Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

An Information-Centric Communication Infrastructure for Real-Time State Estimation of Active Distribution Networks

© 2010-2012 IEEE.The evolution toward emerging active distribution networks (ADNs) can be realized via a real-time state estimation (RTSE) application facilitated by the use of phasor measurement units (PMUs). A critical challenge in deploying PMU-based RTSE applications at large scale is the lack of a scalable and flexible communication infrastructure for the timely (i.e., sub-second) delivery of the high volume of synchronized and continuous synchrophasor measurements. We address this challenge by introducing a communication platform called C-DAX based on the information-centric networking (ICN) concept. With a topic-based publish-subscribe engine that decouples data producers and consumers in time and space, C-DAX enables efficient synchrophasor measurement delivery, as well as flexible and scalable (re)configuration of PMU data communication for seamless full observability of power conditions in complex and dynamic scenarios. Based on the derived set of requirements for supporting PMU-based RTSE in ADNs, we design the ICN-based C-DAX communication platform, together with a joint optimized physical network resource provisioning strategy, in order to enable the agile PMU data communications in near real-time. In this paper, C-DAX is validated via a field trial implementation deployed over a sample feeder in a real-distribution network; it is also evaluated through simulation-based experiments using a large set of real medium voltage grid topologies currently operating live in The Netherlands. This is the first work that applies emerging communication paradigms, such as ICN, to smart grids while maintaining the required hard real-time data delivery as demonstrated through field trials at national scale. As such, it aims to become a blueprint for the application of ICN-based general purpose communication platforms to ADNs

Cost-efficient Low Latency Communication Infrastructure for Synchrophasor Applications in Smart Grids

With the introduction of distributed renewable energy resources and new loads, such as electric vehicles, the power grid is evolving to become a highly dynamic system, that necessitates continuous and fine-grained observability of its operating conditions. In the context of the medium voltage (MV) grid, this has motivated the deployment of Phasor Measurement Units (PMUs), that offer high precision synchronized grid monitoring, enabling mission-critical applications such as fault detection/location. However, PMU-based applications present stringent delay requirements, raising a significant challenge to the communication infrastructure. In contrast to the high voltage domain, there is no clear vision for the communication and network topologies for the MV grid; a full fledged optical fiber-based communication infrastructure is a costly approach due to the density of PMUs required. In this work, we focus on the support of low-latency PMU-based applications in the MV domain, identifying and addressing the trade-off between communication infrastructure deployment costs and the corresponding performance. We study a large set of real MV grid topologies to get an in-depth understanding of the various key latency factors. Building on the gained insights, we propose three algorithms for the careful placement of high capacity links, targeting a balance between deployment costs and achieved latencies. Extensive simulations demonstrate that the proposed algorithms result in low-latency network topologies while reducing deployment costs by up to 80% in comparison to a ubiquitous deployment of costly high capacity links

Synchronized measurement data conditioning and real-time applications

Phasor measurement units (PMU), measuring voltage and current phasor with synchronized timestamps, is the fundamental component in wide-area monitoring systems (WAMS) and reveals complex dynamic behaviors of large power systems. The synchronized measurements collected from power grid may degrade due to many factors and impacts of the distorted synchronized measurement data are significant to WAMS. This dissertation focus on developing and improving applications with distorted synchronized measurements from power grid. The contributions of this dissertation are summarized below. In Chapter 2, synchronized frequency measurements of 13 power grids over the world, including both mainland and island systems, are retrieved from Frequency Monitoring Network (FNET/GridEye) and the statistical analysis of the typical power grids are presented. The probability functions of the power grid frequency based on the measurements are calculated and categorized. Developments of generation trip/load shedding and line outage events detection and localization based on high-density PMU measurements are investigated in Chapters 3 and 4 respectively. Four different types of abnormal synchronized measurements are identified from the PMU measurements of a power grid. The impacts of the abnormal synchronized measurements on generation trip/load shedding events detection and localization are evaluated. A line outage localization method based on power flow measurements is proposed to improve the accuracy of line outage events location estimation. A deep learning model is developed to detect abnormal synchronized measurements in Chapter 5. The performance of the model is evaluated with abnormal synchronized measurements from a power grid under normal operation status. Some types of abnormal synchronized measurements in the testing cases are recently observed and reported. An extensive study of hyper-parameters in the model is conducted and evaluation metrics of the model performance are presented. A non-contact synchronized measurements study using electric field strength is investigated in Chapter 6. The theoretical foundation and equation derivations are presented. The calculation process for a single circuit AC transmission line and a double circuit AC transmission line are derived. The derived method is implemented with Matlab and tested in simulation cases

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Design and Experimental Validation of an LTE-based Synchrophasor Network in a Medium Voltage Distribution Grid

We present and experimentally validate in a real-scale medium voltage (MV) grid a synchrophasor network that exploits the availability of a public 4G LTE communication infrastructure. An 18 buses, 10\,kV feeder located in Huissen, The Netherlands, has been equipped with 10 Phasor Measurement Units (PMUs) connected to the MV grid by means of dedicated voltage and current sensors. The PMUs stream synchrophasor data through a public 4G LTE network via an information-centric networking-based middleware, named C-DAX. The measurements are received and time-aligned at a phasor data concentrator and fed to a real-time state estimation application. The paper presents the various field-trial components and validates the feasibility of exploiting the 4G LTE technology for PMU-based applications. Specifically we assess the performance of the adopted wireless telecommunication infrastructure with and without the C-DAX middleware, as well as the accuracy of the real-time state estimation process

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte