Rearch on RBF Neural Network Fuzzy Control for STATC0M

Li, Yang; Chen, Xiangjie

doi:10.1109/ihmsc.2009.111

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional PID control effect and accuracy is low, will take the PID control with fuzzy control rules -fuzzy PID control of the controlled object. Fuzzy control rules are the result of expert system experience as well as experience gained by using in engineering practice [16] . The principle of fuzzy control is shown in Figure 3: The fuzzy control rules are formulated by taking the error E and the rate of change of error EC as inputs and the set ΔKp, ΔKi , ΔKd as outputs, thus constituting the fuzzy control structure with two inputs and three outputs to find the fuzzy relationship between ΔKp, ΔKi, ΔKd and the errorE, the amount of change of the error EC, so as to satisfy the requirements of the control accuracy [17] .…”

Section: Control Design 31 Fuzzy Control Designmentioning

confidence: 99%

Energy Saving Research on Temperature Control in Data Center Server Rooms

Su,

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

To tackle the challenge of inefficient traditional control methods and energy wastage caused by temperature disparities within the data center’s server room cooling system, this research initiative initially installed temperature and humidity sensors in each cabinet to gather comprehensive data. Subsequently, a detailed mathematical model of the room’s temperature system was formulated to assess the influence of air supply airflow distribution on the temperature distribution. Following this, through the integration of fuzzy theory with the PID control strategy, a sophisticated fuzzy PID controller was developed. Utilizing Simulink, an in-depth simulation analysis of both PID and fuzzy PID temperature control strategies was executed. The outcomes of the simulations underscored that fuzzy PID control effectively addresses the time delay and stability concerns present in conventional PID temperature control systems. Lastly, empirical validation confirmed that the fuzzy PID control algorithm can proficiently manage the server room’s temperature while concurrently decreasing the intake of cold air, thus promoting energy conservation. This control approach showcases commendable adaptability and robust performance characteristics.

show abstract

Section: Control Design 31 Fuzzy Control Designmentioning

confidence: 99%

Energy Saving Research on Temperature Control in Data Center Server Rooms

Su,

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…This method requires a designer with sufficient experience and knowledge of the system. Some researchers (Li et al, 2009;Mishra, 2006) use the neuro-fuzzy method for controlling FACTS devices to improve the transient stability. Such control strategy has some disadvantages, including:…”

Section: Introductionmentioning

confidence: 99%

Improving Transient Stability of a Multi-Machine Power Network Using FACTS Devices and Nonlinear Controller Tuned by PSO

Ashraf¹,

Shojaeian²,

Koofigar³

2013

RJASET

View full text Add to dashboard Cite

Transient stability is one of the main issues in the field of power systems. On the other hand, using FACTS devices has been proposed for the purpose of maximizing the lines conductivity given the restrictions involved, including the issue of stability. This study seeks to take steps towards the improvement of a multi-machine power system stability in faulty conditions, using two FACTS devices including TCSC and SVC. The control system for the FACTS devices is constructed here based on the so called energy function method. Meanwhile, in order to achieve a prescribed optimal performance, the coefficients of the designed controller are tuned by PSO algorithm. Possessing the theoretical analysis, the simulation results are also demonstrated to show the performance of the proposed control algorithm.

show abstract