光伏发电系统运行理论与关键技术研究
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摘要
随着世界经济的不断发展、人口数量的不断增加，我们对能源的需求急剧增加，而化石能源的日益匮乏，以及不断恶化的环境问题，使得人们将目光转向了可再生能源技术。太阳能作为可再生能源中的一种，具有资源无限、无污染及可持续性等优点，发展潜力巨大。由于太阳光照的不稳定性以及太阳能光伏电站发电功率的不断增大，在系统的运行过程中，存在最大功率点跟踪、功率预测、光伏组件温度预测以及故障定位等一些实际问题。论文针对上述问题展开研究，主要内容包括如下五个方面：
首先，论文对江苏省徐州地区的太阳能光照强度，以及光伏发电站的发电量进行了统计和分析。基于江苏艾德太阳能科技有限公司提供的光伏电站运转过程中的历史数据，对各月份的光照强度、温度变化以及光伏电站的发电规律进行了统计，给出了相应的图表，为徐州地区光伏电站的兴建和运行提供了数据支持。进一步分析了光伏系统运行过程中组件温度与环境温度的差别、光伏系统运行过程中输出功率的变化等，为本论文所针对的光伏发电系统运行中关键问题的解决奠定了基础。
进而，针对光伏发电系统最大功率点跟踪问题进行了研究，提出了一种融入免疫优化算法具有自搜索最大功率点功能的光伏电池建模方法，对于给定的光照强度和给定的温度，此模型可以快速计算其相应的最大功率点，为后续最大功率点算法的验证提供了模型基础。在分析和研究了现有各种最大功率点算法优缺点的基础上，结合恒压法在最大功率点处无功率振荡以及扰动观测法使用方便的优点，提出了一种将恒压法和扰动观测法相结合的光伏发电系统最大功率点跟踪的算法，以光伏系统最大功率点工作时的近似电压和光伏发电系统当时的工作电压作为扰动步长变化的依据，解决了原有扰动观测法跟踪速度和功率振荡之间的矛盾，也解决了恒压法不是真正意义上的最大功率点跟踪算法的问题，仿真和实验都验证了所提方法的有效性。
针对太阳光照强度变化的不可预知性导致的光伏阵列输出功率难预测性，提出了利用相似日和加权支持向量机对光伏电站输出功率进行短期预测的方法，给出了相似日的选择方法，并根据相似度确定加权支持向量机的权重系数；利用历史记录中与待预测日相似度比较高的历史日数据，对加权支持向量机进行训练；解决了光伏电站在发电过程中输出功率的不确定性，实现了对光伏电站输出功率的预测。通过与基于人工神经网络的功率预测算法进行对比，说明了所提算法的有效性。
针对在最大功率点跟踪等关键问题中，简单采用环境温度代替光伏组件温度存在的不足，考虑温度变化存在的不确定性，提出了采用高斯模型对光伏发电系统中光伏阵列组件温度进行建模及预测的方法，建立了光伏组件温度与光伏阵列输出功率以及环境温度之间的仿真模型。将所提算法应用于实际系统中，通过与神经网络预测结果进行了对比分析，在考虑不同训练样本选择机制的情况下，通过对预测误差进行分析发现，采用高斯模型对光伏阵列组件温度预测，效果较好；在光伏阵列组件温度预测的过程中，高斯模型对小样本数据具有很好的预测效果，预测误差较小。
针对光伏阵列运行过程中可能存在的故障问题，特别是其小样本特性，提出了将高斯分回归算法用于光伏阵列故障定位的方法。首先，结合光伏阵列传感器最优布置方式，采用二进制码表示光伏阵列工作状态；然后，构建了基于整数回归的故障定位模型，对光伏系统的故障进行定位。将所提算法应用于实际系统中，并与常用的神经网络算法进行对比，仿真结果表明本算法具有较高的定位准确度，验证了所提方法的有效性。
With the succesive development of the world economy and the increase of thepopulation size, the demand on energy is fast growing. The fossil fuels, however,isnow scaring daily, which will further worsen the environment. To deal with suchproblems, the renewable energy technologies have been attached more attentions inrecent years. The solar energy, one of the renewable energy resources, has greatdevelopment potential because it is a kind of unlimited resource, non-polluting andsustainable. Due to the instability of the sun light and the continues increase of powergeneration, there are some operational problems in the running process of aphotovoltaic system, such as the maximum power point tracking, power prediction,PV module temperature prediction and fault localization. These problems will befocused on in this thesis and the following five contents are studied.
First, the solar light intensity and generating capacity of photovoltaic powerstations in Xuzhou, Jiangsu Province were counted and analyzed. Based on theobtained historical data of the photovoltaic power plant provided by Ed Solar EnergyTechnology Co., Ltd in Jiangsu, we thoroughly studied the monthly light intensity,temperature changes and the generation law of the photovoltaic power plant, and alsogave the corresponding charts and analytical results. These results provide somesupport on data for the building and operation of photovoltaic power plants in Xuzhouarea. The differences between the components temperature and the ambient one arefurther addressed. The corresponding results are expected to be bases of the followingstudies of those important problems existing in the running process of a photovoltaicsystem.
