Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.
False data injection attack (FDIA) is a typical cyber-attack aiming at falsifying measurement data for state estimation (SE), which may incur catastrophic consequences on cyber-physical system operation. In this paper, we develop a deep learning based methodology for detection, localization, and data recovery of FDIA on power systems in a coherent and holistic manner. However, the multi-modal probability distributions of both measurements and state variables in SE due to ever-changing operating points and structural/topological changes pose great challenges in detecting and localizing FDIA. To address this challenge, we first propose an enhanced attack model to launch massive FDIA on limited access points. Second, we train an auto-encoder (AE) with a Bayesian change verification (BCV) classifier using N -1 contingencies to detect FDIA with unseen Nk operational topologies. Third, to avoid model collapse caused by multi-modal measurement distribution, an AEbased generative adversarial network (GAN) is derived to generate a diverse candidate set of normal measurement vectors with various operational topologies. Finally, we develop a pattern match algorithm to localize and recover the falsified measurements and state variables by comparing the falsified measurement vectors with the normal measurement vectors in the candidate set. Case studies with IEEE benchmark systems and a modified 415-bus China Southern Grid system are provided to validate the proposed methodology. It shows that the proposed methodology achieves an average 95% accuracy for detection, over 80% accuracy for localization of FDIA, and recovers the measurement and state variables close to their true values.
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