A wind turbine sensorless automatic control systems, analysis, modelling and development of IDA-PBC method

Abstract

<p>Several non-linear controls are developed for controlling a wind energy conversion system equipped with a DFIG double feed asynchronous generator. Exploitation of the aerodynamic power captured by the turbine gives a new instruction to the controller based on passivity by keeping the hypersynchronous regime of the DFIG. An adaptive observer MRAS was developed for the considered as a structure in which the rotor speed is estimated from the measurement of stator and rotor currents. Our goal is to maximize active power, minimize construction costs in order to control wind turbine systems using our passivity and observation method. Best results were obtained using sensorless MPPT and IDA-PBC methods.</p>

A deep dive into enhancing frequency stability in integrated photovoltaic power grids

“…For a load change of ∆𝑃 𝐿 (𝑠) = 0.2 𝑝. 𝑢., the integral controller gain is set to 𝑘 𝐼 = 4. The closedloop transfer function that relates the load change to the frequency deviation ∆𝛺(𝑠) is expressed by (7).…”

“…Among the key influencers of voltage stability are nonlinear electrical loads operating under critical transient conditions [4], [5]. These nonlinear loads exhibit intricate behaviors that can significantly affect overall system performance, introducing harmonics, distorting voltage waveforms, and displaying dynamic responses that complicate voltage stability [6], [7]. Precisely modeling and comprehending the behavior of these nonlinear loads are essential for formulating control equations and developing effective operational strategies.…”

Adaptive doubly fed induction generator’s control driven wind turbine using luenberger observer optimized by genetic algorithm

“…The added value of Boulmane et al [27], results in the estimation of the stator resistance of an induction machine to reduce the error estimation of its rotational speed. Indeed, different techniques have been proposed to estimate an electrical quantity [28], we cite the Kalman filters, the Luenberguer observer, and other methods based on the injection of low or high-frequency currents (known as the signal processing method). In this work, an adaptation of the control law based on the sliding mode technique and using a Luenberger observer is established.…”

Optimization of wind energy conversion systems – an artificial intelligent approach

“…Incremental conductance (IC) algorithm, for instance, does not require sensors and details on the turbine and generator, thus making it more reliable and less expensive [10][11][12][13]. Hill climbing (HC) algorithm applies a different strategy which relies heavily on mathematical optimization approach to find the local optima of a given function [14][15][16][17][18]. In this method, if the current solution is on the right of the best optima, the switching moves the operating point to the left,…”