Monitoring, Control and Protection

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Now showing 1 - 10 of 10
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    Unified Equivalent-circuit Models for Voltage-source Inverters that Capture Averaged Dynamics and Power-flow Solutions in Distribution Networks
    ( 2021-01-05) Lu, Minghui ; Purba, Victor ; Dhople, Sairaj ; Johnson, Brian
    This paper demonstrates how three-phase distribution networks composed of voltage-source inverters can be modeled as a single unified equivalent-circuit network realized with familiar circuit elements. Such a model is derived by representing all physical- and control-subsystem dynamics as equivalent circuits. Two versions are put forth: the first captures averaged dynamics; while the second is a steady-state version of the first and it captures the power-flow solution in sinusoidal steady state. The main challenge in undertaking such an effort is presented by the fact that inverters are composed of subsystems (filters, pulse width modulators, phase-locked loops, controllers, direct-quadrature reference-frame transformations) that belong to multiple domains (physical and control). We demonstrate how all these constituent subsystems can be transcribed as equivalent circuits which then promote a single and unified circuit model that captures network physical- and control-layer dynamics. Numerical simulations for a representative distribution network compare results from the averaged model and the steady-state model with high-fidelity switch-level simulations. The results establish the validity of the circuit-based models and the computational benefits of the proposed approach.
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    Robust Adaptive Nonlinear Kalman Filter for Synchronous Machine Parameter Calibration
    ( 2021-01-05) Zhao, Junbo ; Wang, Shaobu ; Huang, Renke ; Fan, Rui ; Xu, Yijun ; Huang, Zhenyu
    This paper proposes a robust and adaptive nonlinear Kalman filter for synchronous machine parameter calibration. The key idea is to develop the polynomial chaos-based analysis of variance (ANOVA) method for suspicious parameter detection. ANOVA allows us to derive a set of adaptive weights that can be used to address local parameter optimality issue when performing joint state and parameter estimation. It is shown that if erroneous parameters have strong correlations, the widely used methods that augment state and parameter for joint estimation will lead to large biases. By contrast, thanks to the derived adaptive weights for the suspicious parameters, the proposed method can effectively deal with the parameter dependence, yielding much better calibration results. In addition, the robustness of the proposed method enables us to filter non-Gaussian noise. Simulations carried out on the IEEE 39-bus system validate the effectiveness and robustness of the proposed approach.
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    Online Cooperative Feedback Control of Residential Community Microgrids with 100% Renewable Energy
    ( 2021-01-05) Zuo, Kunyu ; Wu, Lei
    The emerging of renewable distributed energy resources (DER) in the residential community opens the door to forming a residential community microgrid for enhancing energy resiliency when the main grid is out of service. However, traditional microgrid controls via the hierarchical feedforward tertiary, secondary, and primary control framework may not be effective for such residential community microgrids, because of high volatility, low inertia, and insufficiency of DERs along with limited supporting facilities. This paper discusses an online feedback scheme, which cooperates the three control layers in real time to ensure operational stability of the microgrid. Besides, to economically dispatch scarce DERs in the tertial feedback control, this paper deduces an increment cost model of battery storage assets based on their degradation costs and depth of discharges. The model is of low computational complexity, thus can be naturally embedded in the proposed online cooperative feedback control scheme to calculate marginal price in real-time. Small-signal analysis and Simulink simulation are conducted to illustrate stability of the proposed online cooperative feedback control scheme, and its economic advantages over the traditional feedforward control scheme.
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    Identification of Linear Power System Models Using Probing Signals
    ( 2021-01-05) Cárdenas Javier, Romel Angel ; Zelaya Arrazabal, Francisco Alexander ; Arrieta Paternina, Mario Roberto ; Wilches-Bernal, Felipe
    This paper compares the accuracy of two methods to identify a linear representation of a power system: the traditional Eigensystem Realization Algorithm (ERA) and the Loewner Interpolation Method (LIM). ERA is based on time domain data obtained using exponential chirp probing signals and LIM system identification method is based on frequency domain data obtained using sinusoidal probing signals. The ERA and LIM methods are evaluated with the noise produced by the nonlinear characteristics of the system, these characteristics are caused by increasing the amplitude of the applied probing signal. The test systems used are: the two-area Kundur system and a reduced order representation of the Northeastern portion of the North American Eastern Interconnection. The results show that the LIM method provides a more accurate identification than the ERA method.
