Location Detection of False Data Injection Attacks in Power Grid Based on Improved Deep Extreme Learning Machine

Abstract

ObjectivesPower systems are facing threats of false data injection attacks. Existing detection methods for false data injection attacks have the problems of insufficient feature learning ability and slow detection speed, making it difficult to locate false data injection attacks rapidly and accurately. Therefore, this study proposes a method for locating false data injection attacks in power grid based on deep extreme learning machine optimized by Levy flying sparrow search algorithm.MethodsThe proposed method uses a deep extreme learning machine as the feature extraction algorithm and basic classifier to achieve rapid and accurate attack location. At the same time, a Levy flying sparrow search algorithm with strong local search ability is employed to optimize the initial weight and bias to further improve the location detection accuracy of the method.ResultsExtensive simulation analyses are conducted on IEEE-14 and IEEE-57 bus power systems. The proposed method achieves a detection accuracy rate of over 94%.ConclusionsCompared with other detection methods, the proposed method demonstrates better detection accuracy and enables faster location detection of false data injection attacks