A Study of Transmission System with Artificial Intelligence Technique

ELECTRICAL ENGINEERING

Modern control theory-based power system controllers have substantial advantages over traditional designs, especially for HVDC transmission. To dampen electromechanical oscillations in AC-DC systems, optimal and model controls of rectifier current regulators have been used. AI controllers, such as fuzzy logic controllers, ANN controllers, and ANFIS, can be utilised to improve the performance of the HVDC system in addition to conventional controllers. The CIGRE Benchmark weak HVDC system is utilised in this proposed effort to analyse and assess the performance of the HVDC system and its ability to improve stability. To simulate the performance of the system, the proposed controller is implemented in MATLAB/Simulink. In this paper, we explain how to use an Artificial Neural Network (ANN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) to perform adaptive tuning of a PI controller in an HVDC system, and compare the different FACTS devices with simulation results to improve the system's stability. The purpose of this paper is to improve the power transfer capability of an HVDC transmission system. The use of artificial intelligence techniques such as fuzzy logic, neural networks, and Neuro-fuzzy logic to construct an effective and intelligent current controller to improve the gain in an HVDC transmission system was explored and reported. FACTS controllers can improve the power flow capacities of an HVDC transmission system. The current research looked at three devices: SSSC, STATCOM, and UPFC. A 12-pulse thyristor converter was used to simulate the HVDC transmission link in MATLAB/SIMULINK. The CIGRE bench model 2 was utilised in all of the trials conducted in this paper.

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