Roles of dynamic state estimation in power system modeling, monitoring and operation

Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.Departamento de Energía de EE. UU TPWRS-00771-202

Online prediction and control of post-fault transient stability based on PMU measurements and multi-task learning

The combined usage of phasor measurement units and machine learning algorithms provides the opportunity for developing response-based wide-area system integrity protection scheme against transient instability in power systems. However, only the transient stability status is usually predicted in the literature, which is not enough for real-time decision-making for response-based emergency control. In this paper, an integrated approach is proposed. The GRU-based predictor is firstly proposed for post-disturbance transient stability prediction. On this basis, a multi-task learning framework is proposed for the identification of unstable machines and also the estimation of generation shedding. Case study on the IEEE 39-bus system demonstrates that, apart from the basic task of transient stability prediction, the proposed GRU-based multi-task predictor can predict the grouping of unstable machines correctly. Moreover, based on the estimated amount of generation shedding, the generated remedial control actions can retain the synchronism of the power system

Machine Learning-Incorporated Transient Stability Prediction and Preventive Dispatch for Power Systems with High Wind Power Penetration

Historically, transient instability has been the most severe stability challenge for most systems. Transient stability prediction and preventive dispatch are two important measures against instability. The former measure refers to the rapid prediction of impending system stability issues in case of a contingency using real-time measurements, and the latter enhances the system stability against preconceived contingencies leveraging power dispatch. Over the last decade, large-scale renewable energy generation has been integrated into power systems, with wind power being the largest single source of increased renewable energy globally. The continuous evolution of the power system poses more challenges to transient stability. Specifically, the integration of wind power can decrease system inertia, affect system dynamics, and change the dispatch and power flow pattern frequently. As a result, the effectiveness of conventional stability prediction and preventive dispatch approaches is challenged. In response, a novel transient stability prediction method is proposed. First, a stability index (SI) that calculates the stability margin of a wind power-integrated power system is developed. In this method, wind power plants (WPPs) are represented as variable admittances to be integrated into an equivalent network during transients, whereby all WPP nodes are eliminated from the system, while their transient effects on each synchronous generator are retained. Next, the calculation of the kinetic and potential energies of a system is derived, and accordingly, a novel SI is put forward. The novel approach is then proposed taking advantage of the machine learning (ML) technique and the newly defined SI. In case of a contingency, the developed SI is calculated in parallel for all possible instability modes (IMs). The SIs are then formed as a vector and applied to an ensemble learning-trained model for transient stability prediction. Compared with the features used in other studies, the SI vector is more informative and discriminative, thus lead to a more accurate and reliable prediction. The proposed approach is validated on two IEEE test systems with various wind power penetration levels and compared to the existing methods, followed by a discussion of results. In addition, to address the issues existing in preventive dispatch for high wind power-integrated electrical systems, an hour-ahead probabilistic transient stability-constrained power dispatching method is proposed. First, to avoid massive transient stability simulations in each dispatching operation, an ML-based model is trained to predict the critical clearing time (CCT) and IM for all preconceived fault scenarios. Next, a set of IM-categorized probabilistic transient stability constraints (PTSCs) are constructed. Based on the predictions, the system operation plan is assessed with respect to the PTSCs. Then, the sensitivity of the probabilistic level of CCT is calculated with respect to the active power generated from the critical generators for each IM category. Accordingly, the implicit PTSCs are converted into explicit dispatching constraints, and the dispatch is rescheduled to ensure the probabilistic stability requirements of the system are met at an economical operating cost. The proposed approach is validated on modified IEEE 68- and 300-bus test systems, wherein the wind power installed capacity accounts for 40% and 50% of the total load, respectively, reporting high computational efficiency and high-quality solutions. The ML-incorporated transient stability prediction and preventive dispatch methods proposed in this research work can help to maintain the transient stability of the system and avoid the widespread blackouts

Adaptive protection and control for wide-area blackout prevention

Technical analyses of several recent power blackouts revealed that a group of generators going out-of-step with the rest of the power system is often a precursor of a complete system collapse. Out-of-step protection is designed to assess the stability of the evolving swing after a disturbance and take control action accordingly. However, the settings of out-of-step relays are found to be unsatisfactory due to the fact that the electromechanical swings that occurred during relay commissioning are different in practice. These concerns motivated the development of a novel approach to recalculate the out-of-step protection settings to suit the prevalent operating condition. With phasor measurement unit (PMU) technology, it is possible to adjust the setting of out-of-step relay in real-time. The setting of out-of-step relay is primarily determined by three dynamic parameters: direct axis transient reactance, quadrature axis speed voltage and generator inertia. In a complex power network, these parameters are the dynamic parameters of an equivalent model of a coherent group of generators. Hence, it is essential to identify the coherent group of generators and estimate the dynamic model parameters of each generator in the system first in order to form the dynamic model equivalent in the system. The work presented in this thesis develops a measurement-based technique to identify the coherent areas of power system network by analysing the measured data obtained from the system. The method is based on multivariate analysis of the signals, using independent component analysis (ICA). Also, a technique for estimating the dynamic model parameters of the generators in the system has been developed. The dynamic model parameters of synchronous generators are estimated by processing the PMU measurements using unscented Kalman filter (UKF).Open Acces

Static and Dynamic State Estimation Applications in Power Systems Protection and Control Engineering

