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The paper describes a manifold learning-based algorithm for big data classification and reduction, as well as parameter identification in real-time operation of a power system. Both black-box and gray-box settings for SCADA- and PMU-based measurements are examined. Data classification is based on diffusion maps, where an improved data-informed metric construction for partition trees is used. Data classification and reduction is demonstrated on the measurement tensor example of calculated transient dynamics between two SCADA refreshing scans. Interpolation/extension schemes for state extension of restriction (from data to reduced space) and lifting (from reduced to data space) operators are proposed. The method is illustrated on the single-phase Motor D example from a very detailed WECC load model, connected to the single bus of a real-world 441-bus power system.

This study proposes a novel flexible hybrid state estimation (SE) algorithm when a realistic communication system with its irregularities is taken into account. This system is modelled by the Network Simulator 2 software tool, which is also used to calculate communication delays and packet drop probabilities. Within this setup, the system observability can be predicted, and the proposed SE can decide between using the static SE (SSE) or the discrete Kalman filter plus SSE-based measurements and time alignment (Forecasting-aided SE). Flexible hybrid SE (FHSE) incorporates both phasor measurement units and supervisory control and data acquisition-based measurements, with different time stamps. The proposed FHSE with detailed modelling of the communication system is motivated by: (i) well-known issues in SSE (time alignment of the measurements, frequent un-observability for fixed SE time stamps etc.); and (ii) the need to model a realistic communication system (calculated communication delays and packet drop probabilities are a part of the proposed FHSE). Application of the proposed algorithm is illustrated for examples with time-varying bus load/generation on two IEEE test cases: 14-bus and 300-bus.

Since the publication of the original paper on power system stability definitions in 2004, the dynamic behavior of power systems has gradually changed due to the increasing penetration of converter interfaced generation technologies, loads, and transmission devices. In recognition of this change, a Task Force was established in 2016 to re-examine and extend, where appropriate, the classic definitions and classifications of the basic stability terms to incorporate the effects of fast-response power electronic devices. This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.

This study describes a new class of system identification procedures, tailored to electric power systems with renewable resources. The procedure described here builds on computational advances in differential geometry, and offers a new, global, and intrinsic characterisation of challenges in data-derived identification of electric power systems. The approach benefits from increased availability of high-quality measurements. The procedure is illustrated on the multi-machine benchmark example of IEEE 14-bus system with renewable resources, but it is equally applicable to identification of other components and systems (e.g. dynamic loads). The authors consider doubly-fed induction generators (DFIG) operating in a wind farm with system level proportional-integral controllers.

This paper describes a new class of system identification procedures that are tailored to electric power systems, in particular to synchronous generators (SGs) and other dynamic components. Our procedure builds on computational advances in differential geometry, and offers a new, global characterization of challenges frequently encountered in system identification of electric power systems. The approach also benefits from increasing availability of high-quality measurements. While the proposed procedure is illustrated on SG example in a multimachine benchmark (IEEE 14-bus and real-world 441-bus power systems), it is equally applicable to identification of other system components, such as loads.

The paper describes a new model reduction procedure tailored to power systems. It uses measurement data to devise a family of reduced order nonlinear models while retaining physical interpretability of parameters and equations. The manifold boundary approximation method (MBAM) uses the Fisher information matrix calculated from measurements to identify the least relevant parameter combination in the original model. Next, it numerically constructs a geodesic on the corresponding statistical manifold originating from the initial parameters in the least relevant parameter direction until a manifold boundary is found. MBAM then identifies a limiting approximation in the mathematical form of the model and removes one parameter combination. The simplified model is recalibrated by fitting its behavior to that of the original model, and the process is repeated as appropriate. MBAM is demonstrated on the example of a synchronous generator (SG), which has been treated extensively in the literature. Implications of the proposed model reduction procedure on large power system models are illustrated on a 441-bus, 72-SG dynamical model.

