Application of Model Predictive Control Algorithm on a Hydro Turbine Governor Control

Beus, Mateo; Pandžić, Hrvoje

doi:10.23919/pscc.2018.8442594

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…The mechanical energy is then converted into electrical energy by the generator. The position of the turbine system is almost equivalent to the generator system, namely turning the input of electrical energy into output [9], [11], [12]. The turbine in the fluid provides a thrust function to rotate the propeller, these rotations cause the turbine shaft to move and be forwarded to the generator to be converted into electrical energy.…”

Section: B System Modelmentioning

confidence: 99%

Hydraulic Power System Control using State Feedback Controller (SFC)

Fahmizal,

Trio Putra,

Fatimawardhani

et al. 2023

jfsc

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In an electrical network, generators work and provide power to the loads. When the load on the generator increases, the speed of the generator decreases then resulting in a reduction in the frequency of the network. This paper was designed within the state feedback controller (SFC) to improve the hydraulic power system performance. The performance of the proposed controller is compared with simple feedback controller (FC) in the simulation environment. The load variation was tested which is 5%, 10%, and 20% variation. The testing results show that in terms of steady state error (SSE) and overshoot, the SFC is superior. In terms of settling time, the FC method is faster. Since it quickly reaches steady event not getting into set-point. The overall, it can be concluded that SFC can give better performance than FC in the frequency control of a hydraulic power system.

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Section: B System Modelmentioning

confidence: 99%

Hydraulic Power System Control using State Feedback Controller (SFC)

Fahmizal,

Trio Putra,

Fatimawardhani

et al. 2023

jfsc

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show abstract

“…As in the previous case, the predictive controller response is competitive with the classical PID response. However, the MPC algorithms in both [14,15] used the same linear model as the controller's internal prediction model and for plant simulation purposes as well.…”

Section: Review Of Mpc Applications In Power Plantsmentioning

confidence: 99%

“…Since hydraulic coupling effect has negative impact on the stability margin in a closed loop, the proposed approach extends the stability margin. In [15], a generalised predictive control algorithm is applied to a linearised model of a high‐head hydro power plant's unit. As in the previous case, the predictive controller response is competitive with the classical PID response.…”

Section: Review Of Mpc Applications In Power Plantsmentioning

confidence: 99%

Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

Beus

Pandžić

2020

IET Renewable Power Generation

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Traditionally, hydro turbine governor applications mainly rely on classical proportional-integral-derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm.

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“…Various MPC systems for control of conventional hydropower governors and/or excitation control have previously been developed [4–7]. In these papers, conventional plants with synchronous generators are investigated.…”

Section: Introductionmentioning

confidence: 99%

“…The damping of power oscillations [8–12] by minimising the frequency deviation between the local generator and the average system frequency is one example. Voltage stability [13], grid frequency control [14–17] and hydro turbine governor control [7] can also be improved by MPC.…”

Section: Introductionmentioning

confidence: 99%

Stability properties of non‐linear model predictive control of variable speed hydropower

Reigstad¹,

Uhlen²

2020

IET Renewable Power Generation

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This study presents a method for small‐signal analysis of an advanced, multi‐variable control system for variable speed hydropower (VSHP) plants. A model predictive controller (MPC) optimises the power plant performance. In parallel, a virtual synchronous generator‐type (VSG) converter control ensures that the VSHP contributes to virtual inertia and frequency control of the power system. The aim of the small‐signal analysis is to parametrise the cost function of the MPC to minimise oscillatory modes between the VSHP hydraulic system and the power system. A state‐space representation of the MPC is developed by assuming a stable steady‐state operating point equal to the reference values of the MPC cost function, and that no constraints are active. This state‐space representation allows for small‐signal analysis of the power system, including the MPC. The results show that the modes between the hydraulic system and the power system are well‐damped and negligible when the costs of deviations in the hydraulic system are low compared to the cost of deviations in the VSG power reference. Thus, these modes do not constrain the tuning of the VSG. The VSHP power output can, therefore, be optimised independently through the VSG controller to damp power oscillations and reduce frequency deviations.

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Application of Model Predictive Control Algorithm on a Hydro Turbine Governor Control

Cited by 6 publications

References 8 publications

Hydraulic Power System Control using State Feedback Controller (SFC)

Hydraulic Power System Control using State Feedback Controller (SFC)

Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

Stability properties of non‐linear model predictive control of variable speed hydropower

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