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This paper proposes a novel online measurement-based Wide-Area Voltage Control (WAVC) method using Phasor Measurement Unit (PMU) data in power systems with Flexible AC Transmission System (FACTS) devices. As opposed to previous WAVC methods, the proposed WAVC does not require any model knowledge or the participation of all buses and considers both active and reactive power perturbations. Specifically, the proposed WAVC method exploits the regression theorem of the Ornstein-Uhlenbeck process to estimate the sensitivity matrices through PMU data online, which are further used to design and apply the voltage regulation by updating the reference points of FACTS devices. Numerical results on the IEEE 39-Bus and IEEE 68-Bus systems demonstrate that the proposed model-free WAVC can provide effective voltage control in various network topologies, different combinations of voltage-controlled and voltage-uncontrolled buses, under measurement noise, and in case of missing PMUs. Particularly, the proposed WAVC algorithm may outperform the model-based WAVC when an undetected topology change happens. [ABSTRACT FROM AUTHOR]

With the current power system being electronicized nowadays, synchrophasor estimation (SE) plays an increasingly critical role in the monitoring, analysis, and protection of the state of the power grid. One proposal has been brought to enhance the commonly used discrete Fourier transform (DFT) algorithm that has a large error due to the inherent spectral leakage and fence effect in asynchronous sampling by interpolations. This article proposes an SE algorithm based on an optimized combined cosine self-convolution (OCCSC) window. Using the sidelobe characteristics of the window functions as the evaluation index, an optimized combined cosine window is obtained by the accelerated particle swarm optimization (APSO) algorithm. The window function’s self-convolution is used to further expand its excellent characteristics. Based on these characteristics, combined with interpolated DFT, simulations and experimental verifications compared with other algorithms show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for the machine learning-based online prediction of generator dynamic behavior in large interconnected power systems. Unlike the existing literature in this domain, which assumes faults occur immediately after a steady-state situation, the proposed method takes the possibility of multiple disturbances into account. It is founded on a simulation-based classification approach to indirectly take advantage of phasor measurement unit (PMU) data, which leads to improvements in robustness against load model uncertainties. Relying on offline scenarios, the method developed conducts multiple time-domain simulations (TDSs) in parallel for a set of feasible two-machine dynamic equivalent models (DEMs) for each case. Thereafter, common descriptive statistics are computed for the rotor angles obtained to form the feature space. The values taken via a feature selection process are then applied as inputs to ensemble decision trees, which train models capable of predicting both stability status and generator grouping ahead of time. In online situations, PMU data are used to create DEMs and the predictors are collected by performing parallel TDSs for DEMs. The functionality of the proposed hybrid machine learning and TDS-based approach is verified on several IEEE test systems, followed by a discussion of results. [ABSTRACT FROM AUTHOR]

Dynamic-state estimation plays a critical role in achieving real-time wide-area monitoring of power systems. On the other hand, false data injection (FDI) attacks are substantial threats, which can undesirably ruin the state estimation results. To tackle this problem, an effective secure hybrid dynamic-state estimation approach that involves a dynamic model of the attack vector is proposed in this article. In the proposed method, an initial estimation of the system states is first obtained using a designed unknown input observer (UIO). Subsequently, based on the system, UIO models, and the initial estimations of the states, a dynamic model for the attack vector is extracted. Ultimately, the attack model is augmented with the main system model for coestimation of the attack and the system states using a Kalman filter. The onset of the FDI attack is rapidly detected by the accurate estimation of the attack vector. The effectiveness of the proposed approach is demonstrated under different FDI attack scenarios by a thorough theoretical analysis as well as simulations on IEEE 14-bus and 57-bus test systems. In order to show that the proposed method can keep up with typical scan rates of commercial phasor measurement units, a series of software-in-the-loop experiments are also conducted and the real-time feasibility of the proposed approach is guaranteed. [ABSTRACT FROM AUTHOR]

Synchronized phasor and frequency measurements are key tools for the monitoring and management of modern power systems. Under dynamic conditions, it is vital to define algorithms that allow accurately measuring time-varying signals with short latencies and high reporting rates. A dynamic phasor model can help the design of these algorithms and, in particular, of those based on the Kalman filter approach. This article proposes a three-phase synchrophasor estimator based on the extended Kalman filter; state variables are obtained from Taylor expansions of amplitudes and phase angles. The underlying dynamic model considers the inherent relationship among the phases and includes harmonics in an effective way. The process noise covariance matrix that allows representing the uncertainty introduced by the dynamic model has been written by considering that practical ac power systems are nearly three-phase symmetric during typical operation. This a priori information allows improving noise rejection and increasing accuracy in the presence of amplitude modulation, as highlighted by the reported simulation results. [ABSTRACT FROM AUTHOR]

