Hardware-in-the-Loop Wind Turbine Simulation Platform for a Laboratory Feeder Model

As part of a larger project to build a laboratory-based smart grid feeder, there was a need to incorporate renewable energy sources into the feeder model. The goal in this project was to create a hardware-in-the-loop (HIL) wind turbine simulation platform. Software is used to calculate a dynamic optimal power point from time-series wind speed data and to generate control signals for the platform's hardware. The hardware consists of a dc motor providing mechanical power to a coupled generator. To validate the HIL simulation platform, time-series wind speed and output power data were used from an installed and operating utility-scale turbine. The steady-state performance of the HIL simulator correlates well with the provided manufacturer's curve, but the dynamic performance of the system is more varied with periods of excellent correlation as well as poor. A richer data set from the utility containing more than wind speed and power output (such as wind direction and turbine yaw position) could provide substantial improvements to the dynamic performance.

I. Introduction

As interest continues to grow in smart grids, there is a need to explore their operation and control. Rather than relying solely on software simulation, a laboratory-scale model feeder is being developed to allow direct experimentation and measurement of how such a feeder might operate. A key feature of the smart grid is distributed generation and to adequately equip the laboratory feeder, a system needed to be developed that adequately replicated the performance of a wind turbine. As in many such lab experiments, it is much more feasible to define and conduct experiments using meaningful models of renewable energy sources rather than the actual sources themselves. These modeled sources can be explicitly controlled and are not the whims of the weather and also provide flexibility to change the characteristics of the sources as needed. By using such sources, the goal of this system is to use recorded time-series data of wind speed along with a software simulation of the turbine dynamics and related controllers to produce control signals that drive an appropriately sized dc motor and generator. Once connected to the feeder, the varying power source will allow for a broader set of experiments dealing with the dynamic behavior of the smart grid.