Smart Grids

—Low-cost, real-time digital signal processors (DSPs) embedded in generic Internet of Things (IoT) edge devices can make significant contributions to distributed edge computing for industrial IoT (IIoT) networks. The DSP considered in this paper is the Texas Instruments (TI) TMS320C28x DSP (C28x). At the edge of the network, the C28x is programmed using low-level Embedded C programming language to construct the Morlet wavelet. Our implementation at this layer is the first known construction of the Morlet wavelet for C28x DSP using Embedded C. At the fog layer, near the edge of the IoT network, where more computing resources exist, the wavelet is then convolved with healthcare (electrocardiogram) and electrical network signals, using Matlab to reduce signal noise, and to identify important parts of examined signals. Convolution results indicates that the distributed computing approach for low-cost generic devices considered in this paper is suitable for use in large IIoT networks.

The traditional multi-level hierarchical structured power grid is facing many systematic, operational, management and capacity challenges. Thus, strategic efforts are required from power enterprises to not only upgrade the existing grid infrastructure but move forward toward self-healing and resilient grid by integrating the diverse energy resources into the power systems through Information and Communication Technologies (ICT), smart sensing and instrumentation, and Distributed Renewable Energy (DRE) resources. To serve the purpose, the concept of new intelligent power grid known as Smart Grid (SG) was introduced. SG promises the direct involvement of all the stakeholders including service provider, grid operators and consumers making it a grid state aware network. However, SG is a new paradigm and its acceptance and implementation brings forth many folded challenges. In this paper, we present the transformational road map and strategic framework of establishing the SG in Pakistan ...

Smart grid utility provider collects consumers' power consumption data for three main reasons: billing, analysis, and operation. Billing needs coarse-grained data where there are no, or minimal, privacy concerns. While analysis and operation needs fine-grained data which can highly explore consumers' privacy. Hence, consumers might be reluctant to allow for operational metering to protect their privacy.This paper presents detail description of a reliable DNA-based privacy-preserving (DNAPP) scheme in smart grid. DNAPP assures robust authentication, confidentiality, message integrity, and non-repudiation across the smart grid as well as assuring high consumers' privacy. The scheme demonstrates many good security features, such as: high complexity of O(n!), lightweight , scalable, minimum overhead, no cryptography key exchange between the communicating parties as each of them can determine the key locally and independently. This scheme does not require any level of modifications to the existing smart grid infrastructure or smart meter. It only requires some software modifications.

The idea of mass deployment of an Advanced Metering Infrastructure (AMI) for Smart Grids has been explored and evaluated in this paper. Since smart meters with a wireless interface that can connect to the utility provider's server via a backhaul network forms the basic building block of an AMI, it is a good paradigm for an M2M application in Smart Grids. The relevant standardization efforts by 3GPP and ETSI are discussed along with other perceived application scenarios for M2M communication. An example architecture is then proposed and evaluated with clusters of smart meters that transfer their data via UMTS network to a server. The performance evaluation is carried out considering network throughput, latency, and the data generated by the smart meters using both TCP and UDP traffic. The results indicate that there is a substantial reduction in delay when UDP traffic is considered when compared to TCP traffic generated by the smart meters.

In this study, we introduce a novel unsupervised countermeasure for smart grid power systems, based on generative adversarial networks (GANs). Given the pivotal role of smart grid systems (SGSs) in urban life, their security is of particular importance. In recent years, however, advances in the field of machine learning, have raised concerns about cyber attacks on these systems. Power systems, among the most important components of urban infrastructure, have, for example, been widely attacked by adversaries. Attackers disrupt power systems using false data injection attacks (FDIA), resulting in a breach of availability, integrity, or confidential principles of the system. Our model simulates possible attacks on power systems using multiple generators in a step-by-step interaction with a discriminator in the training phase. As a consequence, our system is robust to unseen attacks. Moreover, the proposed model considerably reduces the well-known mode collapse problem of GAN-based mode...

ABSTRACT In deregulated power market, the variation of demand curves with the varying weather parameters is a probabilistic problem. The weather parameters, such as temperature, humidity, and precipitation affect the load requirements in non-autonomous wide area smart grid system. We modeled the relationships of the load with aforementioned weather parameters. Moreover, various correlation models, such as Pearson, Spearman, and Kendall are quantitatively analyzed for each independent weather data variables. Furthermore, simple linear regression and multi-linear regression techniques are applied for the statistical varying load model in the wide area smart grid system. The effect of each independent variable on the dependent variable (load) is critically observed using statistical tool R. The linear and non-linear varying patterns exhibited monitoring information for the load control and management of wide area smart grid system.

Nos últimos 100 anos, não houve mudanças revolucionárias na estrutura da rede de energia elétrica em países sul-americanos [1]. Para lidar com a obsolescência tecnológica, com novas demandas e com a modernização da rede elétrica existente, foram propostas as smart grids, as quais permitem uma maior eficiência, confiabilidade e integração na rede elétrica. A eficiência implica em um menor consumo de energia elétrica e um fornecimento de energia de qualidade igual ou superior ao atual, reduzindo custos e o impacto ecológico da geração de energia. A confiabilidade permite a identificação de problemas, como falhas em ativos da rede, fazendo com que a concessionária possa tomar as devidas ações. O objetivo deste trabalho consiste na investigação de mecanismos de monitoração/supervisão com características ubíquas e autonômicas para o suporte das soluções com smart grid visando adequar o sistema elétrico a uma nova realidade.

The growing reliance on Cyber technology for Smart Grid and other Cyber-Physical Systems (CPS) applications in- creases high assurance challenges. Advanced Metering Infras- tructure (AMI) is a critical part of smart grid and failure to address security risks in it might disrupt utilities in their mission to deliver power reliably. The objective of this paper is to find mitigation actions for attacks that might be launched against AMI. The paper introduces a tool called SecAMI that calculates the relationship between the speed at which an attack spreads, the speed at which it can be detected, and the consequences on the availability of the AMI. The results from SecAMI can be used as a performance metric to significantly improve the development and the deployment of Intrusion Detection Systems (IDSs) in an AMI environment. Experimental results with an IDS called Amilyzer show that centralized IDS might not work efficiently in scalable systems comparing with distributed IDS in term of ...