Fred C. Schweppe

Bio: Fred C. Schweppe is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Electric power system & Load management. The author has an hindex of 35, co-authored 74 publications receiving 7843 citations.

Power System Static-State Estimation, Part I: Exact Model

Abstract: The static state of an electric power system is defined as the vector of the voltage magnitudes and angles at all network buses The static-state estimator is a data processing algorithm far converting redundant meter readings and other available information into an estimate of the static-state vector Discussions center on the general nature of the problem, mathematical modeling, an interative technique for calculating the state estimate, and concepts underlying the detection and identification of modeling errors Problems of interconnected systems are considered Results of some initial computer simulation tests are discussed

Recursive state estimation: Unknown but bounded errors and system inputs

Abstract: A method is discussed for estimating the state of a linear dynamic system using noisy observations, when the input to the dynamic system and the observation errors are completely unknown except for bounds on their magnitude or energy. The state estimate is actually a set in state space rather than a single vector. The optimum estimate is the smallest calculable set which contains the unknown system state, but it is usually impractical to calculate this set. A recursive algorithm is developed which calculates a time-varying ellipsoid in state space that always contains the system's true state. Unfortunately the algorithm is still unproven in the sense that its performance has not yet been evaluated. The algorithm is closely related in structure but not in performance to the algorithm obtained when the system inputs and observation errors are white Gaussian processes. The algorithm development is motivated by the problem of tracking an evasive target, but the results have wider applications.

Bad data analysis for power system state estimation

Abstract: The state estimation problem in electric power systems consists of four basic operations: hypothesize structure; estimate; detect; identify. This paper addresses the last two problems with respect to the bad data and structural error problem. The paper interrelates various detection and identification methods (sum of squared residuals, weighted and normalized residuals, nonquadratic criteria) and presents new results on bad data analysis (probability of detection, effect of bad data). The theoretical results are illustrated by means of a 25 bus network.

Power System Static-State Estimation, Part II: Approximate Model

Abstract: The static state of an electric power system is defined as the vector of the voltage magnitudes and angles at all network buses. The static-state estimator is a data-processing algorithm for converting redundant meter readings and other available information into an estimate of the static-state vector. Discussions center on an approximate mathematical model (related to the dc load-flow model). This model yields noniterative-state estimation equations, simplified prediction of effects of network and generation-load pattern changes on network flow, and simplified detection and identification of modeling errors. Results of some initial computer studies on the real power-voltage angle portion of the approximate model are discussed.

Power System Static-State Estimation, Part III: Implementation

Abstract: The static state of an electric power system is defined as the vector of the voltage magnitudes and angles at all network buses. The static-state estimator is a data processing algorithm for converting redundant meter readings and other available information into an estimate of the static-state vector. Discussions center on implementation problems associated with computation time requirements, dimensionality resulting from a large number of buses, and the actual time-varying (nonstatic) character of power systems. Various potentially useful approaches are discussed and compared.

Linear Matrix Inequalities in System and Control Theory

Abstract: Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.

Trends in Microgrid Control

Abstract: The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

False data injection attacks against state estimation in electric power grids

Abstract: A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.