The developed methodologies are proposed to serve as support for control centers and fault analysis engineers. These approaches provide a dependable and effective means of pinpointing and resolving faults, which ultimately enhances power grid reliability. The algorithm uses the Least Absolute Value (LAV) method to estimate the augmented states of the PCB, enabling supervisory monitoring of the system. In addition, the application of statistical analysis based on projection statistics of the system Jacobian as a virtual sensor to detect faults on transmission lines. This approach is particularly valuable for detecting anomalies in transmission line data, such as bad data or other outliers, and leverage points. Through the integration of remote PCB status with virtual sensors, it becomes possible to accurately detect faulted transmission lines within the system. This, in turn, saves valuable troubleshooting time for line engineers, resulting in improved overall efficiency and potentially significant cost savings for the company. When there is a temporary or permanent fault, the generator dynamics will be affected by the transmission line reclosing, which could impact the system\u27s stability and reliability. To address this issue, an unscented Kalman filter (UKF) and optimal performance iterated unscented Kalman filter (IUKF) dynamic state estimation techniques are proposed. These techniques provide an estimate of the dynamic states of synchronous generators, which is crucial for monitoring generator states during transmission lines reclosing for temporary and permanent fault conditions. Several test systems were employed to evaluate reclosing following faults on transmission lines, including the IEEE 14-bus system, Kundur\u27s two-area model, and the reduced Western Electricity Coordinating Council (WECC) model of UTK electrical engineering hardware test bed (HTB). The developed methods offer a comprehensive solution to address the challenges posed by unbalanced faults on transmission lines, such as line-to-line, line-to-line-ground, and line-to-ground faults. Utilities must consider these faults when developing protective settings. The effectiveness of the solution is confirmed by monitoring the reaction of dynamic state variables following transmission lines reclosing after temporary faults and transmission line lockout from permanent faults

Wind Power Integration into Power Systems: Stability and Control Aspects

Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to clean and low-carbon renewable energy sources. Complex stability issues, such as frequency, voltage, and oscillatory instability, are frequently reported in the power grids of many countries and regions (e.g., Germany, Denmark, Ireland, and South Australia) due to the substantially increased wind power generation. Control techniques, such as virtual/emulated inertia and damping controls, could be developed to address these stability issues, and additional devices, such as energy storage systems, can also be deployed to mitigate the adverse impact of high wind power generation on various system stability problems. Moreover, other wind power integration aspects, such as capacity planning and the short- and long-term forecasting of wind power generation, also require careful attention to ensure grid security and reliability. This book includes fourteen novel research articles published in this Energies Special Issue on Wind Power Integration into Power Systems: Stability and Control Aspects, with topics ranging from stability and control to system capacity planning and forecasting

Uncertainty Modeling of Wind Power Generation for Power System Planning and Stability Study

To reduce greenhouse gas emissions, higher penetration levels of renewable energy resources are added to existing power grids. Among them, wind energy resources are becoming a major source of electricity generation. However, wind energy production has a critical downside: intermittency. The intermittent nature of wind energy in combination with the load demand uncertainties, make it difficult to maintain power system stability and reliability. In addition, the uncertainty and variability of wind power generation (WPG) forces power utilities to retain higher levels of spinning reserves (SRs) to maintain power balance in the system. While necessary to ensure grid reliability, the utilization of those reserves often leads to an increase in operating costs of the power system. To ensure the continuous operation of reliable and economically efficient power systems, system operators and planners need to study the impact of WPGs on bulk power systems and determine the best ways to manage their variability. Such studies require efficient and effective probabilistic models characterizing the variable nature of wind power. Therefore, this dissertation develops new methodologies for modeling the uncertainty and variability of WPG. The developed methods are combined with stability indices to form analytical tools for analyzing the impact of increased penetration of wind energy on power system steady-state stability. The case study results show that the developed methods simulate real-world wind power scenarios, which lead to an accurate assessment of the impact of wind generation uncertainty on power systems. With large-scale adoption of renewable energy, a significant amount of conventional generation units could be replaced with wind energy resources. The best way to use the variable WPG and the remaining conventional generation resources, for continuous balance between load and generation, remains to be determined. Within this context, this dissertation investigates the problem of optimal substitution of conventional generation units by wind-powered generators, while considering the variability of WPG and the uncertainties of energy demand. The goal is to ensure that during unplanned wind power unavailability, the system has the ability to meet the load demand, and maintain steady acceptable voltage levels in the grid. A two-stage solution methodology is proposed to the problem in consideration. The first stage determines the best candidates, among conventional generator (CG) resources, for retirement and replacement by WPG resources. The best candidates for wind replacement are selected such that the adverse impacts of wind power intermittency on system stability and reliability are minimized. In the second stage, the expected amount of wind generation to be added at each retired CG bus is determined. The simulation results show that the developed method facilitates the integration of high wind energy with a reduced need for additional spinning reserves in the system

Statistical method for identification of sources of electromechanical oscillations in power systems

The use of real-time continuous dynamics monitoring often indicates dynamic behaviour that was not anticipated by model-based studies. In such cases it can be difficult to locate the sources of problems using conventional tools. This thesis details the possibility of diagnosing the causes of problems related to oscillatory stability using measurement-based data such as active power and mode decay time constant, derived from system models. The aim of this work was to identify dynamics problems independently of an analytical dynamic model, which should prove useful in diagnosing and correcting dynamics problems. New statistical techniques were applied to both dynamic models and real systems which yielded information about the causes of the long decay time constants observed in these systems. Wavelet transforms in conjunction with General Linear Models (GLMs) were used to improve the statistical prediction of decay time constants derived from the system. Logic regression was introduced as a method of establishing important interactions of loadflow variables that contribute to poor damping. The methodology was used in a number of case studies including the 0.62Hz Icelandic model mode and a 0.48Hz mode from the real Australian system. The results presented herein confirm the feasibility of this approach to the oscillation source location problem, as combinations of loadflow variables can be identified and used to control mode damping. These ranked combinations could be used by a system operator to provide more comprehensive control of oscillations in comparison to current techniques