Vijay Devabhaktuni

Smart meters for power grid — Challenges, issues, advantages and status

Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. Integration of smart meters into electricity grid involves implementation of a variety of techniques and software, depending on the features that the situation demands. Design of a smart meter depends on the requirements of the utility company as well as the customer. This paper discusses various features and technologies that can be integrated with a smart meter. In fact, deployment of smart meters needs proper selection and implementation of a communication network satisfying the security standards of smart grid communication. This paper outlines various issues and challenges involved in design, deployment, utilization, and maintenance of the smart meter infrastructure. In addition, several applications and advantages of smart meter, in the view of future electricity market are discussed in detail. This paper explains the importance of introducing smart meters in developing countries. In addition, the status of smart metering in various countries is also illustrated.

Support vector machine based data classification for detection of electricity theft

Most utility companies in developing countries are subjected to major financial losses because of non-technical losses (NTL). It is very difficult to detect and control potential causes of NTL in developing countries due to the poor infrastructure. Electricity theft and billing irregularities form the main portion of NTL. These losses affect quality of supply, electrical load on the generating station and tariffs imposed on electricity consumed by genuine customers. In light of these issues, this paper discusses the problems underlying detection of electricity theft, previously implemented ways for reducing theft. In addition, it presents the approximate energy consumption patterns of several customers involving theft. Energy consumption patterns of customers are compared with and without the presence of theft. A dataset of customer energy consumption pattern is developed based on the historical data. Then, support vector machines (SVMs) are trained with the data collected from smart meters, that represents all possible forms of theft and are tested on several customers. This data is classified based on rules and the suspicious energy consumption profiles are grouped. The classification results of electricity consumption data are also presented.

Cyber security threat analysis and modeling of an unmanned aerial vehicle system

Advances in technology for miniature electronic military equipment and systems have led to the emergence of unmanned aerial vehicles (UAVs) as the new weapons of war and tools used in various other areas. UAVs can easily be controlled from a remote location. They are being used for critical operations, including offensive, reconnaissance, surveillance and other civilian missions. The need to secure these channels in a UAV system is one of the most important aspects of the security of this system because all information critical to the mission is sent through wireless communication channels. It is well understood that loss of control over these systems to adversaries due to lack of security is a potential threat to national security. In this paper various security threats to a UAV system is analyzed and a cyber-security threat model showing possible attack paths has been proposed. This model will help designers and users of the UAV systems to understand the threat profile of the system so as to allow them to address various system vulnerabilities, identify high priority threats, and select mitigation techniques for these threats.

Long-Term Frequency Gradients During Persistent Atrial Fibrillation in Sheep Are Associated With Stable Sources in the Left Atrium

Background— Dominant frequencies (DFs) of activation are higher in the atria of patients with persistent than paroxysmal atrial fibrillation (AF), and left atrial (LA)-to-right atrial (RA) DF gradients have been identified in both. However, whether such gradients are maintained as long-term persistent AF is established remains unexplored. We aimed at determining in vivo the time course in atrial DF values from paroxysmal to persistent AF in sheep and testing the hypothesis that an LA-to-RA DF difference is associated with LA drivers in persistent AF. Methods and Results— AF was induced using RA tachypacing (n=8). Electrograms were obtained weekly from an RA lead and an implantable loop recorder implanted near the LA. DFs were determined for 5-second-long electrograms (QRST subtracted) during AF in vivo and in ex vivo optical mapping. Underlying structural changes were compared with weight-matched controls (n=4). After the first AF episode, DF increased gradually during a 2-week period (7±0.21 to 9.92±0.31 Hz; n=6; P<0.05). During 9 to 24 weeks of AF, the DF values on the implantable loop recorder were higher than the RA (10.6±0.08 versus 9.3±0.1 Hz, respectively; n=7; P<0.0001). Subsequent optical mapping confirmed a DF gradient from posterior LA-to-RA (9.1±1.0 to 6.9±0.9 Hz; P<0.05) and demonstrated patterns of activation compatible with drifting rotors in the posterior LA. Persistent AF sheep showed significant enlargement of the posterior LA compared with controls. Conclusions— In the sheep, transition from paroxysmal to persistent AF shows continuous LA-to-RA DF gradients in vivo together with enlargement of the posterior LA, which harbors the highest frequency domains and patterns of activation compatible with drifting rotors.

