Spatial-temporal adaptive transient stability assessment for power system under missing data

Deep learning-based transient stability assessment framework for large-scale modern power system

Dynamic state estimation (DSE) plays a fundamental role in the monitoring and operation of power systems. With the increasing deployment of phasor measurement units (PMUs) which enables high-sampling-rate measurements, the estimation for power systems in the complex domain is gaining increasing popularity. However, previous works primarily address DSE with only PMU measurements in the real domain. Also, complex-domain techniques are widely developed in signal processing, rather than the estimation area. Obviously, there leaves a critical gap in the DSE field, which will inevitably become an obstacle to the advancement of DSE. Facing these problems, this paper proposes the complex-valued unscented Kalman filter (CUKF) algorithm using complex-valued measurements provided by PMUs, with complex power injection and voltage phasors included, which is a powerful technique designed to accurately estimate states of the power system. Simulation results carried on the IEEE 39-bus system reveal that the proposed method can effectively track states of the power system with complex-valued measurements.

Phasor measurement units (PMUs) equipped with global positioning system (GPS) receivers are the first devices capable of efficiently performing real-time monitoring of remote electrical power systems. Reliability of the monitoring system that exclusively uses PMUs is directly related to the reliability of each unit allocated on the system. As the failure of measurement channels is more common than PMU's failure, a probabilistic evaluation method of the PMUs measurement channels availability for electrical network observability analysis is proposed. In this method, the Markov process is adopted for estimating the measurement channels availability, as well as for determining an equivalent of the state space diagram of all possible operation states of channels for each unit. With the objective to assess the impact of the failure of measurement channels in the observability and reliability of the system, an algorithm that determines the optimal placement of PMUs using a modified binary search, the measurement channels availability for each unit, the global measurement availability, and the critical measurement channels for power system observability analysis was developed. Numerical results using the IEEE 14-bus, IEEE 30-bus, New England 39-bus, and IEEE 118-bus test systems are presented and discussed.

Transient stability assessment (TSA) is one of the major and essential tools for power system operators. It is of utmost importance from the perspective of power system operation and control to develop a fast and accurate TSA that assists system operators in preventing cascading failures, instabilities, and large area blackouts. This paper proposes a convolution neural network (CNN)-based TSA which exploits transient data collected during a fault to make a reliable prediction of system stability. Temporal voltage phasors, which are captured through Phasor Measurement Units (PMUs) simulated on Real-time Digital Simulators (RTDSs), are used as an input feature to the CNN. Real-time simulation cases are modeled and automated for simulating fault scenarios to generate training and testing data. Furthermore, a laboratory-scale data acquisition and storage framework is developed with the integration of RTDSs, virtual Phasor Data Concentrator (OpenPDC), and MySQL. The significance of the proposed work is to provide a TSA tool for power system operators that can be incorporated with contingency analysis tools. The proposed approach is demonstrated through a comprehensive study on the reduced WECC 9-Bus System. Compared to other machine learning (ML) algorithms, the proposed CNN demonstrates the highest accuracy.

The widespread deployment of Phasor Measurement Units (PMUs) on power systems has facilitated the real-time monitoring of the power systems dynamics. Dynamic State Estimators (DSEs) are used by the investigators to estimate and identify the state variables and parameters of the nonlinear dynamic models within the power systems by using the measurements which are mainly provided by the PMUs. This dissertation addresses fundamental research on dynamic state estimation of the power systems and presents innovative and robust dynamic state estimation approaches for estimation/identification of the state variables/parameters associated with the nonlinear dynamic models within the power systems.

With the development of synchronised measurement technique, online dynamic security assessment (DSA) is of great significance to prevent power system blackout. Recently, based on the phasor measurement unit (PMU) data, intelligent data‐driven techniques have been rapidly developed for online DSA owing to their fast decision speed, less data requirement, and decision rule discovery ability. The interpretable decision rules provide useful information for preventive control and post‐event auditing. However, in case of incomplete measurement events, such as PMU failure, communication loss, and phasor data concentrator failure, the accuracy and the transparency of the intelligent models can be significantly impaired by such incomplete data. To overcome this problem, this paper proposes a robust white‐box model that can sustain DSA accuracy and survive the model interpretability and transparency against incomplete PMU data. The proposed model is tested on New England 39‐bus system and demonstrates higher robustness over existing methods.

