Moldel-Based Fault Detection (FD) Of V47/660kW Wind Turbine (WT) With Extended Kalman Filter (EKF)

Renewable energies include: Solar energy, wind energy, geothermal energy, floodwaters, which are used to generate electricity. So many countries have invested heavily in this area, and there are already a lot of investments in this area. One of the most renewable and clean energy sources is wind energy, which is a major contributor to electricity generation in many countries. To exploit wind energy in order to produce electrical energy, we need to know the equipment of the wind power plants and the basis of their work, this equipment is very expensive, and therefore the protection and maintenance of this equipment is very important and necessary. Because in the event of a failure in the wind farm system, in addition to the huge costs involved in repairing and replacing parts, the production of electrical energy also stops, which will cost a lot of money. One of the most important ways to prevent breakdowns in wind turbine systems is to perform fault detection. Wind turbine is one of the most important parts of a wind power plant. Considering the importance of fault detection in wind turbine systems, we are going to work on FD issues in the V47/660 kW wind turbine in this paper. The innovation in this article is the use of the Extended Kalman Filter for fault detection in the V47/660kW wind turbine. For nonlinear systems, the extended Kalman filter is used. This filter, with the noise and uncertainty, provides accurate estimates. In this paper, we describe the system and system model, and then design and simulate the extended kalman filter. Finally, with the residual production (the difference between the system states and the estimates from the Kalman filter), we will analyze the fault of system

کلیدواژه ها:

wind turbine ، EKF ، fault detection ، estimate ، sensor fault ، actuator fault