Scott G. Ghiocel

Phasor-Measurement-Based State Estimation for Synchrophasor Data Quality Improvement and Power Transfer Interface Monitoring

Validation and data quality improvement of phasor data through state estimation is the first step in ensuring that the synchrophasor data is useful for applications in monitoring, visualization, and control. This paper presents a phasor-measurement-based state estimator (PSE) for improving data consistency by identifying angle biases and current scaling errors in the phasor data using the augmented state vector approach. These errors can arise from issues with the Global Positioning Signal (GPS), timing circuits, instrument channels, and/or data channel scaling. The PSE is demonstrated using several sets of disturbance data from the Central New York Power System. The PSE can also provide estimates of line parameters and transformer tap ratios with sufficient measurement redundancy. Finally, the PSE allows the computation of interface power flows for disturbance and stability monitoring.

Missing Data Recovery by Exploiting Low-Dimensionality in Power System Synchrophasor Measurements

This paper presents a new framework of recovering missing synchrophasor measurements (erasures). Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we connect the problem of recovering PMU data erasures with recent advances in low-rank matrix completion methods. Since the existing analysis for matrix completion methods assumes an independent-erasure model that does not capture the correlations in PMU erasures, we propose two models to characterize the temporal and the channel correlations in PMU erasures and provide theoretical guarantees of a matrix completion method in recovering correlated erasures in both models. We also propose an online algorithm that can fill in the missing PMU measurements for real-time applications. Numerical experiments on actual PMU data are conducted to verify the effectiveness of the proposed methods.

A Power Flow Method Using a New Bus Type for Computing Steady-State Voltage Stability Margins

In steady-state voltage stability analysis, it is well-known that as the load is increased toward the maximum loading condition, the conventional Newton-Raphson power flow Jacobian matrix becomes increasingly ill-conditioned. As a result, the power flow fails to converge before reaching the maximum loading condition. To circumvent this singularity problem, continuation power flow methods have been developed. In these methods, the size of the Jacobian matrix is increased by one, and the Jacobian matrix becomes non-singular with a suitable choice of the continuation parameter. In this paper, we propose a new method to directly eliminate the singularity by reformulating the power flow problem. The central idea is to introduce an AQ bus in which the bus angle and the reactive power consumption of a load bus are specified. For steady-state voltage stability analysis, the voltage angle at the load bus can be varied to control power transfer to the load, rather than specifying the load power itself. For an AQ bus, the power flow formulation consists of only the reactive power equation, thus reducing the size of the Jacobian matrix by one. This reduced Jacobian matrix is nonsingular at the critical voltage point. We illustrate the method and its application to steady-state voltage stability using two example systems.

An Adaptive Wide-Area Power System Damping Controller using Synchrophasor Data

This paper presents an adaptive wide-area interarea mode damping controller for power systems using synchrophasor data. A key consideration in the control design is the time delay in computing the phasor quantities and the variable communication network latency for controllers to use remote synchrophasor data. The adaptive switching controller comprises several phase compensators, each designed for a specific data latency. Based on the latency of the arriving synchrophasor data, the adaptive controller will select the appropriate compensator to use. The design is illustrated with a two-area power system. Applications to large power systems will be discussed.

A Simultaneous Perturbation Approach for Solving Economic Dispatch Problems With Emission, Storage, and Network Constraints

In this paper, an environmental economic dispatch problem with storage, network, and inter-temporal constraints is considered. An approach based on the simultaneous perturbation technique is proposed to deal with the equality and inequality constraints in the economic dispatch problem. The algorithm has been implemented using Matlab and tested on a 6-bus, 5-generator system and a 140-bus, 48-generator system. The effects of cap-and-trade policies, energy storage, and transmission line flow limits in economic dispatch are discussed. The simulations reveal that the method can handle a variety of constraints with good convergence performance.

Preliminary synchronized phasor data analysis of disturbance events in the US Eastern Interconnection

This paper presents analysis results of synchronized phasor data from 10 disturbance events recorded in the US Eastern Interconnection (EI). The phasor data covers a wide region in the EI, allowing for the study of disturbance propagation, interarea modes, and oscillations in voltages and currents. The analysis is not straightforward because the EI is a meshed system with adequate interarea mode damping. Disturbances involving tripping a single large generator unit produce very short interarea swing responses. Islanding events involving regions at the perimeter, however, provide more prominent responses for analysis.

A Low-Rank Matrix Approach for the Analysis of Large Amounts of Power System Synchrophasor Data

With the installation of many new multi-channel phasor measurement units (PMUs), utilities and power grid operators are collecting an unprecedented amount of high-sampling rate bus frequency, bus voltage phasor, and line current phasor data with accurate time stamps. The data owners are interested in efficient algorithms to process and extract as much information as possible from such data for real-time and off-line analysis. Traditional data analysis typically analyze one channel of PMU data at a time, and then combine the results from the individual analysis to arrive at some conclusions. In this paper, a spatial-temporal framework for efficient processing of blocks of PMU data is proposed. A key property of these PMU data matrices is that they are low rank. Using this property, various data management issues such as data compression, missing data recovery, data substitution detection, and disturbance triggering and location can be processing using singular-value based algorithms and convex programming. These functions are illustrated using some historical data from the Central New York power system.

Modeless reconstruction of missing synchrophasor measurements

This paper presents a new framework of reconstructing missing synchrophasor measurements (erasures) without the modeling of power system dynamics. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we connect the problem of reconstructing PMU data erasures with the recent advance in low-rank matrix completion methods. Erasures can be reconstructed through existing computationally-efficient algorithms such as singular value thresholding (SVT) and information cascading matrix completion (ICMC). Numerical experiments on actual PMU data are conducted to verify the effectiveness of the proposed method. Since existing analysis for matrix completion methods assumes an independent-erasure model that does not capture the correlation among PMU data erasures, we propose two models to characterize temporal correlation and channel correlation in PMU data erasures. We provide theoretical guarantees of the ICMC algorithm in reconstructing correlated erasures in both models.

Identification of “unobservable” cyber data attacks on power grids

This paper presents a new framework of identifying cyber data attacks on synchrophasor measurements. We focus on detecting “unobservable” cyber data attacks that cannot be detected by any existing detection method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the unobservable cyber attack identification problem as a matrix decomposition problem where the observed data matrix is the sum of a low-rank matrix plus a linear projection of a column-sparse matrix. We propose a convex-optimization-based decomposition method and provide its theoretical guarantee in the attack identification. Numerical experiments on actual PMU data and synthetic data are conducted to verify the effectiveness of the proposed method.

A voltage sensitivity study on a power transfer path using synchrophasor data

This paper uses synchrophasor measurements on a power transfer interface in the US Eastern Interconnection (EI) to develop voltage sensitivities with respect to increases in transfer flow. The disturbance event considered in this analysis involved a sudden loss of generation, resulting in increased flows on lines close to the disturbance location. The increased flow would immediately cause the bus voltages on the transfer path to drop. With high sampling rate synchrophasor data, the voltage and power flow relationship on a power transfer interface would form a segment of the familiar PV-curve. This paper uses phasor measurements from a disturbance event in the EI to develop a Thevenin equivalent circuit and evaluate the voltage sensitivity with respect to power transfer using this reduced model.