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

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

Synchrophasor Data Analytics for Control and Protection Applications in Smart Grids

RÉSUMÉ Des réseaux intelligents sont des réseaux d’énergie fortement distribués où les technologies d’énergie et des services sont intégrés avec des informations, des communications et contrôlent des technologies. Puisque les sources d’énergie renouvelable deviennent plus efficaces et rentables, les réseaux intelligents peuvent livrer la puissance propre, durable, sécuritaire, et fiable aux consommateurs. Cependant, l’utilisation rapide de sources d’énergie renouvelable provoque des défis techniques en termes de surveillance, le contrôle et la protection des réseaux électriques. En fait, l’énergie renouvelable implique les phénomènes qui sont naturellement stochastiques comme la lumière du soleil et le vent. Donc, les réseaux intelligents devraient être capables de surveiller et répondre aux changements tant dans fournisseur d’énergie que dans la demande. L’évolution des réseaux électriques provoque aussi le déploiement de nombreuses unités de mesure sans précédent et d’intelligents appareils de mesure. En vertu des systèmes de communications, les signaux en temps réel et les données peuvent être échangés entre les composants des réseaux intelligents. Le flux de données en temps réel fournit une occasion unique pour des applications axées sur les données et des outils pour démultiplier la modernisation de réseaux et la résilience. Les unités de mesure de phaseur sont les dispositifs spécialisés qui acquièrent le phaseur synchronisé (synchrophasor) des données des réseaux électriques. L’analytique de données Synchrophasor peut potentiellement étre plus performant que des méthodes traditionnelles en termes de prise de décisions. Spécifiquement, l’analytique de données est des approches qualitatives/quantitatives et les algorithmes qui rassemblent et traitent des données pour en fin de compte améliorer la conscience situationnelle dans des réseaux électriques. Motivé par ce fait, cette thèse présente des solutions viables pour l’analytique de données synchrophasor dans le but d’améliorer la surveillance, le contrôle et la protection de réseaux de distribution. La thèse se concentre sur trois fonctionnalités qui sont portées de basé sur l’analytique de données synchrophasor: Détection de perturbation centralisée, surveillance de production décentralisée (PD) et la protection “backup” coordonnée. L’objectif de surveillance de perturbation est de réaliser la détection rapide et fiable de tension/des déviations de fréquence qui affectent la stabilité de réseau. La surveillance de PD est liée à la détection de la présence/absence de ressources énergétiques pour la gestion du flux de puissance.----------ABSTRACT Smart grids are highly distributed energy networks where energy technologies and services are integrated with information, communications and control technologies. As renewable energy sources are becoming more efficient and cost–effective, the smart grids can deliver safe, clean, sustainable and reliable power to consumers. However, the rapid utilization of renewable energy sources brings about technical challenges in terms of monitoring, control, and protection of power systems. In fact, renewable energy involves phenomena which are naturally stochastic such as sunlight and wind. Therefore, the smart grids should be capable of monitoring and responding to changes in both power supply and demand. The evolution of the power systems also gives rise to deployment of unprecedented number of measurement units and smart meters. By virtue of communications systems, real-time signals and data can be exchanged between components of the smart grids. The flow of real-time data provides a unique opportunity for data-driven applications and tools to leverage grid modernization and resiliency. Phasor measurement units are specialized devices that acquire synchronized phasor (synchrophasor) data from the power systems. Synchrophasor data analytics can potentially outperform traditional methods in terms of decision making. Specifically, data analytics are qualitative/quantitative approaches and algorithms that collect and process data to ultimately improve situational awareness in the power systems. Motivated by this fact, this thesis presents viable solutions for synchrophasor data analytics with the aim of improving monitoring, control and protection of power distribution grids. The thesis focuses on three functionalities that are carried out based on synchrophasor data analytics: Centralized disturbance detection, monitoring of distributed generation (DG) systems, and coordinated backup protection. The objective of disturbance monitoring is to achieve fast and reliable detection of voltage/frequency deviations that affect the network stability. The DG monitoring is concerned with detecting presence/absence of energy resources for management of the flow of power. Disturbance and DG monitoring tools pave the way for adaptive backup protection of active distribution networks. The adaptive backup protection scheme ensures the post-fault stability by detecting line faults within a permissible tolerance time. The coordination between control and backup protection systems leads to fast recovery of voltage/frequency and minimizes power outage. The efficacy and reliability of the developed methods and algorithms are validated by extensive computer simulations based on different benchmarks

