15,313 research outputs found
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
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Search Techniques for Multi-Objective Optimization of Mixed-Variable Systems Having Stochastic Responses
A method is proposed for solving stochastic multi-objective optimization problems. Such problems are typically encountered when one desires to optimize systems with multiple, often competing, objectives that do not have a closed form representation and must be estimated via simulation. A two-stage method is proposed that combines generalized pattern search/ranking and selection (GPS/R&S) and and Mesh Adaptive Direct Search (MADS) developed for single-objective stochastic problems with three multi-objective methods: interactive techniques for the specification of aspiration/reservation levels, scalarization functions, and multi-objective ranking and selection. This combination is devised specifically so as to keep the desirable convergence properties of GPS/R&S and MADS while extending application to the multi-objective case
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