This paper develops a systematic framework for analyzing how low frequency forced oscillations propagate in electric power systems. Using this framework, the paper shows how to mathematically justify the so-called Dissipating Energy Flow (DEF) forced oscillation source location technique. The DEF's specific deficiencies are pinpointed, and its underlying energy function is analyzed via incremental passivity theory. This analysis is then used to prove that there exists no passivity transformation (i.e. quadratic energy function) which can simultaneously render all components of a lossy classical power system passive. The paper goes on to develop a simulation-free algorithm for predicting the performance of the DEF method in a generalized power system, and it analyzes the passivity of three non-classical load and generation components. The proposed propagation framework and performance algorithm are both tested and illustrated on the IEEE 39-bus New England system and the WECC 179-bus system. [ABSTRACT FROM AUTHOR]

In power system state estimation, the robust least absolute value robust dynamic estimator is well known. However, the covariance of the state estimation error cannot be obtained easily. In this article, an analytical equation is derived using influence function approximation to analyze the covariance of the robust least absolute value dynamic state estimator. The equation gives insights into the precision of the estimation and can be used to express the variances of the state estimates as functions of measurement noise variances, enabling the selection of sensors for specified estimator precision. Simulations on the IEEE 14-bus, 30-bus, and 118-bus systems are given to illustrate the usefulness of the equation. Monte Carlo experiments can also be used to determine the covariance, but many data points are needed and hence many runs are required to achieve convergence. Our result shows that to obtain the covariance of the state estimation error, the analytical equation proposed in this article is four orders of magnitude faster than a 10 000-run Monte Carlo experiment on both the IEEE 14-bus and 30-bus systems. [ABSTRACT FROM AUTHOR]

A robust observer for performing power system dynamic state estimation (DSE) of a synchronous generator is proposed. The observer is developed using the concept of $\mathcal {L}_{\infty }$ stability for uncertain, nonlinear dynamic generator models. We use this concept to (i) design a simple, scalable, and robust dynamic state estimator and (ii) obtain a performance guarantee on the state estimation error norm relative to the magnitude of uncertainty from unknown generator inputs, and process and measurement noises. Theoretical methods to obtain upper and lower bounds on the estimation error are also provided. Numerical tests validate the performance of the $\mathcal {L}_{\infty }$ -based estimator in performing DSE under various scenarios. The case studies reveal that the derived theoretical bounds are valid for a variety of case studies and operating conditions, while yielding better performance than existing power system DSE methods. [ABSTRACT FROM AUTHOR]

Fast harmonic phasor measurements are very useful in power system protection applications, e.g., high-impedance fault location and intelligent method-based islanding detection. To address this problem, a novel harmonic phasor estimator is proposed in this paper, especially for P-class phasor measurement units (PMUs). The exponential functions are used to model the decaying dc (ddc) component, and the optimal time constant (TC) set for the ddc model is searched based on an enumeration method. In addition, the dynamic harmonic phasors are modeled based on the Shannon sampling theorem. As a result, a filter bank is designed for harmonic phasor and frequency estimations. The rate of change of frequency (ROCOF) of the harmonic is estimated based on a second-order fitting method. The response time of the proposed method for harmonic phasor estimation is always no longer than 40 ms. Simulation test results indicate that the proposed method is more accurate than the Taylor–Fourier transform (TFT) under ddc offset and frequency deviation conditions. In particular, the proposed method is more robust to ddc’s TC variations. An experimental test is implemented to demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

An adaptive decentralized control scheme is proposed for real-time control of oscillatory dynamics and overall stability improvement of an interconnected power system. A standard framework of continuous-time (CT) infinite-horizon optimal control paradigm is defined and an extended online actor-critic (AC) algorithm based on policy iteration is used for its solution. The AC structure uses neural networks (NNs) whose weights are updated adaptively. The proof for the convergence of the scheme guarantees the system stability. The unobservable internal states of the synchronous machine are estimated using a numerically stable, swift and relatively accurate decentralized dynamic state estimator (DDSE) based on spherical-radial cubature rule. Applicability of the developed scheme has been demonstrated on a benchmark power system (IEEE 2.2, 16 machine 68 bus system) model via nonlinear time-domain simulations. Multi-processor technology based scaled laboratory setup was used for controller performance validation in real-time. [ABSTRACT FROM AUTHOR]

A new multi-input multi-output (MIMO) observer-driven reduced copy (ORC), i.e., MIMO-ORC architecture is proposed for wide-area damping control using multiple doubly fed induction generator (DFIG)-based wind farms to mitigate the issue of intermittent observations with multiple feedback signals and actuators. In this context, the concepts of cyber-physical self-coupling and cross-coupling are introduced, and their impact on the deterioration of closed-loop performance with data-dropout is quantified using an analytical derivation. A framework for the stability analysis of MIMO-ORC architecture is also proposed. Finally, time-domain simulations show the superiority of the proposed approach over its single-input single-output ORC counterpart in a 16-machine New England–New York power system. [ABSTRACT FROM AUTHOR]

