Study on Power Grid Partition and Attack Strategies Based on Complex Networks

Based on the community discovery method in complex network theory, a power grid partition method considering generator nodes and network weightings is proposed. Firstly, the weighted network model of a power system is established, an improved Fast-Newman hierarchical algorithm and a weighted modular Q function index are introduced, and the partitioning algorithm process is practically improved combined with the characteristics of the actual power grid. Then, the partition results of several IEEE test systems with the improved algorithm and with the Fast-Newman algorithm are compared to demonstrate its effectiveness and correctness. Subsequently, on the basis of subnet partition, two kinds of network attack strategies are proposed. One is attacking the maximum degree node of each subnet, and the other is attacking the maximum betweenness node of each subnet. Meanwhile, considering the two traditional intentional attack strategies, that is, attacking the maximum degree nodes or attacking the maximum betweenness nodes of the whole network, the cascading fault survivability of different types of networks under four attack strategies is simulated and analyzed. It was found that the proposed two attack strategies based on subnet partition are better than the two traditional intentional attack strategies.

Klasifikasi Data Tidak Lengkap Dengan Pendekatan Fuzzy Grid Partition

Klasifikasi data tidak lengkap dapat di proses langsung dengan cara tertentu untuk mendapatkan aturannya atau diperoleh dari pengetahuan para pakar. Ketergantungan terhadap pakar akan sulit memodelkan implikasi logis manusianya, tidak tersedianya framework proses pemodelan, dan biaya pakar yang mahal. Kesulitan tersebut dapat diatasi dengan mendapatkan aturan dari data yang bersifat uncertain dengan menerapkan metode dari sistem fuzzy yang dibangun berdasarkan konsep fuzzy if-then rules. Pendekatan metode pada penelitian ini adalah metode fuzzy grid partition untuk mengklasifikasikan data tidak lengkap. Data yang digunakan adalah data cuaca yang terdiri data kelembaban udara sebagai konklusi, data temperatur, curah hujan, lamanya penyinaran matahari dan kecepatan angin sebagai anteseden. Tahapan penelitian dimulai dengan menginput data set tidak lengkap, merubah data tidak lengkap menjadi data lengkap, menormalisasi data, membangkitkan aturan dan melakukan proses klasifikasi data. Hasil penelitian menghasilkan 22 aturan untuk mengklasifikasi data dengan tingkat akurasi 66,67%, tingkat error 33,33% dan jumlah data unclass adalah 0

Grid Partition Variable Step Alpha Shapes Algorithm

On the basis of Alpha Shapes boundary extraction algorithm for discrete point set, a grid partition variable step Alpha Shapes algorithm is proposed to deal with the shortcomings of the original Alpha Shapes algorithm in the processing of nonuniform distributed point set and multiconcave point set. Firstly, the grid partition and row-column index table are established for the point set, and the point set of boundary grid partition is quickly extracted. Then, the average distance of the k -nearest neighbors of the point is calculated as the value of α . For the point set of boundary grid partition extracted in the previous step, Alpha Shapes algorithm is used to quickly construct the point set boundary. The proposed algorithm is verified by experiments of simulated point set and measured point set, and it has high execution efficiency. Compared with similar algorithms, the larger the number of point sets is, the more obvious the execution efficiency is.

Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems

Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by special devices. Methodology. The construction of a fuzzy neural network is carried out using networks based on adaptive neuro-fuzzy system ANFIS. The network generated using the Grid Partition algorithm without clustering and Subtractive Clustering. Results. The paper presents a comparative analysis of fuzzy neural networks of various architectures in terms of increasing the accuracy of reproducing the moisture content of transformer oil. For training and testing fuzzy neural networks, the results of continuous monitoring of the temperature of the upper and lower layers of transformer oil during two months of operation used. Considered twenty four variants of the architecture of ANFIS models, which differ in the membership functions, the number of terms of each input quantity, and the number of training cycles. The results of using the constructed fuzzy neural networks for reproducing the dynamics of moisture content of transformer oil during a month of operation of the transformer are presented. The reproducing accuracy was assessed using the root mean square error and the coefficient of determination. The test results indicate the sufficient adequacy of the proposed models. Consequently, the RMSE value for the network constructed using Grid Partition method was 0.49, and for the network built using the Subtractive Clustering method – 0.40509.

An improved algorithm for the maximal information coefficient and its application

The maximal information coefficient (MIC) captures both linear and nonlinear correlations between variable pairs. In this paper, we proposed the BackMIC algorithm for MIC estimation. The BackMIC algorithm adds a searching back process on the equipartitioned axis to obtain a better grid partition than the original implementation algorithm ApproxMaxMI. And similar to the ChiMIC algorithm, it terminates the grid search process by the χ 2 -test instead of the maximum number of bins B( n , α ). Results on simulated data show that the BackMIC algorithm maintains the generality of MIC, and gives more reasonable grid partition and MIC values for independent and dependent variable pairs under comparable running times. Moreover, it is robust under different α in B( n , α ). MIC calculated by the BackMIC algorithm reveals an improvement in statistical power and equitability. We applied (1-MIC) as the distance measurement in the K-means algorithm to perform a clustering of the cancer/normal samples. The results on four cancer datasets demonstrated that the MIC values calculated by the BackMIC algorithm can obtain better clustering results, indicating the correlations between samples measured by the BackMIC algorithm were more credible than those measured by other algorithms.