International audience; Microgrids have been identified as key components of modern electrical systems to facilitate the integration of renewable distributed generation units. Their analysis and controller design require the development of advanced (typically model-based) techniques naturally posing an interesting challenge to the control community. Although there are widely accepted reduced order models to describe the dynamic behavior of microgrids, they are typically presented without details about the reduction procedure—hampering the understanding of the physical phenomena behind them. Preceded by an introduction to basic notions and definitions in power systems, the present survey reviews key characteristics and main components of a microgrid. We introduce the reader to the basic functionality of DC/AC inverters, as well as to standard operating modes and control schemes of inverter-interfaced power sources in microgrid applications. Based on this exposition and starting from fundamental physics, we present detailed dynamical models of the main microgrid components. Furthermore, we clearly state the underlying assumptions which lead to the standard reduced model with inverters represented by controllable voltage sources, as well as static network and load representations, hence, providing a complete modular model derivation of a three-phase inverter-based microgrid.

This paper describes a manifold learning algorithm for big data classification and parameter identification in real-time operation of power systems. We assume a black-box setting, where only SCADA-based measurements at the point of interest are available. Data classification is based on diffusion maps, where an improved data-informed metric construction for partition trees is used. Data reduction is demonstrated on an hourly measurement tensor example, collected from the power flow solutions calculated for daily load/generation profiles. Parameter identification is performed on the same example, generated via randomly selected input parameters. The proposed method is illustrated on the case of the static part (ZIP) of a detailed WECC load model, connected to a single bus of a real-world 441-bus power system.

The paper explores interleaved and coordinated refinement of physics- and data-driven models in describing transient phenomena in large-scale power systems. We develop and study an integrated analytical and computational data-driven gray box environment needed to achieve this aim. Main ingredients include computational differential geometry-based model reduction, optimization-based compressed sensing, and a finite approximation of the Koopman operator. The proposed two-step procedure (the model reduction by differential geometric (information geometry) tools, and data refinement by the compressed sensing and Koopman theory based dynamics prediction) is illustrated on the multi-machine benchmark example of IEEE 14-bus system with renewable sources, where the results are shown for doubly-fed induction generator (DFIG) with local measurements in the connection point. The algorithm is directly applicable to identification of other dynamic components (for example, dynamic loads).

This paper proposes a tractable and scalable algorithm to identify and analyze bifurcation points of a large-scale power system model, which are directly related to dynamic voltage instability problems. Different types of bifurcations are analyzed, including: saddle-node (fold), Hopf, singularity-induced and limit-induced. An algorithm that combines optimization and predictor-corrector procedure is proposed for equilibrium tracing. The algorithm is based on calculation of only critical (rightmost and closest-to-zero) eigenvalues. The proposed algorithm is extended to the case of dynamic voltage stability assessment for power systems with optimized topology (simultaneously subjected to the topology control changes and generation re-dispatch). The proposed approach is illustrated on two (medium- and large-scale real-world) test power systems.

The paper describes a global identification procedure for dynamic power system models in the form of differential and algebraic equations. Power system models have a number of features that makes their improvement challenging – they are multi-level, multi-user and multi-physics. Not surprisingly, they are nonlinear and time varying, both in terms of states (memory variables) and parameters, and discrete structures, such as graphs, are strongly blended with continuous dynamics, resulting in network dynamics. The transient stability models are used as a prototypical example. Our method is based on information geometry, and uses advances in computational differential geometry to characterize high-dimensional manifolds in the space of measurements. In the case of network parameters, a comparison is presented with circuit-theoretic techniques. The results are illustrated on the case of IEEE 14-bus test system with 58 parameters in our realization.

In this paper we propose approximate bisimulation relations and functions for reduction of power system dynamic models in differential-algebraic (descriptor) form. The full-size dynamic model is obtained by linearization of the nonlinear transient stability model. We generalize theoretical results on approximate bisimulation relations and bisimulation functions, originally derived for a class of constrained linear systems, to linear systems in descriptor form. An algorithm for transient stability assessment is proposed and used to determine whether the power system is able to maintain the synchronism after a large disturbance. Two benchmark power systems are used to illustrate the proposed algorithm and to evaluate the applicability of approximate bisimulation relations and bisimulation functions for reduction of the power system dynamic models.

