A new power swing detection method in power systems with large‐scale wind farms based on modified empirical‐mode decomposition method

High energy consumption around the world and concerns over climate change and high greenhouse gas emissions have led to a significant increase in using renewable energy resources, particularly wind power plants. In spite of using small wind farms in distribution networks as distributed generation, the interest in employing large‐scale wind farms in transmission levels has grown significantly in recent years. The presence of large‐scale wind farms with a synchronous generator to supply the energy demand can affect the power swing parameters. These effects are in a way that can mislead power swing detection methods and cause undesirable tripping of transmission line due to mal‐operation of distance relay against power swing . Therefore, in this paper, a method has been presented based on modified empirical mode decomposition (EMD) method to differentiate fault from power swing in the networks connected to the large‐scale wind farms. The results prove the efficiency of the presented method in detecting the power swing in large‐scale wind farm networks. Finally, a comparison is conducted between the proposed method and similar methods which proves the efficiency and superiority of the proposed method in terms of noise tolerance, detection fault detection speed, and multi‐mode power swing detection compared to the current methods. 1. Using modified EMD with PMU to reduce the effect of the large‐scale wind turbines for power swing detection. 2. Detecting 3‐phase fault simultaneouse with the power swing in the power systems with large‐scale wind turbines. 3. Detecting different power swing types including stable, unstable, and multi‐mode. 4. A proper performance during noisy signal .