Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation
Dongran Song
<p><span class="fontstyle0">Wind direction is random and time-varying, which is arduous to be accurately predicted. The yaw control based on the predicted wind direction is limited by the accuracy of the wind direction prediction, which leads to narrow improvement in the energy capture efficiency of the wind turbine (WT). For this issue, a Stochastic Model Predictive Yaw Control (SMPYC) strategy based on Intelligent Scenarios Generation (ISG) is proposed. Herein, in view of the uncertainty of wind direction prediction, the ISG method is proposed to generate scenarios that characterize it, then the yaw action optimized through the proposed scenario-based SMPYC is performed to improve the energy capture efficiency of WTs. Specifically, ISG creates an optimization problem from scenarios generation in each control period, and the co-evolution bonobo optimizer is improved to solve the optimal scenarios in real time for this high-dimensional multimodal problem. The proposed SMPYC based on ISG is tested using historical wind direction data, and its effectiveness and advantages under different accuracy of wind direction prediction are validated by the test results. The proposed SMPYC reduces the yaw time ratio by 0.35%- 1.58% and improves the energy capture efficiency by 0.26%-0.43% in comparison with the baseline MPYC. For a 5 MW WT, the gained energy production could reach 1.14</span><span class="fontstyle2">–</span><span class="fontstyle0">1.88 </span><span class="fontstyle3">× </span><span class="fontstyle0">10</span><span class="fontstyle0" style="font-size:6pt;">5 </span><span class="fontstyle0">kWh in a year, which corresponds to an additional annual profit of 68,000</span><span class="fontstyle2">–</span><span class="fontstyle0">110,000 yuan. Consequently, the proposed method is promising to enhance the energy capture efficiency and has important application value for reducing the cost of wind power.</span>
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