The paper proposes a scalable and tractable algorithm for dynamic topology optimization of power systems involving changes in branch on/off status, while respecting small-signal stability (SSS) constraints. A procedure for fast updates of the system matrices (in descriptor form) and without additional full matrix inversions is proposed. To additionally reduce the computation time, only critical eigenvalues (right-most or those in a specified damping ratio and frequency range) are calculated. A quadratic optimization approach is proposed for optimized generation re-dispatch to satisfy SSS constraints. The approach is applied to two (medium- and large-scale) real-world test power systems.

The paper proposes a power system state estimation algorithm in the presence of irregular sensor sampling and random communication delays. Our state estimator incorporates Phasor Measurement Units (PMU) and SCADA measurements. We use an Extended Kalman filter based algorithm for time alignment of measurements and state variables. Time stamps are assumed for PMU, SCADA and state estimation. Application of the proposed algorithm is illustrated for hourly/daily load/generation variations on two test examples: 14-bus and 118-bus.

Load modeling has been extensively studied in power systems. The problem is intrinsically hard, as a simple description is sought for a large collection of heterogeneous physical devices. One aspect of model simplification has to do with the number of parameters needed to describe a dynamic load. With the rich tapestry of methods proposed in the literature as a backdrop, this paper introduces a new approach to simplify the load models and estimate the parameters. Our method is based on information geometry which combines information theory with computational differential geometry to derive global estimation results and shed a new light on difficulties commonly encountered when fitting widely used models to the measurement data. The results are compared with the literature using simulations on the IEEE 14 bus benchmark system.

In contemporary power system practice, the stability limits are typically computed off-line and stored in databases to be monitored by system operators (dispatchers) in the real-time environment. Several sources of uncertainty affect such computations and consequently reasonable stability margins must be taken into account when determining operation limits. Despite these precautions, unplanned outages and planned switching actions (such as topology control [TC] actions) may cause operational conditions not considered at operation planning stages, and consequently system operators are left with no pertinent stability information. Online stability assessment has been proposed as an additional line of defense in which stability limits are computed based on the actual power system condition, which decreases the uncertainty, thus providing more accurate stability operation limits. The online stability assessment can be performed for the (real-time) operating point only, or additionally for a region around this condition [1].

The paper describes a REI-based procedure for estimating parameters of a dynamic model from measurements in the boundary buses/branches. Parameter identification of equivalent synchronous generators in fictitious buses is performed by Weighted Least Square (WLS) nonlinear optimization to minimize the difference between online measurements and transient responses of reduced power system.

This paper proposes a scalable algorithm to identify and analyze bifurcation points of a large power system model of dynamic voltage instability. Different types of bifurcations are analyzed, such as: saddle-node (fold), Hopf, singularity-induced, and limit-induced bifurcations. The interval bisection based algorithm is proposed for determination of the steady-state equilibrium point. The proposed algorithm is based on calculation of critical eigenvalues (least damped or closest-to-zero) only. The approach is applied to two (medium- and large-scale real-world) test power systems.

The paper addresses analytical and practical aspects of integration of equation-based (classical) and signal-derived [artificial neural network (ANN)] dynamic models for transient analysis of large-scale dynamical systems. Our signal-based part is based on two ANNs, and is derived from measurements at boundary points. In this paper, we describe this hybrid modeling technique, and focus on: 1) a least square-based mechanism for on-line correction of dynamic variable predictions that is based on actual operating conditions; 2) the resilience of the algorithm to missing measurements due to failed communication links; and 3) a complete two-way interaction between the differential-algebraic equation based subsystem and the ANN-based subsystem. The paper demonstrates the feasibility of implementing our approach in standard power system software by integrating the ANN-based model with the transient analysis toolbox from Matlab. We illustrate capabilities of the proposed approach for transient analysis on a benchmark multi-machine example derived from the New England/New-York interconnected power system.

