41,138 research outputs found
An internal model approach to (optimal) frequency regulation in power grids with time-varying voltages
This paper studies the problem of frequency regulation in power grids under
unknown and possible time-varying load changes, while minimizing the generation
costs. We formulate this problem as an output agreement problem for
distribution networks and address it using incremental passivity and
distributed internal-model-based controllers. Incremental passivity enables a
systematic approach to study convergence to the steady state with zero
frequency deviation and to design the controller in the presence of
time-varying voltages, whereas the internal-model principle is applied to
tackle the uncertain nature of the loads.Comment: 16 pages. Abridged version appeared in the Proceedings of the 21st
International Symposium on Mathematical Theory of Networks and Systems, MTNS
2014, Groningen, the Netherlands. Submitted in December 201
Stabilization of structure-preserving power networks with market dynamics
This paper studies the problem of maximizing the social welfare while
stabilizing both the physical power network as well as the market dynamics. For
the physical power grid a third-order structure-preserving model is considered
involving both frequency and voltage dynamics. By applying the primal-dual
gradient method to the social welfare problem, a distributed dynamic pricing
algorithm in port-Hamiltonian form is obtained. After interconnection with the
physical system a closed-loop port-Hamiltonian system of differential-algebraic
equations is obtained, whose properties are exploited to prove local asymptotic
stability of the optimal points.Comment: IFAC World Congress 2017, accepted, 6 page
Performance-based control system design automation via evolutionary computing
This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD)
automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient
evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional
designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’
controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of
a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on
a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing,
with good closed-loop performance and robustness in the presence of practical constraints and perturbations
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