空间负荷预测对有配电网的规划建设具有重要意义,为了提高配电网空间负荷预测的精度,文中提出基于极限学习机(ELM)的配电网空间负荷预测算法,采用粒子群优化(PSO)模型的参数.首先根据用地性质将负荷分类,再通过模糊C均值(FCM)算法对每一类负荷进行聚类分析,建立精细化的负荷密度指标体系.根据待预测地块的特性指标选取训练样本,代入ELM训练,提高预测精度.通过搜索的数据对实例进行仿真试验,通过对比未引入FCM算法的相对误差、未引入PSO算法的相对误差以及采用PSO-ELM算法的相对误差可得,文中提出的PSO-ELM算法具有较高精度,满足实际工程的要求.

空间负荷预测对配电网规划建设具有重要意义.为了提高配电网空间负荷预测精度,文中提出基于熵权法与灰色关联分析-极限学习机(GRA-ELM)的配电网空间负荷预测方法.首先,将规划区域内的小区按用地性质划分,分析不同类型负荷的影响因素,建立空间负荷密度指标体系;其次,利用熵权法对不同类型负荷的负荷密度指标进行权重分配;然后,应用GRA挑选出与待测地块负荷密度指标相似的训练样本;最后,将样本带入经粒子群优化(PSO)算法参数处理后的极限学习机(ELM)进行训练,得到预测结果.通过实例对所提方法的性能进行仿真验证,结果表明,所提方法相对其他方法的空间负荷预测精度更高.

当前对于短期负荷预测的研究主要针对影响因素的分析以及模型的改进,很少有对模型的鲁棒性进行研究.以极限学习机(extreme learning machine,ELM)作为研究对象,针对ELM模型的鲁棒性问题进行了深入的研究,并将其应用到短期负荷预测问题中.ELM模型的鲁棒性受损失函数的影响,当前ELM模型在处理含异常点样本时,鲁棒性差、预测精度较低.针对该问题,提出了一种基于p阶最大相关熵准则的损失函数,并将该损失函数应用到ELM模型中,以提高其在短期负荷预测问题中的鲁棒性.提出了一种估计实际样本中异常点百分比的计算方法,在建立短期负荷预测模型之前,估计出实际负荷样本中的异常点百分比.仿真结果表明,在异常点超过12％的样本中,提出的算法模型具有更好的鲁棒性以及预测精度.

针对独立直流微电网中功率分配问题,基于Hamilton能量理论提出分布式协同控制策略.首先对单组燃料电池独立直流微电网模型设计预反馈控制,得到端口受控耗散Hamilton模型;然后将单组燃料电池拓展为相互通信的多组燃料电池的独立直流微电网,设计分布式协同控制策略,使得用电负荷在每组系统中得到合理分配;最后通过仿真验证分布式协同控制的有效性.结果表明,燃料电池输出的有功功率一致且直流母线电压都稳定在其参考值.

夏季受高温天气的影响,由降温设备所引起的气象负荷日趋变大.针对气象负荷获取困难以及负荷预测精度不高的问题,提出一种新的气象负荷预测方法.首先,为获得准确的气象负荷数据,采用生长曲线来描述基础负荷的增长特性,通过剔除基础负荷来获得气象负荷数据;其次,考虑到夏季高温天气的气温累积效应,需要对高温天气的日最高温度进行修正,提出一种基于气象负荷的温度修正方法及相应模型;最后,建立粒子群优化的极限学习机负荷预测模型,分别对总负荷和气象负荷进行预测.算例分析结果表明,基于生长曲线与气温累积效应提升了负荷预测效果,验证了所提算法和模型的有效性.

针对独立直流微电网稳定性问题,提出了一种基于滑模变结构的电流无源双闭环控制策略,提高了系统的响应速度和抗干扰能力.首先,建立独立直流微电网系统数学模型,针对该模型应用无源性理论设计系统的控制输入,得到系统的端口受控耗散Hamilton(port-controlled hamiltonian with dissipation,PCH-D)模型.然后,针对系统的直流母线电压的稳定性问题,应用滑模变结构方法,当系统用电负荷、分布式能源和参数突变时,母线电压仍能稳定在其期望值上,并得到燃料电池电流的参考值.最后,通过Matlab仿真进行验证,对比本文控制策略与传统单一的无源控制策略,说明所提出的控制方法具有更好的动态特性和稳态特性.