In this paper, we address the issue of quantifying model uncertainties in a deregulated power market. We are interested in a worst-case analysis of effects of network parameter uncertainties on optimization problems that are solved on the level of an independent system operator (ISO). The focus is on linear programming (LP) formulations because of their practical relevance. After briefly reviewing applications of different interval analysis tools (interval matrix inversion and various contractors and solvers for systems of interval equations), we propose two tailored algorithms. In the first (basic LP approach), we propose another solver for a system of linear interval equations, while in the second (power system-customized LP approach), we use features of the power system model to reduce the width of the interval solution. The interval-based results are compared with a deterministic robust noninterval matrix inversion-based solution of a model of the benchmark 68-node New England/New York interconnected power system. Presented analyzes demonstrate that even small parameter variations can have significant impact on optimization.

On August 14, 2003, a cascading outage of transmission and generation facilities in the North American Eastern Interconnection resulted in a blackout of most of New York state as well as parts of Pennsylvania, Ohio, Michigan, and Ontario, Canada. On September 23, 2003, nearly four million customers lost power in eastern Denmark and southern Sweden following a cascading outage that struck Scandinavia. Days later, a cascading outage between Italy and the rest of central Europe left most of Italy in darkness on September 28. These major blackouts are among the worst power system failures in the last few decades. The Power System Stability and Power System Stability Controls Subcommittees of the IEEE PES Power System Dynamic Performance Committee sponsored an all day panel session with experts from around the world. The experts described their recent work on the investigation of grid blackouts. The session offered a unique forum for discussion of possible root causes and necessary steps to reduce the risk of blackouts. This white paper presents the major conclusions drawn from the presentations and ensuing discussions during the all day session, focusing on the root causes of grid blackouts. This paper presents general conclusions drawn by this Committee together with recommendations based on lessons learned.

In this paper, we address the issue of quantifying effects of model uncertainty on transmission security in a power market. The paper addresses two types of uncertainties: 1) structural (e.g., branch outages), which are modeled by contingency scenarios, and 2) perturbations of model parameters (e.g., branch impedance), which are described by sensitivity analysis. The paper embeds these considerations in an optimization procedure that checks if transmission security is maintained and, when necessary, generates minimal corrections in existing contractual transactions that ensure security. Given our interest in real-time applications, we consider problem formulations guaranteeing solution existence and global optimality. A detailed sensitivity analysis based on approximate matrix inversion and linear programming perturbation theory is performed. The paper proposes a linearized ac power flow model to describe the transmission network. The model is studied on a benchmark example of the New England/New York interconnection with 68 nodes.

The problem of defining and classifying power system stability has been addressed by several previous CIGRE and IEEE Task Force reports. These earlier efforts, however, do not completely reflect current industry needs, experiences and understanding. In particular, the definitions are not precise and the classifications do not encompass all practical instability scenarios. This report developed by a Task Force, set up jointly by the CIGRE Study Committee 38 and the IEEE Power System Dynamic Performance Committee, addresses the issue of stability definition and classification in power systems from a fundamental viewpoint and closely examines the practical ramifications. The report aims to define power system stability more precisely, provide a systematic basis for its classification, and discuss linkages to related issues such as power system reliability and security.

The paper addresses practical capabilities of artificial neural networks (ANNs) in developing measurement-based continuous-time dynamic equivalents for power systems. Our method is based on a set of measurements at boundary nodes between a subsystem that is to be modeled in detail ("retained" portion of the system) and the part that is to be replaced by a simplified ("equivalent") model. We are particularly interested in combining standard physics-based models with signal-based models derived from measurements. We utilize a color-coding scheme to distinguish between physics-based models (clear or white box) at one end, the signal-based models (opaque or black box) at the opposite end, and mixed (gray box) models in the middle. The paper also proposes a way for combining classical and ANN-based equivalents in a hybrid model implemented in a standard software environment for transient analysis (in this case, ETMSP). Our conclusions are based on simulations performed on a model of a benchmark multimachine power system derived from the WSCC system.