A low-cost INS/GPS integration methodology based on random forest regression

This paper, for the first time, introduces a random forest regression based Inertial Navigation System (INS) and Global Positioning System (GPS) integration methodology to provide continuous, accurate and reliable navigation solution. Numerous techniques such as those based on Kalman filter (KF) and artificial intelligence approaches exist to fuse the INS and GPS data. The basic idea behind these fusion techniques is to model the INS error during GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby maintaining the continuity and improving the navigation solution accuracy. KF based approaches possess several inadequacies related to sensor error model, immunity to noise, and computational load. Alternatively, neural network (NN) proposed to overcome KF limitations works unsatisfactorily for low-cost INS, as they suffer from poor generalization capability due to the presence of high amount of noise. In this study, random forest regression has shown to effectively model the highly non-linear INS error due to its improved generalization capability. To evaluate the proposed method effectiveness in bridging the period of GPS outages, four simulated GPS outages are considered over a real field test data. The proposed methodology illustrates a significant reduction in the positional error by 24-56%.

Measures and setbacks for controlling electricity theft

Most of the utility companies in developing countries incur huge losses because of the non-technical losses (NTL). It is very difficult to detect and control potential causes of NTL in developing countries due to their poor infrastructure. Electricity theft and billing irregularities form a major chunk of NTL. These losses affect quality of supply, electrical load on the generating station and tariff imposed on electricity consumed by genuine customers. This paper discusses various factors those influence the consumer to make an attempt to steal electricity In addition, some handy cases where electricity theft are detected will be illustrated. In view of these ill effects, some methods for detection and estimation of the theft will be discussed. This paper also illustrates several methods to quantify and control theft. In essence, setbacks for implementation of these measures and techniques will be illustrated in detail. Motivation of this work is to conserve the interest of utility companies in providing quality electricity to genuine customers at affordable tariff.

A conceptual design using harmonics to reduce pilfering of electricity

Electricity theft is a major problem in developing countries and it has been very difficult for the utility companies to detect and fight against the people responsible for theft. This paper proposes an architectural design of smart meter, external control station, harmonic generator, and filter circuit, which can detect and chastise the appliances of people responsible for electricity theft. The motivation of this work is to deject the illegal consumers, and conserve and effectively utilize energy. As well, smart meters are designed to provide data of various parameters related to instantaneous power consumption. Dynamic behavior of such meters can be managed and controlled by utility companies. Total loss in the distribution feeder is computed by the external control station from the values of total load consumption and technical losses in the distribution feeder. If considerable amount of non-technical losses are detected at any given feeder, harmonic generator is operated for introducing harmonics into the feeder for destroying the appliances of the illegal consumers. In addition, harmonic analysis of the distribution feeder and consumer appliances due to the presence of harmonics is carried out to estimate the effect of induced harmonics. For illustration, cost-benefit analysis for implementation/maintenance of the proposed system in India is presented.

Demand response simulation implementing heuristic optimization for home energy management

This paper introduces optimized operation of household appliances in a Demand-Side Management (DSM) based simulation tool. DSM can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. The principal purpose of the simulation tool is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customers cost. An optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for optimizing the DSM operation of the tool. The tool also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the tool is illustrated through an example case study for various household situations.

A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS

Land Vehicle Navigation (LVN) mostly relies on integrated system consisting of Inertial Navigation System (INS) and Global Positioning System (GPS). The combined system provides continuous and accurate navigation solution when compared to standalone INS or GPS. Different fusion methodology such as those based on Kalman filtering and particle filtering has been proposed that estimates and models the INS error during the GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby improving its accuracy. However, these fusion approaches possess several inadequacies related to sensor error model, immunity to noise and computational load. Alternatively, Neural Network (NN) based approaches has been proposed. In the case of low-cost INS, the NN suffers from poor generalization capability due to the presence of high amount of noises. The paper thus introduces a novel and hybrid fusion methodology utilizing Dempster-Shafer (DS) theory augmented by Support Vector Machines (SVM), known as DS-SVM. The INS and GPS data fusion is carried using DS fusion whereas SVM models the INS error. During GPS availability, DS provides accurate solution; whereas during outages, the trained SVM model corrects the INS error thereby improving the positioning accuracy. The proposed methodology is evaluated against the existing Artificial Neural Network (ANN) and the Random Forest Regression (RFR) methodology. A total of 20-87% improvement in the positional accuracy was found against ANN and RFR.

A hybrid neural network model and encoding technique for enhanced classification of energy consumption data

Total losses in transmission and distribution (T&D) of electrical energy including nontechnical losses (NTL) are huge and are affecting the good interest of utility company and its customers. In this context, importance of customer load profile evaluation for detection of illegal consumers is explained in this paper. Classification of the customers based on load profile evaluation using SVMLIB requires us to choose training function and related parameters. Selecting these parameters would consume a lot of time and is not suggestible evaluation of real time electricity consumption patterns, as, the suspicious profiles are to be predicted instantly. In light of this issue, this paper implements a neural network (NN) model and suggests a hierarchical model for enhanced estimation of the classification efficiency, if that data was classified using support vector machines (SVM). In addition, this paper proposes an encoding technique that can identify illegal consumers with better efficiency and faster classification of data.