Advanced Autonomous Voltage-Control Method using Sensor Data in a Distribution Power System

被引:1
作者
Takahashi, Naoyuki [1 ]
机构
[1] Cent Res Inst Elect Power Ind, Yokosuka, Kanagawa, Japan
来源
2019 2ND INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST 2019) | 2019年
关键词
Power distribution system; Autonomous voltage control; Database;
D O I
10.1109/sest.2019.8849001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
PVs have been rapidly incorporated within power distribution systems after the operation of Feed In Tariff (FIT). Step Voltage Regulator (SVR) with Line Drop Compensator method has been installed to the power distribution system to control rising voltages due to the reverse flow from PVs. LDC controls the distribution voltage based on the estimated voltage obtained via sensor measurements in real-time. However, using LDC, it is difficult to maintain the distribution voltage within a specified range because the estimation error expands with increasing PV installed capacity. In Japan, a type of automatic switch with voltage and current sensors (IT-switch) has been installed to detect an accident point in the power distribution system. IT-switching can monitor line voltage and current in each phase, and measured data are transferred to data servers. This paper proposes an advanced, autonomous voltage-control method to improve the estimation accuracy and voltage control performance of SVR, using the reference database to address the voltage deviation problem. The proposed method estimates a voltage width of the control target voltage in real-time as a reference voltage by using the measured (online) data and database (offline) data. To determine the validity of the proposed method, we conducted numerical simulations and compared the voltage control performance and the influence of SVR life span using a model power distribution system. The results indicates that the proposed method improved the voltage control performance and SVR life span by improving the estimation accuracy through the integration of real-time measured data and the reference database.
引用
收藏
页数:6
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