基于核函数极限学习机的分布式光伏短期功率预测

伴随中国农村电网的较快发展,分布式光伏的集成应用是实现新能源就地消纳的重要途径。国家相关政策已对分布式光伏的快速发展进行了相关规划,国家电网公司也出台政策为分布式光伏接入提供便利条件与技术支持,相关的分布式光伏发电功率预测技术需要进行深入研究。针对用户侧分布式光伏发电系统,考虑预测系统的成本约束和运行需求,以及农村电网应用特点,提出一种基于核函数极限学习机的分布式光伏功率预测方法。对于不同容量的分布式光伏发电系统,使用核函数极限学习机构建分布式光伏短期功率预测模型,使用基于权重的训练样本筛选方法提高预测模型计算效率,并通过粒子群算法优化模型参数。预测模型使用低成本的非数值天气预报采样信息,对几十千瓦级的分布式光伏,预测相对误差仅16%～18%,能在低功耗处理器上实现10ms内完成单次发电功率预测,在简化低权重属性后能基本保持原有精度,同时在分布式光伏随机覆尘或逆变器故障条件下预测误差基本不变,具有较高的适应能力。

Distributed photovoltaic short-term power output forecasting based on extreme learning machine with kernel

Abstract: Along with the rapid development of rural power network in our country, the power demand increases and creates the environment for distributed energy access to the agricultural power network. Taking integration application program with the distributed photovoltaic power output systems through the rural power network and other application forms is a realization and effective method for the new clean energy can be absorbed at local, reducing carbon emissions and environmental pollution. In our country, the national government had developed and introduced relevant policies, and making plans toward to the rapid and healthy development of distributed photovoltaic power output systems for a period of time in the near future. In addition to the national planning, the State Grid Corporation of China also proposed their policies about macro planning and access technologies, that making convenience to the legal proceedings for the personal property distributed photovoltaic access, and offering the related technical support for the distributed photovoltaic power output system access. In order to make sure the operation stability and economy of micro-grid and distribution network with the distributed photovoltaic power output systems access at rural power network, the distributed photovoltaic power output forecasting technology need to be deeply researched combining with the forecasting system application environment characteristics of the rural power network. For the distributed photovoltaic power output system at user side, a short-term PV power output forecasting method based on the algorithm that extreme learning machine with kernel (ELM_k) is proposed. This method considers the operation cost constraints of short-term power output forecasting system, such as does not depending on the high cost of numerical weather prediction. This forecasting system also considers the application characteristics of rural power network, such as the lack of professional maintenance for the distributed photovoltaic panels and related inverter equipment. For the distributed photovoltaic systems with different capacities, the PV power output short-term forecasting model was built based on ELM_k algorithm. Taking the training samples filtration based on attributes weight to improve the computational efficiency of the PV power output forecasting model. The parameters of the forecasting model that relevant with ELM_k algorithm and samples filtration method is optimized through the particle swarm optimization algorithm. The forecasting model uses the low-cost samples with non-numerical weather prediction. For the distributed photovoltaic power output systems at tens of kilowatt, the mean absolute relative error was only 16-18%, and can complete a single power output forecasting within 10 milliseconds, when it implemented on the lower power processor, furthermore, this forecasting method can basically maintain the original accuracy when the low weight attributes were simplified. At the same time, under the distribution photovoltaic operation circumstance of random dust overlying and inverter partial failure, the prediction error remains largely unchanged, which proves the high adaptability of this forecasting model.