Matching Items (6)

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Error detection and error correction for PMU data as applied to power system state estimators

Description

In modern electric power systems, energy management systems (EMSs) are responsi-ble for monitoring and controlling the generation system and transmission networks. State estimation (SE) is a critical `must run successful' component within the EMS software. This is dictated by the

In modern electric power systems, energy management systems (EMSs) are responsi-ble for monitoring and controlling the generation system and transmission networks. State estimation (SE) is a critical `must run successful' component within the EMS software. This is dictated by the high reliability requirements and need to represent the closest real time model for market operations and other critical analysis functions in the EMS. Tradi-tionally, SE is run with data obtained only from supervisory control and data acquisition (SCADA) devices and systems. However, more emphasis on improving the performance of SE drives the inclusion of phasor measurement units (PMUs) into SE input data. PMU measurements are claimed to be more accurate than conventional measurements and PMUs `time stamp' measurements accurately. These widely distributed devices meas-ure the voltage phasors directly. That is, phase information for measured voltages and currents are available. PMUs provide data time stamps to synchronize measurements. Con-sidering the relatively small number of PMUs installed in contemporary power systems in North America, performing SE with only phasor measurements is not feasible. Thus a hy-brid SE, including both SCADA and PMU measurements, is the reality for contemporary power system SE. The hybrid approach is the focus of a number of research papers. There are many practical challenges in incorporating PMUs into SE input data. The higher reporting rates of PMUs as compared with SCADA measurements is one of the salient problems. The disparity of reporting rates raises a question whether buffering the phasor measurements helps to give better estimates of the states. The research presented in this thesis addresses the design of data buffers for PMU data as used in SE applications in electric power systems. The system theoretic analysis is illustrated using an operating electric power system in the southwest part of the USA. Var-ious instances of state estimation data have been used for analysis purposes. The details of the research, results obtained and conclusions drawn are presented in this document.

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2013

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Analysis of synchronization and accuracy of synchrophasor measurements

Description

In electric power systems, phasor measurement units (PMUs) are capable of providing synchronized voltage and current phasor measurements which are superior to conventional measurements collected by the supervisory control and data acquisition (SCADA) system in terms of resolution and accuracy.

In electric power systems, phasor measurement units (PMUs) are capable of providing synchronized voltage and current phasor measurements which are superior to conventional measurements collected by the supervisory control and data acquisition (SCADA) system in terms of resolution and accuracy. These measurements are known as synchrophasor measurements. Considerable research work has been done on the applications of PMU measurements based on the as-sumption that a high level of accuracy is obtained in the field. The study in this dissertation is conducted to address the basic issue concerning the accuracy of actual PMU measurements in the field. Synchronization is one of the important features of PMU measurements. However, the study presented in this dissertation reveals that the problem of faulty synchronization between measurements with the same time stamps from different PMUs exists. A Kalman filter model is proposed to analyze and calcu-late the time skew error caused by faulty synchronization. In order to achieve a high level of accuracy of PMU measurements, inno-vative methods are proposed to detect and identify system state changes or bad data which are reflected by changes in the measurements. This procedure is ap-plied as a key step in adaptive Kalman filtering of PMU measurements to over-come the insensitivity of a conventional Kalman filter. Calibration of PMU measurements is implemented in specific PMU instal-lation scenarios using transmission line (TL) parameters from operation planning data. The voltage and current correction factors calculated from the calibration procedure indicate the possible errors in PMU measurements. Correction factors can be applied in on-line calibration of PMU measurements. A study is conducted to address an important issue when integrating PMU measurements into state estimation. The reporting rate of PMU measurements is much higher than that of the measurements collected by the SCADA. The ques-tion of how to buffer PMU measurements is raised. The impact of PMU meas-urement buffer length on state estimation is discussed. A method based on hy-pothesis testing is proposed to determine the optimal buffer length of PMU meas-urements considering the two conflicting features of PMU measurements, i. e. un-certainty and variability. Results are presented for actual PMU synchrophasor measurements.

