Economic power schedule and transactive energy through an intelligent centralized energy management system for a DC residential distribution system

Direct current (DC) residential distribution systems (RDS) consisting of DC living homes will be a significant integral part of future green transmission. Meanwhile, the increasing number of distributed resources and intelligent devices will change the power flow between the main grid and the demand side. The utilization of distributed generation (DG) requires an economic operation, stability, and an environmentally friendly approach in the whole DC system. This paper not only presents an optimization schedule and transactive energy (TE) approach through a centralized energy management system (CEMS), but also a control approach to implement and ensure DG output voltages to various DC buses in a DC RDS. Based on data collection, prediction and a certain objectives, the expert system in a CEMS can work out the optimization schedule, after this, the voltage droop control for steady voltage is aligned with the command of the unit power schedule. In this work, a DC RDS is used as a case study to demonstrate the process, the RDS is associated with unit economic models, and a cost minimization objective is proposed that is to be achieved based on the real-time electrical price. The results show that the proposed framework and methods will help the targeted DC residential system to reduce the total cost and reach stability and efficiency

Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, yet Effective Time Series CNN-Based Approach

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on detecting malicious applications that are spawned as a separate thread during application execution, hence detecting stealthy malware patterns at run-time remains a critical challenge. Stealthy malware refers to harmful cyber attacks in which malicious code is hidden within benign applications and remains undetected by traditional malware detection approaches. In this paper, we first present a comprehensive review of recent advances in hardware-assisted malware detection studies that have used standard ML techniques to detect the malware signatures. Next, to address the challenge of stealthy malware detection at the processor’s hardware level, we propose StealthMiner, a novel specialized time series machine learning-based approach to accurately detect stealthy malware trace at run-time using branch instructions, the most prominent HPC feature. StealthMiner is based on a lightweight time series Fully Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series data and utilizes them to accurately recognize the trace of stealthy malware. Our analysis demonstrates that using state-of-the-art ML-based malware detection methods is not effective in detecting stealthy malware samples since the captured HPC data not only represents malware but also carries benign applications’ microarchitectural data. The experimental results demonstrate that with the aid of our novel intelligent approach, stealthy malware can be detected at run-time with 94% detection performance on average with only one HPC feature, outperforming the detection performance of state-of-the-art HMD and general time series classification methods by up to 42% and 36%, respectively

A power system and synchrophasor communication network co-simulation testbed with a real-time cyber security application

The development of smart grids facilitates the deployment of phasor measurement units (PMUs) to improve the system stability and reliability. The growing installation of PMUs provides grid operators wide-area situational awareness while introducing additional vulnerabilities to power systems from the cyber security point of view. Thus, not only the online method to handle such vulnerabilities real-time but also the corresponding power system simulation environments with appropriate time-fidelity are needed. This thesis presents two major works: an interactive, extensible environment for power system simulation and a real-time malicious PMU data detection method. The first part introduces such an environment that operates with power system models in the PMU time frame, including data visualization and interactive control action capabilities. The flexible and extensible capabilities are demonstrated by interfacing with a synchrophasor communication network simulation, which is a testbed for developing real-time PMU data related applications. The second part proposes an online method to detect ongoing contingencies in the system and malicious data attack on its underlying synchrophasor communication network. To do so, the principal component analysis is applied to leverage the spatial and temporal correlations among the PMU data, and the method is implemented in the synchrophasor network simulation for data collection and tests. Pattern match and data reconstruction are proposed to identify incident types and find their most possible locations. The thesis illustrates the extensibility of the interactive simulation environment and the effectiveness of the proposed method with a 150 buses case

A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering

This paper is an extended version of our paper published in 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 5–10 August 2018.Copyright: © 2021 by the authors. Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Sustainable Technology and Elderly Life

The coming years will see an exponential increase in the proportion of elderly people in our society. This accelerated growth brings with it major challenges in relation to the sustainability of the system. There are different aspects where these changes will have a special incidence: health systems and their monitoring; the development of a framework in which the elderly can develop their daily lives satisfactorily; and in the design of intelligent cities adapted to the future sociodemographic profile. The discussion of the challenges faced, together with the current technological evolution, can show possible ways of meeting the challenges. There are different aspects where these changes will have a special incidence: health systems and their monitoring; the development of a framework in which the elderly can develop their daily lives satisfactorily; and in the design of intelligent cities adapted to the future sociodemographic profile. This special issue discusses various ways in which sustainable technologies can be applied to improve the lives of the elderly. Six articles on the subject are featured in this volume. From a systematic review of the literature to the development of gamification and health improvement projects. The articles present suggestive proposals for the improvement of the lives of the elderly. The volume is a resource of interest for the scientific community, since it shows different research gaps in the current state of the art. But it is also a document that can help social policy makers and people working in this domain to planning successful projects

Model-Free Methods to Analyze Pmu Data in Real-Time for Situational Awareness and Stability Monitoring

This dissertation presents and evaluates model-free methodologies to process Phasor Measurement Unit (PMU) data. Model-based PMU applications require knowledge of the system topology, most frequently the system admittance matrix. For large systems, the admittance matrix, or other system parameters, can be time-consuming to integrate into supporting PMU applications. These data sources are often sensitive and can require permissions to access, delaying the implementation of model-based approaches. This dissertation focuses on evaluating individual model-free applications to efficiently perform functions of interest to system operators for real-time situational awareness. Real-time situational awareness is evaluated with respect to central digitization where the PMU data is archived, and delays from telecommunication and system architecture are not considered. The PMU data available to utilities is often a subset of the overall system. Even without full observability, PMU data for observable portions of the system provides valuable, high-resolution information about the current system state. Methods are needed that can analyze and generate critical insight about the system in real-time to assist in detection and mitigation of major system events. All chapters address methodologies that can derive their output solely from the PMU signals. These methodologies are evaluated for their reliability and computational efficiency, considering a specific task of interest. Inter-area oscillations and poorly damped electromechanical modes are dangerous when undetected for extended periods of time, eventually leading to blackouts when unstable parameters are present. Prony Analysis and Matrix Pencil Method were selected in Chapter 4 for their proven effectiveness of estimating the dominant modes of an input signal; for purposes of this dissertation, the signal of interest for oscillation analysis is real power. The speed of convergence, accuracy of the methods, and viability when applied to utility PMU data were assessed to determine suitability to online system operation. Matrix Pencil Method was determined to provide more robust and computationally efficient estimation of key system modes for both simulated and real utility PMU data. The biorthogonal discrete wavelet transform, which can correlate frequency data to a time-domain solution, was utilized in Chapter 3 to create a methodology for event detection and classification for a subset of selected events. The derived methodology was shown to be effective for identification and classification of load and capacitor switch events, as well as breaker operation and faults. Methods to mimic the power flow Jacobian from discrete measurements are derived to assess system stability and eigenvalues in Chapter 2. These methods were effective for fast detection of unstable system parameters. Chapter 5, the most significant contribution of this dissertation, details derivations of a mathematical reduced system model and power flow Jacobian variants for more robust instability detection, system weak point identification, mitigation techniques, and state estimation capabilities. Considering the functions of all evaluated and developed model-free methodologies, event detection, event classification, detection of poorly damped oscillatory modes, and instability detection and mitigation can be achieved for situational awareness