Optimal Placement of PMU for Fast Robust Power System Dynamic State Estimation using UKF–GBDT Technique

This paper proposes a fast and robust dynamic state estimation technique based on model transformation method using the proposed hybrid technique. The proposed hybrid method is the combination of Unscented Kalman Filter (UKF) and Gradient Boosting Decision Tree (GBDT), hence commonly referred to as the UKF–GBDT technique. The proposed model transformation approach is accomplished by taking the active power generator measured as input variable and derived frequency as rate of change of frequency measurements of phasor measurement units (PMU) as dynamic generator output variable model. The proposed hybrid technique is also formulated to deal with data quality issues, and the rate of change of frequency and frequency measurements may be skewed in the presence of rigorous disruption or communication problems. This permits to obtain discrete-time linear dynamic equations in state space based on the linear Kalman filter (LKF). With this proper control, this model alleviates filter divergence problems, which can be a severe issue if the nonlinear model is utilized in greatly strained operating system conditions, and gives quick estimate of rotor speeds together with angles through transient modes if only the transient stability with control is concerned. In the case of long-term dynamics, the outcome of governor’s response in long-term system dynamics is offset together with mechanical power at rotor speed and the state vector angles for joint evaluation. At last, the performance of the proposed method is simulated in MATLAB/Simulink and the performance is compared to the existing methods like UKF, GBDT and ANN. The proposed technique is simulated under three case studies like IEEE 14-, 30- and 118-bus systems.

Ensemble kalman filter and particle filter-based state estimation on electrical power systems

In this article, a referential study of the sequential importance sampling particle filter with a systematic resampling and the ensemble Kalman filter is provided to estimate the dynamic states of several synchronous machines connected to a modified 14-bus test case, when a balanced three-phase fault is applied at a bus bar near one of the generators. Both are supported by Monte Carlo simulations with practical noise and model uncertainty considerations. Such simulations were carried out in MATLAB by the Power System Toolbox, whereas the evaluation of the Particle Filter and the Ensemble Kalman Filter by script files developed inside the toolbox. The results obtained show that the particle filter has higher accuracy and more robustness to measurement and model noise than the ensemble Kalman filter, which helps support the feasibility of the method for dynamic state estimation applications.

Hybrid Physics-Based Adaptive Kalman Filter State Estimation Framework

State-of-the art physics-model based dynamic state estimation generally relies on the assumption that the system’s transition matrix is always correct, the one that relates the states in two different time instants, which might not hold always on real-life applications. Further, while making such assumptions, state-of-the-art dynamic state estimation models become unable to discriminate among different types of anomalies, as measurement gross errors and sudden load changes, and thus automatically leads the state estimator framework to inaccuracy. Towards the solution of this important challenge, in this work, a hybrid adaptive dynamic state estimator framework is presented. Based on the Kalman Filter formulation, measurement innovation analytical-based tests are presented and integrated into the state estimator framework. Gross measurement errors and sudden load changes are automatically detected, identified, and corrected, providing continuous updating of the state estimator. Towards such, the asymmetry index applied to the measurement innovation is introduced, as an anomaly discrimination method, which assesses the physics-model-based dynamic state estimation process in different piece-wise stationary levels. Comparative tests with the state-of-the-art are presented, considering the IEEE 14, IEEE 30, and IEEE 118 test systems. Easy-to-implement-model, without hard-to-design parameters, build-on the classical Kalman Filter solution, highlights potential aspects towards real-life applications.

A Hybrid Framework for Detecting and Eliminating Cyber-Attacks in Power Grids

The work described in this paper aims to detect and eliminate cyber-attacks in smart grids that disrupt the process of dynamic state estimation. This work makes use of an unsupervised learning method, called hierarchical clustering, in an attempt to create an artificial sensor to detect two different cyber-sabotage cases, known as false data injection and denial-of-service, during the dynamic behavior of the power system. The detection process is conducted by using an unsupervised learning-enhanced approach, and a decision tree regressor is then employed for removing the threat. The dynamic state estimation of the power system is done by Kalman filters, which provide benefits in terms of the speed and accuracy of the process. Measurement devices in utilities and buses are vulnerable to communication interruptions between phasor measurement units and operators, who can be easily manipulated by false data. While Kalman filters are incapable of detecting the majority of such cyber-attacks, this article proves that the proposed unsupervised machine learning method is able to detect more than 90 percent of the mentioned attacks. The simulation results on the IEEE 9-bus with 3-machines and IEEE 14-bus with 5-machines systems verify the efficiency of the proposed approach.