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With the development of communication technology, power and information systems have become deeply coupled and have become the most massive and complex cyber-physical power systems (CPPSs), resulting in certain risks to the safe operation of power systems. In this paper, a CPPS security assessment method based on the expected failure method and considering combined information attacks is proposed. Due to the large number of nodes in modern information systems, enumerating all possible combinations of system failures for evaluation and analysis is unrealistic. By considering the topological relationship of the CPPS and the electrical properties of coupled physical nodes, the idea of first screening information nodes is proposed in this paper to filter the information nodes. The impact of combined information attacks on the expected physical fault handling process is analyzed, and a safety evaluation index for the distribution network is provided. In addition, the proposed method is applied to the IEEE-118 system and compared with previous evaluation methods to verify its effectiveness.

Blackouts have severely damaged cyber-physical power systems over the years, resulting in malfunctions that have rapidly spread within the electrical network. Selecting a set of critical nodes for human control can avoid similar situations. We propose a critical node evaluation method based on pinning control theory that uses a minimum nonzero eigenvalue of a modified Laplacian matrix as the evaluation index. Computational complexity can be markedly reduced using matrix analysis theory to sort screening conditions. Multiple nodes are controlled to form critical node groups in the directed weighted cyber-physical power system. We can thus more accurately find an optimal set of controlled nodes compared with other critical node evaluation strategies. Through theoretical and simulated verifications, we conclude that the addition of two nodes, which are used as active and standby dispatching centers of the communication network, is more effective and can result in a cyber-physical power system with better connectivity.

Wind power is a new energy source, for which the forecasting accuracy is related to the feasibility of various decision-making schemes for the power grid. The random, fluctuating and uncertain characteristics of wind power make accurate point forecasting very difficult and result in low practical application value. Therefore, a novel wind-power interval prediction method is proposed in this paper. First, variational model decomposition (VMD) is applied to the historical time sequence for wind power, followed by using an extreme learning machine (ELM) to establish a prediction model for the intrinsic mode function (IMF). An autoregressive integrated moving average (ARIMA) is used to establish a prediction model for the residual components, and the predicted IMF and residual components are reconstructed to predict the wind power. Finally, iterative self-organizing data analysis technique algorithm (ISODATA) are used with the error sequence to perform clustering segmentation, and adaptive diffusion kernel density estimation (ADKDE) is used to fit the probability density function for the prediction error of each segment and thereby obtain the cumulative distribution function and the final interval prediction under a given confidence level (CL). The results of a simulation based on real wind farm data from Hunan Province, China, from January to April 2020 show that the proposed method can significantly improve interval coverage.

Scan matching methods have been widely applied in the fields of autonomous localization and mapping. However, in structured environments where feature differences are less significant, such as long straight corridors, conventional positioning algorithms often suffer from characteristic mismatching, resulting in lower accuracy. As such, a new global localization algorithm, based on environmental difference evaluation and correlation scan matching fusion, is proposed in this study. In this process, the surrounding space was evaluated using a priori understanding of the environment based on a linear fit. Corresponding evaluation and positioning results from correlation scan matching were then modified using dynamic selection and a posture updating strategy. The performance of the proposed technique was compared with other conventional methods using open datasets exhibiting long straight features and a series of tests conducted in a physical corridor. Results showed that the proposed algorithm could effectively improve localization accuracy in narrow environments. The translation and rotation absolute pose errors were reduced by an average of 27.29% and 25.82%, respectively, compared with a correlation matching approach that does not consider the surrounding geometry. These results suggest the proposed technique offers higher adaptability and positioning accuracy in narrow environments.