Aditya Sundararajan

Frequency band for HAN and NAN communication in Smart Grid

Smart Grid metering and control applications require fast and secured two-way communication. IEEE 802.15.4 based ZigBee is one of the leading communication protocols for Advanced Metering Infrastructure (AMI). In North America, ZigBee supports two distinguished frequency bands - 915 MHz and 2.4 GHz. In Home Area Network (HAN) of AMI, home appliances communicate with smart meters whereas the communication among neighboring meters is termed as Neighborhood Area Network (NAN). In this study, optimum frequency bands for NAN and HAN communication have been proposed based on the throughput, reliability and scalability. We evaluated and compared the performance of bands 868/915 MHz and 2.4 GHz for AMI context. The solution also meets the requirements for Smart Grid communication standards as recommended by the US Department of Energy (DOE).

RSS based loop-free compass routing protocol for data communication in advanced metering infrastructure (AMI) of Smart Grid

Communication is the heart of the Smart Grid. Smart Grid metering and control applications require fast, reliable and secured two-way communication network. In this study, random signal strength (RSS) based localization of smart meters of Advanced Metering Infrastructure (AMI) has been proposed using the location information of meters whose position are known. Based on the location information, a loop free routing protocol has been developed. As a consequence, loop freedom, local routing decision and limited flooding, faster and reliable data transmission can be achieved.

Defending mechanisms for protecting power systems against intelligent attacks

The power system forms the backbone of a modern society, and its security is of paramount importance to nations economy. However, the power system is vulnerable to intelligent attacks by attackers who have enough knowledge of how the power system is operated, monitored and controlled. This paper proposes a game theoretic approach to explore and evaluate strategies for the defender to protect the power systems against such intelligent attacks. First, a risk assessment is presented to quantify the physical impacts inflicted by attacks. Based upon the results of the risk assessment, this paper represents the interactions between the attacker and the defender by extending the current zero-sum game model to more generalized game models for diverse assumptions concerning the attackers motivation. The attacker and defenders equilibrium strategies are attained by solving these game models. In addition, a numerical illustration is demonstrated to warrant the theoretical outcomes.

A review of cathode and anode materials for lithium-ion batteries

Lithium ion batteries are one of the most commercially sought after energy storages today. Their application widely spans from Electric Vehicle (EV) to portable devices. Their lightness and high energy density makes them commercially viable. More research is being conducted to better select the materials for the anode and cathode parts of Lithium (Li) ion cell. This paper presents a comprehensive review of the existing and potential developments in the materials used for the making of the best cathodes, anodes and electrolytes for the Li-ion batteries such that maximum efficiency can be tapped. Observed challenges in selecting the right set of materials is also described in detail. This paper also provides a brief history of battery technology and their wide applicability in the energy market today, the chemistry and principle of operation behind the batteries, and their potential applications even beyond the energy sector. Safety concerns related to Li-ion batteries have also been taken into account considering recent events.

Performance evaluation of optimal photovoltaic-electrolyzer system with the purpose of maximum Hydrogen storage

Power electronics-based electrolyzer systems are prevalently in current use. This paper proposes the more recently employed directly coupled photovoltaic (PV) electrolyzer systems. Equipped with accurate electrical models of the advanced alkaline electrolyzer, PV system and Hydrogen storage tank simulated using MATLAB, the systems performance for a full week is analyzed using Miami, Floridas meteorological data. A multi-level Genetic Algorithm (GA)-based optimization facilitates maximum hydrogen production, minimum excess power generation, and minimum energy transfer loss. The crucial effect of temperature on the overall system performance is also accounted for by optimizing this parameter using GA, maintaining operating conditions close to the Maximum Power Point (MPP) of the PV array. The results of the analysis have been documented to show that the optimal system for a 10 kW electrolyzer can produce, on an average, Hydrogen of 0.0176 mol/s, when the system is operating with 6.3% power loss and 2.4% power transfer loss.