Modern power systems are large in size and complex in features; the data collected by Phasor Measurement Units (PMUs) are often noisy and contaminated; and the machine learning models that have been applied to the transient stability assessment (TSA) of power systems are not sufficiently capable of capturing long-distance dependencies. All these issues make it difficult for data mining-based power system TSA methods to have sufficient accuracy, timeliness, and robustness. To solve this problem, this paper proposes a power system TSA model based on the transformer and neighborhood rough set. The model first uses the neighborhood rough set to deal with the redundant features of the power system trend data and then uses the transformer model to train the TSA model, in which various normalization methods such as Batch Normalization and Layer Normalization are introduced in the process to obtain better evaluation performance and speed up the convergence rate of the model. Finally, the model is evaluated by two evaluation indicators, F1−measure and accuracy, with values of 99.61% for accuracy and 0.9972 for F1−measure, as soon as the tests on noise contamination and missing data test results on the IEEE39 system show that the NRS-Transformer model proposed in this paper is superior in terms of prediction accuracy, training speed, and robustness.

Convolutional neural network-based power system transient stability assessment and instability mode prediction

This paper is concerned about optimal placement of synchronized phasor measurements that can monitor voltage and current phasors along network branches. Earlier investigations on placement of phasor measurement units (PMUs) have assumed that a PMU could be placed at a bus and would provide bus voltage phasor as well as current phasors along all branches incident to the bus. This study considers those PMUs which are designed to monitor a single branch by measuring the voltage and current phasors at one end of the monitored branch. It then determines the optimal location of such PMUs in order to make the entire network observable. The paper also addresses the reliability of the resulting measurement design by considering loss or failure of PMUs as well as contingencies involving line or transformer outages. Developed placement strategies are illustrated using IEEE test systems.

Short-term voltage stability (STVS) problem has become more prominent in today’s power systems, and the deployment of phasor measurement units (PMU) opens the way for real-time STVS assessment. The existing approaches for real-time STVS assessment suffer from long observation windows and lack mitigation to the impact of PMU measurement noise. Considering those inadequacies, this paper proposes a noise-tolerant temporal-adaptive STVS assessment approach with the following salient features: 1) the adoption of probabilistic classification enables the early unstable event detection ability, which significantly improves the assessment speed; 2) the probabilistic classifier consists of noisy-tolerant ensemble models that are able to learn the impact of measurement noise, which improves the assessment robustness against measurement noise. The proposed approach is tested on New England 39-bus system, and its fast assessment speed and excellent robustness against measurement noise are verified by comparative studies with existing approaches.

Guest Editorial: Situational awareness of integrated energy systems

Nowadays, the increase in electrical energy consumption and power system restructuring have posed new challenges to the operation, control, and monitoring of power systems. In this situation, the supervisory control and data acquisition (SCADA) system is not enough to ensure power system security and stability. The SCADA is often unable to measure data of all buses simultaneously. In addition, the sampling rate in this system is not enough for some power system applications. Therefore, the information obtained from SCADA does not show power system dynamics properly. In order to improve the power system monitoring, wide area measurement system (WAMS) has been developed to overcome the problems of SCADA system. Phasor measurements units (PMUs) are the main part of WAMS system and it basically consists of three essential processes including collecting, transmitting, and analyzing data. WAMS receives obtained data via a high speed communication links. After data processing and extracting appropriate information, decisions are made to improve the power system performance. Efficient use of power system data to achieve a secure operation strategy is targeted using the WAMS system. Due to the effective role of WAMS and PMUs in the reliable operation of power system, it is necessary to study their concept and applications in this chapter. The history of PMU and its structure is presented in this chapter. In addition, the necessity of WAMS for future power system and its difference from SCADA system have been investigated in this chapter. Different algorithms and application of the WAMS are also introduced in this chapter which can be implemented to improve the performance of the future power system.

