Distributed Dynamic State Estimation of Power Systems

Applying Kalman filtering techniques to dynamic state estimation is a developing research area in modern power systems. Compared to traditional steady state estimators, the Kalman filter is able to track dynamic state variables both efficiently and accurately. However, in large-scale and wide-area interconnected power systems, the combination of computational complexity-primarily due to the very large number of measurements-and slow processing speeds present a significant challenge. To help address this challenge we have developed an approach we call Reduced Measurement-space Dynamic State Estimation (ReMeDySE). We present the method in the context of the Kalman filter, however it can also be applied to other state estimation methods such as particle filters. In addition, although we present the method in the context of power systems, it is suitable for real-time and massive calculations in any large-scale state tracking systems. Finally, the method lends itself well to modern parallel computation techniques for further improvements.

Power system state estimation is one of the most important functions of power system control centers. In recent years, the complexity of power system state estimation has significantly increased due to the growing number of distributed, including renewable energy sources, electric vehicles, the demand response technologies, and the increased risk of cyber-attacks. Under these conditions, state estimation methods, which consider information about the time-correlation of the power system states have a great potential. The correlation is described by a transition model. The well-known state estimation methods usually use one single model. However, in case of stochastic behavior of the load and generation, it is impossible to assert the adequacy of the chosen model over the entire observation interval. Therefore, the paper proposes a multi-model forecasting power system state estimation method, which has lower errors in comparison with a single-model assessment at the moments of the lowest accuracy of the latter. Multi-model parameter estimation is used based on three procedures of the single-model Kalman filtering estimation and various transition models based on autoregressive and vector autoregressive analyzes, as well as Holt's exponential smoothing. Uniting the single-model estimation has been carried out according to the criterion of the minimum of variance of the resulting estimate. An algorithm of multi-model power system state estimation has been developed. Its version has been analyzed using a three-model forecasting-aided estimation using linear transition models. The state of the IEEE 30-bus test power system has been assessed by the means of simulation modeling. The maximum accuracy increase of the multi-model estimation in comparison with the single model is set up. 26,1 % is for autoregressive analysis; 16,9 % is for vector regression analysis and 37,7 % is for Holt’s exponential smoothing. The proposed method of multi-model state estimation has higher robustness and accuracy at the moments of the lowest accuracy of single-model estimation. It is advisable to use the method to solve control tasks of power systems with rapidly changing dynamic modes.

According to the contradiction between the static power system state estimation and the actual operation of power system, based on the study of the classical algorithms of power system state estimation, apply the gray system theory of power system based on GM (1, 1) mode to the state estimation of power system, and create a new dynamic power system state estimation algorithm, for example to a four-node power system network test for State estimation simulation. Measurement of a known algorithm used to predict the gray system theory. On the other hand, measurement of unknown quantity, using the result that come from the known quantity, then get all parameters with character of power system state through statistical analysis of simulation results that the normal measurement system, the method is feasible and has a good filtering effect. The power system state estimation algorithm based on the gray system theory of Power System based on GM (1, 1) model has high estimating precision, fast calculation, use small memory and the adaptability of the measurement system is very strong, so it has good prospects.

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.

Recently, owing to the high penetration of renewable power generations, random access to dynamical loads and wide application of power-electronic devices, the modern power systems exhibit new features such as high dynamics, low inertia and weak voltage and frequency support. As a powerful tool in revealing the dynamical evolution of the modern power systems, the dynamic state estimation (DSE) scheme gains a surge of research enthusiasms. Note that in actual power systems, various styles of power generations and mixed measurements generated by the supervisory control and data acquisition (SCADA) system and the phasor measurement unit (PMU) pose higher demands on the DSE. In addition, the measurements with anomalies such as gross errors, outliers and incomplete information are often encountered due to the unreliable measurement facilities, constrained communication resources and cyber-threats. Such anomalies, if not tactfully handled, would largely impair the DSE performance. In this paper, a systematic and timely review with respect to DSE for power systems is provided. First, several typical models for power systems include quasi-steady model, synchronous-generator-based multi-machine model and renewable power generation model are introduced. Then the measurement facilities of power systems and their features as well as the measurements with anomalies are reviewed, respectively. Subsequently, the DSE schemes are recalled and discussed from the perspectives of framework, methodology, anomaly-resistant ability and application, respectively. Finally, some possible future research topics are outlined for the DSE of power systems.

Guest Editorial: Situational awareness of integrated energy systems

Dynamic equivalent state estimation for multi-area power systems with synchronized phasor measurement units

In this paper, we propose a fully-distributed algorithm for load-based dynamic state estimation in power systems. Unscented Kalman filter techniques are used to accommodate a nonlinear measurement model. The method requires only local information and a limited amount of information from neighboring areas rather than global, system-wide information. We propose to use the local dependence of the power system measurement equations in order to achieve an approximate decoupling of the load-based dynamic state estimation problem. Our method has reduced computation and communication requirements compared to existing methods. Various numerical experiments demonstrate its effectiveness.

