209 research outputs found

    Synchrophasor Assisted Efficient Fault Location Techniques In An Active Distribution Network

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    Reliability of an electrical system can be improved by an efficient fault location identification for the fast repair and remedial actions. This scenario changes when there are large penetrations of distributed generation (DG) which makes the distribution system an active distribution system. An efficient use of synchrophasors in the distribution network is studied with bidirectional power flow, harmonics and low angle difference consideration which are not prevalent in a transmission network. A synchrophasor estimation algorithm for the P class PMU is developed and applied to identify efficient fault location. A fault location technique using two ended synchronized measurement is derived from the principle of transmission line settings to work in a distribution network which is independent of line parameters. The distribution systems have less line length, harmonics and different sized line conductors, which affects the sensitivity of the synchronized measurements, Total Vector Error (TVE) and threshold for angular separation between different points in the network. A new signal processing method based on Discrete Fourier Transform (DFT) is utilized to work in a distribution network as specified in IEEE C37.118 (2011) standard for synchrophasor. A specific P and M classes of synchrophasor measurements are defined in the standard. A tradeoff between fast acting P class and detailed measurement M class is sought to work specifically in the distribution system settings which is subjected to large amount of penetrations from the renewable energy

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

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    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

    Machine Learning Applications for Dynamic Security Assessment in presence of Renewable Generation and Load Induced Variability

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    abstract: Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs). This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.Dissertation/ThesisMasters Thesis Electrical Engineering 201

    Synchrophasor-based Fault Location Detection and Classification, in Power Systems, using Artificial Intelligence

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    With the introduction of sophisticated electronic gadgets which cannot sustain interruption in the provision of electricity, the need to supply uninterrupted and reliable power supply, to the consumers, has become a crucial factor in the present-day world. Therefore, it is customary to correctly identify fault locations in an electrical power network, in order to rectify faults and restore power supply in the minimum possible time. Many automated fault location detection algorithms have been proposed, however, prior art requires topological and physical information of the electrical power network. This thesis presents a new method of detecting fault locations, in transmission as well as distribution networks, using state-of-the-art machine learning algorithms on the real-time synchrophasor measurements obtained from the network. The proposed method first generates a bus admittance matrix from the synchrophasor data and then uses a neural network to identify the faulty buses. It is independent of network-specific data of the electrical power network. The proposed algorithm is evaluated using actual outage data from a real transmission system of Southwest Power Pool, in the year 2015. The results of the system implemented in python shows that the proposed method can detect fault locations with 100% accuracy

    Wide-Area Measurement-Driven Approaches for Power System Modeling and Analytics

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    This dissertation presents wide-area measurement-driven approaches for power system modeling and analytics. Accurate power system dynamic models are the very basis of power system analysis, control, and operation. Meanwhile, phasor measurement data provide first-hand knowledge of power system dynamic behaviors. The idea of building out innovative applications with synchrophasor data is promising. Taking advantage of the real-time wide-area measurements, one of phasor measurements’ novel applications is to develop a synchrophasor-based auto-regressive with exogenous inputs (ARX) model that can be updated online to estimate or predict system dynamic responses. Furthermore, since auto-regressive models are in a big family, the ARX model can be modified as other models for various purposes. A multi-input multi-output (MIMO) auto-regressive moving average with exogenous inputs (ARMAX) model is introduced to identify a low-order transfer function model of power systems for adaptive and coordinated damping control. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify an online measurement-based transfer function model that can be used to tune the oscillation damping controller. A demonstration on hardware testbed may illustrate the effectiveness of the proposed adaptive and coordinated damping controller. In fact, measurement-driven approaches for power system modeling and analytics are also attractive to the power industry since a huge number of monitoring devices are deployed in substations and power plants. However, most current systems for collecting and monitoring data are isolated, thereby obstructing the integration of the various data into a holistic model. To improve the capability of utilizing big data and leverage wide-area measurement-driven approaches in the power industry, this dissertation also describes a comprehensive solution through building out an enterprise-level data platform based on the PI system to support data-driven applications and analytics. One of the applications is to identify transmission-line parameters using PMU data. The identification can obtain more accurate parameters than the current parameters in PSS®E and EMS after verifying the calculation results in EMS state estimation. In addition, based on temperature information from online asset monitoring, the impact of temperature change can be observed by the variance of transmission-line resistance