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    Fast Fault Location Method for a Distribution System with High Penetration of PV
    ( 2021-01-05) Jimenez Aparicio, Miguel ; Grijalva, Santiago ; Reno, Matt
    Distribution systems with high levels of solar PV may experience notable changes due to external conditions, such as temperature or solar irradiation. Fault detection methods must be developed in order to support these changes of conditions. This paper develops a method for fast detection, location, and classification of faults in a system with a high level of solar PV. The method uses the Continuous Wavelet Transform (CWT) technique to detect the traveling waves produced by fault events. The CWT coefficients of the current waveform at the traveling wave arrival time provide a fingerprint that is characteristic of each fault type and location. Two Convolutional Neural Networks are trained to classify any new fault event. The method relays of several protection devices and doesn’t require communication between them. The results show that for multiple fault scenarios and solar PV conditions, high accuracy for both location and type classification can be obtained.
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    Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics
    ( 2021-01-05) Baembitov, Rashid ; Dokic, Tatjana ; Kezunovic, Mladen ; Hu, Yi ; Obradovic, Zoran
    A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Under the receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs. Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events. 93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec.
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    Evaluation of Mode Meters Robust to Forced Oscillations using Field-Measured Data
    ( 2021-01-05) Follum, Jim ; Agrawal, Urmila ; Etingov, Pavel
    Mode meters are tools used by power system operators to continuously monitor a system's small-signal stability. They do so by estimating the system's electromechanical modes of oscillation. When a system undergoes a forced oscillation, mode meters may become biased because the two types of oscillation cannot be distinguished. Modified mode meter algorithms robust to this bias have been proposed in prior research, but these studies were based primarily on simulated data. In this paper, modified least squares and Yule-Walker mode meter algorithms are evaluated using field-measured data from phasor measurement units (PMUs). Results show that the sensitivities of the least squares algorithm make it impractical for use given the complexities of real-world forced oscillations. However, the modified Yule-Walker algorithm is shown to perform well and has significant potential for practical deployment in mode meter tools.
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    A Novel Approach for Security Analysis using Shift Factors for Limited Synchrophasor Observability
    ( 2021-01-05) Abu-Jaradeh, Backer ; Beshir, Mohammed
    The adoption of synchrophasor technology has increased rapidly in the past decade. Many system operators have made synchrophasor applications available to operators, to reveal hidden operating conditions, and increase grid resiliency. The development of Linear State Estimation provided an innovative method to solve system states linearly at a faster rate, and serve as a backup to EMS should the conventional State Estimator fail to solve. Advanced applications were developed to take advantage of LSE solution to provide operators with alternative contingency analysis applications using synchrophasors data [6]. However, currently explored applications are presumed to run iteratively every couple of minutes, and therefore not taking advantage of high resolution of measurements available in synchrophasors. This work proposes a method to monitor system limits by leveraging linearization methods for contingency analysis, to better utilize the benefits of synchrophasors. Also, a practical approach is proposed to handle lack of full observability, to ensure tool operability with the industry infrastructure.
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    A Distributed Scheme for Stability Assessment in Large Scale Structure-Preserving Models via Singular Perturbation
    ( 2021-01-05) Sun, Andy ; Gholami, Amin
    Assessing small-signal stability of power systems composed of thousands of interacting generators is a computationally challenging task. To reduce the computational burden, this paper introduces a novel condition to assess and certify small-signal stability. Using this certificate, we can see the impact of network topology and system parameters (generators’ damping and inertia) on the eigenvalues of the system. The proposed certificate is derived from rigorous analysis of the classical structure-preserving swing equation model and has a physically insightful interpretation related to the generators’ parameters and reactive power. To develop the certificate, we use singular perturbation techniques, and in the process, we establish the relationship between the structure-preserving model and its singular perturbation counterpart. As the proposed method is fully distributed and uses only local measurements, its computational cost does not increase with the size of the system. The effectiveness of the scheme is numerically illustrated on the WSCC system.
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