3,008 research outputs found
Passivity - Based Control and Stability Analysis for Hydro-Solar Power Systems
Los sistemas de energía modernos se están transformando debido a la inclusión de renovables no convencionales fuentes de energía como la generación eólica y fotovoltaica. A pesar de que estas fuentes de energía son buenas alternativas para el aprovechamiento sostenible de la energía, afectan el funcionamiento y la estabilidad del sistema de energía, debido a su naturaleza inherentemente estocástica y dependencia de las condiciones climáticas. Además, los parques solares y eólicos tienen una capacidad de inercia reducida que debe ser compensada por grandes generadores síncronos en sistemas hidro térmicos convencionales, o por almacenamiento de energía dispositivos. En este contexto, la interacción dinámica entre fuentes convencionales y renovables debe ser estudiado en detalle. Para 2030, el Gobierno de Colombia proyecta que el poder colombiano El sistema integrará en su matriz energética al menos 1,2 GW de generación solar fotovoltaica. Por esta razón, es necesario diseñar controladores robustos que mejoren la estabilidad en los sistemas de energía. Con alta penetración de generación fotovoltaica e hidroeléctrica. Esta disertación estudia nuevas alternativas para mejorar el sistema de potencia de respuesta dinámica durante y después de grandes perturbaciones usando pasividad control basado. Esto se debe a que los componentes del sistema de alimentación son inherentemente pasivos y permiten formulaciones hamiltonianas, explotando así las propiedades de pasividad de sistemas eléctricos. Las principales contribuciones de esta disertación son: una pasividad descentralizada basada control de los sistemas de control de turbinas hidráulicas para sistemas de energía de múltiples máquinas para estabilizar el rotor acelerar y regular el voltaje terminal de cada sistema de control de turbinas hidráulicas en el sistema como, así como un control basado en PI pasividad para las plantas solares fotovoltaicas
Distributed Optimal Frequency Control Considering a Nonlinear Network-Preserving Model
This paper addresses the distributed optimal frequency control of power
systems considering a network-preserving model with nonlinear power flows and
excitation voltage dynamics. Salient features of the proposed distributed
control strategy are fourfold: i) nonlinearity is considered to cope with large
disturbances; ii) only a part of generators are controllable; iii) no load
measurement is required; iv) communication connectivity is required only for
the controllable generators. To this end, benefiting from the concept of
'virtual load demand', we first design the distributed controller for the
controllable generators by leveraging the primal-dual decomposition technique.
We then propose a method to estimate the virtual load demand of each
controllable generator based on local frequencies. We derive incremental
passivity conditions for the uncontrollable generators. Finally, we prove that
the closed-loop system is asymptotically stable and its equilibrium attains the
optimal solution to the associated economic dispatch problem. Simulations,
including small and large-disturbance scenarios, are carried on the New England
system, demonstrating the effectiveness of our design
Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning
In this paper we present an online wide-area oscillation damping control
(WAC) design for uncertain models of power systems using ideas from
reinforcement learning. We assume that the exact small-signal model of the
power system at the onset of a contingency is not known to the operator and use
the nominal model and online measurements of the generator states and control
inputs to rapidly converge to a state-feedback controller that minimizes a
given quadratic energy cost. However, unlike conventional linear quadratic
regulators (LQR), we intend our controller to be sparse, so its implementation
reduces the communication costs. We, therefore, employ the gradient support
pursuit (GraSP) optimization algorithm to impose sparsity constraints on the
control gain matrix during learning. The sparse controller is thereafter
implemented using distributed communication. Using the IEEE 39-bus power system
model with 1149 unknown parameters, it is demonstrated that the proposed
learning method provides reliable LQR performance while the controller matched
to the nominal model becomes unstable for severely uncertain systems.Comment: Submitted to IEEE ACC 2019. 8 pages, 4 figure
Lyapunov Functions Family Approach to Transient Stability Assessment
Analysis of transient stability of strongly nonlinear post-fault dynamics is
one of the most computationally challenging parts of Dynamic Security
Assessment. This paper proposes a novel approach for assessment of transient
stability of the system. The approach generalizes the idea of energy methods,
and extends the concept of energy function to a more general Lyapunov Functions
Family (LFF) constructed via Semi-Definite-Programming techniques. Unlike the
traditional energy function and its variations, the constructed Lyapunov
functions are proven to be decreasing only in a finite neighborhood of the
equilibrium point. However, we show that they can still certify stability of a
broader set of initial conditions in comparison to the traditional energy
function in the closest-UEP method. Moreover, the certificates of stability can
be constructed via a sequence of convex optimization problems that are
tractable even for large scale systems. We also propose specific algorithms for
adaptation of the Lyapunov functions to specific initial conditions and
demonstrate the effectiveness of the approach on a number of IEEE test cases
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