The paper proposes an artificial neural network (ANN)-based strategy for identification of reduced-order dynamic equivalents of power systems. This large-signal model is formulated in continuous-time and is therefore compatible with standard models of power system components. In a departure from previous works on the subject, we do not postulate a particular model structure for the equivalent, hence the label nonparametric. The approach uses only measurements at points where internal (retained) and external (reduced) systems are interfaced, and requires no knowledge of parameters and topology of the external subsystem. The procedure consists of two conceptual steps: (1) the first ("bottleneck") ANN is used to extract "states" of the reduced-order equivalent; and (2) the second (recurrent) ANN is embedded in an ordinary differential equations (ODEs) solver, and trained to approximate the "right-hand side," using the states extracted at the first step. We also describe an extension in which a third ANN is used to synthesize missing interface measurements from a historical database of system responses to various disturbances. We illustrate the capabilities of the approach on a multimachine benchmark example derived from the WSCC system.

The paper provides a unified exposition of various definitions of reactive (or inactive) power and of various compensation methods. In particular, it presents a comparative Hilbert space analysis of definitions used in power system studies and in electric drives (the so called instantaneous reactive power). The paper casts several compensation strategies (such as compensation without energy storage and compensation with linear shunt components) in a common framework, and utilizes the concept of orthogonal projections on suitable subspaces as the main analytical and computational tool. Various notions are illustrated on two examples.

In this paper we discuss the estimation of the domain of attraction of equilibria in power systems and propose a new passivity-based controller design methodology for excitation control of synchronous generators. The methodology goes beyond the widely popular damping injection (L"gV) schemes, to actually shape the total energy function via modification of the energy transfer between the mechanical and electrical components of the system. Applying the procedure it is shown that a, properly tuned, linear state feedback enlarges both the estimates and the actual domain of attraction, thus increasing critical clearing time for faults. This is illustrated in two case studies, including a benchmark comparison with the classical control scheme.

Summary form only given, as follows. This paper presents application of the dynamic phasor modeling technique to unbalanced polyphase power systems. The proposed technique is a polyphase generalization of the dynamic phasor approach, and it is applicable to nonlinear power system models. In a steady-state, the dynamic phasors reduce to standard phasors from AC circuit theory. The technique produces results that are very close to those obtained from time-domain simulations. Simulations in terms of dynamic phasors typically allow larger integration steps than the standard time-domain formulation. The authors present simulations of unbalanced faults involving a three-phase synchronous generator connected to an infinite bus through a transmission line, and demonstrate that models based on dynamic phasors provide very accurate descriptions of observed transients.

In this paper, the authors present a unifying framework for design of controllers that suppress low-frequency oscillations in power systems. The proposed methodology is based on the physical notion of dissipativity, and on its system-theoretic counterparts. They illustrate the capabilities of this framework in four application areas: power system stabilizers (PSSs); supplementary control for high-voltage DC (HVDC) lines; auxiliary control for static VAr systems (SVCs); and regulation of thyristor-controlled series capacitors (TCSCs). All case studies involve a standard benchmark case of a multi-machine power system, and all controllers use only locally available information.

In their previous work, the authors have studied the phasor dynamics of thyristor-controlled series capacitors (TCSCs), and derived associated models for studying power swing oscillations in power systems. This paper presents the use of a dynamic phasor model of the TCSC in studies of subsynchronous resonance (SSR). Their earlier dynamic phasor models of the TCSC are further simplified here, and tested at typical subsynchronous frequencies. The phasor model of the TCSC is directly compatible with the conventional models used for the other system components relevant to SSR. The proposed approach to SSR analysis is an attractive alternative to approaches based on sampled-data models, and to torque-per-unit-velocity methods. The dynamic phasor approach has been tested on the IEEE first benchmark SSR model, showing outstanding agreement with more computationally demanding alternative methods for eigenvalue analysis.

In this paper we address the problem of supression of low frequency oscillations in power systems. These oscillations appear in strongly interconnected networks because of load and topology changes, and they may cause loss of synchronism and generator tripping. We propose the utilisation of passivation techniques to design power system stabilizers for the synchronous generators. The generator to be controlled is described by a standard lagrange model, with three forcing terms: the mechanical torque coming from the turbine, the terminal voltage of the network and the field voltage, which is our control variable. In view of the significant differences between the mechanical and the electrical time scales, the first signal can be treated as a constant disturbance. The terminal voltage may be viewed as the output of an operator, -defined by the remaining part of the network-, which is in feedback interconnection with the generator. Our basic assumption is that the network is always absorbing energy from the generator, whence the interconnection subsystem (as viewed from the generator) is passsive. The control objective is then to close a loop around the field voltage so as to passivize the generator system. We characterize, in terms of a simple linear matrix inequality, a class of linear state-feedback controllers which achieve this objective.

