基于功率全程可调的风电场优化调度策略
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摘要
近年来风电产业发展迅速,随着风力发电容量在新能源电力系统中的比重日益增加,为保证电力系统安全运行,风电场有必要进一步提高自身的运行水平。为了减少出力波动性和随机性对电网的冲击,风电场需要增加自身出力的控制调度水平,保证机组输出功率满足电网侧的负荷需求。围绕这一目标,本文在短期风速预测,风电机组全程功率可调,风电场机组优化调度策略三个方面进行了研究,主要进行了以下工作：
建立了基于AdaBoost-BP神经网络和最小二乘支持向量机算法的短期风速预测模型,并用风电场现场数据进行仿真验证,证明了短期风速预测的有效性。风速信号在各个频域上分别具有相同的特点和规律,在风速预测过程中的数据预处理方法中,选择最优小波包变换作为信号分析手段,在小波分解的基础上,增加了更多高频信号的分析,提供了比小波变换更高的分辨率。论文建立了最优小波包变换与最小二乘支持向量机结合的短期风速预测模型,通过实际风速数据测试,结果表明了采用最优小波包变换多尺度分析方法的预测精度有了明显提升,更有助于风力发电系统的安全运行和经济调度。
针对短期风速预测的不同应用途径,给出了两种短期风速预测方式：多步预测与信息粒化预测,通过仿真实例分析了两种应用方式的对比。电网对区域能源中风电场的调度往往都是连续的过程,风速多步预测可以更好的反映未来风速变化的趋势,为电网连续调度提供条件；粒化预测能够消除冗余数值,计算较准确的风速水平和分布,更适用于分析不同风电场或不同机组未来的出力特性。
提出了基于风机有功功率模型的单一机组有功功率全程可调策略。传统的风机运行方式是在额定风速下尽可能地吸收风能,保持桨距角为最小值,调节转速值达到最优叶尖速比：额定风速以上调节桨距角保持额定功率。本文通过建立风机的有功功率模型,分析风机运行特性,提出机组有功功率全程可调策略,在风速允许的条件下,小范围功率调节通过发电机侧的转子转矩来调节,中大范围功率调节通过桨距角和转速联合调节,实现对风电机组功率全程可调。
提取机组特征向量,建立了风电场特征矩阵,通过历史数据分析风电场各机组性能；其次由于机组众多,对所有机组进行聚类分析,设置不同的机组调度群,降低调度问题的维数；最后在电网调度指令对风电场出力进行限制的情况下,考虑机组相对损耗,建立机组功率优化调度策略。
In recent years, wind power has developed rapidly. As the proportion of wind power capacity in the new energy power system is increasing, wind farms should improve the operation and control level in order to ensure the safety. For the less volatility and less impact from randomness, wind farms should increase their output control level to meet the demand of power grid. Around this goal, paper did some research on short-term wind speed forecast, adjustable power of wind turbine unit, optimal load dispatch strategy in wind farm.
The thesis includes the following aspects:
This paper established short-term wind speed forecast model based on AdaBoost-BP neural network and least squares support vector machine (LSSVM), then did the simulation with field data and proved the effectiveness of the short-term wind speed forecast. As characteristics and laws are the same in wind speed signal of the same frequency part, we choose the wavelet packet transform as the data preprocessing method. In basis of wavelet transform, wavelet packet transform provides more analysis of high frequency signal with higher resolution. Paper established short-term wind speed forecast model with optimal wavelet packet transform and LSSVM. Through the test with field data, results show higher prediction precision by using optimal wavelet packet transform.
For the different purpose of short-term wind speed forecast, paper gave two types of wind speed forecast:multistep forecast and information granulation forecast. Then paper analyzed the contrast of two application modes through the simulation. Scheduling instruction to wind farm from power grid are usually serial numbers. Wind speed multistep forecast can show the variation trend in the future for grid continuous scheduling. Information granulation forecast can eliminate data redundancy and get accurate wind speed level and distribution for analyzing output of different wind farms or different wind power units in the future.
It is proposed the adjustable power strategy based on wind power model. Traditional wind unit operation uses the most power strategy and keeps the pitch angle minimum and optimal tip speed ratio; keep rated power by adjusting the pitch angle. Paper established the wind turbine power model, analyzed wind units characteristic, proposed adjust wind turbine power strategy in the full range. Within wind speed, strategy chooses generator torque for small range adjustment and pitch angle and generator torque together for large range adjustment.
