Simulation of Load Control Scheme in Power Plant Based on Condensate Throttling Prediction Model

Author(s):  
Miaomiao Zhang ◽  
Ze Song ◽  
Xiaoyong Zhang ◽  
Liangyu Ma ◽  
Wei Dan ◽  
...  
2009 ◽  
Vol E92-B (6) ◽  
pp. 2327-2331 ◽  
Author(s):  
Chang Soon KANG ◽  
Junsu KIM ◽  
Dan Keun SUNG
Keyword(s):  

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 753 ◽  
Author(s):  
Jianfeng Dai ◽  
Yi Tang ◽  
Jun Yi

High-penetration wind power will count towards a significant portion of future power grid. This significant role requires wind turbine generators (WTGs) to contribute to voltage and reactive power support. The maximum reactive power capacity (MRPC) of a WTG depends on its current input wind speed, so that the reactive power regulating ability of the WTG itself and adjacent WTGs are not necessarily identical due to the variable wind speed and the wake effect. This paper proposes an adaptive gains control scheme (AGCS) for a permanent magnet synchronous generator (PMSG)-based wind power plant (WPP) to provide a voltage regulation service that can enhance the voltage-support capability under load disturbance and various wind conditions. The droop gains of the voltage controller for PMSGs are spatially and temporally dependent variables and adjusted adaptively depending on the MRPC which are a function of the current variable wind speed. Thus, WTGs with lower input wind speed can provide greater reactive power capability. The proposed AGCS is demonstrated by using a PSCAD/EMTDC simulator. It can be concluded that, compared with the conventional fixed-gains control scheme (FGCS), the proposed method can effectively improve the voltage-support capacity while ensuring stable operation of all PMSGs in WPP, especially under high wind speed conditions.


2020 ◽  
Vol 174 ◽  
pp. 115294 ◽  
Author(s):  
I. Mathews ◽  
E.H. Mathews ◽  
J.H. van Laar ◽  
W. Hamer ◽  
M. Kleingeld

Author(s):  
V. STEPHAN ◽  
K. DEBES ◽  
H.-M. GROSS ◽  
F. WINTRICH ◽  
H. WINTRICH

We present a new control scheme for an industrial hard-coal combustion process in a power plant based on reinforcement-learning in combination with neural networks. To comply with the great requirements for environmental protection, the plant operator is interested in a minimization of the nitrogen oxides emission and a maximization of the efficiency factor, while other process parameters have to be kept within predefined limits. To cope with both the tremendous action and state space of the power plant, we present a multiagent-reinforcement-system consisting of 4 agents, which are realized by relatively simple neural function approximators. We demonstrate that our multiagent-system was able to significantly reduce the overall air consumption of the real combustion process of the power plant.


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