The aim of this paper is to answer the question: Are the Łódź Hills useful for electrical energy production from wind energy or not? Due to access to short-term data related to wind measurements (the period of 2008 and 2009) from a local meteorological station, the measure – correlate – predict approach have been applied. Long-term (1979‒2016) reference data were obtained from ECWMF ERA-40 Reanalysis. Artificial neural networks were used to calculate predicted wind speed. The obtained average wind speed and wind power density was 4.21 ms⁻¹ and 70 Wm⁻¹, respectively, at 10 m above ground level (5.51 ms⁻¹, 170 Wm⁻¹ at 50 m). From the point of view of Polish wind conditions, Łódź Hills may be considered useful for wind power engineering.
Focus of the vibration expert community shifts more and more towards diagnosing machines subjected to varying rotational speeds and loads. Such machines require order analysis for proper fault detection and identification. In many cases phase markers (tachometers, encoders, etc) are used to help performing the resampling of the vibration signals to remove the speed fluctuations and smearing from the spectrum (order tracking). However, not all machines have the facility to install speed tracking sensors, due to design or cost reasons, and the signal itself has to then be used to extract this information. This paper is focused on the problem of speed tracking in wind turbines, which represent typical situations for speed and load variation. The basic design of a wind turbine is presented. Two main types of speed control i.e. stall and pitch control are presented,. The authors have investigated two methods of speed tracking, using information from the signal (without relying on a speed signal). One method is based on extracting a reference signal to use as a tachometer, while the other is phase-based (phase demodulation). Both methods are presented and applied to the vibration data from real wind turbines. The results are compared with each other and with the actual speed data.
Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.