The ambient noise in synchrophasor measurements carries core dynamic signatures of a power system and its spectrum can strongly reflect the dynamic properties and operating conditions of the system and its loads. Typically, the ambient noise spectrum is estimated from a window of measurements captured during steady state operating conditions of the system. The presence of any Forced Oscillation (FO) in the measurement window severely biases the estimated ambient noise spectrum. In this article, an algorithm is proposed for separable estimation of the ambient noise spectrum in the presence of periodic FOs in synchrophasor measurements. The algorithm utilizes the Thomson's multitaper spectral estimation and harmonic analysis techniques and is capable of effectively reducing the bias in estimating the ambient noise spectrum due to the presence of FOs in the measurements. The performance of the estimator is analyzed theoretically and the theoretical results are verified experimentally using simulation studies. Application of the algorithm to field measured data illustrates that the algorithm is capable of effectively reducing the bias introduced by FOs in estimating the ambient noise spectrum from the synchrophasor measurements containing both ambient noise and FOs. [ABSTRACT FROM AUTHOR]

Fast dynamics and transient events are becoming more and more frequent in power systems, due to the high penetration of renewable energy sources and the consequent lack of inertia. In this scenario, Phasor Measurement Units (PMUs) are expected to track the monitored quantities. Such functionality is related not only to the PMU accuracy (as per the IEC/IEEE 60255-118-1 standard) but also to the PMU reporting rate (RR). High RRs allow tracking fast dynamics, but produce many redundant measurement data in normal conditions. In view of an effective tradeoff, the present paper proposes an adaptive RR mechanism based on a real-time selection of the measurements, with the target of preserving the information content while reducing the data rate. The proposed method has been tested considering real-world datasets and applied to four different PMU algorithms. The results prove the method effectiveness in reducing the average data throughput as well as its scalability at PMU concentrator or storage level. • Phasor Measurement Unit (PMU) • Synchrophasor, frequency and ROCOF tracking error metrics. • Adaptive reporting rate for PMU. • Data throughput optimisation. • Measurement accuracy. [ABSTRACT FROM AUTHOR]

Synchronized phasor and frequency measurements are key tools for the monitoring and management of modern power systems. Under dynamic conditions, it is vital to define algorithms that allow accurately measuring time-varying signals with short latencies and high reporting rates. A dynamic phasor model can help the design of these algorithms and, in particular, of those based on the Kalman filter approach. This article proposes a three-phase synchrophasor estimator based on the extended Kalman filter; state variables are obtained from Taylor expansions of amplitudes and phase angles. The underlying dynamic model considers the inherent relationship among the phases and includes harmonics in an effective way. The process noise covariance matrix that allows representing the uncertainty introduced by the dynamic model has been written by considering that practical ac power systems are nearly three-phase symmetric during typical operation. This a priori information allows improving noise rejection and increasing accuracy in the presence of amplitude modulation, as highlighted by the reported simulation results. [ABSTRACT FROM AUTHOR]

This paper proposes a switched system method (SSM) for the fusion state estimation (SE) under denial-of-service (DoS) attacks in power systems. One of the subsystems in the SSM employs the widely used dynamic model with unit state transition matrix and zero control input. The performance of this model is favorable when the system operates in normal conditions but tends to suffer while the system state dramatically changes. Moreover, its accuracy and convergence is sensitive to the value of the process noise covariance matrix. The other subsystem, with some opposite characteristics, utilizes a pseudo-dynamic model derived from the linearization of the power flow equation. A switching rule based on the innovations is established to make a tradeoff between the two subsystems with regard to estimation accuracy, converge speed, and computing time. Apart from the difference in the reporting rate of various measurements, cyber attacks, such as DoS attacks may result in the absence of measurements in the snapshot of SE at the finest time scale. To deal with this problem, the linear extrapolation and multistep prediction techniques are involved. Simulation studies are conducted on the IEEE-14 bus test system and a 33 bus distribution system under various scenarios, and the performance of the proposed method is discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework for probabilistic data-driven prediction of unstable groups of coherent generators in interconnected power systems. In contrast to existing techniques in which deterministic classification or forecasting approaches are applied to an offline database, the current study relies on a prediction interval (PI)-based method to tackle prediction uncertainties. First, similarity coefficients (SCs) are considered as internal outputs and calculated for all offline cases. Then, at some generator terminals as selected via a feature selection process, voltage values are measured and used as the input features of the prediction tool. Quantile regression forest is conducted to generate PIs, in which several intervals with certain probabilities are predicted for SCs between any pair of generators. Thereafter, the obtained PIs are used to shape an empirical cumulative distribution function of SCs; a Monte Carlo simulation is then conducted to find a reliable estimate of possible grouping patterns. Finally, a decision-making phase is employed to draw clear distinctions among various parts of the most plausible grouping pattern with respect to a reliability index. This approach can offer power system operators wider flexibility to select a corrective control strategy. The effective performance of the developed approach is demonstrated on several IEEE test systems, followed by a discussion of results. [ABSTRACT FROM AUTHOR]