In order to more accurately tracking the maximum power point of a photovoltaicsystem, an immune genetic algorithm based simulation model which can search itsmaximum power point is established here. For a given light intensity and temperature,the theoretical maximum power point of the current condition can be quicklycalculated with the simulation model, which can be adopted as a reference forvalidating the subsequent maximum power point tracking algorithm. Then, bycombining the advantages of constant voltage method and disturbance observermethod, we propose a novel maximum power point tracking algorithm. According tothe approximate voltage of maximum power point and photovoltaic system operatingvoltage, the disturbance steps are changed, which can well solve the contradiction of the tracking speed and the power oscillation of original disturbance observer method.Meanwhile, the problem of constant voltage method that the tracked maximum powerpoint may not a true one can also be refrained from. The effectiveness of the proposedmethod is experimentally validated.
The output power of PV arrays is difficult to be predicted due to unpredictablechanges in sun light intensity. To solve this problem, the method of short-termforecasts of the output power is here addressed by introducing weighted supportvector machine and similar days. The weights of the training data for training thepredictive model are determined according to the similar degree of the selectedsimilar days. To demonstrate the performance of the proposed algorithm, we appliedthe obtained data to experimentally validate it. The effectiveness of our algorithm isillustrated and it outperforms the often used neural network in high predictionaccuracy with small number of training data.
In existing research involving component temperature, its value is oftensubstituted with the ambient temperature for simplicity. But the two temperatures arequite different, which will greatly influence the accuracy of simulation and maximumpower point tracking in the constant voltage method. To solve these problems, wehere first focused on the prediction of the PV array components temperature by use ofGaussian Process. The prediction model based on Gaussian Process is constructedwith a set of samples. To sufficiently illustrate the performance of the algorithm,different sizes of samples are considered in the simulations. The results comparedwith the neural network based algorithm show that our method by use of GaussianProcess has a better prediction to a small sample of data.
During the running process of a photovoltaic system, faults often occur atdifferent time and places, which will inevitably impact the output power of the system.Therefore, the accurate and reliable localization of the faults are very important. Tothis end, a fault localization method using Gaussian Process is then given here.According to the placement of the voltage and current sensors, the fault modesexpreesed by binary numbers are converted into integers and the Gaussian Process isconstructed to approximate these integers. With newly measured voltages and currents,the corresponding integer values of the system can be easily calculated and thecorresponding operating models can be obtained. A plenty of experiments e.g.,different fault modes, are conducted to validate the effectiveness of the algorithm, andthe results demonstrate that the proposed algorithm has a higher localization accuracy than the neural network one.
首先，论文对江苏省徐州地区的太阳能光照强度，以及光伏发电站的发电量进行了统计和分析。基于江苏艾德太阳能科技有限公司提供的光伏电站运转过程中的历史数据，对各月份的光照强度、温度变化以及光伏电站的发电规律进行了统计，给出了相应的图表，为徐州地区光伏电站的兴建和运行提供了数据支持。进一步分析了光伏系统运行过程中组件温度与环境温度的差别、光伏系统运行过程中输出功率的变化等，为本论文所针对的光伏发电系统运行中关键问题的解决奠定了基础。
进而，针对光伏发电系统最大功率点跟踪问题进行了研究，提出了一种融入免疫优化算法具有自搜索最大功率点功能的光伏电池建模方法，对于给定的光照强度和给定的温度，此模型可以快速计算其相应的最大功率点，为后续最大功率点算法的验证提供了模型基础。在分析和研究了现有各种最大功率点算法优缺点的基础上，结合恒压法在最大功率点处无功率振荡以及扰动观测法使用方便的优点，提出了一种将恒压法和扰动观测法相结合的光伏发电系统最大功率点跟踪的算法，以光伏系统最大功率点工作时的近似电压和光伏发电系统当时的工作电压作为扰动步长变化的依据，解决了原有扰动观测法跟踪速度和功率振荡之间的矛盾，也解决了恒压法不是真正意义上的最大功率点跟踪算法的问题，仿真和实验都验证了所提方法的有效性。
针对太阳光照强度变化的不可预知性导致的光伏阵列输出功率难预测性，提出了利用相似日和加权支持向量机对光伏电站输出功率进行短期预测的方法，给出了相似日的选择方法，并根据相似度确定加权支持向量机的权重系数；利用历史记录中与待预测日相似度比较高的历史日数据，对加权支持向量机进行训练；解决了光伏电站在发电过程中输出功率的不确定性，实现了对光伏电站输出功率的预测。通过与基于人工神经网络的功率预测算法进行对比，说明了所提算法的有效性。
针对在最大功率点跟踪等关键问题中，简单采用环境温度代替光伏组件温度存在的不足，考虑温度变化存在的不确定性，提出了采用高斯模型对光伏发电系统中光伏阵列组件温度进行建模及预测的方法，建立了光伏组件温度与光伏阵列输出功率以及环境温度之间的仿真模型。将所提算法应用于实际系统中，通过与神经网络预测结果进行了对比分析，在考虑不同训练样本选择机制的情况下，通过对预测误差进行分析发现，采用高斯模型对光伏阵列组件温度预测，效果较好；在光伏阵列组件温度预测的过程中，高斯模型对小样本数据具有很好的预测效果，预测误差较小。
针对光伏阵列运行过程中可能存在的故障问题，特别是其小样本特性，提出了将高斯分回归算法用于光伏阵列故障定位的方法。首先，结合光伏阵列传感器最优布置方式，采用二进制码表示光伏阵列工作状态；然后，构建了基于整数回归的故障定位模型，对光伏系统的故障进行定位。将所提算法应用于实际系统中，并与常用的神经网络算法进行对比，仿真结果表明本算法具有较高的定位准确度，验证了所提方法的有效性。
