Pravin Chopade

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Loads in a power distribution system network are mostly inductive and lead to poor power factor. In order to utilize the generated power optimally it is necessary to maintain close-to-unity power factor. Power factor correction is possible by introducing the capacitive loads in the circuit, as to nullify the effect of inductive loading. Due to simplicity of(More)
A Novel or Methodological approach to comprehensively analyze the vulnerability of interdependent infrastructures is introduced in our paper; two types of vulnerability are considered: structural vulnerability and functional vulnerability. For structural vulnerability, infrastructures topologies are the only information while operating regimes of different(More)
Community structure is thought to be one of the main organizing principles in most complex networks. Big data and complex networks represent an area which researchers are analyzing worldwide. Of special interest are groups of vertices within which connections are dense. In this paper we begin with discussing community dynamics and exploring complex network(More)
Identifying network communities is one of the most important tasks when analyzing complex networks. Most of these networks possess a certain community structure that has substantial importance in building an understanding regarding the dynamics of the large-scale network. Intriguingly, such communities appear to be connected with unique spectral property of(More)
Community detection is a fundamental component of large network analysis. In both academia and industry, progressive research has been made on problems related to community network analysis. Community detection is gaining significant attention and importance in the area of network science. Regular and synthetic complex networks have motivated intense(More)
We discuss the robustness of large networks such as Smart Power Grid and Supervisory Control and Data Acquisition (SCADA) network. We put forward mathematical models of robustness that reflect power network dynamics and the Laplacian of the network. We used nodal degree distributions, algebraic connectivity, and Fiedler eigenvalue parameters for analyzing(More)
Erratum After the publication of this work [1], we noticed that an incorrect version of Table two (Table 1 here) was published. An incorrect version of Algorithm four (Algorithm 1 here) was also published. The correct versions of Table two and Algorithm four are provided here and have been updated in the original article. The publisher apologises for any(More)
In this work we put forward our novel approach using graph partitioning and Micro-Community detection techniques. We firstly use algebraic connectivity or Fiedler Eigenvector and spectral partitioning for community detection. We then used modularity maximization and micro level clustering for detecting micro-communities with concept of community energy. We(More)
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