An algorithm based on Thomson's multitaper spectral estimation and harmonic analysis techniques is proposed for the detection and frequency estimation of periodic forced oscillations in a power system. Unlike traditional periodogram-based techniques, the method does not rely on true ambient noise spectrum of the system, and can operate continuously in an online environment without requiring any system knowledge except synchrophasor measurements. It compares a test statistic derived from only measurements against a threshold, and the threshold is established from general expressions of the test statistic's distribution and the probability of false alarm. Performance of the detector is explicitly expressed using probability of detection expression, and evaluated using simulation studies. Results upon application of this algorithm to field measured synchrophasor data suggest usability of the technique for forced oscillation detection in practical monitoring of the power system. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of the rotor speeds of synchronous machines by means of phasor measurement units. This estimation is aimed at online monitoring of electromechanical transients and transient stability analysis. The proposed technique is based on the concept of frequency divider formula recently published by the first two authors in these Transactions. The dynamic state estimation is formally stated as a convex optimization problem and a thorough discussion of the sensitivity analysis of the optimal solution is provided. The case study considers a dynamic 1479-bus model of the all-island Irish system and discusses the effect of bad data, noise, and latency on the proposed estimation technique. [ABSTRACT FROM AUTHOR]

Locating the sources of forced low-frequency oscillations in power systems is an important problem. A number of proposed methods demonstrate their practical usefulness, but many of them rely on strong modeling assumptions and provide poor performance in certain cases for reasons still not well understood. This paper proposes a systematic method for locating the source of a forced oscillation by considering a generator's response to fluctuations of its terminal voltages and currents. It is shown that a generator can be represented as an effective admittance matrix with respect to low-frequency oscillations, and an explicit form for this matrix, for various generator models, is derived. Furthermore, it is shown that a source generator, in addition to its effective admittance, is characterized by the presence of an effective current source, thus giving a natural qualitative distinction between source and nonsource generators. Detailed descriptions are given of a source detection procedure based on this developed representation, and the method's effectiveness is confirmed by simulations on the recommended testbeds (e.g., WECC 179-bus system). This method is free of strong modeling assumptions and is also shown to be robust in the presence of measurement noise and generator parameter uncertainty. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven real-time event identification method based on the measurements of Phasor Measurement Units (PMUs). The central idea is to characterize an event by the low-dimensional subspace spanned by the dominant singular vectors of a data matrix that contains spatial–temporal blocks of PMU data. The subspace representation is robust to system initial conditions and characterizes the system dynamics. A dictionary of subspaces that correspond to different events are established offline, and an event is identified online with the most similar event in the dictionary through subspace comparison. The compact subspace representation reduces the dictionary size and the computational time of the event identification method. Numerical experiments on both simulated events in an IEEE 68-bus power system and the recorded data in New England validate the proposed method. [ABSTRACT FROM AUTHOR]

Accurate estimation of dynamic states (such as synchronous machine rotor angle and speed) is critical for monitoring and controlling transient stability. Extended Kalman filter (EKF)-based approaches have been developed for estimating dynamic states. In order to improve the EKF’s performance in estimating dynamic states of a synchronous machine, this paper proposes a multi-step adaptive interpolation (MSAI) approach to achieve a balance between estimation accuracy and computational time. This approach consists of three major steps. First, two indexes are calculated to quantify the nonlinearity of the state transition function and measurement function, respectively. Second, based on the nonlinearity indexes, the interpolation factor is determined using a finite state machine model. And finally, to mitigate the negative impact of nonlinearity on the estimation accuracy, pseudo-measurements are added between consecutive measurements through linear interpolation. A simple example is used to validate the proposed nonlinear indexes. The two-area four-machine system and 16-machine 68-bus system are used to evaluate the effectiveness of the proposed MSAI approach. It is shown through the Monte-Carlo method that the estimation accuracy can be improved through interpolation. In addition, a good tradeoff between estimation accuracy and computational time can be achieved effectively through the proposed MSAI approach. [ABSTRACT FROM AUTHOR]

