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

Abstract

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.

The state-of-art of design and research for Pelton turbine casing, weight estimation, counterpressure operation and scientific challenges

“…Control algorithms for Pelton turbines are also important research topics under investigation [ 64 , 65 ].…”

Optimal Control Strategy of Turbine Governor Parameters Based on Improved Beetle Antennae Search Algorithm

“…The optimization and adjustment methods can be generally divided into two categories: one is to redesign the turbine governor by combining the fuzzy theory, collaborative control, neural network and other advanced control algorithms [9][10][11][12] to replace the classic PID Governor. The other is based on particle swarm optimization, bacterial colony optimization and other intelligent optimization strategies [13][14][15][16][17][18], which optimizes the parameters of the classic PID governor to adapt to different operating conditions of the prime mover. This kind of method makes full use of the good robustness of the classical PID control strategy and is widely used in Engineering practice.…”

Nonlinear Control of Non-Combined Integrated Governor for Kaplan Turbine Generator Set

“…So far, many scholars have presented several control schemes based on single-regulation turbines (generally Francis turbines), such as intelligent control [3], [4], robust control [5], [6], and nonlinear control [7], [8]. However, few integrated control laws have been developed for governor and excitation [9], [10], and few works have been devoted to the coordinate control of KTGS [11].…”