This paper addresses the analysis and design issues in load frequency control (LFC) for a power system entity that participates in interconnection. The basic physical control mechanisms are the same whether the entity is a vertically integrated utility, an independent system operator or a virtual control area in a fully de-regulated market. Goals of this paper are twofold: to examine a rationale for known heuristic design guidelines; and to suggest an orderly methodology for control synthesis. Motivated by the large uncertainty in dynamic models of power system components and their interconnections, this paper proposes a physically motivated passivity objective as a means to achieve effective closed-loop control. Quantitative feedback theory provides convenient graphical tools for suggested design and analysis methods. A simulation model of a two-system interconnection is used as an illustration.

This paper presents a probabilistic approach to the characterization of dynamic properties of harmonics in power systems. In this framework it is assumed that, in addition to a deterministic component, harmonics contain a stochastic component that is modeled by a Markov chain. This mathematically tractable approach enables a power system analyst to study dynamics of harmonics, while accommodating the possibility that the underlying stochastic process may have a finite memory. The suggested framework thus removes the uncorrelated-ness ("whiteness") assumption for the temporal evolution of harmonics, which is often not supported by actual measurements. Mathematical formulae to compute the first and second moments of the resulting random waveforms are presented for the practically relevant case of finite-dimensional discrete-time Markov chains. The approach is illustrated on computer simulations and on actual data taken from a residential load.

This letter describes a physics-based methodology for decomposing the apparent power into orthogonal components. The framework unifies several results from the literature and enables useful extensions and generalizations.

This paper presents a probabilistic approach to the characterization of dynamical models of induction machine clusters. The authors' method derives bounds on eigenvalue variations for linearized models expressed in terms of stochastic norms. In their examples of the modeling of power system loads, this characterization tends to be less conservative than alternative deterministic approaches. They consider examples of induction machines with different ratings (classes), and allow for wide variations of electrical and mechanical parameters. They describe a stochastic norm approach to: (1) efficiently describe the dynamical model variations for a cluster of similar machines without having to perform repeated eigenvalue calculations, e.g. in a wind farm application; and (2) suggest the order of the reduced model in power system load modeling where the tightness of the bounds of eigenvalue variations is used for guidance in decisions regarding the number of different classes that would efficiently represent a given composite load.

This paper develops simple but accurate continuous-time, large-signal dynamic models for a thyristor-controlled series capacitor (TCSC). The models are based on the representation of voltages and currents as time-varying Fourier series, and focus on the dynamics of the short-term Fourier coefficients. Truncating the series to keep only the fundamental components provides a natural dynamic generalization of the familiar quasi-static phasor representation. Systematic simplifications or refinements of their fundamental phasor dynamics model yield a variety of other models. The authors establish that even the simplest of these models predicts the dynamic behavior of the TCSC very accurately. Their phasor models provide a powerful basis for simulation, more than an order of magnitude faster than detailed time-domain circuit simulation. The phasor models present an attractive alternative to sampled-data models, having the advantages of simpler representation, modularity and direct compatibility with the phasor models that are routinely used to represent the dynamics of generators and other power system components.

This paper proposes a new approach to reduction of power system dynamic models based on approximate bisimulation relations. Advantages of these relations for time domain analysis over the model reduction techniques commonly used in system theory are pointed out. The approach is applied to the transient stability analysis in power systems. We propose an algorithm that identifies whether the power system is able to maintain the synchronism after a disturbance, based on linearization and approximate bisimulation relations. During the time simulation, the linearized model is transformed into the appropriate form and reduced using the approximate bisimulation relations. This reduced (and linear) model is used in the numerical integration instead of the original nonlinear one, while the accuracy criterion is satisfied. The New England benchmark power system is used to verify the described algorithm.