Dynamic state estimation of a power system provides essential information about its inherent dynamic change. Nonlinear Kalman filters (NKFs) have been identified as potential versatile tools in performing state estimations. One key challenge of using NKFs lies in the fact that the available observations are inaccurate due to the presence of non-Gaussian noise that degrades the filtering precision and robustness. Some robust NKFs are developed to correct the state estimation via using robust optimality criteria or approximating the posterior density. Considering that the essence in these robust NKFs is to weight the error covariance and noise variance, we propose a novel method that can directly act on measurement to weight error covariance and noise variance. The novel method is based on a square root unscented Kalman filter that can enhance the numerical stability. Then, a weighting factor is applied to the measurement model for alleviating the effect of abnormal measurement errors. The weighting factor is derived from consideration of an anisotropy covariance function and an upper envelope of the traditional “sinc” function for suppressing large measurement errors and retaining the information of very large errors. We call the new filter the square root unscented Kalman filter with modified measurement (SRUKF-MM). The proposed SRUKF-MM is applied to state estimation of the Western System Coordinating Council (WSCC) 3-machine system and the Northeastern Power Coordinating Council (NPCC) 48-machine system in a non-Gaussian noise environment. Simulation results show that the proposed method achieves significantly improved filtering performance compared to other related NKFs.

The integration of high-penetration renewables, electric vehicles, and active demand-response schemes has introduced unprecedented volatility into modern power grids. Weighted Least Squares (WLS) remains the industry standard for Power System State Estimation (PSSE). However, its iterative nature may lead to slow convergence or even divergence when poorly initialized, particularly in large and complex transmission power systems. To address this, warm-start techniques using Machine Learning (ML) have been proposed. This paper introduces a deep Residual Network (ResNet) framework to generate high-quality initial guesses for WLS and rigorously compares its performance against Convolutional Neural Networks (CNN), Long Short Term Memory networks (LSTM), and Generative Adversarial Networks (GAN). Evaluations on the IEEE 30 bus test system under realistic measurement noise demonstrate that ResNet-based warm starts reduce the iteration count of WLS by up to 60%. The novelty lies in leveraging ResNet’s skip connections to learn incremental state corrections, which closely aligns with the warm-start requirements and results in improved convergence, generalization, and computational efficiency compared to alternative ML models.

State estimation is a very critical component in smart grid, a typical energy‐based cyber‐physical system. Kalman filter has been widely used in the dynamic state estimation of power systems. Although a large number of research efforts have been made on the robustness and filtering effectiveness, little effort has been conducted on cyber attacks against Kalman filtering. To address this issue, in this paper we systematically compare three representative Kalman filtering techniques and formalize the problem of anomaly detection against false data injection attacks in Kalman filter. On the basis of our modeling results, we investigate five novel attack approaches that can bypass the anomaly detection. To defend against those attacks, we develop two countermeasures: the enhancement of Kalman filtering and the temporal‐based detection algorithm. We conduct extensive performance evaluation and our data validates our theoretical finding well. Copyright © 2013 John Wiley & Sons, Ltd.

This paper presents an algorithm for state estimation in power systems that include FACTS devices and PMUs. The FACTS devices are equipments of special purpose capable of changing the natural behavior of transmission systems. They may be able to influence the voltage, the active power or the reactive power flows based on predefined targets so that the classical power flow and steady state calculations must be adapted. Furthermore, PMU measurements are increasing in number so that the accuracy in steady state calculations can be improved. A state estimation algorithm was developed by considering the behavior of TCSC and SVC devices, while including some improvements due to the integration of synchronized measurements. A Matlab application was developed and simulations were performed on various test networks. In this paper, results obtained on the IEEE 30 bus test system only will be presented.

Cyber-Resilient Smart Cities: Detection of Malicious Attacks in Smart Grids

Robust adaptive Unscented Kalman Filter with gross error detection and identification for power system forecasting-aided state estimation

The Kalman-filter-based algorithms as the mainstream algorithms of dynamic state estimation of power systems have been extensively used to provide accurate data for power system applications. However, few comparisons are made to show their advantages and disadvantages. In this paper, four Kalman-filter-based algorithms (i.e., extended Kalman filter, unscented Kalman filter, cubature Kalman filter, and ensemble Kalman filter) are compared to show their differences from implementation complexity, estimation accuracy and calculation efficiency, the resistance to measurement errors, and the sensitivity to system scales. Finally, the simulation results on the 3-machine, 10-machine, and 48-machine power systems show their advantages and disadvantages.