performed with this new model. The data were obtained by real-time measurements from a 63-m-high wind measurement station built at the coordinates of UTM E 263254 and N 4173479, altitude 1313 m. Two different scenarios were created using actual data and applied to all models. It was observed that the error values in the designed new hybrid metaheuristic model were lower than those of the other models.

Flexible energy load identification in intelligent manufacturing for demand response using a neural network integrated particle swarm optimization

2020 ◽  
pp. 095440622093365
Author(s):  
Md Monirul Islam ◽  
Zeyi Sun ◽  
Ruwen Qin ◽  
Wenqing Hu ◽  
Haoyi Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Demand Response ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
System Stability ◽  
Management Tool ◽  
Swarm Optimization ◽  
Demand Response Programs

Various demand response programs have been widely established by many utility companies as a critical load management tool to balance the demand and supply for the enhancement of power system stability in smart grid. While participating in these demand response programs, manufacturers need to develop their optimal demand response strategies so that their energy loads can be shifted successfully according to the request of the grid to achieve the lowest energy cost without any loss of production. In this paper, the flexibility of the electricity load from manufacturing systems is introduced. A binary integer mathematical model is developed to identify the flexible loads, their degree of flexibility, and corresponding optimal production schedule as well as the power consumption profiles to ensure the optimal participation of the manufacturers in the demand response programs. A neural network integrated particle swarm optimization algorithm, in which the learning rates of the particle swarm optimization algorithm are predicted by a trained neural network based on the improvement of the fitness values between two successive iterations, is proposed to find the near optimal solution of the formulated model. A numerical case study on a typical manufacturing system is conducted to illustrate the effectiveness of the proposed model as well as the solution approach.

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