A Hydro Power Plant Linear Parameter Varying Control Framework

Linear Parameter Varying Modeling and Control of a Nonlinear Power Plant

“…A base model parameter-dependent variations, e.g., corresponding to 0.8pu gate position level, can be considered such that S(s, ρ) = ρ. S ସ (s) (18) can be written. However, using (14) …”

“…As the Matlab implementations followed a oneparameter LPV modeling [18], the important steps of the LPV framework implementations are emphasized. The time-varying parameters, (ρ 1 (t)) and (ρ 2 (t)) in the LPV formulations and pv in the code, was defined by using the 'pvec' command as pv=pvec('box',[0.3 0.5;48 80]), with proper range values in a multidimensional box while omitting the rate of variations, i.e., the time-varying parameters are assumed to vary in the interval of [0.3 0.5] for a 11 and [48 80] for a 21 .…”

“…The two-parameter hydro plant LPV modeling performance was compared with the one-parameter [18] hydro plant by using the same parameter variations. The one-and two-parameter LPV system step responses are given in Fig.…”

“…Since the LPV framework addresses the plant uncertainties by using the actual linear time-varying parameter, it is expected to be superior, yet, with additional computational complexity, over traditional robust control theory that exploits block or parametric uncertainty in worst-case considerations to obtain corresponding optimal and fixed controllers. LPV frameworks have been effectively used in many problems, including system identification, invalidation, and control synthesis for nonlinear economic stabilization problems [15], LPV modeling of lithium-ion battery temperature-dependent nonlinear dynamics [16], LPV control framework for superior transient responses of an electric generator [17], LPV modeling and control of a one-parameter hydro power plant [18], LPV control synthesis for fuel cell power generation air-supply system model uncertainties [19], LPV control synthesis for a nonlinear induction machine dynamics [20], LPV digital control setup of systems with time-delays [21], LPV modeling of a battery via the subspace method [22], LPV synthesis for an adaptive active microgravity isolation approach [23], and LPV modeling perspectives [24]. In addition, the LPV modeling and optimization framework can be extended to other smart grid components such as photovoltaic resources, wind generators, coal units, and load models including demandside management feature by properly identifying a-priori unknown but measurable linear varying parameters for future times and incorporating them during the LPV framework.…”

“…This paper investigates a two-parameter hydro plant LPV modeling and closed loop control operations and compares its performance against a one-parameter hydro plant case [18]. The hydro plant time-varying parameters are studied for most effective real-time nonlinear dynamic characterization and associated modeling and control performances are presented.…”