Intelligent approach for processmodelling and optimization on electrical dischargemachining of polycrystalline diamond

Polycrystalline diamond (PCD) is increasingly becomes an important material used in the industry for cutting tools of difficult-to-machine materials due to its excellent characteristics such as hardness, toughness and wear resistance. However, its applications are restricted because of the PCD material is difficult to machine. Therefore, electrical discharge machining (EDM) is an ideal method suitable for PCD materials due to its non-contact process nature. The performance of EDM, however, is significantly influenced by its process parameters and type of electrode. In this study, soft computing technique was utilized to optimize the performance of the EDM in roughing condition for eroding PCD with copper tungsten or copper nickel electrode. Central composite design with five levels of three machining parameters viz. peak current, pulse interval and pulse duration has been used to design the experimental matrix. The EDM experiment was conducted based on the design experimental matrix. Subsequently, the effectiveness of EDM on shaping PCD with copper tungsten and copper nickel was evaluated in terms of material removal rate (MRR) and electrode wear rate (EWR). It was found that copper tungsten electrode gave lower EWR, in comparison with the copper nickel electrode. The predictive model of radial basis function neural network (RBFNN) was developed to predict the MRR and EWR of the EDM process. The prominent predictive ability of RBFNN was confirmed as the prediction errors in terms of mean-squared error were found within the range of 6.47E−05 to 7.29E−06. Response surface plot was drawn to study the influences of machining parameters of EDM for shaping PCD with copper tungsten and copper nickel. Subsequently, moth search algorithm (MSA) was used to determine the optimal machining parameters, such that the MRR was maximized and EWR was minimized. Based on the obtained optimal parameters, confirmation test with the absolute error within the range of 1.41E−06 to 5.10E−05 validated the optimization capability of MSA

Advanced Wide-Area Monitoring System Design, Implementation, and Application

Wide-area monitoring systems (WAMSs) provide an unprecedented way to collect, store and analyze ultra-high-resolution synchrophasor measurements to improve the dynamic observability in power grids. This dissertation focuses on designing and implementing a wide-area monitoring system and a series of applications to assist grid operators with various functionalities. The contributions of this dissertation are below: First, a synchrophasor data collection system is developed to collect, store, and forward GPS-synchronized, high-resolution, rich-type, and massive-volume synchrophasor data. a distributed data storage system is developed to store the synchrophasor data. A memory-based cache system is discussed to improve the efficiency of real-time situation awareness. In addition, a synchronization system is developed to synchronize the configurations among the cloud nodes. Reliability and Fault-Tolerance of the developed system are discussed. Second, a novel lossy synchrophasor data compression approach is proposed. This section first introduces the synchrophasor data compression problem, then proposes a methodology for lossy data compression, and finally presents the evaluation results. The feasibility of the proposed approach is discussed. Third, a novel intelligent system, SynchroService, is developed to provide critical functionalities for a synchrophasor system. Functionalities including data query, event query, device management, and system authentication are discussed. Finally, the resiliency and the security of the developed system are evaluated. Fourth, a series of synchrophasor-based applications are developed to utilize the high-resolution synchrophasor data to assist power system engineers to monitor the performance of the grid as well as investigate the root cause of large power system disturbances. Lastly, a deep learning-based event detection and verification system is developed to provide accurate event detection functionality. This section introduces the data preprocessing, model design, and performance evaluation. Lastly, the implementation of the developed system is discussed

Algorithms to Improve Performance of Wide Area Measurement Systems of Electric Power Systems

