Taking account of the fact that the existing knowledge‐driven detection process for false data injection attacks (FDIAs) has been in a passive detection state for a long time and ignores the advantages of data‐driven active capture of features, an active and passive hybrid detection method for power cyber‐physical systems (CPS) FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper. First, the shortcomings of the traditional AKF algorithm in terms of filtering divergence and calculation speed are analyzed. The state estimation algorithm based on non‐negative positive‐definite adaptive Kalman filter (NDAKF) is improved, and a passive detection method of FDIAs is constructed, with similarity Euclidean distance detection and residual detection at its core. Then, combined with the advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and feature‐expression ability, an active detection method of FDIAs based on a GRU‐CNN hybrid neural network is proposed. Finally, the results of joint knowledge‐driven and data‐driven parallel detection are used to define a mixed fixed‐calculation formula, and an active and passive hybrid detection method of FDIAs is established, considering the characteristic constraints of the parallel mode.

The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for these models to accurately describe spatiotemporal deduction processes, such as microgrid CPS attack identification, risk propagation, safety assessment, defense control, and cascading failure. To address this problem, a modeling method for the coupling relations of microgrid CPS driven by hybrid spatiotemporal events is proposed in the present work. First, according to the topological correlation and coupling logic of the microgrid CPS, the cyclical conversion mechanism of information flow and energy flow is analyzed, and a microgrid CPS architecture with multi-agents as the core is constructed. Next, the spatiotemporal evolution characteristic of the CPS is described by hybrid automata, and the task coordination mechanism of the multi-agent CPS terminal is designed. On this basis, a discrete-continuous correlation and terminal structure characteristic representation method of the CPS based on heterogeneous multi-groups are then proposed. Finally, four spatiotemporal events, namely state perception, network communication, intelligent decision-making, and action control, are defined. Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios.

Here, a novel Gaussian bare‐bones bat algorithm (GBBBA) and its modified version named as dynamic exploitation Gaussian bare‐bones bat algorithm (DeGBBBA) are proposed for solving optimal reactive power dispatch (ORPD) problem. The optimal reactive power dispatch (ORPD) plays a fundamental role in ensuring stable, secure, reliable as well as economical operation of the power system. The proposed GBBBA employs the Gaussian distribution in updating the bat positions in an effort to mitigate the premature convergence problem associated with the standard BA. DeGBBBA is an advanced variant of GBBBA in which a modified Gaussian distribution is introduced so as to allow the dynamic adaptation of exploitation and exploitation in the proposed algorithm. Both GBBBA and DeGBBBA are used to determine the optimal settings of generator bus voltages, tap setting transformers and shunt reactive sources in order to minimize the active power loss, total voltage deviations and voltage stability index. The performance and effectiveness of the proposed algorithms are demonstrated on three power system test bus including the standard IEEE 14‐bus, IEEE 57‐bus and IEEE 118‐bus. Simulation results show that GBBBA and DeGBBBA are robust and effective in solving the ORPD problem.

This paper presents a personalized recommendation model of electric power information maintenance & operation knowledge based on collaborative filtering. First, by introducing implicit rating mechanism, it transforms the learning behavior of operation and maintenance personnel into implicit rating of electric power information operation and maintenance knowledge. Then, it improves the shortcomings of the traditional collaborative recommendation filtering. Last, based on improving collaborative recommendation filtering, it builds a personalized recommendation model, and recommends the knowledge of electric power information operation & maintenance. The experimental outcomes show that the personalized recommendation model can effectively improve recommendation effect, which is of practical significance.