Pravin Chopade

Reactive power management and voltage control of large Transmission System using SVC (Static VAR Compensator)

The role of the transmission network in the Power System is to transmit the power generated in the power plants to the load centers and the interconnected power systems. The transmission of electric power has to take place in the most efficient way in addition to providing flexibility in the process. Flexible A.C. Transmission System (FACTS) promotes the use of static controllers to enhance the controllability and increase the power transfer capability. Providing reactive shunt compensation with shunt-connected capacitors and reactors is a well established technique to get a better voltage profile in a power system. Shunt capacitors are inexpensive but lack dynamic capabilities, thus some form of dynamically controlled reactive power compensation becomes essential. This feature is provided by Static VAR Compensator (SVC). The work presented here also compares SVC with fixed capacitor compensation and documents the superiority of SVC using Computer Simulation and its performance for reactive power management and better voltage control.

Structural and functional analytics for community detection in large-scale complex networks

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 structural parameters. We put forward structural and functional models for analyzing complex networks under situations of perturbations. We introduce modified adjacency and modified Laplacian matrices. We further introduce network or degree centrality (weighted Laplacian centrality) based on modified Laplacian, weighted micro-community centrality. We discuss its robustness and importance for micro-community detection for social and technological complex networks with overlapping communities. We also introduce ’k-clique sub-community’ overlapping community detection based on degree and weighted micro-community centrality. The proposed algorithms use optimal partition of k-clique sub-community for modularity optimization. We establish relationship between degree centrality and modularity. This proposed method with modified adjacency matrix helps us solve NP-hard problem.

Critical infrastructure interdependency modeling: Using graph models to assess the vulnerability of smart power grid and SCADA networks

We discuss a framework for quantitative vulnerability assessment of critical infrastructure systems. We focus on the smart electric power delivery systems, i.e., electricity transmission and distribution smart grids, along with SCADA and EMS systems. We introduce concepts and results from graph and social network theories, and apply them to the study of the WSCC-Smart Power Grid Network-SCADA-EMS (WSSE) System. We also calculate values of the topological characteristics of the networks and compare their error and attack tolerances, i.e., their performance when vertices are removed, randomly or in a malicious way. Also, we study the possible topology generation models such as the random graph, small-world, and scale-free models. The WSEE system was found to follow the scale-free graph model of social network theory.

New centrality measures for assessing smart grid vulnerabilities and predicting brownouts and blackouts

This paper proposes mathematical models based on the electrical properties of smart grids for conducting vulnerability analyses and predicting brownouts and blackouts. Definitions of pseudo-Laplacian, pseudo-adjacency and pseudo-degree matrices for smart grids are introduced to specify new centrality measures with electrical interpretations. The centrality measures are used to rank the relative importance of nodes (e.g., generating stations or substations) and edges (e.g., transmission lines or buses) of a graph corresponding to a power grid network and to assess the overall vulnerability of the network. The reliability of using the centrality measures to predict brownouts and blackouts is demonstrated in the face of random and targeted attacks. Monte-Carlo simulations are used to analyze attacks on smart grid networks and to assess the performance of the centrality measures. The simulations employ the IEEE 30-bus, IEEE 57-bus and IEEE 300-bus networks as well as the WSCC 4941-bus real power grid. Every scenario in the Monte-Carlo simulations involves the removal of a subset of buses and performing a complete nonlinear Newton-Raphson power flow analysis to compute the power traffic matrix and the corresponding centrality. The Monte-Carlo simulations conclusively demonstrate that electrical centrality measures based on the power traffic matrix are reliable indicators of the total unsatisfied load ratio (i.e., the load taken offline due to an attack divided by the total load demand). A key result is that, if the total centrality score of the removed buses exceeds a threshold estimated via Monte-Carlo simulation, then a sudden and dramatic jump to a blackout situation is ensured.

Node attributes and edge structure for large-scale big data network analytics and community detection

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 the graph Laplacian of the adjacency matrix and we exploit this connection by using modified relationship between Laplacian and adjacency matrix. We propose modularity optimization based on a greedy agglomerative method, coupled with fast unfolding of communities in large-scale networks using Louvain community finding method. Our proposed modified algorithm is linearly scalable for efficient identification of communities in huge directed/undirected networks. The proposed algorithm shows great performance and scalability on benchmark networks in simulations and successfully recovers communities in real network applications. In this paper, we develop communities from node attributes and edge structure. New modified algorithm statistically models the interaction between the network structure and the node attributes which leads to more accurate community detection as well as helps for identifying robustness of the network structure. We also show that any community must contain a dense Erdos-Renyi (ER) subgraph. We carried out comparisons of the Chung and Lu (CL) and Block Two-Level Erdos-Renyi (BTER) models with four real-world data sets. Results demonstrate that it accurately captures the observable properties of many real-world networks.

Structural and functional vulnerability analysis for survivability of Smart Grid and SCADA network under severe emergencies and WMD attacks

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 infrastructures are further taken into consideration to analyze functional vulnerability. In this paper, we propose a flexible and extensible framework for network survivability testing and evaluation based interdependency modeling of Smart Grid and SCADA network. We developed novel approach for vulnerability reduction considering both structural and functional vulnerability. We computed interdependent effect between these networks under different conditions. We used MATLAB for simulation and analysis of methodology developed by us for interdependency analysis. The contribution of this paper provides a feasible research approach for the normalization of network survivability testing and evaluation and the promotion of network survivability research.

Novel Smart Grid and SCADA system interdependency networks for future's clean, sustainable and green energy

A novel approach of interdependency modeling of Smart Grid and Supervisory Control and Data Acquisition (SCADA) networks is introduced. Methodological approach for vulnerability reduction considering both structural and functional vulnerability is introduced in this paper. The analysis of cyber-attacks on Smart Grid and SCADA network is discussed. Paper discusses importance and need of clean, sustainable and green energy for the future. Paper also analyzes interdependent Smart Grid and SCADA network under severe emergency situations. The contribution of this paper provides novel network with integrated infrastructures for active and survivable network operation. Thus novel Smart Grid and SCADA interdependency network will act as futures clean, sustainable and Green energy model.

Back to the future Renewable Energy Sources and green Smart Grid

There are advantages and disadvantages of the green power generation technologies using Renewable Energy Sources (RES) such as solar, wind energy, fuel cells, and biomass power generation, which can be used for generating distributed power.

Copper cables: Should they be phased out as last mile broadband solutions?

The fundamental building block of last mile broadband connections for the telecommunications industry is the copper cabling systems that have traditionally underpinned the networks. Time has seen tremendous change in this core component of the network, with result being silica-based fiber optics are championed as the de facto replacement technology for traditional copper cabling in the last mile. This paper will provide a survey and discussion of the current status of copper cabling in networks. It will examine the strengths of copper verses fiber optics. It will explore the effectiveness of recent technological advances made in delivering broadband over copper. It will also review the current economic models relating to cable deployments. Finally, an analysis will be presented that attempts to answers the question of phasing out copper.

Analyzing smart power grid and SCADA network robustness using the node degree distribution and algebraic connectivity under vulnerability and WMD attacks

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 the robustness of real world power grid network. We also discussed the performance of Western Electric Coordinating Council (WECC) US grid under Weapons of mass destruction (WMD) attacks.