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2012

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A study on constrained state estimators

Description

This study focuses on state estimation of nonlinear discrete time systems with constraints. Physical processes have inherent in them, constraints on inputs, outputs, states and disturbances. These constraints can provide additional information to the estimator in estimating states from the

This study focuses on state estimation of nonlinear discrete time systems with constraints. Physical processes have inherent in them, constraints on inputs, outputs, states and disturbances. These constraints can provide additional information to the estimator in estimating states from the measured output. Recursive filters such as Kalman Filters or Extended Kalman Filters are commonly used in state estimation; however, they do not allow inclusion of constraints in their formulation. On the other hand, computational complexity of full information estimation (using all measurements) grows with iteration and becomes intractable. One way of formulating the recursive state estimation problem with constraints is the Moving Horizon Estimation (MHE) approximation. Estimates of states are calculated from the solution of a constrained optimization problem of fixed size. Detailed formulation of this strategy is studied and properties of this estimation algorithm are discussed in this work. The problem with the MHE formulation is solving an optimization problem in each iteration which is computationally intensive. State estimation with constraints can be formulated as Extended Kalman Filter (EKF) with a projection applied to estimates. The states are estimated from the measurements using standard Extended Kalman Filter (EKF) algorithm and the estimated states are projected on to a constrained set. Detailed formulation of this estimation strategy is studied and the properties associated with this algorithm are discussed. Both these state estimation strategies (MHE and EKF with projection) are tested with examples from the literature. The average estimation time and the sum of square estimation error are used to compare performance of these estimators. Results of the case studies are analyzed and trade-offs are discussed.

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2013

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Consequences of false data injection on power system state estimation

Description

The electric power system is one of the largest, most complicated, and most important cyber-physical systems in the world. The link between the cyber and physical level is the Supervisory Control and Data Acquisition (SCADA) systems and Energy Management

The electric power system is one of the largest, most complicated, and most important cyber-physical systems in the world. The link between the cyber and physical level is the Supervisory Control and Data Acquisition (SCADA) systems and Energy Management Systems (EMS). Their functions include monitoring the real-time system operation through state estimation (SE), controlling the system to operate reliably, and optimizing the system operation efficiency. The SCADA acquires the noisy measurements, such as voltage angle and magnitude, line power flows, and line current magnitude, from the remote terminal units (RTUs). These raw data are firstly sent to the SE, which filters all the noisy data and derives the best estimate of the system state. Then the estimated states are used for other EMS functions, such as contingency analysis, optimal power flow, etc.

In the existing state estimation process, there is no defense mechanism for any malicious attacks. Once the communication channel between the SCADA and RTUs is hijacked by the attacker, the attacker can perform a man-in-middle attack and send data of its choice. The only step that can possibly detect the attack during the state estimation process is the bad data detector. Unfortunately, even the bad data detector is unable to detect a certain type of attack, known as the false data injection (FDI) attacks.

Diagnosing the physical consequences of such attacks, therefore, is very important to understand system stability. In this thesis, theoretical general attack models for AC and DC attacks are given and an optimization problem for the worst-case overload attack is formulated. Furthermore, physical consequences of FDI attacks, based on both DC and AC model, are addressed. Various scenarios with different attack targets and system configurations are simulated. The details of the research, results obtained and conclusions drawn are presented in this document.

