Fast Processing Intelligent Wind Farm Controller for Production Maximisation

Tanvir Ahmad,Juveria Anwar,Peter Matthews,Abdul Basit,Behzad Kazemtabrizi,Olivier Coupiac

doi:10.3390/en12030544

Abstract

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines particle swarm optimisation (PSO) with a turbulence intensity–based Jensen wake model (TI–JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ( C P ) or deflecting wakes by applying yaw-offsets for maximising net farm production. Firstly, TI–JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimised strategies are evaluated using simulations based on TI–JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 s for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

Highlights

Wind farms take advantages of economies of scale for reducing levelised cost of energy by clustering turbines together
Wake effects are negligible in above-rated conditions [5], only below-rated conditions were assumed in the simulations
turbulence intensity–based Jensen wake model (TI–JM) used Supervisory Control And Data Acquisition (SCADA) data for Brazos-row and Sole de Moulin Vieux (SMV) for tuning the initial value of k according to the conditions

Summary

Introduction

Wind farms take advantages of economies of scale for reducing levelised cost of energy by clustering turbines together. Turbines in this cluster interact with each other aerodynamically through wake effects. Wake losses in the crosswind directions can be as high as 50% due to close spacing [4], reducing wind farm efficiency to as low as 40% [5]. Another way of reducing wake effects is global optimisation of the whole wind farm using coordinated control. Coordinated control based on global optimisation of the whole wind farm, instead of local optimisation of individual turbines, can result in increased annual energy production [6]

Methods

Results

Conclusion