Most existing phasor measurement units (PMUs) are noncausal PMUs and they compensate for the group delay in PMU applications by shifting time stamps, which may result in high latency that will impair performance of PMU applications. This paper presents causal PMUs to reduce the group delay caused latency without shifting time stamps, and studies the impact of causality on performance of PMU algorithms. The paper first analyzes how causal and noncausal PMUs compensate for the group delay caused by digital filters and it is found that causal PMUs normally reduce latency by the group delay, whereas noncausal PMU will not. Then, the impact of causality on performance of PMU algorithms is investigated and analyzed by using the Hydro Quebec Research Institute (IREQ) PMU model and the IEEE PMU model. Finally, the simulation results carried out on two test systems highlight the difference between the causal method and the noncausal method under fault conditions in realistic multimachine setups. [ABSTRACT FROM AUTHOR]

In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKF-GPS) is proposed and compared with five existing approaches, including UKF-schol, UKF- \boldsymbol \kappa , UKF-modified, UKF- \boldsymbol \Delta Q , and the square-root UKF (SR-UKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is found that UKF-schol, UKF- \boldsymbol \kappa , and UKF- \boldsymbol \Delta Q do not work well in some estimations while UKF-GPS works well in most cases. UKF-modified and SR-UKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability. [ABSTRACT FROM PUBLISHER]

This paper presents a double suboptimal-scaling-factor adaptive strong tracking Kalman filter (DSTKF)-based phasor measurement unit algorithm which can meet the accuracy requirement of the IEEE standard C37.118.1 under the dynamic condition. This method uses a $k$ th Taylor polynomial to linearize the complex exponential of the signal model, and estimates the dynamic phasor using DSTKF. The antialias filter unit for DSTKF algorithm is included to suppress high-order harmonics which will augment computing burden. By applying double suboptimal-scaling factors, DSTKF adjusts the process noise covariance matrix and the prediction covariance matrix in real time. Thereafter, amplitude and frequency measurement performances under various dynamic conditions are assessed in detail. Theoretical analysis and comprehensive experimental results validate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Synchrophasors are an emerging power system wide-area dynamic performance monitoring and control technology. The IEEE standard C37.118.1 outlines specifications for the construction of synchrophasor units, in which two reference synchrophasor models are proposed for protection (P-type) and measurement (M-type) purposes. This paper develops improved synchrophasor models for power system dynamic stability/performance evaluation based on the reference architecture proposed in the IEEE standard C37.118.1, considering frequency error (FE) and the total vector error (TVE) as the main performance indices. The quadrature demodulation filters are designed explicitly considering the impact of harmonic distortion and out-of-band interference, and thus optimized quadrature demodulation filters improve estimation performance during adverse conditions. Furthermore, a special emphasis is placed on the input low-pass filter to attenuate high-frequency components above the Nyquist rate. The improved synchrophasor models are rigorously tested to ensure compliance with performance requirements stipulated in the IEEE standard. Results show that improved synchrophasor models significantly enhance the FE while ensuring compliance requirements for the TVE. [ABSTRACT FROM PUBLISHER]

With power networks undergoing an unprecedented transition from traditional power systems to modern electric grids integrated with renewable energy sources, maintaining frequency stability of generators in modern power systems has become one of the major concerns. Targeting this issue, in this paper, we propose a novel frequency restoration strategy in photovoltaics (PV)-connected power systems using decentralized dynamic state estimation technique and PV power plant as a contingency power source. When a sudden increase in load demand occurs, the output power of PV panels is increased in order to compensate for the shortage of real power capacity of the generator, in order to restore the frequency of a certain generator bus bar. An unscented Kalman filter-based decentralized dynamic estimation is utilized in this study to estimate the frequency of a selected generator bus bar with local noisy voltage and current measurement data acquired by using phasor measurement units. Solar luminous intensity may vary over a period of time in different seasons, weather conditions, etc., which causes the variations in the output power of PV power plants. This irradiance uncertainty is also considered in this study. The proposed control strategy not only incorporates the frequency deviations of a generator bus-bar, but also takes into account the tie-line power deviations under disturbances. Simulation results demonstrate the capacity of proposed control schemes in restoring the frequency of generator bus-bars and also maintaining the tie-line power flowing between adjoining areas at it scheduled value. [ABSTRACT FROM PUBLISHER]

Over the past several years, great strides have been made in the effort to monitor the small-signal stability of power systems. These efforts focus on estimating electromechanical modes, which are a property of the system that dictate how generators in different parts of the system exchange energy. Though the algorithms designed for this task are powerful and important for reliable operation of the power system, they are susceptible to severe bias when forced oscillations are present in the system. Forced oscillations are fundamentally different from electromechanical oscillations in that they are the result of a rogue input to the system, rather than a property of the system itself. To address the presence of forced oscillations, the frequently used AutoRegressive Moving Average (ARMA) model is adapted to include sinusoidal inputs, resulting in the AutoRegressive Moving Average plus Sinusoid (ARMA+S) model. From this model, a new Two-Stage Least Squares algorithm is derived to incorporate the forced oscillations, thereby enabling the simultaneous estimation of the electromechanical modes and the amplitude and phase of the forced oscillations. The method is validated using simulated power system data as well as data obtained from the western North American power system and Eastern Interconnection. [ABSTRACT FROM PUBLISHER]

