Improvement of the performance of STATCOM in terms of voltage profile using ANN controller

Abstract

The electronic equipments are extremely sensitive to variation in electric supply. The increasing of a nonlinear system with several interconnected unpredicted and non-linear loads are causing some problems to the power system. The major problem facing the power system is power quality, controlling of reactive power and voltage drop. A static synchronous compensator (STATCOM) is an important device commonly used for compensation purposes, it can provide reactive support to a bus to compensate voltage level. In this paper, the Artificial Neural Network (ANN) controlled STATCOM has been designed to replace the conventional PI controller to enhance the STATCOM performance. The ANN controller is proposed due to its simple structure, adaptability, robustness, considering the power grid non linearities. The ANN is trained offline using data from the PI controller. The performance of STATCOM with case of Load increasing and three-phase faults case was analyzed using MATLAB/Simulink software on the IEEE 14-bus system. The comprehensive result of the PI and ANN controllers has demonstrated the effectiveness of the proposed ANN controller in enhancing the STATCOM performance for Voltage profile at different operating conditions. Furthermore, it has produced better results than the conventional PI controller.

PV solar farm as static synchronous compensator for power compensation in hybrid system using Harris Hawks optimizer technique

“…Because of the time of day, the production of PV and wind energy, and the demand for load, the electric converter behaves as if it were a standard electric converter. In the dark state, PV is not available and wind power does not pay for the load demand, that power converter acts as STATCOM compensation and energy management through the use of a generated controller [24], [25]. The two modes of STATCOM operation are described below.…”

A novel methodology for optimal location of reactive compensation through deep neural networks

“…For example ( Guo et al., 2021 ), discusses three commonly used machine-learning methods used for load forecasting, the support vector machine method, the random forest regression method, and the long short-term memory neural network method to forecast the load in the short term reducing the levels of uncertainty that this process entails. Also ( Alatshan et al., 2020 ); changes the conventional PI control for one based on Artificial Neural Network (ANN) on a STATCOM to improve its performance giving it adaptability, and robustness, considering the non-linearities of the electrical network.…”