The paper explores the influence of topology changes (due to the change in transmission branch on/off status) and injection perturbations (due to economic load/generation redispatching) on the eigenvalue sensitivity of the power system dynamic model. These control actions are analyzed as part of dynamic transmission topology control. An algorithm for fast updates of the system matrices (in descriptor form) and without additional full matrix inversions for topology changes is proposed. To further reduce the computation time, only critical, right-most (poorly damped) and most sensitive poles to multiple input parameter changes are calculated by subspace accelerated Rayleigh quotient iteration (SARQI) and Sensitive Pole algorithms, respectively. The described approach is applied to two (small- and medium-scale) test power systems.

We describe a method for generating causal, stable, numerically robust, IIR, customizable (application specific), perfect reconstruction (PR) filter-banks with zero (or adjustable) reconstruction delay. The ideas presented in this paper extend the concept of Dynamic Phasors as well as the concept of the so-called Phasor Bank Transform, which constitute specific subclasses of filter-banks. We provide an explicit compact characterization of the optimally-robust reconstruction stage of this transform, which can be used with any (customized) choice of its analysis stage. Since the Phasor Bank transform allows causal (real-time) delay-free perfect reconstruction of any input waveform, it is perfectly suited for (closed-loop) control applications. Although the motivating application area of Phasor Banks is Power Systems, such as tracking of time-variant harmonic content under both steady-state (nearly-periodic) and transient operating conditions, the technique presented here can be used in a variety of closed loop applications.

This paper addresses analysis and design issues in automatic generation control (AGC) for a power system that participates in an interconnection. The paper goals are twofold: to examine a rationale for known heuristic design guidelines and to suggest an orderly methodology to improve these guidelines. Robust performance and stability frameworks are suggested to reduce dependence on model fidelity. Together with a physically motivated passivity objective is proposed, they are viable sources for such guidelines. Quantitative feedback theory provides convenient tools for suggested design and analysis methods. A simulation model of a two system interconnection is used as an illustration.

This chapter contains sections titled: Introduction Model-Based Fault Detection Detuning Detection and Accommodation on IFOC-Driven Induction Motors Broken Rotor Bar Detection on IFOC-Driven Induction Motors Fault Detection on Power Systems Conclusions ]]>

This letter provides a unified exposition of various definitions of reactive (or inactive) power. In particular, it presents a comparative frequency-domain analysis of definitions used in power system studies and in electric drives (the so-called instantaneous reactive power). Concepts are illustrated on a common trample.

This brief provides a formulation and solution for the problem of optimizing power flows in polyphase power systems with significant source (line) impedance. An optimal solution considering significant line impedance has already been obtained in recent works. Unfortunately, it relies on network and load parameters that are not easy to determine during operation. This motivates our interest in a suboptimal, easy to implement solution that relies only on the measurements of the load voltage and current, so as to allow precise control of power delivered to the compensated load as well as the real power flowing out of the compensator, while reducing line losses to within a few percent of the theoretical minimum. Our compensator tracks variations in both network and load conditions, continuously adjusting its current so as to reduce the power dissipated in the line impedance, for both linear and nonlinear loads. Properties of the adaptive compensation procedure are illustrated for an asymmetrical three-phase induction motor supplied with unbalanced nonsinusoidal voltages.

The paper proposes a new framework for characterizing instantaneous non-active (reactive) power in general polyphase systems. The framework is based on the concept of multivectors, which are a higher-order generalization of vectors, and on the idea of the geometric product, which blends the inner (scalar) and the outer (wedge) products. This framework is representation-free, and thus extends readily to any polyphase system and any choice of coordinates, such as symmetric sequence components. The new framework sheds a new light on the well known 3-phase quantities, and provides a deeper understanding of connections with average (phasor-based) non-active power quantities.

