Parameter Optimization of Steam Generator Feedwater Controller Based on Particle Swarm Optimization

2013 ◽  
Vol 291-294 ◽  
pp. 2496-2499
Author(s):  
Rui Xiang ◽  
Rui Yu ◽  
Zhi Wu Ke ◽  
Ke Long Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Water Level ◽  
Steam Generator ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Step Response ◽  
Swarm Optimization ◽  
Response Characteristics ◽  
Water Lever

The U-tube steam generator(SG), as the joint of primary and secondary circuits, is one of the crucial components in a PWR plant. Because of the swell-and-shrink phenomena, it is very difficult to control the water level of the steam generator. Presently the three-element controller in cascade configuration with two PID controller is widely use. But the determination of the six optimal PID parameters of the cascade controller is a major problem. This paper studies the application of the particle swarm optimization(PSO) methods in determining the parameters of cascade controller of SG water lever. The SG is modeled in Simulink and the PSO algorithm is implemented in MATLAB. Comparing with conventional PID parameter, the proposed method is more efficient in improving the step response characteristics while controlling the water level of SG.

Parameter Optimization for AP1000 Steam Generator Feedwater Control System Using Particle Swarm Optimization Algorithm

2016 ◽  
Author(s):  
Shifa Wu ◽  
Pengfei Wang ◽  
Jiashuang Wan ◽  
Xinyu Wei ◽  
Fuyu Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Water Level ◽  
Steam Generator ◽  
Parameter Optimization ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Control Performance ◽  
Level Control ◽  
Swarm Optimization

The U-tube Steam Generator (UTSG) of AP1000 Nuclear Power Plant (NPP) is the crucial component transferring heat from the primary loop to the secondary loop to make steam. The UTSG of AP1000 NPP is a highly complex, nonlinear and time-varying system and its parameters vary with operating conditions. Therefore, it is difficult and challenging to well control the water level of AP1000 UTSG by tuning the PID controller parameter in a traditional way, especially when the system is undergoing a sharp transient. To achieve better control performance, the Particle Swarm Optimization (PSO) algorithm was applied for the parameter optimization of the AP1000 UTSG feedwater control system in this study. First, the mathematical model of AP1000 UTSG was established and the objective function was developed with the system constraints considered. Second, the simulation platform was built and then the simulation was conducted in MATLAB/Simulink environment. Finally, the optimized parameters were obtained and the feedwater control system with optimized parameters was simulated against that without optimized. The simulation results demonstrate that optimized parameters of AP1000 UTSG feedwater control system can significantly improve the water level control performance with smaller overshoot and faster response. Therefore, the PSO based optimization method can be applied to optimizing AP1000 UTSG feedwater control system parameters to provide much better control capabilities.

scholarly journals Design Stable Controller for PUMA 560 Robot with PID and Sliding Mode Controller Based on PSO Algorithm

2020 ◽  
Vol 13 (6) ◽  
pp. 487-499
Author(s):  
Hanan Akkar ◽  
◽  
Suhad Haddad ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Step Response ◽  
Sliding Mode Controller ◽  
Nonlinear Controller ◽  
Swarm Optimization ◽  
Integral Sliding Mode

The most significant challenge facing the researcher in the field of robotics is to control the robot manipulator with appropriate overall performance. This paper focuses mainly on the novel Intelligent Particle Swarm Optimization (PSO) algorithm that was used for optimizing and tuning the gain of conventional Proportional Integral Derivative (PID), and improve the parameters of dynamic design in Sliding Mode Control (SMC), which is considered a strong nonlinear controller for controlling highly nonlinear systems, particularly for multi-degree serial link robot manipulator. Additional modified Integral Sliding Mode Controller (ISMC) was implemented to the design of dynamic system with high control theory of sliding mode controller. Intelligent Particle Swarm Optimization (PSO) algorithm was introduced for developing the nonlinear controller. The algorithm demonstrates superior performance in determining the appropriate gains and parameters value in harmony with robot scheme dynamic layout in order to achieve suitable and stable nonlinear controller, besides reduce the chattering phenomenon. PUMA robot manipulator that was used as study case in this work, shows perfect result in step response, with acceptable steady state, and overshoot, besides, eliminating the disadvantage of chattering in conventional SMC. Matlab / Simulink presents to increase the speed of matrix calculation in forward, inverse kinematics and dynamic model of manipulator. Comparison was made between the proposed method with existing methods. Result shows that integral sliding mode with PSO (ISMC/PSO) gave best result for stable step response, minimum mean square error with best objective function, and stable torque.

Application of Particle Swarm Optimization in Design of PID Controller for AVR System

2013 ◽  
Vol 2 (3) ◽  
pp. 1-17 ◽  
Author(s):  
H. F. Abu-Seada ◽  
W. M. Mansor ◽  
F. M. Bendary ◽  
A. A. Emery ◽  
M. A. Moustafa Hassan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Stability Margin ◽  
Pso Algorithm ◽  
Step Response ◽  
Voltage Response ◽  
Swarm Optimization ◽  
Avr System ◽  
Terminal Voltage

This paper presents a method to get the optimal tuning of Proportional Integral Derivative (PID) controller parameters for an AVR system of a synchronous generator using Particle Swarm Optimization (PSO) algorithm. The AVR is not initially robust to variations of the power system parameters. Therefore, it was necessary to use PID controller to increase the stability margin and to improve performance of the system. Fast tuning of optimum (PID) controller parameter yield high quality solution. New criteria for time domain performance evaluation was defined. Simulation for comparison between the proposed method and Ziegler-Nichols method is done. The proposed method was indeed more efficient also. The terminal voltage step response for AVR model will be discussed in different cases and the effect of adding rate feed back stabilizer to the model on the terminal voltage response. Then the rate feedback will be compared with the proposed PID controller based on use of (PSO) method to find its coefficients. Different simulation results are presented and discussed.

Research on Double Closed-Loop DC Speed-Regulation System Based on Improved Particle Swarm Optimization

2013 ◽  
Vol 846-847 ◽  
pp. 317-320 ◽  
Author(s):  
Le Peng Song ◽  
Han Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Step Response ◽  
Regulation System ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Improved Pso

For the defects of the parameter tuning and optimization of the PID controller uses an improved Particle Swarm Optimization (IPSO) algorithm to apply on the dual closedloop DC speed tuning system and adjust PID controller parameters online. The optimization result of adopting step response of the improved PSO algorithm is analyzed. It shows that using the improved PSO algorithm will obtain better dynamic performance, follow faster and more robustness than the traditional engineering design method. It provides a good performance of practical method for PID parameters optimization.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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