MPPT of Permanent Magnet Synchronous Generator in Tidal Energy Systems Using Support Vector Regression

Abstract

In this paper, an improved Maximum Power Point Tracking (MPPT) algorithm for a tidal power generation system using a Support Vector Regression (SVR) is proposed. To perform this MPPT, a tidal current speed sensor is needed to track the maximum power. The use of these sensors has a lack of reliability, requires maintenance, and has a disadvantage in terms of price. Therefore, there is a need for a sensorless MPPT control algorithm that does not require information on tidal current speed and rotation speed that improves these shortcomings. Sensorless MPPT control methods, such as SVR, enables the maximum power to be output by comparing the relationship between the output power and the rotational speed of the generator. The performance of the SVR is influenced by the selection of its parameters which is optimized during the offline training stage. SVR has a strength and better response than the neural network since it ensures the global minimum and avoids being stuck at local minima. This paper proposes a high-efficiency grid-connected tidal current generation system with a permanent magnet synchronous generator back-to-back converter. The proposed algorithm is verified experimentally and the results confirm the excellent control characteristics of the proposed algorithm.