We calculated the unit feature vector, established wind farm feature matrix. As the number of units in wind farm, we analyzed the clustering of all units, then set up several level clusters to reduce the dimension of unit commitment problem. Last, in the condition of wind farm power limit from power grid, we solved the unit commitment problem by considering units relative loss.
建立了基于AdaBoost-BP神经网络和最小二乘支持向量机算法的短期风速预测模型,并用风电场现场数据进行仿真验证,证明了短期风速预测的有效性。风速信号在各个频域上分别具有相同的特点和规律,在风速预测过程中的数据预处理方法中,选择最优小波包变换作为信号分析手段,在小波分解的基础上,增加了更多高频信号的分析,提供了比小波变换更高的分辨率。论文建立了最优小波包变换与最小二乘支持向量机结合的短期风速预测模型,通过实际风速数据测试,结果表明了采用最优小波包变换多尺度分析方法的预测精度有了明显提升,更有助于风力发电系统的安全运行和经济调度。
针对短期风速预测的不同应用途径,给出了两种短期风速预测方式：多步预测与信息粒化预测,通过仿真实例分析了两种应用方式的对比。电网对区域能源中风电场的调度往往都是连续的过程,风速多步预测可以更好的反映未来风速变化的趋势,为电网连续调度提供条件；粒化预测能够消除冗余数值,计算较准确的风速水平和分布,更适用于分析不同风电场或不同机组未来的出力特性。
提出了基于风机有功功率模型的单一机组有功功率全程可调策略。传统的风机运行方式是在额定风速下尽可能地吸收风能,保持桨距角为最小值,调节转速值达到最优叶尖速比：额定风速以上调节桨距角保持额定功率。本文通过建立风机的有功功率模型,分析风机运行特性,提出机组有功功率全程可调策略,在风速允许的条件下,小范围功率调节通过发电机侧的转子转矩来调节,中大范围功率调节通过桨距角和转速联合调节,实现对风电机组功率全程可调。
提取机组特征向量,建立了风电场特征矩阵,通过历史数据分析风电场各机组性能；其次由于机组众多,对所有机组进行聚类分析,设置不同的机组调度群,降低调度问题的维数；最后在电网调度指令对风电场出力进行限制的情况下,考虑机组相对损耗,建立机组功率优化调度策略。
In recent years, wind power has developed rapidly. As the proportion of wind power capacity in the new energy power system is increasing, wind farms should improve the operation and control level in order to ensure the safety. For the less volatility and less impact from randomness, wind farms should increase their output control level to meet the demand of power grid. Around this goal, paper did some research on short-term wind speed forecast, adjustable power of wind turbine unit, optimal load dispatch strategy in wind farm.
The thesis includes the following aspects:
This paper established short-term wind speed forecast model based on AdaBoost-BP neural network and least squares support vector machine (LSSVM), then did the simulation with field data and proved the effectiveness of the short-term wind speed forecast. As characteristics and laws are the same in wind speed signal of the same frequency part, we choose the wavelet packet transform as the data preprocessing method. In basis of wavelet transform, wavelet packet transform provides more analysis of high frequency signal with higher resolution. Paper established short-term wind speed forecast model with optimal wavelet packet transform and LSSVM. Through the test with field data, results show higher prediction precision by using optimal wavelet packet transform.
For the different purpose of short-term wind speed forecast, paper gave two types of wind speed forecast:multistep forecast and information granulation forecast. Then paper analyzed the contrast of two application modes through the simulation. Scheduling instruction to wind farm from power grid are usually serial numbers. Wind speed multistep forecast can show the variation trend in the future for grid continuous scheduling. Information granulation forecast can eliminate data redundancy and get accurate wind speed level and distribution for analyzing output of different wind farms or different wind power units in the future.
It is proposed the adjustable power strategy based on wind power model. Traditional wind unit operation uses the most power strategy and keeps the pitch angle minimum and optimal tip speed ratio; keep rated power by adjusting the pitch angle. Paper established the wind turbine power model, analyzed wind units characteristic, proposed adjust wind turbine power strategy in the full range. Within wind speed, strategy chooses generator torque for small range adjustment and pitch angle and generator torque together for large range adjustment.
We calculated the unit feature vector, established wind farm feature matrix. As the number of units in wind farm, we analyzed the clustering of all units, then set up several level clusters to reduce the dimension of unit commitment problem. Last, in the condition of wind farm power limit from power grid, we solved the unit commitment problem by considering units relative loss.
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