Real-Time Machine Learning Models To Detect Cyber And Physical Anomalies In Power Systems

A Smart Grid is a cyber-physical system (CPS) that tightly integrates computation and networking with physical processes to provide reliable two-way communication between electricity companies and customers. However, the grid availability and integrity are constantly threatened by both physical faults and cyber-attacks which may have a detrimental socio-economic impact. The frequency of the faults and attacks is increasing every year due to the extreme weather events and strong reliance on the open internet architecture that is vulnerable to cyber-attacks. In May 2021, for instance, Colonial Pipeline, one of the largest pipeline operators in the U.S., transports refined gasoline and jet fuel from Texas up the East Coast to New York was forced to shut down after being attacked by ransomware, causing prices to rise at gasoline pumps across the country. Enhancing situational awareness within the grid can alleviate these risks and avoid their adverse consequences. As part of this process, the phasor measurement units (PMU) are among the suitable assets since they collect time-synchronized measurements of grid status (30-120 samples/s), enabling the operators to react rapidly to potential anomalies. However, it is still challenging to process and analyze the open-ended source of PMU data as there are more than 2500 PMU distributed across the U.S. and Canada, where each of which generates more than 1.5 TB/month of streamed data. Further, the offline machine learning algorithms cannot be used in this scenario, as they require loading and scanning the entire dataset before processing. The ultimate objective of this dissertation is to develop early detection of cyber and physical anomalies in a real-time streaming environment setting by mining multi-variate large-scale synchrophasor data. To accomplish this objective, we start by investigating the cyber and physical anomalies, analyzing their impact, and critically reviewing the current detection approaches. Then, multiple machine learning models were designed to identify physical and cyber anomalies; the first one is an artificial neural network-based approach for detecting the False Data Injection (FDI) attack. This attack was specifically selected as it poses a serious risk to the integrity and availability of the grid; Secondly, we extend this approach by developing a Random Forest Regressor-based model which not only detects anomalies, but also identifies their location and duration; Lastly, we develop a real-time hoeffding tree-based model for detecting anomalies in steaming networks, and explicitly handling concept drifts. These models have been tested and the experimental results confirmed their superiority over the state-of-the-art models in terms of detection accuracy, false-positive rate, and processing time, making them potential candidates for strengthening the grid\u27s security

Distributed Machine Learning Approach to Fast Frequency Response-based Inertia Estimation in Low Inertia Grids

Recent updates to the IEEE 1547-2018 standard allow active participation of distributed energy resources (DERs) in power grid services with the goal of increased grid reliability and resiliency. With the rapid growth of DERs towards a low inertia converter-dominated grid, the DERs can provide fast frequency response (FFR) services that can quickly counteract the change in system frequency through inertial support. However, in low voltage grids, frequency and voltage face dynamics coupling due to a high resistance to reactance ratio and cannot be controlled separately as in the bulk electric grid. Due to the coupling effect, the control of one parameter also affects the dynamics of the other parameter. A part of this work highlights the role of DERs to provide grid ancillary services underscoring the challenges of combined voltage and frequency control in low voltage grids. Increasing penetration of renewable energy sources (RES) also decreases the power system inertia, there by affecting the stability of bulk grid. The stochastic nature of RES makes the power system inertia a time-varying quantity. Furthermore, converter-dominated grids have different dynamics compared to conventional grids and therefore estimates of the inertia constant using existing dynamic power system models are unsuitable. This work proposes a novel inertia estimation technique based on convolutional neural networks (CNN) that use local frequency measurements. The model uses a non-intrusive excitation signal to perturb the system and measure frequency using a phase-locked loop. The estimated inertia constants, have significant accuracy for the training, validation, and testing sets. Additionally, the proposed approach can be applied over traditional inertia estimation methods that do not incorporate the dynamic impact of renewable energy sources. The frequency response of power systems changes drastically when multi-area power systems with interconnected tie-lines are considered. Furthermore, higher penetration of RES increases the stochasticity in interconnected power systems. Hence, it is important to estimate the multi-area parameters ensuring communication and coordination between each of the areas. A robust and secure client-server-based distributed machine learning framework is used to estimate power system inertia in a two-area system. The proposed approach can be efficiently optimized to increase the training performance. It is important to analyze the performance of a trained machine learning model in a real-world scenario with unknown dynamics. A pre-trained CNN is tested on a system with model predictive controller (MPC)-based virtual inertia (VI) unit. Results show that the frequency and inertial response of conventional synchronous generators-based system differs drastically as compared to the system with non-synchronous generator-based VI support

