Abstract
An intelligent wind power smoothing control using fuzzy neural network (FNN) is proposed in this study. First, the modeling of wind power generator and the designed battery energy storage system (BESS) are introduced. The BESS is consisted of a bidirectional interleaved DC/DC converter and a 3-arm 3-level inverter. Then, the network structure of the FNN and its online learning algorithms are described in detail. Moreover, actual wind data is adopted as the input to the designed wind power generator model. Furthermore, the three-phase output currents of the wind power generator are converted to dq-axis current components. The resulted q-axis current is the input of the FNN power smoothing control and the output is a gentle wind power curve to achieve the effect of wind power smoothing. The difference of the actual wind power and smoothed power is supplied by the BESS. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the FNN power smoothing control. In the experimentation, a digital signal processor (DSP) based BESS is built using two TMS320F28335. From the experimental results of various wind variation sceneries, the effectiveness of the proposed intelligent wind power smoothing control is verified.
Highlights
In recent years, global warming caused by emissions of greenhouse gases has hugely attracted the attention of governments and general public
If a large number of wind turbines or wind farms are directly connected to the power system, the need to maintain the power and load balancing in terms of the power system stability at all times and to ensure a smooth supply of electricity owing to an instantaneous change of wind power becomes a very important issue [1, 2]
The difference of the actual wind power and smoothed power is supplied by the battery energy storage system (BESS)
Summary
Global warming caused by emissions of greenhouse gases has hugely attracted the attention of governments and general public. The use of battery energy storage system (BESS) to reduce the volatility of the output power of wind farm with various power smoothing control methods is an important research topic [10,11,12,13]. Examples are the adjusting of the time constant of a resilient low-pass filter using two-time-scale coordination [11], a duallayer control strategy consisting of a fluctuation mitigation control [12], and application of the concept of automatic segmentation moving average method [13] These control methods can enable the BESS effectively managing the impact of output power variation of the wind farm. In [14], the use of fuzzy control rules to adjust the parameters of Kalman Filter was proposed to smooth the output power fluctuation of a wind power generation system.
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