A self-tuning variable frequency control for multi-level contactless Electric Vehicle charger

This paper proposes a self-tuning controller for a multi-level inductive Electric Vehicle (EV) battery charger application. By controlling the energy injection frequency of the Inductive Power Transfer (IPT) system, multiple charging levels (4 in number) are achieved. Maximum performance is leveraged by the proposed IPT system using its capability of self-tuning the switching operations to the natural resonant frequency of the IPT system, thereby benefiting from soft-switching operations (zero-current switching).Either a digital or analog control circuit can be used to implement the proposed controller. The results are shown by simulating the proposed controller with different charging levels using MATLAB/Simulink. These results show that the proposed controller effectively enables IPT-based contactless EV charging at multiple charging levels, with soft-switching operations. This self-tuning capability is specifically useful in IPT systems that have variable resonance frequency.

A Generic Framework for EEG-Based Biometric Authentication

Biometric systems are a part and parcel of everyones lives these days. However, with the increase in their use, the security risks associated with them have equally increased. Hence, there is an increased need to develop systems which use biometrics efficiently and ensure the authentication is integral and effective. This paper aims to introduce the concept of using Electro Encephalogram (EEG), commonly known as brain waves, as a biometric. A wavelet based feature extraction method is proposed, that uses visual and auditory evoked potentials. The future scope, pros and cons of this biometric are analyzed next.

Toward a Smart City of Interdependent Critical Infrastructure Networks

A smart city requires synergistic interaction between several functionally interdependent networks like energy, transportation, water, oil, gas, and emergency services to provide on-demand, reliable services to prosumers. The sustainability of smart city can be guaranteed only through ubiquitous communication and decentralized information exchange between optimization and computational models for the operation, visibility, and control of each constituent network. With the city spanning different societies and jurisdictions, the models must also account for challenges like interoperability, security, latency, resiliency, policymaking, and social behavior. Solutions in the current literature address these challenges in each network exclusively, but the interdependency between them is not properly emphasized. The chapter addresses this gap in research by considering smart city networks with special emphasis on energy, communication, data analytics, and transportation. It introduces each of these networks, identifies state of the art in them and explores open challenges for future research. As its key contribution to the literature, the chapter brings out the interdependencies between these networks through realistic examples and scenarios, identifying the critical need to design, develop, and implement solutions that value such dependencies. Thus, the chapter aims to serve as a starting point for researchers entering the domain of smart city and is interested in conducting cross-functional research across its different interdependent networks.

Power quality and voltage profile analyses of high penetration grid-tied photovoltaics: A case study

Installed Photovoltaic (PV) capacity across the smart distribution grid has been on the rise in order to reduce greenhouse gas emissions. However, under high penetration of PV, there could be potential impacts on the operation and planning of distribution networks. In order to evaluate the impacts of grid-tied PV, a case study on power quality and voltage profile analyses is conducted using a 1.1 MW AC grid-tied PV power plant located at Florida International University. As part of the power quality analysis, study explores Total Harmonic Distortion (THD) and high power and high energy ramp rate analysis. Current THD is posed to trigger problems when generation is highly intermittent wherein Voltage THD does not have a tight relationship with power output. For voltage profile analysis, the case study considers peak and minimum daytime load scenarios under different levels of penetration, including the existing level, and appraises the plants current and potential impacts in steady-state and time-series scenarios. The effect of using smart inverters with grid-support functions is also simulated. Results show that some major problems like voltage deviations and feeder losses can be expected at 60% PV penetration in minimum daytime load. The number of switching operations for voltage regulators also increase when smart inverters operate at Volt/VAr control mode. Results of the case study are discussed to highlight the significance of these issues in high penetration scenarios.

A distributed intelligent framework for electricity theft detection using benford's law and stackelberg game

Electricity theft is a major contributor of nontechnical losses in the distribution systems of the smart grid. However, owing to the resource-limitations of smart meters and the privacy requirement of electricity usage data, theft detection has become a challenging task for electric utilities. To address this problem, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed and implemented in this paper. It is equipped with Benfords Analysis for initial but powerful diagnostics on smart meter big data. A Stackelberg game-theoretic model is formulated to analyze the strategic interactions between one utility and multiple electricity thieves, which is applied to data flagged suspicious by Benfords Analysis. The Stackelberg equilibrium provides sampling rate and threshold to conduct a Likelihood Ratio Test (LRT) to detect potentially fraudulent meters. The framework is validated on real interval electricity usage data from an electric utility in Florida to filter fraudulent meters in a community.