Power Quality and Electromagnetic Interference Noise Problems of Fluorescent Lamp System to Control Systems

2014 ◽  
Vol 20 (10) ◽  
pp. 1798-1802 ◽  
Author(s):  
Chutipon Uyaisom ◽  
Werachet Khan-Ngern
Author(s):  
Okan Ozgonenel ◽  
◽  
Kubra Nur Akpinar ◽  

Electrical power systems are expected to transmit continuously nominal rated sinusoidal voltage and current to consumers. However, the widespread use of power electronics has brought power quality problems. This study performs classification of power quality disturbances using an artificial neural network (ANN). The most appropriate ANN structure was determined using the Box-Behnken experimental design method. Nine types of disturbance (no fault, voltage sag, voltage, swell, flicker, harmonics, transient, DC component, electromagnetic interference, instant interruption) were investigated in computer simulations. The feature vectors used in the identification of the different types of disturbances were produced using the discrete wavelet transform and principal component analysis. Our results show that the optimized feed forward multilayer ANN structure successfully distinguishes power quality disturbances in simulation data and was also able to identify these disturbances in real time data from substations.


2010 ◽  
Vol 13 (2) ◽  
pp. 29-36
Author(s):  
Anh Huy Quyen ◽  
Anh Viet Truong ◽  
Nhung Thi Hong Le

Control voltage in power system is always necessary to guarantee power quality and reduce power loss of productivity. This paper presents the construction of voltage control fuzzy system in transmission network with variety control devices, used fuzzy Mamdani controllers. Fuzzy controllers associated and combined control actives to create an unified fuzzy control systems capable of automatically controlling the voltage at the nodes in power system by retaining nodes voltage in a while desired values with the satisfying restrictive conditions. Through the investigative results on 30bus IEEE standard network demonstrated the effectiveness of the proposed fuzzy algorithm compared with conventional techniques sensitive tree.


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