Fault recorders can track the transients in the power system during a fault. However, they cannot provide a wide area picture of the power system operating condition. The provision to track the transients is of great interest for the power system operators. In this paper, a two-stage state estimator (SE), based on both conventional and phasor measurement unit (PMU) measurements, is presented. The estimator is able to track the dynamics of the power system states during a fault. The advantage of the proposed SE is that it does not require full power system observability by the PMU measurements. The two-stage SE was successfully tested on the IEEE 14 and 118 bus systems. [ABSTRACT FROM PUBLISHER]

In recent years, techniques of using system disturbance data to validate generator models have been widely discussed. Dynamic model validation and calibration is becoming one of the important applications to smart grid initiative. As this kind of technique is utilized to validate generator model, procedures of data screening and reprocessing are essential because raw data obtained from measurement is not always satisfactory. Regarding model parameter calibration, system model is usually quite complicated with various parameters interacting with each other. Artificial intelligent tool is the prior option to save the laborious tuning process and enhance the parameter accuracy. This paper presents a guideline to validate and calibrate parameters of generating units using the record data from phasor measurement unit. Associated procedures for signal filtering on the measurement data, key parameters screening, intelligent search of model parameters, and cross check of legitimate parameters will be discussed in detail. Finally, two historical disturbance cases that happened in the Taiwan power (Taipower) system are applied in accordance with the proposed guideline to demonstrate its effectiveness on generator parameter validation and calibration. [ABSTRACT FROM PUBLISHER]

We consider the problem of planning the location and size of sensors and actuators to achieve optimal dynamic performance. Using basic results from control and convex optimization, we formulate mixed-integer semidefinite programs for the actuator placement and sizing to obtain the linear quadratic regulator with the lowest cost, and the sensor placement to obtain the Kalman filter with the lowest error. The two formulations are nearly identical due to the duality of optimal linear control and estimation. We also pose similar problems in terms of observability and controllability, which result in smaller mixed-integer semidefinite programs. Since the mixed-integer semidefinite programing is not yet a mature technology, we also use greedy heuristics in conjunction with continuous semidefinite programming. The approach is demonstrated on two modern applications from power systems: the placement and sizing of energy storage for regulation and the placement of phasor measurement units for estimation. [ABSTRACT FROM PUBLISHER]

Accurate and fast estimation of power system phasors is the key to reliable operation of power system and its control/protective equipment. There have been many studies on phasor estimation techniques under dynamic and transient conditions. Although the performance under dynamic conditions of estimators based on the static phasor model has improved, significant errors still exist during large dynamic deviations. Two fast and precise dynamic phasor estimation algorithms under power system oscillations and off-nominal frequency conditions are described in this paper. The methods use the signal model under these dynamic conditions, linearize them by using Taylor’s series expansion, and estimate the phasor using least squares technique. Frequency and its rate of change are also calculated using adjacent phasors with minimum complexity. Results obtained show that the performance errors of the proposed methods are way below the minimum requirement of the standard and better than other similar dynamic phasor algorithms. [ABSTRACT FROM PUBLISHER]

This paper proposes an effective scheme for real-time operation and protection of microgrids based on the distributed dynamic state estimation (DDSE) that is applied to a single renewable distributed energy resource (DER) or other components. First, the DDSE can be used for setting-less component protection that applies dynamic state estimation on a component under protection with real-time measurements and dynamic models of the component. Based on the results, the well-known chi-square test yields the confidence level that quantifies the goodness of fit of models to measurements, indicating the health status of the component. With this approach, renewable DERs in microgrids can be protected on an autonomous and adaptive basis. Meanwhile, the estimated state variables of each component are converted to phasor data with time tags, and then collected to the distributed energy resources management (DERMS) of microgrids. These aggregated phasor data that are once filtered by the DDSE are input to the static state estimation in the DERMS along with unfiltered data sent from conventional meters, relays, and digital fault recorders, ultimately generating real-time operating conditions of microgrids. This paper also provides numerical simulations for comparing the DDSE-based approach with the conventional centralized state estimation in terms of data accuracy and computational speeds. [ABSTRACT FROM AUTHOR]

This paper presents a new approach in adaptive out-of-step (OOS) protection settings in power systems in real time. The proposed method uses extended equal area criterion (EEAC) to determine the critical clearing time and critical clearing angle of the system, which are vital information for the OOS protection setting calculation. The dynamic model parameters and the coherency groups of the system for EEAC analysis are determined in real time to ensure that the newly calculated settings suit with the prevalent system operating condition. The effectiveness of the method is demonstrated in simulated data from a 16-machine 68-bus system model. [ABSTRACT FROM PUBLISHER]

