Abstract
In this chapter, the Neural Sliding Mode Field Oriented Control (N-SMFOC), the Neural Sliding Mode Linearization (N-SML), and the Neural Inverse Optimal Control (N-IOC) schemes are proposed for DFIG prototype. By using neural identifiers, an adequate model of the DFIG and of the DC-link are obtained for different grid scenarios, which helps the controllers to reject disturbances caused by fault grid conditions and/or parameter variations. The proposed controllers performances are evaluated for time-varying tracking reference, parameter variations, and win d speed changing effects. In addition, both of them are tested in presence of three different grid fault conditions: single-phase-to-ground, two-phase-to-ground, and three-phase-to-ground.
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Sánchez, E.N., Djilali, L. (2020). Neural Control Synthesis. In: Neural Control of Renewable Electrical Power Systems. Studies in Systems, Decision and Control, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-47443-0_4
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DOI: https://doi.org/10.1007/978-3-030-47443-0_4
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