    Machine Learning Based Detection of False Data Injection Attacks in Wide Area Monitoring Systems

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    The Smart Grid (SG) is an upgraded, intelligent, and a more reliable version of the traditional Power Grid due to the integration of information and communication technologies. The operation of the SG requires a dense communication network to link all its components. But such a network renders it prone to cyber attacks jeopardizing the integrity and security of the communicated data between the physical electric grid and the control centers. One of the most prominent components of the SG are Wide Area Monitoring Systems (WAMS). WAMS are a modern platform for grid-wide information, communication, and coordination that play a major role in maintaining the stability of the grid against major disturbances. In this thesis, an anomaly detection framework is proposed to identify False Data Injection (FDI) attacks in WAMS using different Machine Learning (ML) and Deep Learning (DL) techniques, i.e., Deep Autoencoders (DAE), Long-Short Term Memory (LSTM), and One-Class Support Vector Machine (OC-SVM). These algorithms leverage diverse, complex, and high-volume power measurements coming from communications between different components of the grid to detect intelligent FDI attacks. The injected false data is assumed to target several major WAMS monitoring applications, such as Voltage Stability Monitoring (VSM), and Phase Angle Monitoring (PAM). The attack vector is considered to be smartly crafted based on the power system data, so that it can pass the conventional bad data detection schemes and remain stealthy. Due to the lack of realistic attack data, machine learning-based anomaly detection techniques are used to detect FDI attacks. To demonstrate the impact of attacks on the realistic WAMS traffic and to show the effectiveness of the proposed detection framework, a Hardware-In-the-Loop (HIL) co-simulation testbed is developed. The performance of the implemented techniques is compared on the testbed data using different metrics: Accuracy, F1 score, and False Positive Rate (FPR) and False Negative Rate (FNR). The IEEE 9-bus and IEEE 39-bus systems are used as benchmarks to investigate the framework scalability. The experimental results prove the effectiveness of the proposed models in detecting FDI attacks in WAMS

    Performance Improvement of Wide-Area-Monitoring-System (WAMS) and Applications Development

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    Wide area monitoring system (WAMS), as an application of situation awareness, provides essential information for power system monitoring, planning, operation, and control. To fully utilize WAMS in smart grid, it is important to investigate and improve its performance, and develop advanced applications based on the data from WAMS. In this dissertation, the work on improving the WAMS performance and developing advanced applications are introduced.To improve the performance of WAMS, the work includes investigation of the impacts of measurement error and the requirements of system based on WAMS, and the solutions. PMU is one of the main sensors for WAMS. The phasor and frequency estimation algorithms implemented highly influence the performance of PMUs, and therefore the WAMS. The algorithms of PMUs are reviewed in Chapter 2. To understand how the errors impact WAMS application, different applications are investigated in Chapter 3, and their requirements of accuracy are given. In chapter 4, the error model of PMUs are developed, regarding different parameters of input signals and PMU operation conditions. The factors influence of accuracy of PMUs are analyzed in Chapter 5, including both internal and external error sources. Specifically, the impacts of increase renewables are analyzed. Based on the analysis above, a novel PMU is developed in Chapter 6, including algorithm and realization. This PMU is able to provide high accurate and fast responding measurements during both steady and dynamic state. It is potential to improve the performance of WAMS. To improve the interoperability, the C37.118.2 based data communication protocol is curtailed and realized for single-phase distribution-level PMUs, which are presented in Chapter 7.WAMS-based applications are developed and introduced in Chapter 8-10. The first application is to use the spatial and temporal characterization of power system frequency for data authentication, location estimation and the detection of cyber-attack. The second application is to detect the GPS attack on the synchronized time interval. The third application is to detect the geomagnetically induced currents (GIC) resulted from GMD and EMP-E3. These applications, benefited from the novel PMU proposed in Chapter 6, can be used to enhance the security and robust of power system
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