Enhancing System Stability with FACTS Devices Using Deep Neural Network Algorithm

The implementation of FACTS-enabled devices has been widely adopted in power systems to enhance their stability and reliability. However, construction of FACTS devices and a means of controlling them would need a significant amount of expertise and can be time-consuming and expensive. There has been a surge of curiosity around the application of artificial intelligence techniques such as neural networks to enhance the design and control of FACTS devices. Using a deep neural network, this research study presents a novel method to improve the stability of power systems using FACTS devices. The proposed approach involves training a deep neural network model using historical data on power system operation and stability. The trained neural network is then used improving the control parameters FACTS instruments in real-time to improve system stability. Here, we assess how well the planned approach on a test system and compare it to traditional control methods. Our According to the findings, the suggested method is able to provide superior system stability and faster response times compared to traditional control methods. Overall, our study highlights the potential of using neural network algorithms to enhance the design and control of FACTS devices in power systems, leading to improved stability and reliability of the grid.