An increasing number of wind turbine generators (WTGs) are being installed in modern power system networks. As a result, substantial achievements have been made in developing generic wind models to represent the dynamic behavior of WTGs on the grid. The Western Electricity Coordinating Council (WECC) has developed and proposed four generic models for positive sequence stability analysis of WTG. These models provide a good first step in terms of simulating the dynamic response of WTGs on the power system grid. Since it requires accurate parameters to predict the true response of the WTG, challenges associated with the simulation of dynamic response of WTGs in the field still remain. This paper proposes a procedure to estimate parameters of dynamic equivalent model of WTG by using the measurement data from phasor measurement units (PMUs). Hybrid dynamic simulation is used to reduce external system by employing input signal on PMU bus. Stochastic approximation is adopted in searching optimized model parameter values. A two-WTG subsystem in Electric Reliability Council of Texas (ERCOT) is presented as a case study to address the importance of correct model in system operation strategy. Two most commonly used generic WTG models have been utilized to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

An algorithm for the detection and frequency estimation of periodic forced oscillations in power systems is proposed. The method operates by comparing the periodogram of synchrophasor measurements to a detection threshold. This threshold is established by deriving a general expression for the distribution of the periodogram and is related to the algorithm's probabilities of false alarm and detection. Unlike classic detection algorithms designed for use with white Gaussian noise, the proposed algorithm uses a detection threshold that varies with frequency to account for the colored nature of synchrophasor measurements. Further, a detection method based on multiple segments of data is also proposed to improve the algorithm's performance as a monitoring tool in the online environment. A design approach that helps to ensure that the best available probability of detection from any one detection segment is constantly increasing with the duration of the forced oscillation is also developed. Results from application of the detection algorithm to simulated and measured power system data suggest that the algorithm provides the expected detection performance and can be used to detect forced oscillations in practical monitoring of power systems. [ABSTRACT FROM AUTHOR]

Online disturbance location is of significant importance to wide-area response-based situational awareness, stability analysis and control of the power systems. This paper presents an adaptive online disturbance location method that considers the anisotropy of the frequency propagation speed (FPS). The method geographically partitions the studied power system into several different regions offline. It then extracts the FPS vector for events occurring in each region. The method uses one event simulation for each region. The online simulations take into account changes in the generation, load and power grid topology. The location of the disturbance in each region is estimated specifically based on the speed vector of the region. Parallel computing is used to accelerate the estimation procedure. A part of the North and Central China interconnected power grid, which covers 7 provinces, is selected as an example to verify the effectiveness of the proposed disturbance location method and to analyze the speedup properties of the parallel computing. [ABSTRACT FROM AUTHOR]

Oscillations threaten the stability of a power system. Timely detecting oscillations can improve operators' situational awareness of system stability and enable remedial reactions. To detect oscillations during their early stages, this paper proposes a cross-coherence method using multiple-channel phasor measurement unit (PMU) data. Oscillations are related to the peaks in coherence spectra and can be detected by visual inspection and setting up a threshold. Using simulation data, it is shown that the proposed coherence method can detect oscillations even under low signal-to-noise ratios. Three algorithms for estimating coherence spectra are implemented and evaluated. Their performances are compared using Monte Carlo methods. Based on the comparison, this paper makes some recommendations for proper use of the algorithms. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a new “event-oriented” method of online load modeling for the Illinois Institute of Technology (IIT) microgrid based on synchrophasor data produced by phasor measurement units. Several load models and their parameter estimation methods are proposed. The online load modeling process is based on an adjustable sliding window applied to two different types of load step changes. The load modeling tests and related analysis of the synchrophasor data from the IIT microgrid are demonstrated. [ABSTRACT FROM PUBLISHER]

Synchrophasor Standards have evolved since the first one was introduced in 1995, IEEE 1344-1995. IEEE Standard C37.118-2005 introduced the evaluation of measurement accuracy under steady state conditions and interference rejection. It also defined a messaging system for communication of phasor data. In 2009, the IEEE started a joint project with IEC to harmonize real time communications defined in C37.118 with the IEC 61850 communication standard. This led to the need to split the C37.118 into two standards, one for communication and one for measurements. This paper presents an overview of IEEE Standard C37.118.1-2011, which includes the measurement part of the previous standard. This standard strengthens the 2005 steady-state requirements and extends them to include measurement performance under dynamic system conditions. Frequency and rate of change of frequency (ROCOF) measurement was defined and requirements for these measurements were also added. Amendment IEEE Std. C37.118.1a-2014 modified some performance requirements. These changes are also included in this paper. [ABSTRACT FROM AUTHOR]

