78 research outputs found
Estimating the Propagation of Interdependent Cascading Outages with Multi-Type Branching Processes
In this paper, the multi-type branching process is applied to describe the
statistics and interdependencies of line outages, the load shed, and isolated
buses. The offspring mean matrix of the multi-type branching process is
estimated by the Expectation Maximization (EM) algorithm and can quantify the
extent of outage propagation. The joint distribution of two types of outages is
estimated by the multi-type branching process via the Lagrange-Good inversion.
The proposed model is tested with data generated by the AC OPA cascading
simulations on the IEEE 118-bus system. The largest eigenvalues of the
offspring mean matrix indicate that the system is closer to criticality when
considering the interdependence of different types of outages. Compared with
empirically estimating the joint distribution of the total outages, good
estimate is obtained by using the multitype branching process with a much
smaller number of cascades, thus greatly improving the efficiency. It is shown
that the multitype branching process can effectively predict the distribution
of the load shed and isolated buses and their conditional largest possible
total outages even when there are no data of them.Comment: Accepted by IEEE Transactions on Power System
Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian
In this paper the empirical observability Gramian calculated around the
operating region of a power system is used to quantify the degree of
observability of the system states under specific phasor measurement unit (PMU)
placement. An optimal PMU placement method for power system dynamic state
estimation is further formulated as an optimization problem which maximizes the
determinant of the empirical observability Gramian and is efficiently solved by
the NOMAD solver, which implements the Mesh Adaptive Direct Search (MADS)
algorithm. The implementation, validation, and also the robustness to load
fluctuations and contingencies of the proposed method are carefully discussed.
The proposed method is tested on WSCC 3-machine 9-bus system and NPCC
48-machine 140-bus system by performing dynamic state estimation with
square-root unscented Kalman filter. The simulation results show that the
determined optimal PMU placements by the proposed method can guarantee good
observability of the system states, which further leads to smaller estimation
errors and larger number of convergent states for dynamic state estimation
compared with random PMU placements. Under optimal PMU placements an obvious
observability transition can be observed. The proposed method is also validated
to be very robust to both load fluctuations and contingencies.Comment: Accepted by IEEE Transactions on Power System
Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks
Kalman filters and observers are two main classes of dynamic state estimation
(DSE) routines. Power system DSE has been implemented by various Kalman
filters, such as the extended Kalman filter (EKF) and the unscented Kalman
filter (UKF). In this paper, we discuss two challenges for an effective power
system DSE: (a) model uncertainty and (b) potential cyber attacks. To address
this, the cubature Kalman filter (CKF) and a nonlinear observer are introduced
and implemented. Various Kalman filters and the observer are then tested on the
16-machine, 68-bus system given realistic scenarios under model uncertainty and
different types of cyber attacks against synchrophasor measurements. It is
shown that CKF and the observer are more robust to model uncertainty and cyber
attacks than their counterparts. Based on the tests, a thorough qualitative
comparison is also performed for Kalman filter routines and observers.Comment: arXiv admin note: text overlap with arXiv:1508.0725
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