With the continuous expansion of the power system scale and extensive application of phasor measurement units (PMUs), the secure operation of power systems has been increasingly concerned. In this paper, an integrated scheme for online dynamic security assessment (DSA) based on feature selection and regression prediction is proposed. First, partial mutual information (PMI) and the Pearson correlation coefficient (PCC) are used to select the key variables in the feature selection process. Second, an iterated random forest (IRF) is applied to predict the transient stability margin (TSM) based on the selected variables. Combining the feature selection process and regression prediction, a DSA model is constructed. Finally, a spatial-temporal dynamic visualization approach is proposed, which can intuitively provide real-time dynamic security information of power systems. The integrated scheme, which is tested on the IEEE 39-bus system and a practical 1648-bus system provided by the software PSS/E, exhibits desirable assessment accuracy and is suitable for online application. In the robustness test, some impact factors for power system operation are considered, such as topology change, variation of generator/load power distribution and variation of load characteristics. Moreover, the impacts of missing data and measurement noise are studied.

The uncertainty of wind power brings many challenges to the operation and control of power systems, especially for the joint operation of multiple wind farms. Therefore, the study of the joint probability density function (JPDF) of multiple wind farms plays a significant role in the operation and control of power systems with multiple wind farms. This research was innovative in two ways. One, an adaptive bandwidth improvement strategy was proposed. It replaced the traditional fixed bandwidth of multivariate nonparametric kernel density estimation (MNKDE) with an adaptive bandwidth. Two, based on the above strategy, an adaptive multi-variable non-parametric kernel density estimation (AMNKDE) approach was proposed and applied to the JPDF modeling for multiple wind farms. The specific steps of AMNKDE were as follows: First, the model of AMNKDE was constructed using the optimal bandwidth. Second, an optimal model of bandwidth based on Euclidean distance and maximum distance was constructed, and the comprehensive minimum of these distances was used as a measure of optimal bandwidth. Finally, the ordinal optimization (OO) algorithm was used to solve this model. The scenario results indicated that the overall fitness error of the AMNKDE method was 8.81% and 11.6% lower than that of the traditional MNKDE method and the Copula-based parameter estimation method, respectively. After replacing the modeling object the overall fitness error of the comprehensive Copula method increased by as much as 1.94 times that of AMNKDE. In summary, the proposed approach not only possesses higher accuracy and better applicability but also solved the local adaptability problem of the traditional MNKDE.

In the increasingly complex cyber-environment, appropriate sustainable maintenance of substation auto systems (SASs) can lead to many positive effects on power cyber-physical systems (CPSs). Evaluating the cybersecurity risk of power CPSs is the first step in creating sustainable maintenance plans for SASs. In this paper, a mathematical framework for evaluating the cybersecurity risk of a power CPS is proposed considering both the probability of successful cyberattacks on SASs and their consequences for the power system. First, the cyberattacks and their countermeasures are introduced, and the probability of successful cyber-intruding on SASs is modeled from the defender’s perspective. Then, a modified hypergraph model of the SAS’s logical structure is established to quantitatively analyze the impacts of cyberattacks on an SAS. The impacts will ultimately act on the physical systems of the power CPS. The modified hypergraph model can describe more information than a graph or hypergraph model and potentially can analyze complex networks like CPSs. Finally, the feasibility and effectiveness of the proposed evaluation method is verified by the IEEE 14-bus system, and the test results demonstrate that this proposed method is more reasonable to assess the cybersecurity risk of power CPS compared with some other models.

To optimize the operating temperature and flow velocity of cooling water in a high voltage direct current (HVDC) thyristor valve cooling system, the erosion corrosion characteristics of aluminum electrodes in deionized water at various temperatures were studied. With increasing water temperature, the corrosion current of the aluminum electrode gradually increases and the charge transfer impedance gradually decreases, thus, the corrosion of aluminum tends to become serious. The aluminum electrode in 50 °C deionized water has the most negative corrosion potential (−0.930 V), the maximum corrosion current (1.115 × 10⁻⁶ A cm⁻²) and the minimum charge transfer impedance (8.828 × 10⁻⁶ Ω), thus, the aluminum corrosion at this temperature is the most serious. When the temperature of deionized water increases, the thermodynamic activity of the ions and dissolved oxygen in the deionized water increases, and the mass transfer process accelerates. Therefore, the electrochemical corrosion reaction of the aluminum surface will be accelerated. The corrosion products covering the aluminum electrode surface are mainly Al(OH)₃. With increasing water temperature, the number of pits and grooves formed by corrosion on the aluminum surface increased. In this paper, the molar activation energy Ea and the equilibrium constant K of the aluminum corrosion reaction with various temperatures are calculated. This clarifies the effect of temperature on the aluminum corrosion reaction, which provides a basis for protecting aluminum from corrosion. The results of this study will contribute to research that is focused on the improvement of production techniques used for HVDC thyristor valve cooling systems.