With the succesive development of the world economy and the increase of thepopulation size, the demand on energy is fast growing. The fossil fuels, however,isnow scaring daily, which will further worsen the environment. To deal with suchproblems, the renewable energy technologies have been attached more attentions inrecent years. The solar energy, one of the renewable energy resources, has greatdevelopment potential because it is a kind of unlimited resource, non-polluting andsustainable. Due to the instability of the sun light and the continues increase of powergeneration, there are some operational problems in the running process of aphotovoltaic system, such as the maximum power point tracking, power prediction,PV module temperature prediction and fault localization. These problems will befocused on in this thesis and the following five contents are studied.
First, the solar light intensity and generating capacity of photovoltaic powerstations in Xuzhou, Jiangsu Province were counted and analyzed. Based on theobtained historical data of the photovoltaic power plant provided by Ed Solar EnergyTechnology Co., Ltd in Jiangsu, we thoroughly studied the monthly light intensity,temperature changes and the generation law of the photovoltaic power plant, and alsogave the corresponding charts and analytical results. These results provide somesupport on data for the building and operation of photovoltaic power plants in Xuzhouarea. The differences between the components temperature and the ambient one arefurther addressed. The corresponding results are expected to be bases of the followingstudies of those important problems existing in the running process of a photovoltaicsystem.
In order to more accurately tracking the maximum power point of a photovoltaicsystem, an immune genetic algorithm based simulation model which can search itsmaximum power point is established here. For a given light intensity and temperature,the theoretical maximum power point of the current condition can be quicklycalculated with the simulation model, which can be adopted as a reference forvalidating the subsequent maximum power point tracking algorithm. Then, bycombining the advantages of constant voltage method and disturbance observermethod, we propose a novel maximum power point tracking algorithm. According tothe approximate voltage of maximum power point and photovoltaic system operatingvoltage, the disturbance steps are changed, which can well solve the contradiction of the tracking speed and the power oscillation of original disturbance observer method.Meanwhile, the problem of constant voltage method that the tracked maximum powerpoint may not a true one can also be refrained from. The effectiveness of the proposedmethod is experimentally validated.
The output power of PV arrays is difficult to be predicted due to unpredictablechanges in sun light intensity. To solve this problem, the method of short-termforecasts of the output power is here addressed by introducing weighted supportvector machine and similar days. The weights of the training data for training thepredictive model are determined according to the similar degree of the selectedsimilar days. To demonstrate the performance of the proposed algorithm, we appliedthe obtained data to experimentally validate it. The effectiveness of our algorithm isillustrated and it outperforms the often used neural network in high predictionaccuracy with small number of training data.
In existing research involving component temperature, its value is oftensubstituted with the ambient temperature for simplicity. But the two temperatures arequite different, which will greatly influence the accuracy of simulation and maximumpower point tracking in the constant voltage method. To solve these problems, wehere first focused on the prediction of the PV array components temperature by use ofGaussian Process. The prediction model based on Gaussian Process is constructedwith a set of samples. To sufficiently illustrate the performance of the algorithm,different sizes of samples are considered in the simulations. The results comparedwith the neural network based algorithm show that our method by use of GaussianProcess has a better prediction to a small sample of data.
During the running process of a photovoltaic system, faults often occur atdifferent time and places, which will inevitably impact the output power of the system.Therefore, the accurate and reliable localization of the faults are very important. Tothis end, a fault localization method using Gaussian Process is then given here.According to the placement of the voltage and current sensors, the fault modesexpreesed by binary numbers are converted into integers and the Gaussian Process isconstructed to approximate these integers. With newly measured voltages and currents,the corresponding integer values of the system can be easily calculated and thecorresponding operating models can be obtained. A plenty of experiments e.g.,different fault modes, are conducted to validate the effectiveness of the algorithm, andthe results demonstrate that the proposed algorithm has a higher localization accuracy than the neural network one.
引文
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