sustainability Article MPPT of Permanent Magnet Synchronous Generator in Tidal Energy Systems Using Support Vector Regression Ahmed G. Abo-Khalil 1,2, * and Ali S. Alghamdi 1   Citation: Abo-Khalil, A.G.; Alghamdi, A.S. MPPT of Permanent Magnet Synchronous Generator in Tidal Energy Systems Using Support Vector Regression. Sustainability 2021, 13, 2223. https://doi.org/10.3390/ su13042223 Academic Editor: M. Sergio Campobasso Received: 10 December 2020 Accepted: 2 February 2021 Published: 19 February 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia; aalghamdi@mu.edu.sa 2 Department of Electrical Engineering, College of Engineering, Assuit University, Assuit 71515, Egypt * Correspondence: a.abokhalil@mu.edu.sa 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. Keywords: PMSG; maximum power point; Support Vector Regression 1. Introduction Tidal power generation has recently been spotlighted as an alternative energy that can solve the problem of energy depletion while minimizing environmental deterioration. Tidal power generation is characterized by predictable power generation and high reliability, different from other renewable energy sources. It is a method of producing electricity by converting the flow energy of the tide into the rotational energy of the turbine. Due to these advantages, research on horizontal axis tidal turbines has been actively conducted. For tidal power, strong tidal current is essential, and a tidal current generator can be installed in an area where a flow rate of 1.0 m/s or higher occurs. The study of 106 potential locations all over the world for the use of currents calculates that around 50 TWh/year could be extracted from marine currents [1]. Tidal power generation has numerous advantages, some of these advantages are explained below [2]: • It is a predictable resource as it depends on the tides. • It has a slight environmental impact, but much less than other electricity generation systems, both renewable and conventional. • A tidal turbine with a current speed of between 2 and 3 m/s can obtain about four times more annual power than an equivalent wind turbine. So the increase in cost of both installation and maintenance of the tidal power system is more than offset by the increase in production. However, the marine environment is considerably harsher than on land where wind turbines are located. In addition, the problem of corrosion due to being in a marine Sustainability 2021, 13, 2223. https://doi.org/10.3390/su13042223 https://www.mdpi.com/journal/sustainability