Power system operation has become increasingly complex due to high load growth and increasing market pressure. The occurrence of major blackouts in many power systems around the world has necessitated the use of synchrophasor based Wide Area Measurement Systems (WAMS) for grid monitoring. Synchrophasor technology is comparatively new in the area of power systems. Phasor measurement units (PMUs) and phasor data concentrators (PDCs) are new to the substations and control centers. Even though PMUs have been installed in many power grids, the number of installed PMUs is still low with respect to the number of buses or lines. Currently, WAMS systems face many challenges. This thesis is an attempt towards solving some of the technical problems faced by the WAMS systems. This thesis addresses four problems related to synchrophasor estimation, synchrophasor quality detection, synchrophasor communication and synchrophasor application. In the first part, a synchrophasor estimation algorithm has been proposed. The proposed algorithm is simple, requires lesser computations, and satisfies all the steady state and dynamic performance criteria of the IEEE Standard C37.118.1-2011 and also suitable for protection applications. The proposed algorithm performs satisfactorily during system faults and it has lower response time during larger disturbances. In the second part, areas of synchrophasor communication which can be improved by applying compressive sampling (CS) are identified. It is shown that CS can reduce bandwidth requirements for WAMS networks. It is also shown that CS can successfully reconstruct system dynamics at higher rates using synchrophasors reported at sub-Nyquist rate. Many synchrophasor applications are not designed to use fault/switching transient synchrophasors. In this thesis, an algorithm has been proposed to detect fault/switching transient synchrophasors. The proposed algorithm works satisfactorily during smaller and larger step changes, oscillations and missing data. Fault transient synchrophasors are not usable in WAMS applications as they represent a combination of fault and no-fault scenario. In the fourth part, two algorithms have been proposed to extract fault synchrophasor from fault transient synchrophasor in PDC. The proposed algorithms extract fault synchrophasors accurately in presence of noise, off-nominal frequencies, harmonics, and frequency estimation errors

Event and Intrusion Detection Systems for Cyber-Physical Power Systems

High speed data from Wide Area Measurement Systems (WAMS) with Phasor Measurement Units (PMU) enables real and non-real time monitoring and control of power systems. The information and communication infrastructure used in WAMS efficiently transports information but introduces cyber security vulnerabilities. Adversaries may exploit such vulnerabilities to create cyber-attacks against the electric power grid. Control centers need to be updated to be resilient not only to well-known power system contingencies but also to cyber-attacks. Therefore, a combined event and intrusion detection systems (EIDS) is required that can provide precise classification for optimal response. This dissertation describes a WAMS cyber-physical power system test bed that was developed to generate datasets and perform cyber-physical power system research related to cyber-physical system vulnerabilities, cyber-attack impact studies, and machine learning algorithms for EIDS. The test bed integrates WAMS components with a Real Time Digital Simulator (RTDS) with hardware in the loop (HIL) and includes various sized power systems with a wide variety of implemented power system and cyber-attack scenarios. This work developed a novel data processing and compression method to address the WAMS big data problem. The State Tracking and Extraction Method (STEM) tracks system states from measurements and creates a compressed sequence of states for each observed scenario. Experiments showed STEM reduces data size significantly without losing key event information in the dataset that is useful to train EIDS and classify events. Two EIDS are proposed and evaluated in this dissertation. Non-Nested Generalized Exemplars (NNGE) is a rule based classifier that creates rules in the form of hyperrectangles to classify events. NNGE uses rule generalization to create a model that has high accuracy and fast classification time. Hoeffding adaptive trees (HAT) is a decision tree classifier and uses incremental learning which is suitable for data stream mining. HAT creates decision trees on the fly from limited number of instances, uses low memory, has fast evaluation time, and adapts to concept changes. The experiments showed NNGE and HAT with STEM make effective EIDS that have high classification accuracy, low false positives, low memory usage, and fast classification times

