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This paper presents a Group Search Optimizer with Multiple Producer (GSOMP) to solve the voltage control problem of the power systems penetrated with wind power. Although the utilization of wind turbines in power systems reduces the fuel cost and carbon emission efficiently, it also affects the reactive power flow, which might result in negative efffect on the power losses and voltage stability. In...
This paper proposes a novel nonlinear multi-loop control method for the coordinated control of the Unified Power Flow Controller (UPFC) using high gain state and perturbation observers (HGSPO). The UPFC is decoupled into four independent subsystems. The optimal output feedback control of each subsystem is achieved with the estimates of states and perturbations obtained by the HGSPO. A four-loop nonlinear...
This paper presents a new algorithm which employs morphological filter and instantaneous reactive power theory for phasor measurement. A preprocessor is proposed to removes the decaying DC component from the input signal with a delay of two samples. The effects of sampling rate, time constant, frequency shift, harmonics and fault inception angle on the performance of the proposed algorithm have been...
This paper focuses on implementing a dimensional Q-learning (DQL) for solving reactive power optimization with discrete control variables. The proposed algorithm applies the traditional Q-learning to search the feasible region dimensionally, so that the memory amount of each agent can be largely reduced. Meanwhile, the safety margin of voltage amplitude and reactive power output of generators are...
With the increasing penetration of distributed generation (DG) systems based on renewable energies into the power grid, the fault ride-through capability of them is stressed by recent grid codes. Aiming to adapting this requirement, this paper proposes a voltage sensorless predictive direct power control (PDPC) for the grid-interactive DC/AC converter utilized in DG systems. In order to realize a...
In this paper, a novel predictive direct power control (PDPC) strategy is proposed for three-phase AC/DC converters, which is based on a straightforward regulation of instantaneous active and reactive power. The converter controlled by the proposed strategy realizes operations without line-side voltage sensors by introducing the virtual flux (VF) concept and a grid voltage estimation method based...
This paper presents a novel perturbation observer-based multiloop control method for the integrated control of the doubly-fed induction generator-based wind turbine (DFIG-WT) in a multimachine power system. The DFIG-WT is decoupled into four independent subsystems in accordance with four outputs. A perturbation state is introduced into each subsystem, and the optimal output feedback control of each...
This paper proposes a probabilistic model for optimal joint allocation of energy and spinning reserve to determine energy and spinning reserve capacities. The model takes into consideration both the hourly changes of load demands and the probability of generators' contingencies. The objective function aims at minimizing not only fuel costs caused by power generation but also the costs associated with...
Pollution of the power plant has caused harmful environmental effects due to the emission of greenhouse gas. The pollution can be reduced by adjusting the real power outputs of different power plants. However, the relocation of real power outputs results an additional outlay in the system. In order to eliminate the conflict between the cost and emission, an optimal power flow is introduced in this...
This paper presents Function Optimization by Learning Automata (FOLA) for the power flow problem which aims to achieve economic power system dispatch and voltage stability enhancement in dynamic wind power integrated systems. Dividing each dimension into a certain number of cells, FOLA undertakes the dimensional search, and has the ability of memorizing history through the values of cells that have...
This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems, in particular for those in a high-dimensional space. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated...
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