This paper develops a self-coherence method for detecting sustained oscillations using phasor measurement unit (PMU) data. Sustained oscillations decrease system performance and introduce potential reliability issues. Timely detection of the oscillations at an early stage provides the opportunity for taking remedial reaction. Using high-speed time-synchronized PMU data, this paper details a self-coherence method for detecting sustained oscillation, even when the oscillation amplitude is lower than ambient noise. Simulation and field measurement data are used to evaluate the proposed method's performance. It is shown that the proposed method can detect sustained oscillations and estimate oscillation frequencies with a low signal-to-noise ratio. Comparison with a power spectral density method also shows that the proposed self-coherence method performs better. [ABSTRACT FROM PUBLISHER]

Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data. The four methods are extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter. The statistical performance of each algorithm is compared using Monte Carlo methods and a two-area-four-machine test system. Under the statistical framework, robustness against measurement noise and process noise, sensitivity to sampling interval, and computation time are evaluated and compared for each approach. Based on the comparison, this paper makes some recommendations for the proper use of the methods. [ABSTRACT FROM PUBLISHER]

Phasor measurement units (PMUs) provide the basis for synchrophasor measurements, which are good data sources for capturing power system dynamic behavior. The recently published IEEE Standard C37.118.1-2011 characterizes the dynamic performance of synchrophasor measurements with two performance class filters: P-class and M-class. Practical implementation of a PMU to meet all dynamic requirements is a challenging task. Performance of an actual PMU with the proposed reference performance class filters is presented with a brief overview of the PMU performance evaluation process and the sources of errors in the test environment. Based on the test results, some inadequacies in the reference M-class filter algorithm provided in the standard are revealed, and several minor inconsistencies in the error limits imposed by the standard are highlighted. Finally, some modifications to the M-class reference filter and suggestions to rectify the minor issues in the new standard are proposed. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) are time synchronized sensors primarily used for power system state estimation. Despite their increasing incorporation and the ongoing research on state estimation using measurements from these sensors, estimation with imperfect phase synchronization has not been sufficiently investigated. Inaccurate synchronization is an inevitable problem that large scale deployment of PMUs has to face. In this paper, we introduce a model for power system state estimation using PMUs with phase mismatch. We propose alternating minimization and parallel Kalman filtering for state estimation using static and dynamic models, respectively, under different assumptions. Numerical examples demonstrate the improved accuracy of our algorithms compared with traditional algorithms when imperfect synchronization is present. We conclude that when a sufficient number of PMUs with small delays are employed, the imperfect synchronization can be largely compensated in the estimation stage. [ABSTRACT FROM PUBLISHER]

In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a nonlinear system with non-Gaussian noise. The proposed extended PF improves robustness of the basic PF through iterative sampling and inflation of particle dispersion. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF's performance is evaluated and compared with the basic PF, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF). The extended PF results showed high accuracy and robustness against measurement and model noise. [ABSTRACT FROM PUBLISHER]

In this paper, estimation of dynamic phasors, under power-system oscillations, has been proposed using subspace-based techniques. A high sampling rate and few modifications in the subspace-based techniques are suggested to estimate the voltage phasor with a fundamental frequency component without using antialiasing filter to the input signal. The total least squares estimation of signal parameters via rotational invariance techniques and the propagator method are the two subspace-based techniques used for estimating the frequency, as well as the amplitude and the phase, respectively. A compensation for the amplitude and the phase has been proposed to address the dynamic nature of the power-system oscillations. The suggested techniques for the phasor estimation have been tested on simultaneous multiple amplitude and phase oscillations in the presence of noise and also for a fault scenario in New England 39-bus system. The hardware requirement of the proposed phasor estimation technique has also been discussed for its practical viability. [ABSTRACT FROM PUBLISHER]

The contribution of this paper is the application of subspace system identification techniques, to derive a low-order black-box state-space model of a power system with many controllable devices using global signals. This model is a multiinput, multioutput open system model describing the power oscillatory behavior of the power system. The input signals are the controllable setpoints of the controllable devices, the output signals are the speed of selected generators measured by a wide-area measurement system. This paper describes how to achieve and preprocess the data to use subspace techniques to estimate and validate to finally assign an accurate model. This new approach can be used directly to design a central coordinating controller for all of the relevant controllable devices, with the aim to increase the damping of the modes in the system. Previously presented methods use local measurements or output signals dependent on the actual operational point. The benefit of the presented method is that the used output signals are independent of the system state. This makes it possible to use state-feedback control to combine the controllable devices to coordinately damp the modes. The presented method is applied in the CIGRÉ Nordic 32-bus system including two HVDC links. The case study demonstrates that accurate low-order state-space models can be estimated and validated by using the described method to accurately model the system's power oscillatory behavior. [ABSTRACT FROM AUTHOR]