The widespread deployment of Phasor Measurement Units (PMUs) makes it possible to monitor the power system operating conditions on-line in near real time. Among all operating conditions, forced oscillations (FOs) have came into people's attention and been studied only recently. Mostly, FOs are rogue inputs to the system originating from malfunctioning equipment. The FOs have a negative impact on the estimation of system modes and mode shapes when not properly accounted for. In this paper, the change-point detection decision statistics based upon the output-only covariance subspace method proposed in our previous work [1] is used in a sequential detection method, the cumulative sum (CUSUM) procedure, to achieve better detection performance. We tested the procedure to detect the low-amplitude forced oscillations in power systems buried in system nature responses and find that the algorithm is able to achieve lower false alarm and missed detection rate at the same detection delay as compared with our previous work.

Small signal stability problems are one of the major threats to grid stability and reliability in California and the western U.S. power grid. An unstable oscillatory mode can cause large-amplitude oscillations and may result in system breakup and large-scale blackouts. There have been several incidents of system-wide oscillations. Of them, the most notable is the August 10, 1996 western system breakup produced as a result of undamped system-wide oscillations. There is a great need for real-time monitoring of small-signal oscillations in the system. In power systems, a small-signal oscillation is the result of poor electromechanical damping. Considerable understanding and literature have been developed on the small-signal stability problem over the past 50+ years. These studies have been mainly based on a linearized system model and eigenvalue analysis of its characteristic matrix. However, its practical feasibility is greatly limited as power system models have been found inadequate in describing real-time operating conditions. Significant efforts have been devoted to monitoring system oscillatory behaviors from real-time measurements in the past 20 years. The deployment of phasor measurement units (PMU) provides high-precision time-synchronized data needed for estimating oscillation modes. Measurement-based modal analysis, also known as ModeMeter, uses real-time phasor measure-ments to estimate system oscillation modes and their damping. Low damping indicates potential system stability issues. Oscillation alarms can be issued when the power system is lightly damped. A good oscillation alarm tool can provide time for operators to take remedial reaction and reduce the probability of a system breakup as a result of a light damping condition. Real-time oscillation monitoring requires ModeMeter algorithms to have the capability to work with various kinds of measurements: disturbance data (ringdown signals), noise probing data, and ambient data. Several measurement-based modal analysis algorithms have been developed. They include Prony analysis, Regularized Ro-bust Recursive Least Square (R3LS) algorithm, Yule-Walker algorithm, Yule-Walker Spectrum algorithm, and the N4SID algo-rithm. Each has been shown to be effective for certain situations, but not as effective for some other situations. For example, the traditional Prony analysis works well for disturbance data but not for ambient data, while Yule-Walker is designed for ambient data only. Even in an algorithm that works for both disturbance data and ambient data, such as R3LS, latency results from the time window used in the algorithm is an issue in timely estimation of oscillation modes. For ambient data, the time window needs to be longer to accumulate information for a reasonably accurate estimation; while for disturbance data, the time window can be significantly shorter so the latency in estimation can be much less. In addition, adding a known input signal such as noise probing signals can increase the knowledge of system oscillatory properties and thus improve the quality of mode estimation. System situations change over time. Disturbances can occur at any time, and probing signals can be added for a certain time period and then removed. All these observations point to the need to add intelligence to ModeMeter applications. That is, a ModeMeter needs to adaptively select different algorithms and adjust parameters for various situations. This project aims to develop systematic approaches for algorithm selection and parameter adjustment. The very first step is to detect occurrence of oscillations so the algorithm and parameters can be changed accordingly. The proposed oscillation detection approach is based on the signal-noise ratio of measurements.

La evaluación de los parámetros eléctricos mediante unidades de medición fasorial en los sistemas de potencia es fundamental, ya que con los datos obtenidos se realiza la estimación del estado de la operación de los mismos. En la presente investigación se plantea el problema para el despliegue óptimo de unidades de medición fasorial respetando restricciones de observabilidad, redundancia y contingencias N-1. La minimización de unidades considera la salida de una línea de transmisión o la falla de una unidad de medición fasorial y garantiza el 100 % de observabilidad de sistema de potencia; para la optimización se utilizó programación lineal entera mixta. El algoritmo propuesto fue probado con los modelos de prueba del IEEE de 9, 14, 30 y 118 nodos.