Online disturbance prediction for enhanced availability in smart grids

A gradual move in the electric power industry towards Smart Grids brings new challenges to the system's efficiency and dependability. With a growing complexity and massive introduction of renewable generation, particularly at the distribution level, the number of faults and, consequently, disturbances (errors and failures) is expected to increase significantly. This threatens to compromise grid's availability as traditional, reactive management approaches may soon become insufficient. On the other hand, with grids' digitalization, real-time status data are becoming available. These data may be used to develop advanced management and control methods for a sustainable, more efficient and more dependable grid. A proactive management approach, based on the use of real-time data for predicting near-future disturbances and acting in their anticipation, has already been identified by the Smart Grid community as one of the main pillars of dependability of the future grid. The work presented in this dissertation focuses on predicting disturbances in Active Distributions Networks (ADNs) that are a part of the Smart Grid that evolves the most. These are distribution networks with high share of (renewable) distributed generation and with systems in place for real-time monitoring and control. Our main goal is to develop a methodology for proactive network management, in a sense of proactive mitigation of disturbances, and to design and implement a method for their prediction. We focus on predicting voltage sags as they are identified as one of the most frequent and severe disturbances in distribution networks. We address Smart Grid dependability in a holistic manner by considering its cyber and physical aspects. As a result, we identify Smart Grid dependability properties and develop a taxonomy of faults that contribute to better understanding of the overall dependability of the future grid. As the process of grid's digitization is still ongoing there is a general problem of a lack of data on the grid's status and especially disturbance-related data. These data are necessary to design an accurate disturbance predictor. To overcome this obstacle we introduce a concept of fault injection to simulation of power systems. We develop a framework to simulate a behavior of distribution networks in the presence of faults, and fluctuating generation and load that, alone or combined, may cause disturbances. With the framework we generate a large set of data that we use to develop and evaluate a voltage-sag disturbance predictor. To quantify how prediction and proactive mitigation of disturbances enhance availability we create an availability model of a proactive management. The model is generic and may be applied to evaluate the effect of proactive management on availability in other types of systems, and adapted for quantifying other types of properties as well. Also, we design a metric and a method for optimizing failure prediction to maximize availability with proactive approach. In our conclusion, the level of availability improvement with proactive approach is comparable to the one when using high-reliability and costly components. Following the results of the case study conducted for a 14-bus ADN, grid's availability may be improved by up to an order of magnitude if disturbances are managed proactively instead of reactively. The main results and contributions may be summarized as follows: (i) Taxonomy of faults in Smart Grid has been developed; (ii) Methodology and methods for proactive management of disturbances have been proposed; (iii) Model to quantify availability with proactive management has been developed; (iv) Simulation and fault-injection framework has been designed and implemented to generate disturbance-related data; (v) In the scope of a case study, a voltage-sag predictor, based on machine- learning classification algorithms, has been designed and the effect of proactive disturbance management on downtime and availability has been quantified

TOWARDS OPTIMAL OPERATION AND CONTROL OF EMERGING ELECTRIC DISTRIBUTION NETWORKS

The growing integration of power-electronics converters enabled components causes low inertia in the evolving electric distribution networks, which also suffer from uncertainties due to renewable energy sources, electric demands, and anomalies caused by physical or cyber attacks, etc. These issues are addressed in this dissertation. First, a virtual synchronous generator (VSG) solution is provided for solar photovoltaics (PVs) to address the issues of low inertia and system uncertainties. Furthermore, for a campus AC microgrid, coordinated control of the PV-VSG and a combined heat and power (CHP) unit is proposed and validated. Second, for islanded AC microgrids composed of SGs and PVs, an improved three-layer predictive hierarchical power management framework is presented to provide economic operation and cyber-physical security while reducing uncertainties. This scheme providessuperior frequency regulation capability and maintains low system operating costs. Third, a decentralized strategy for coordinating adaptive controls of PVs and battery energy storage systems (BESSs) in islanded DC nanogrids is presented. Finally, for transient stability evaluation (TSE) of emerging electric distribution networks dominated by EV supercharging stations, a data-driven region of attraction (ROA) estimation approach is presented. The proposed data-driven method is more computationally efficient than traditional model-based methods, and it also allows for real-time ROA estimation for emerging electric distribution networks with complex dynamics