Analysis of the primary control system of a hydropower plant in isolated model

Zoby, Maria Regina Gomes; Yanagihara, Jurandir Itizo

doi:10.1590/s1678-58782009000100002

Cited by 5 publications

(3 citation statements)

References 6 publications

(5 reference statements)

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“…The most frequently used controller for HPP control is the PID [5], [6]. This type of controller is sensitive to small errors and is more capable of quick response.…”

Section: Figure 4 Simulink Model Configuration For Pid Models [2]mentioning

confidence: 99%

“…It does not matter if it is linear or non-linear, the HPP model which is a real case, experimentally verified, used in this paper, is proposed by the working group of IEEE, as shown in Figure 3 [2], [3], [4], [5]. According to the proposed HPP model and the HPP control, previous research was settled with traditional proportional-integral (PI), proportional integral derivative (PID) [5], [6], or proportional integralproportional derivative (PI-PD) controllers [6]. It is concluded in [6] that with PI controller, the plant represents the best performances, while in [2], [5], [7], PID controller is represented as a more proper and successful HPP control technique.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network-based Neurocontroller for Hydropower Plant Control

Koleva¹,

Lazarevska²,

Babunski³

2022

TEM Journal

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In this paper, the behavior of a system dynamics is represented where neuro-controller is designed, trained, and implemented. The development of the mathematical models is based on suggestions and recommendations from the literature issued by the working group of IEEE. According to the mathematical models, simulation is developed in Simulink software. MATLAB/Simulink software was used to represent the difference between the conventional PID controller and artificial neural network (ANN) neuro-controller. Nonlinear autoregressive-moving average (NARMA-L2) has been used for control simulation of the hydro-power plant (HPP) with neuro-controllers on one hand, and conventional PID control on the other hand.

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“…The most frequently used controller for HPP control is the PID [5], [6]. This type of controller is sensitive to small errors and is more capable of quick response.…”

Section: Figure 4 Simulink Model Configuration For Pid Models [2]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-based Neurocontroller for Hydropower Plant Control

Koleva¹,

Lazarevska²,

Babunski³

2022

TEM Journal

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“…The Proportional-Integral (PI) controller in HPP frequency control could be found to be a successful strategy in [3] and [4]. While in [5] are studied four types of controller strategies, traditional, PI, PID, and PI-PD but PI is represented as the one which has the best performance over the traditional, PID, and PI-PD within the stability criteria whereas the mechanical power follows the demanded power curve the best.…”

mentioning

confidence: 99%

New Approach in Hydropower Plant Control Based on Neural Networks

Koleva¹,

Babunski²,

Zaev³

et al. 2022

EEE

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A new approach to efficient, faster, and intelligent hydropower plant (HPP) control, where constituent equipment is described with highly non-linear mathematical models based on the recommendation from the working group of IEEE on prime movers, is represented in this paper. HPP stability and high efficiency are important factors dependent on the dynamic changes in the energy system demands and the starting time of the plant because the obtained energy is very flexible to those changes in the energy system. This paper is shown and analysed the implementation of the artificial neural network-based controller with PID as an auxiliary controller which helped achieve better behaviour, faster plant stabilization, and operation. The benefits of new technologies and possibilities led to improvements in HPP control and faster system operation. This is achieved by using MATLAB® – Deep Learning Toolbox whereas the simulations are prepared in Simulink. Artificial Neural Networks (ANN) as a technique used in the HPP control systems have advantages in getting a stable and faster response but the complexity of the structure behind the neural networks (NN), meaning algorithms, number of hidden layers, training function, activation function can complicate and destabilize the process. In this paper, the focus is put on the mechanical power responses improvement and the advantages of implementing new technologies contrary to the problems that can occur by using them such as plant destabilization by implementing minor changes, fitting parameters, learning, and training processes, number of hidden layers/neurons, number of epochs, etc.

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Stability and dynamic analysis of doubly-fed variable speed pump turbine governing system based on Hopf bifurcation theory

Gao

Nan

et al. 2021

Renewable Energy

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Analysis of the primary control system of a hydropower plant in isolated model

Cited by 5 publications

References 6 publications

Artificial Neural Network-based Neurocontroller for Hydropower Plant Control

Artificial Neural Network-based Neurocontroller for Hydropower Plant Control

New Approach in Hydropower Plant Control Based on Neural Networks

Stability and dynamic analysis of doubly-fed variable speed pump turbine governing system based on Hopf bifurcation theory

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