A new strategy based on ANN for controlling the electronic on‐load tap changer.

Summary: On‐load tap changer (OLTC) is utilized in regulating the voltage in electric systems. Recently, electronic on‐load tap changer (EOLTC) has been presented to eliminate the OLTC limitations and drawbacks. This paper presents an intelligent controller for EOLTC. The proposed controller aims to regulate the load voltage via fast and accurate adjusting the EOLTC when load voltage or power system voltage is changed. A multilayer feed forward neural network (MFFNN) controller is used in this proposal. The designing, training, and testing of the proposed intelligent controller are done in a MATLAB/Simulink environment. The performance of the EOLTC with the proposed MFFNN controller is achieved using two test distribution systems. The first one consists of a 220‐V source installed with a 50‐kW electrical load during a 220/210 V transformer and a transmission of short line. The second test system consists of an 11‐kV source connected to a (500 kW + 300 kVAR) industrial load through a 11/0.4‐kV, 800‐kVA transformer. This test system is a part of a real distribution feeder picked from North Delta Electric Distribution Company in Egypt. Several cases for the load voltage decreasing and increasing are used to verify the effectiveness of the proposed intelligent controller. The proposed controller shows fast and accurate response. [ABSTRACT FROM AUTHOR]