Peak-ratio analysis method for enhancement of LOM protection using M class PMUs

A novel technique for loss of mains (LOM) detection, using Phasor Measurement Unit (PMU) data, is described in this paper. The technique, known as the Peak Ratio Analysis Method (PRAM), improves both sensitivity and stability of LOM protection when compared to prevailing techniques. The technique is based on a Rate of Change of Frequency (ROCOF) measurement from M-class PMUs, but the key novelty of the method lies in the fact that it employs a new “peak-ratio” analysis of the measured ROCOF waveform during any frequency disturbance to determine whether the potentially-islanded element of the network is grid connected or not. The proposed technique is described and several examples of its operation are compared with three competing LOM protection methods that have all been widely used by industry and/or reported in the literature: standard ROCOF, Phase Offset Relay (POR) and Phase Angle Difference (PAD) methods. It is shown that the PRAM technique exhibits comparable performance to the others, and in many cases improves upon their abilities, in particular for systems where the inertia of the main power system is reduced, which may arise in future systems with increased penetrations of renewable generation and HVDC infeed

Power System Frequency Measurement Based Data Analytics and Situational Awareness

This dissertation presents several measurement-based research from power system wide-area dynamics data analytics to real-time situational awareness application development. All the research are grounded on the power system phasor measurements provided by wide-area Frequency Monitoring Network (FNET/GridEye), which collects the Global Positioning System (GPS) signal synchronized power system phasor measurements at distribution networks. The synchronized frequency measurement at FNET/GridEye enables real-time monitoring of bulk power systems (BPSs) and allows the dynamics interpretation of power system disturbances. Research on both the dynamic and ambient frequency measurements are conducted in this dissertation.The dynamics refer to the frequency measurement when the system is experiencing sudden contingencies. This dissertation focuses on two types of contingency: generation trip and oscillation and conducts both data analytics and corresponding real-time applications. Historical generation trip events in North America are analyzed in purpose to develop a frequency measurement based indicator of power systems low inertia events. Then the frequency response study is extended to bulk power systems worldwide to derive its association with system capacity size. As an essential parameter involved in the frequency response, the magnitude of the power imbalances is estimated based on multiple linear regression for improved accuracy. With respect to situational awareness, a real-time FNET/GridEye generation trip detection tool is developed for PMU use at power utilities and ISOs, which overcomes several challenges brought by different data situations.Regarding the oscillation dynamics, statistical analysis is accomplished on power system inter-area oscillations demonstrating the yearly trend of low-frequency oscillations and the association with system load. A novel real-time application is developed to detect power systems sustained oscillation in large area. The application would significantly facilitate the power grid situational awareness enhancement and system resiliency improvement.Furthermore, an additional project is executed on the ambient frequency measurement at FNET/GridEye. This project discloses the correlation between power system frequency and the electric clock time drift. In practice, this technique serves to track the time drifts in traffic signal systems

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness

Impact Assessment, Detection, And Mitigation Of False Data Attacks In Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

Impact Assessment, Detection, and Mitigation of False Data Attacks in Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

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

Data-Driven Situation Awareness for Power System Frequency Dynamics

As the penetration of renewable energy increases, system inertia decreases, causing changes in system frequency dynamics. The power industry desires situation awareness of power system frequency dynamics to ensure secure and economic operation and control. Moreover, FNET/Grideye has abundant measured data from power systems, making it possible to conduct data-driven situation awareness studies on power system frequency dynamics. This doctoral dissertation proposes several contributions: (a) Two accurate generator trip event MW estimation methods are proposed, in which one is based on long window RoCoF and another is based on multi-Beta values; (b) Two real-time system inertia estimation approaches are developed using ambient frequency fluctuation and pump turn-off events, along with techniques for improving RoCoF calculation in event-based inertia estimation; (c) An adaptive PV reserve estimation algorithm is established to provide PV reserve while saving energy for PV resources; (d) A practical load composition estimation tool is built for the industry to easily obtain essential load model parameters. Although conducting research using actual data from power systems for practical application is challenging and compilated, the proposed data-driven situation awareness methods in this doctoral dissertation solve practical problems and offer clear theoretical explanations for the industry. These methods address one of the key challenges for operating a high-renewable power grid and pave the way for the U.S. carbon-free power sector by 2035

Data-driven Estimation of the Power Grid Inertia with Increased Levels of Renewable Generation Resources

The thesis investigates methods for estimating inertia in systems at different levels of renewable energy penetrations. Estimating renewable generators\u27 inertia is challenging because their structures differ from traditional generators. Moreover, the power generated from renewable energy resources is not stable, depending on weather conditions. When a power grid has a disturbance, photovoltaic inverter control influences a power grid inertia by different controllers, such as power factor and reactive power control, to bring a power grid back to a steady state. The changing reactive power impacts the frequency, which strongly relates to inertia and increases the inertia estimation problem. Several papers proposed different approaches to estimating renewable generators\u27 inertia. The two main categories of estimating inertia are model-based and measurement-based methods. The model-based methods mimic an actual renewable generator behavior to calculate inertia. It is a complicated model specialized for specific renewable devices, but unlike the measurement-based methods, it can estimate the inertia in the steady state. The measurement-based methods find the patterns in measured data and use classification or regression functions to calculate inertia. A measurement model can monitor a power grid in real time. However, the method needs parameter oscillation, representing power imbalance in a power grid. This thesis proposes three measurement-based models to estimate inertia for systems under levels of photovoltaic systems: Symbolic Aggregate Approximation, Back Propagation Neural Network, and Minimum Volume Enclosing with a Gradient Descent Machine Model. The measurement-based inertia estimation models need large-scale system measurement data. PowerWorld Simulator has a function to analyze the transient stability, which is utilized in this thesis to generate simulated data for this. Reducing photovoltaic output power can mimic the impact of weather changes. Different types of photovoltaic controllers have various behavior. The Symbolic Aggregate Approximation transfers continuous data into discrete data. The advantage of this method over other techniques is its ability to compress large-scale data and the reduced data storage requirements. Hence, the model demonstrates the best performance for estimating the inertia. The Minimum Volume Enclosing Ellipsoid visualizes measurement data, including frequency, generator output power, and bus voltage, on a 3-dimensional space. The volume of the enclosed ellipsoid is the output that yields label inertia. During a fault in a power system, the volume of the ellipsoid increases. The Gradient Descent Model estimates an optimal regression curve to match volume with label inertia as the estimated inertia. The Back Propagation Neural Network is a nonlinear classification method. With multiple layers and neurons, this method can efficiently cluster complex input features, such as the frequency of all buses and generator output power. The error between the estimated inertia and the label inertia is used to modify the branches\u27 weight to reduce error. The disadvantage of the second and third models is that they do not have a better performance than the first one