Electric power systems are among most spatially extended engineered systems. Their stable operation in the presence of disturbances and outages is made possible by real-time control over communication networks. Communication delay is one of the unavoidable side-effects of using a computer network to interconnect sensors, actuators and control centers/nodes. We propose here a delay canceling approach based on time-stamping of transmitted signals, which can successfully mitigate the destabilizing effects of delay. Time stamping is enabled by the availability of precise time signals derived from the Global Positioning System (GPS) in various nodes of the power system. Such signals are part of Phasor Measurement Units (PMUs) that are increasingly being deployed as a key ingredient of system-wide monitoring and control. We show that our time-stamping-based delay canceling method makes it possible to overcome very large delays in the estimation-control loop while avoiding adverse effects on the stability of the system.

This paper studies the dissipativity properties of full order dynamic models of synchronous generators. It is shown that, under widely accepted assumptions, these models satisfy a balance between the internal storage of a generalized energy and a suitably defined power supply associated to the stator and excitation circuits. This property has particular relevance since it allows to incorporate the detailed model of synchronous machines to classical energy functions used to analyze power system stability. The dissipation inequality implies also that the linear model around the equilibrium point meet a convex condition in the frequency domain, able to be exploited in the stability analysis of interconnected systems. The impact of excitation control and resistive losses on these properties is studied through a numerical example.

The paper presents a non-traditional approach to assessment of global dynamics in power systems. The method is based on nonlinear contraction analysis of a generalized time-varying, one-machine infinite bus (OMIB) equivalent of the system, and can be applied to establish global contraction for the nonlinear system response following perturbations. It has a potential to complement traditional stability studies as it does not depend on the precise knowledge of the (post-fault) system equilibrium. In our procedure, a time-varying OMIB model of the system is constructed first. Based upon this model, nonlinear contraction theory is used next to investigate the onset of the divergence of system trajectories. In particular, an OMIB-based metric is suggested that accounts for nonlinear and time-varying behavior and allows contraction analysis of system dynamics and the study of robustness properties. The developed procedures are tested on a large-scale power system model that exhibits complex inter-area oscillations following major contingencies. Results from both contraction and conventional analyses are presented.

The approach adopted in this paper for the transient stabilization of multimachine power systems sees the entire network as the (structure-preserving) interconnection of the network components. We #rst show that, under certain assumptions, these models admit a Hamiltonian description and are, then, shown to be cyclo-dissipative. Our main contribution is the identification-in terms of a linear matrix inequality (LMI)-of a class of power systems with resistive loads and leaky lines for which we can design excitation controllers that (locally) stabilize the desired equilibrium point. Since the LMI depends explicitly on the controller gain, a decentralized control action can be easily computed. The proposed technique is applied to a classical example

Boundary load flow exemplifies a class of power (load) flow solution procedures that explicitly incorporates uncertainties in system data. Its importance for operation and control of modern power systems is increasing due to changes in system operation following deregulation and due to the emergence of new types of sources (e.g., distributed generation). The quantification of effects of uncertainties in system parameters (e.g., branch admittances) and in measurements (e.g., SCADA) is a fundamental task in generalized boundary (robust) power flow methodology. In this paper we propose a family of ellipsoidal approximations to uncertainty propagation in linearized power flow equations. After providing a precise uncertain power flow problem formulation, we describe an approximate solution based on the geometry of ellipsoids in high dimensions. While computationally intensive, the method is potentially applicable to large-scale power systems. We illustrate its capabilities on a benchmark example of the New England/New-York interconnection with 68 nodes

In this paper we address the issue of quantifying transmission security in a deregulated power market. In particular, we are interested in effects of model uncertainties on characterization of system security. The paper addresses two types of uncertainties: 1) structural uncertainties (e.g., line outages) are modeled by contingency scenarios (by utilizing the matrix inversion lemma in the constraint sets), and 2) perturbation of model parameters (by utilizing first-order perturbation matrix theory in the optimization criterion and the constraint sets). The paper embeds these considerations in an optimization procedure that checks if transmission security is maintained, and, when necessary, generates minimal corrections in existing contractual transactions that ensure security. Given our interest in real-time applications, we consider problem formulations based on convex optimization, thus assuring the solution existence and uniqueness. The paper proposes a convex form of a linearized AC power flow model to describe the transmission network. Model is applied on example standard 68-node New England/New-York interconnected power system.