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Date Created
2015

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State Estimation for Enhanced Monitoring, Reliability, Restoration and Control of Smart Distribution Systems

Description

The Smart Grid initiative describes the collaborative effort to modernize the U.S. electric power infrastructure. Modernization efforts incorporate digital data and information technology to effectuate control, enhance reliability, encourage small customer sited distributed generation (DG), and better utilize assets. The

The Smart Grid initiative describes the collaborative effort to modernize the U.S. electric power infrastructure. Modernization efforts incorporate digital data and information technology to effectuate control, enhance reliability, encourage small customer sited distributed generation (DG), and better utilize assets. The Smart Grid environment is envisioned to include distributed generation, flexible and controllable loads, bidirectional communications using smart meters and other technologies. Sensory technology may be utilized as a tool that enhances operation including operation of the distribution system. Addressing this point, a distribution system state estimation algorithm is developed in this thesis. The state estimation algorithm developed here utilizes distribution system modeling techniques to calculate a vector of state variables for a given set of measurements. Measurements include active and reactive power flows, voltage and current magnitudes, phasor voltages with magnitude and angle information. The state estimator is envisioned as a tool embedded in distribution substation computers as part of distribution management systems (DMS); the estimator acts as a supervisory layer for a number of applications including automation (DA), energy management, control and switching. The distribution system state estimator is developed in full three-phase detail, and the effect of mutual coupling and single-phase laterals and loads on the solution is calculated. The network model comprises a full three-phase admittance matrix and a subset of equations that relates measurements to system states. Network equations and variables are represented in rectangular form. Thus a linear calculation procedure may be employed. When initialized to the vector of measured quantities and approximated non-metered load values, the calculation procedure is non-iterative. This dissertation presents background information used to develop the state estimation algorithm, considerations for distribution system modeling, and the formulation of the state estimator. Estimator performance for various power system test beds is investigated. Sample applications of the estimator to Smart Grid systems are presented. Applications include monitoring, enabling demand response (DR), voltage unbalance mitigation, and enhancing voltage control. Illustrations of these applications are shown. Also, examples of enhanced reliability and restoration using a sensory based automation infrastructure are shown.

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Date Created
2012

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Topology attacks on power system operation and consequences analysis

Description

The large distributed electric power system is a hierarchical network involving the

transportation of power from the sources of power generation via an intermediate

densely connected transmission network to a large distribution network of end-users

at the lowest level of the hierarchy. At

The large distributed electric power system is a hierarchical network involving the

transportation of power from the sources of power generation via an intermediate

densely connected transmission network to a large distribution network of end-users

at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans-

mission/ distribution), the system is managed and monitored with a combination of

(a) supervisory control and data acquisition (SCADA); and (b) energy management

systems (EMSs) that process the collected data and make control and actuation de-

cisions using the collected data. However, at all levels of the hierarchy, both SCADA

and EMSs are vulnerable to cyber attacks. Furthermore, given the criticality of the

electric power infrastructure, cyber attacks can have severe economic and social con-

sequences.

This thesis focuses on cyber attacks on SCADA and EMS at the transmission

level of the electric power system. The goal is to study the consequences of three

classes of cyber attacks that can change topology data. These classes include: (i)

unobservable state-preserving cyber attacks that only change the topology data; (ii)

unobservable state-and-topology cyber-physical attacks that change both states and

topology data to enable a coordinated physical and cyber attack; and (iii) topology-

targeted man-in-the-middle (MitM) communication attacks that alter topology data

shared during inter-EMS communication. Specically, attack class (i) and (ii) focus on

the unobservable attacks on single regional EMS while class (iii) focuses on the MitM

attacks on communication links between regional EMSs. For each class of attacks,

the theoretical attack model and the implementation of attacks are provided, and the

worst-case attack and its consequences are exhaustively studied. In particularly, for

class (ii), a two-stage optimization problem is introduced to study worst-case attacks

that can cause a physical line over

ow that is unobservable in the cyber layer. The long-term implication and the system anomalies are demonstrated via simulation.

For attack classes (i) and (ii), both mathematical and experimental analyses sug-

gest that these unobservable attacks can be limited or even detected with resiliency

mechanisms including load monitoring, anomalous re-dispatches checking, and his-

torical data comparison. For attack class (iii), countermeasures including anomalous

tie-line interchange verication, anomalous re-dispatch alarms, and external contin-

gency lists sharing are needed to thwart such attacks.

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Date Created
2015