Aiming at the defects of chicken swarm optimization algorithm, such as easy to fall into local optimal, premature convergence and slow convergence, a chicken swarm optimization algorithm based on quantum behavior is proposed in this paper. A quantized potential well model is established based on the individual information of chicken swarm. According to the existing individual extremum and global extremum obtained by the original updating formula, Monte Carlo random sampling is adopted to complete the updating of individual extremum, and the search is conducted at a parallel Angle near individual extremum and global extremum, which improves the local search performance of the algorithm. At the same time, the convergence of quantum-behavior chicken swarm optimization algorithm is discussed in this paper, and QCSO is proved to be a globally convergent optimization algorithm. The optimization capability of QCSO is tested by using basic test function, and the results show that the optimization performance of this algorithm is greatly improved compared with the original algorithm.

随着分布式光伏的发展以及电动汽车的接入对配电网的影响,传统的空间负荷预测已经不能满足要求,因此本文提出了考虑光伏出力以及电动汽车接入的空间负荷预测方法.首先基于LSSVM与负荷密度指标法预测传统空间负荷;然后,根据规划区的光伏影响因素预测光伏输出功率大小,采用蒙特卡洛算法模拟电动汽车的充电负荷;最后,将光伏输出功率、电动汽车充电功率与传统空间负荷叠加得出总的空间负荷.以绍兴市某城区为例,预测结果表明,考虑了光伏出力以及电动汽车接入会明显的提高空间负荷预测的精度.

With the development of distributed photovoltaic (PV) and the impact of electric vehicles (EVs) access on the distribution network, the traditional spatial load forecasting can no longer meet the requirements. Therefore, a spatial load forecasting method considering PV output and EVs access is proposed. Firstly, the traditional spatial load was predicted based on LS-SVM and load density index method, and then the photovoltaic output power is predicted according to the photovoltaic influence factors in the planning area. Monte Carlo algorithm is adopted to simulate the charging load of electric vehicles, and finally the photovoltaic output power, electric vehicles charging power and traditional space load are superimposed to obtain the total space load. Taking a certain urban area of Shaoxing as an example, the prediction results show that the accuracy of spatial load predicting can be significantly improved by considering PV output and EVs access.

With large-scale development of offshore wind power and the increasing scale of power grid interconnection, more and more attention has been drawn to the stable operation of wind power units. When the wide area measurement system (WAMS) is applied to the power system, the time delay mainly occurs in the signal measurement and transmission of the power system. When 10MW wind turbines transmit information through complex communication network, time delay often exists, which leads to the degradation of performance and instability for system. This affects the normal operation of a wind farm. Therefore, in this paper, the distributed control problem of doubly fed wind turbines with input time delay is studied based on the Hamiltonian energy theory. Firstly, the Port-controlled Hamiltonian system with Dissipation (PCH-D) model is implemented with the Hamiltonian energy method. Then, the Casimir function is introduced into the PCH-D model of the single wind turbine system to stabilize the time delay. The wind turbine group is regarded as one network and the distributed control strategy is designed, so that the whole wind turbine cluster can remain stable given a time delay occurring in the range of 30–300 ms. Finally, simulation results show that the output power of the wind turbine cluster with input delay converges to the expected value rapidly and remains stable. Additionally, the system error caused by time delay is greatly reduced. This control method can effectively solve the problem of input time delay and improve the stability of the wind turbine cluster. Moreover, the method proposed in this paper can adopt the conventional time step of dynamic simulation, which is more efficient in calculation. This method has adaptability in transient stability analysis of large-scale power system, however, the third-order mathematical model used in this paper cannot be used to analyze the internal dynamics of the whole power converter.