| 引用本文: | 刘 军,汪继勇.基于风电机组健康状态的风电场功率分配研究[J].电力系统保护与控制,2020,48(20):106-113. |
| LIU Jun,WANG Jiyong.Research on power distribution of a wind farm based on the healthy state of wind turbines[J].Power System Protection and Control,2020,48(20):106-113 |
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| 摘要: |
| 评价风电机组健康状态，合理分配风电场中风电机组的功率，对降低风电场的运维成本有着重要意义。首先，利用风电机组监控与数据采集(supervisory control and data acquisition, SCADA)系统中的风电机组历史运行数据，训练基于长短期记忆(long short-term memory, LSTM)网络的风电机组输出功率预测模型。然后，根据预测功率和风电机组实测输出功率的偏差幅值将风电机组的健康状态分为良好、一般、较差等三个类别。最后，综合考虑风电场中每台机组的健康状态、最大发电能力和电网调度部门对风电场下达的发电指令，建立目标函数和约束条件，采用遗传算法进行求解，得到分配给每台机组的功率。仿真结果表明，所提出的方法不仅能够根据风电机组的健康状态合理分配机组功率，而且能够满足调度中心下达的风电场总的发电功率。 |
| 关键词: 风电机组 风电场 状态评估 功率分配 LSTM网络 |
| DOI：DOI: 10.19783/j.cnki.pspc.191489 |
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| 基金项目:陕西省重点研发计划项目资助(2017GY-061) |
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| Research on power distribution of a wind farm based on the healthy state of wind turbines |
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LIU Jun, WANG Jiyong
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(School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China)
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| Abstract: |
| Evaluating the health status of wind turbines and reasonably allocating their output power is of great significance in reducing the operational and maintenance costs of wind farms. First, an output power prediction model of wind turbines based on a Long Short-Term Memory (LSTM) network is trained using the historical operational data in the Supervisory Control and Data Acquisition (SCADA) system. Then, according to the deviation amplitude between measured output power of wind turbines and its predicted output power, the health status of wind turbines can be divided into three categories: good, average, and bad. Finally, for the health status of each wind turbine, the maximum generating capacity of each wind turbine and power generation directives from power grid dispatching departments to wind farms are established, along with the objective function and constraint formula. A genetic algorithm is used to solve the problem. Thus the output power allocated to each wind turbine is obtained. The simulation results show that the method proposed can not only distribute the power reasonably according to the health status of the wind turbine, but also meet the total power of the wind farm given by the dispatching center.
This work is supported by Shaanxi Provincial Key Research and Development Plan of China (No. 2017GY-061). |
| Key words: wind turbine wind farm health status evaluation power allocation LSTM network |
