Reliability and fault tolerance modelling of multiprocessor systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Reliability evaluation by analytic modelling constitute an important issue of designing a reliable multiprocessor system. In this thesis, a model for reliability and fault tolerance analysis of the interconnection network is presented, based on graph theory. Reliability and fault tolerance are considered as deterministic and probabilistic measures of connectivity. Exact techniques for reliability evaluation fail for large multiprocessor systems because of the enormous computational resources required. Therefore, approximation techniques have to be used. Three approaches are proposed, the first by simplifying the symbolic expression of reliability; the other two by applying a hierarchical decomposition to the system. All these methods give results close to those obtained by exact techniques.Consejo Nacional de Ciencia y Tecnologia" (National Council for Science and Technology of Mexico) and "Instituto de Investigaciones Electricas" (Institute for Electrical Research

Data Challenges and Data Analytics Solutions for Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

Data Mining Framework for Monitoring Attacks In Power Systems

Vast deployment of Wide Area Measurement Systems (WAMS) has facilitated in increased understanding and intelligent management of the current complex power systems. Phasor Measurement Units (PMU\u27s), being the integral part of WAMS transmit high quality system information to the control centers every second. With the North American Synchro Phasor Initiative (NAPSI), the number of PMUs deployed across the system has been growing rapidly. With this increase in the number of PMU units, the amount of data accumulated is also growing in a tremendous manner. This increase in the data necessitates the use of sophisticated data processing, data reduction, data analysis and data mining techniques. WAMS is also closely associated with the information and communication technologies that are capable of implementing intelligent protection and control actions in order to improve the reliability and efficiency of the existing power systems. Along with the myriad of advantages that these measurements systems, informational and communication technologies bring, they also lead to a close synergy between heterogeneous physical and cyber components which unlocked access points for easy cyber intrusions. This easy access has resulted in various cyber attacks on control equipment consequently increasing the vulnerability of the power systems.;This research proposes a data mining based methodology that is capable of identifying attacks in the system using the real time data. The proposed methodology employs an online clustering technique to monitor only limited number of measuring units (PMU\u27s) deployed across the system. Two different classification algorithms are implemented to detect the occurrence of attacks along with its location. This research also proposes a methodology to differentiate physical attacks with malicious data attacks and declare attack severity and criticality. The proposed methodology is implemented on IEEE 24 Bus reliability Test System using data generated for attacks at different locations, under different system topologies and operating conditions. Different cross validation studies are performed to determine all the user defined variables involved in data mining studies. The performance of the proposed methodology is completely analyzed and results are demonstrated. Finally the strengths and limitations of the proposed approach are discussed

Investigation Of Multi-Criteria Clustering Techniques For Smart Grid Datasets

The processing of data arising from connected smart grid technology is an important area of research for the next generation power system. The volume of data allows for increased awareness and efficiency of operation but poses challenges for analyzing the data and turning it into meaningful information. This thesis showcases the utility of clustering algorithms applied to three separate smart-grid data sets and analyzes their ability to improve awareness and operational efficiency. Hierarchical clustering for anomaly detection in phasor measurement unit (PMU) datasets is identified as an appropriate method for fault and anomaly detection. It showed an increase in anomaly detection efficiency according to Dunn Index (DI) and improved computational considerations compared to currently employed techniques such as Density Based Spatial Clustering of Applications with Noise (DBSCAN). The efficacy of betweenness-centrality (BC) based clustering in a novel clustering scheme for the determination of microgrids from large scale bus systems is demonstrated and compared against a multitude of other graph clustering algorithms. The BC based clustering showed an overall decrease in economic dispatch cost when compared to other methods of graph clustering. Additionally, the utility of BC for identification of critical buses was showcased. Finally, this work demonstrates the utility of partitional dynamic time warping (DTW) and k-shape clustering methods for classifying power demand profiles of households with and without electric vehicles (EVs). The utility of DTW time-series clustering was compared against other methods of time-series clustering and tested based upon demand forecasting using traditional and deep-learning techniques. Additionally, a novel process for selecting an optimal time-series clustering scheme based upon a scaled sum of cluster validity indices (CVIs) was developed. Forecasting schemes based on DTW and k-shape demand profiles showed an overall increase in forecast accuracy. In summary, the use of clustering methods for three distinct types of smart grid datasets is demonstrated. The use of clustering algorithms as a means of processing data can lead to overall methods that improve forecasting, economic dispatch, event detection, and overall system operation. Ultimately, the techniques demonstrated in this thesis give analytical insights and foster data-driven management and automation for smart grid power systems of the future

Distribution System Disturbance Analysis and Outage Management Using Hybrid Data-Driven and Physics-Based Approaches

Securing cyber-power distribution systems (DS) against malicious events is critical with the integration of distributed energy resources (DERs), supporting automation and increasing vulnerabilities. Situational awareness utilizing power data (e.g., data from distribution phasor measurement units (D-PMUs)) and cyber data (e.g., network packets data) is the main focus of this dissertation by means of which, an opportunity for real-time monitoring and decision-making is provided. To further improve the DS’s reliable and resilient operation, in this Ph.D. work, the aim has been put towards the development of an automated tool consisting of multiple modules to precisely investigate any type of data anomalies, followed by root cause finding. For this purpose, a data aggregation scheme is developed to synchronize the resolution and time stamp of multiple metering sources throughout the DS, using an enhancement of the conventional Kalman Filter, named Ensemble Extended Kalman Filter (EEKF). EEKF is implemented as an automated module by exploiting the real-time measurements as well as deriving the system physics. Furthermore, this dissertation develops online cyber-physical event detection and classification as well as proposal of the novel Outage Root Cause Analysis (ORCA) system. Different sections of the work have been tested on IEEE and OPAL-RT test systems as well as real-filed measurements from installed actual hardwares

Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15