The recent growth of new renewable energy sources has necessitated fresh avenues for research that searches for new methods to optimise the electrical network and reduce power losses. As this issue represents a combinational problem, the larger electrical systems present more complexity – the reason why this problem remains unsolved. This research aims to develop an algorithm that suggests a set of configurations in large offshore wind power clusters to maintain harmonics at levels that let the clusters operate without operational problems caused by harmonics. The major case studies of harmonics in offshore wind farms are focused on the importance of harmonic analysis for obtaining better performance from the design of the components. The interactions between the components of the system affect harmonic performance, which, in turn, may yield adverse effects such as resonances that can cause instability and operational problems that could shut off one or more wind turbines, causing a significant fault if not controlled promptly. Consequently, it is essential to reduce these adverse effects, and it is in this context that this study locates the proposed algorithm. This research pays attention to harmonic minimisation in combination with power losses minimisation, thus implementing an evolutionary algorithm to be applied to offshore wind power clusters. The project is divided into two primary goals: First, developing the objective functions to calculate power flow and harmonic propagation.Second, implementing the objective functions to develop a multi-objective evolutionary algorithm that can minimise harmonic propagation and power losses between the different components of a grid. Once it is obtained a set of better configurations of the offshore wind farm, data is used and helps to the designer to make recommendations for possible control strategies in large offshore wind farm projects that will deliver the offshore capacity generated into the onshore AC grid.
|Date of Award||10 Jun 2019|
- University Of Strathclyde
|Supervisor||Olimpo Anaya-Lara (Supervisor) & Kwok Lo (Supervisor)|