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.

With the global trend to develop digital substation automation systems, measurement devices are required to be reliable, of small size and light weight and of acceptable accuracy in a wide frequency band. This article presents a combined high voltage direct current measurement method which comprises the above-mentioned features that makes it suitable for smart grids protection and control applications. The proposed measurement method utilizes Hall sensor array for dc current measurement. DC voltage is measured using a voltage divider circuit while the harmonic currents are measured using a square Rogowski coil made of four straight bars along with a high precision digital integration algorithm. A four-spectral line interpolation fast Fourier transform algorithm based on trapezoidal convolution window is proposed to improve the extraction accuracy of the dc and harmonic components from the measured signal. Experimental results show that the error variation of the proposed method is less than 0.098% for voltage measurement while it is less than 0.104% for current measurement. The harmonic measurement ratio error is less than 0.2% and the angle error is less than 8'.

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.

Due to the rapid development of phasor measurement units (PMUs) and the wide area of interconnection of modern power systems, the security of power systems is confronted with severe challenges. A novel framework based on data for static voltage stability margin (VSM) assessment of power systems is presented. The proposed framework can select the key operation variables as input features for the assessment based on partial mutual information (PMI). Before the feature selection procedure is completed by PMI, a feature preprocessing approach is applied to remove redundant and irrelevant features to improve computational efficiency. Using the selected key variables, a voltage stability assessment (VSA) model based on iterated random forest (IRF) can rapidly provide the relative VSM results. The proposed framework is examined on the IEEE 30-bus system and a practical 1648-bus system, and a desirable assessment performance is demonstrated. In addition, the robustness and computational speed of the proposed framework are also verified. Some impact factors for power system operation are studied in a robustness examination, such as topology change, variation of peak/minimum load, and variation of generator/load power distribution.

With the continuous development of microgrids (MGs), a hybrid structure consisting of single-phase MGs and three-phase MGs has been widely promoted. When each submicrogrid (SMG) pursues its economic benefit, it can easily cause a three-phase power unbalance for a grid-connected three/single-phase hybrid multimicrogrid (MMG). To address this problem, an optimization model based on the multileader and multifollower game theory is proposed for a grid-connected three/single-phase hybrid MMG in this paper. In the proposed optimization model, the three-phase unbalance constraints are integrated into the utility functions of the leaders and followers to reduce the impact of the unbalance constraints on the benefits of the participants. The performance of the proposed optimization model is verified by a series of tests. The test results show that the proposed optimization model can effectively reduce the three-phase power unbalance factor of the system and improve the profit of the three/single-phase hybrid MMG.