73 research outputs found
An Efficient Multifidelity Model for Assessing Risk Probabilities in Power Systems under Rare Events
Risk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy
A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow
In this paper, we propose a robust data-driven process model whose
hyperparameters are robustly estimated using the Schweppe-type generalized
maximum likelihood estimator. The proposed model is trained on recorded
time-series data of voltage phasors and power injections to perform a
time-series stochastic power flow calculation. Power system data are often
corrupted with outliers caused by large errors, fault conditions, power
outages, and extreme weather, to name a few. The proposed model downweights
vertical outliers and bad leverage points in the measurements of the training
dataset. The weights used to bound the influence of the outliers are calculated
using projection statistics, which are a robust version of Mahalanobis
distances of the time series data points. The proposed method is demonstrated
on the IEEE 33-Bus power distribution system and a real-world unbalanced
240-bus power distribution system heavily integrated with renewable energy
sources. Our simulation results show that the proposed robust model can handle
up to 25% of outliers in the training data set.Comment: Submitted to the IEEE Transactions on Power System
On the Definition of Cyber-Physical Resilience in Power Systems
In recent years, advanced sensors, intelligent automation, communication
networks, and information technologies have been integrated into the electric
grid to enhance its performance and efficiency. Integrating these new
technologies has resulted in more interconnections and interdependencies
between the physical and cyber components of the grid. Natural disasters and
man-made perturbations have begun to threaten grid integrity more often. Urban
infrastructure networks are highly reliant on the electric grid and
consequently, the vulnerability of infrastructure networks to electric grid
outages is becoming a major global concern. In order to minimize the economic,
social, and political impacts of power system outages, the grid must be
resilient. The concept of a power system cyber-physical resilience centers
around maintaining system states at a stable level in the presence of
disturbances. Resilience is a multidimensional property of the electric grid,
it requires managing disturbances originating from physical component failures,
cyber component malfunctions, and human attacks. In the electric grid
community, there is not a clear and universally accepted definition of
cyber-physical resilience. This paper focuses on the definition of resilience
for the electric grid and reviews key concepts related to system resilience.
This paper aims to advance the field not only by adding cyber-physical
resilience concepts to power systems vocabulary, but also by proposing a new
way of thinking about grid operation with unexpected disturbances and hazards
and leveraging distributed energy resources.Comment: 20 pages. This is a modified versio
Kullback-Leibler Divergence-Guided Copula Statistics-Based Blind Source Separation of Dependent Signals
In this paper, we propose a blind source separation of a linear mixture of
dependent sources based on copula statistics that measure the non-linear
dependence between source component signals structured as copula density
functions. The source signals are assumed to be stationary. The method
minimizes the Kullback-Leibler divergence between the copula density functions
of the estimated sources and of the dependency structure. The proposed method
is applied to data obtained from the time-domain analysis of the classical
11-Bus 4-Machine system. Extensive simulation results demonstrate that the
proposed method based on copula statistics converges faster and outperforms the
state-of-the-art blind source separation method for dependent sources in terms
of interference-to-signal ratio.Comment: Submitted to the ISGT NA 202
- …