Fast online identification of power system dynamic behaviour

This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors

Estimation of inherent governor dead-band and regulation using unscented Kalman filter

The inclusion of the governor droop and dead-band in dynamic models helps to reproduce the measured frequency response accurately and is a key aspect of model validation. Often, accurate and detailed turbine-governor information are not available for various units in an area control centre. The uncertainty in the droop also arise from the nonlinearity due to the governor valves. The droop and deadband are required to tune the secondary frequency bias factors, and to determine the primary frequency reserve. Earlier research on droop estimation did not adequately take into account the effect of dead-band and other nonlinearities. In this paper, unscented Kalman filter is used in conjunction with continuously available measurements to estimate the governor droop and the dead-band width. The effectiveness of the approach is demonstrated through simulation

Feasibility study of applicability of recurrence quantification analysis for clustering of power system dynamic responses

A methodology based on Recurrence Quantification Analysis (RQA) for the clustering of generator dynamic behavior is presented. RQA is a nonlinear data analysis method, which is used in this paper to extract features from measured generator rotor angle responses that can be used to cluster generators in groups with similar oscillatory behavior. The possibility of extracting features relevant to damping and frequency of oscillations present in power systems is studied. The k-Means clustering algorithm is further used to cluster the generator responses in groups exhibiting well or poorly damped oscillations, based on the extracted features from RQA. The effectiveness of RQA is investigated using simulated responses from a modified version of the IEEE 68 bus network, including renewable energy resources

Feasibility Study of Applicability of Recurrence Quantification Analysis for Clustering of Power System Dynamic Responses

A methodology based on Recurrence Quantification Analysis (RQA) for the clustering of generator dynamic behavior is presented. RQA is a nonlinear data analysis method, which is used in this paper to extract features from measured generator rotor angle responses that can be used to cluster generators in groups with similar oscillatory behavior. The possibility of extracting features relevant to damping and frequency of oscillations present in power systems is studied. The k-Means clustering algorithm is further used to cluster the generator responses in groups exhibiting well or poorly damped oscillations, based on the extracted features from RQA. The effectiveness of RQA is investigated using simulated responses from a modified version of the IEEE 68 bus network, including renewable energy resources

Fast online identification of power system dynamic behaviour

This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors

Impact of false data injection attacks in wide area damping control

Wide area measurement based damping controllers are used to mitigate the inter-area oscillations in a large geographically distributed power system. The performance of wide area damping control (WADC) heavily relies on cyber and physical infrastructure. As the measured input signals used in WADC are transferred to the controller location via a communication channel, it is prone to cyber-attacks. The attacker can inject malicious data into the WADC measurements and/or control signals. This paper focuses on modeling and analyzing the impact of different types of false data injection (FDI) attacks on the WADC control signals, namely, sinusoidal attack, triangular attack, saw-tooth attack, ramp attack, pulse attack, random attack, and replay attack. The control architecture for analyzing these attacks consists of power system stabilizers placed on each generator for damping of local modes and an H2/H∞ based WADC controller for damping of inter-area modes in Kundur’s 4-machine 2-area test system. Different types of attacks were compared for their severity, and it has been found that a sinusoidal attack has the highest severity of all the analyzed FDI attacks. The results obtained in this paper will be useful in implementing the cyber-attack detection and mitigation algorithms