6,258 research outputs found
Resilient Distributed Energy Management for Systems of Interconnected Microgrids
In this paper, distributed energy management of interconnected microgrids,
which is stated as a dynamic economic dispatch problem, is studied. Since the
distributed approach requires cooperation of all local controllers, when some
of them do not comply with the distributed algorithm that is applied to the
system, the performance of the system might be compromised. Specifically, it is
considered that adversarial agents (microgrids with their controllers) might
implement control inputs that are different than the ones obtained from the
distributed algorithm. By performing such behavior, these agents might have
better performance at the expense of deteriorating the performance of the
regular agents. This paper proposes a methodology to deal with this type of
adversarial agents such that we can still guarantee that the regular agents can
still obtain feasible, though suboptimal, control inputs in the presence of
adversarial behaviors. The methodology consists of two steps: (i) the
robustification of the underlying optimization problem and (ii) the
identification of adversarial agents, which uses hypothesis testing with
Bayesian inference and requires to solve a local mixed-integer optimization
problem. Furthermore, the proposed methodology also prevents the regular agents
to be affected by the adversaries once the adversarial agents are identified.
In addition, we also provide a sub-optimality certificate of the proposed
methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201
Diagnosability Verification Using Compositional Branching Bisimulation
This paper presents an efficient diagnosability
verification technique, based on a general abstraction approach. More specifically, branching bisimulation including state labels with explicit divergence (BBSD) is defined. This bisimulation preserves the temporal logic property that verifies diagnosability. Based on a proposed BBSD algorithm, compositional abstraction for modular diagnosability verification is shown
to offer a significant state space reduction in comparison to state-of-the-art techniques. This is illustrated by verifying non-diagnosability analytically for a set of synchronized components, where the abstracted solution is independent of the number of components and the number of observable events
Fault Diagnosis for Polynomial Hybrid Systems
Safety requirements of technological processes trigger an increased demand for elaborate fault diagnosis tools. However, abrupt changes in system behavior are hard to formulate with continuous models but easier to represent in terms of hybrid systems. Therefore, we propose a set-based approach for complete fault diagnosis of hybrid polynomial systems formulated as a feasibility problem. We employ mixed-integer linear program relaxation of this formulation to exploit the presence of discrete variables. We improve the relaxation with additional constraints for the discrete variables. The efficiency of the method is illustrated with a simple two-tank example subject to multiple faults
Learning-Based Real-Time Event Identification Using Rich Real PMU Data
A large-scale deployment of phasor measurement units (PMUs) that reveal the
inherent physical laws of power systems from a data perspective enables an
enhanced awareness of power system operation. However, the high-granularity and
non-stationary nature of PMU time series and imperfect data quality could bring
great technical challenges to real-time system event identification. To address
these issues, this paper proposes a two-stage learning-based framework. At the
first stage, a Markov transition field (MTF) algorithm is exploited to extract
the latent data features by encoding temporal dependency and transition
statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided
convolutional neural network (CNN) is established to efficiently and accurately
identify operation events. The proposed method fully builds on and is also
tested on a large real dataset from several tens of PMU sources (and the
corresponding event logs), located across the U.S., with a time span of two
consecutive years. The numerical results validate that our method has high
identification accuracy while showing good robustness against poor data
quality
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