Event and Intrusion Detection Systems for Cyber-Physical Power Systems

High speed data from Wide Area Measurement Systems (WAMS) with Phasor Measurement Units (PMU) enables real and non-real time monitoring and control of power systems. The information and communication infrastructure used in WAMS efficiently transports information but introduces cyber security vulnerabilities. Adversaries may exploit such vulnerabilities to create cyber-attacks against the electric power grid. Control centers need to be updated to be resilient not only to well-known power system contingencies but also to cyber-attacks. Therefore, a combined event and intrusion detection systems (EIDS) is required that can provide precise classification for optimal response. This dissertation describes a WAMS cyber-physical power system test bed that was developed to generate datasets and perform cyber-physical power system research related to cyber-physical system vulnerabilities, cyber-attack impact studies, and machine learning algorithms for EIDS. The test bed integrates WAMS components with a Real Time Digital Simulator (RTDS) with hardware in the loop (HIL) and includes various sized power systems with a wide variety of implemented power system and cyber-attack scenarios. This work developed a novel data processing and compression method to address the WAMS big data problem. The State Tracking and Extraction Method (STEM) tracks system states from measurements and creates a compressed sequence of states for each observed scenario. Experiments showed STEM reduces data size significantly without losing key event information in the dataset that is useful to train EIDS and classify events. Two EIDS are proposed and evaluated in this dissertation. Non-Nested Generalized Exemplars (NNGE) is a rule based classifier that creates rules in the form of hyperrectangles to classify events. NNGE uses rule generalization to create a model that has high accuracy and fast classification time. Hoeffding adaptive trees (HAT) is a decision tree classifier and uses incremental learning which is suitable for data stream mining. HAT creates decision trees on the fly from limited number of instances, uses low memory, has fast evaluation time, and adapts to concept changes. The experiments showed NNGE and HAT with STEM make effective EIDS that have high classification accuracy, low false positives, low memory usage, and fast classification times

Real-time data operations and causal security analysis for edge-cloud-based Smart Grid infrastructure

The electric power grids are one of the fundamental infrastructures of modern society and are among the most complex networks ever made. Recent development in communications, sensing and measurement techniques has completely changed the traditional electric power grid and has brought us the intelligent electric power grid known as Smart Grid. As a critical cyber-physical system (CPS), Smart Grid is an integration of physical components, sensors, actuators, control centers, and communication networks. The key to orchestrate large scale Smart Grid is to provide situational awareness of the system. And situational awareness is based on large-scale, real-time, accurate collection and analysis of the monitoring and measurement data of the system. However, it is challenging to guarantee situational awareness of Smart Grid. On the one hand, connecting a growing number of heterogeneous programmable devices together introduces new security risks and increases the attack surface of the system. On the other hand, the tremendous amount of measurements from sensors spanning a large geographical area can result in a reduction of available bandwidth and increasing network latency. Both the lack of security protection and the delayed sensor data impede the situational awareness of the system and thus limit the ability to efficiently control and protect large scale Smart Grids in time-critical scenarios. To target the aforementioned challenge, in this thesis, I propose a series of frameworks to provide and guarantee situational awareness in Smart Grid. Taking an integrated approach of edge-cloud design, real-time data operations, and causal security analysis, the proposed frameworks enhance security protection by anomaly detection and managing as well as causal reasoning of alerts, and reduce traffic volume by online data compression. Extensive experiments by real or synthetic traffic demonstrate that the proposed frameworks achieve satisfactory performance and bear great potential practical value

Experimental Comparison of Multicast Authentication for Wide Area Monitoring Systems

Multicast is proposed as a preferred communication mechanism for many power grid applications. One of the biggest challenges for multicast in smart grid is ensuring source authentication without violating the stringent time requirement. The research community and standardization bodies have proposed several authentication mechanisms for smart grid multicast applications. In this paper, we evaluate different authentication schemes and identify the best candidates for phasor data communication in wide area monitoring systems (WAMS). We first do an extensive literature review of existing solutions and establish a short list of schemes to evaluate. Second we make an experimental comparison of the chosen schemes in an operational smart grid pilot and evaluate the performance of these schemes by using the following metrics: computation, communication and key management overheads. The best candidates we consider are two variants of ECDSA, TV-HORS and three variants of Incomplete-key-set. We find ECDSA without pre-computed tokens and all the Incomplete-key-set variants are inapplicable for WAMS due to their high computation overhead. The ECDSA variant that uses pre-computed tokens and TV-HORS perform well in all metrics; however, TV-HORS has potential drawbacks due to a large key management overhead as a result of the frequent distribution of a large public key per source