Volume 17, No. 1, 2020
Power Quality Improvement Using Dvr Control Designed With Ann-Fuzzy In Matalab
B. Gopal , Pannala Krishna Murthy
Abstract
Industrial loads are particularly vulnerable to disruptions in the power supply and must be carefully managed to prevent disruptions in production. Semiconductor devices are often responsible for providing the majority of the power required by an industrial load. That is why you need a dynamic voltage restorer, which can detect voltage drops and restore the proper voltage level quickly and precisely. In this paper, the simulation of dynamic voltage restorer (DVR) was made by using an artificial neural network. DVR injects the voltage in the system when voltage sag or swell occurred due to fault. The ANN is trained online by data generated by the uncompensated model. The problem of voltage sags and its severe impact on sensitive loads is well known. To solve this problem, the DVR is a modern and important custom power device for compensation voltage sags in power distribution systems. The Dynamic Voltage Restorer (DVR) is fast, flexible and efficient solution to voltage sag problem. The DVR is a series compensator used to mitigate voltage sags and to restore load voltage to its rated value. In this paper, an overview of the DVR, its functions, configurations, components, operating modes, voltage injection methods and closed loop control of the DVR output voltage are reviewed along with the device capabilities and limitations. A power system model with a programmable power source is used to include 3rd and 5th harmonics. The systems’ response for load voltage is evaluated for with and without DVR scenarios. It has been noted that the proposed DVR based strategy has effectively managed the voltage distortion, and a smooth compensated load voltage was achieved. The load voltage THD percentage was approximately 18% and 23% with insertion 3rd and 5th harmonics in the supply voltage, respectively. The inclusion of the proposed DVR has reduced THD around less than 4% in both cases.
Pages: 573-592
Keywords: DVR, Sag, Facts, ANN, Harmonics. Fuzzy.