光伏发电并网系统的相关技术研究
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摘要
世界光伏产业和市场在严峻的能源形势和人类生态环境形势的压力下,进入到了快速发展时期,光伏电池产量逐年增加。因此,开展光伏并网发电相关技术的研究具有重要的理论意义和实用价值。本文以光伏发电并网系统为研究对象,对光伏并网系统的建模、控制及对配电网电压质量的影响和对策,光伏及其它分布式电源并网系统中逆变器的故障特征和保护方案,光伏发电系统的短期发电功率预测,光伏及其它分布式电源并网系统失步保护等方面进行了深入的研究。
     采用外特性等效的方法推导了大容量光伏电源的数学模型;针对传统扰动法存在步长固定、功率损耗等问题,提出了“改进扰动观察法”的最大功率跟踪方案;给出了LCL滤波器的设计过程,并与L滤波器和LC滤波器的性能进行了对比分析;采用同步PI双环控制策略实现单极式三相电压型桥式逆变器的并网控制。在此基础上,搭建了大容量光伏并网发电系统模型,为本文其它章节的研究提供了有效平台,并对分布式电源接入配电网对电压波动、电压分布以及谐波影响和防止对策进行了较深入的分析。
     并网逆变器作为光伏及其它分布式电源与系统并网的能量转换与控制核心,其运行情况不仅影响和决定自身的使用寿命,也影响到并网系统是否能够稳定、安全、高效地运行。本文基于开关函数分析了并网逆变器在内部功率开关管短路、断路和并网交流侧出口处发生不对称短路时的直流侧电流的故障特征,深入研究了直流侧电流中基波和二次谐波的幅值随故障类型的变化规律,提出了在现有逆变器保护配置的基础上增加基波过电流保护和二次谐波过电流保护的逆变器保护新方案,无需添加硬件成本,能全面、灵敏、快速反映并网逆变器的所有故障。
     准确地预测光伏电源在未来特定时间段内的发电功率,对电网规划、经济调度、电力交易和分布式电源最优组合运行管理等都有重要的意义。本文分析了天气因素对发电功率的影响,提出了基于BP神经网络和反馈型神经网络的光伏发电功率预测模型,给出了神经网络各层节点个数、模型评估、学习算法等方面的设计方法。在此基础上,鉴于神经网络初始化权值阈值随机性较大,容易陷入局部最优等问题,利用混沌自适应粒子群优化算法的全局收敛能力和强鲁棒性对反馈型神经网络进行初始化设计,提出了基于混沌自适应粒子群优化算法与反馈型神经网络相结合的发电功率预测模型,并对以上三种模型的预测结果进行了分析对比。
     越来越多的光伏及其它分布式电源接入电网,对电力系统的运行稳定性产生了一定影响。本文针对光伏及其它分布式电源并网系统中存在的失步问题,分析了逆变型分布式电源并网系统失步和传统旋转型电源电网失步的区别,利用公共耦合节点的视在阻抗角偏移量来判断失步情况,进而提出适用于分布式并网系统的基于视在阻抗角偏移的失步保护新原理。仿真实验验证了本文提出的失步保护判据原理清晰,不受三相短路的影响。
The world photovoltaic (PV) industry and market have been accessing into arapid period under the pressure of severe energy and human ecology environmentsituation. With the increasing of the PV battery production year by year, PV powergeneration has become the new tendency of the usage of renewable energy. Therefore,the study of related technologies of grid-connected PV power generation has a veryimportant theoretical significance and practical utility. In this paper, grid-connectedPV power generation system is taken as the research object, and deep study has beenmade in the following aspects: the build and control of PV grid-connected systemmodel and its influence and countermeasure of distribution network voltage quality,the fault characteristics and protection scheme of the inverters in PV and otherdistributed generation (DG) grid-connected system, the forecast of the short-termgeneration power of PV power system, and the out-of-step protection in the PV andother DG grid-connected system.
     The method of external characteristic equivalence is used to deduce themathematical model of high capacity PV source. Aiming to the problems of step fixedand power loss of traditional perturbation and observation (P&O) algorithm, animproved P&O algorithm of the maximum power point tracking (MPPT) is proposed.The design process of LCL filter are introduced and compared with the performanceof L and LC filters. Gird-connection control of the monopolar three-phase voltagesource bridge type inverter is realized by using the synchronous PI dual-loop controlstrategy. On the basis, the high capacity grid-connected PV generation system modelis built to provide an effective platform for the research of other chapters. At the sametime, the voltage fluctuation, voltage distribution and harmonics is deeply researchedunder the condition of distribution network with DG accessed.
     The grid-connected inverter, whose operating condition affects not only its ownservice life but also the stability, security, and high efficiency of the whole PVgrid-connected system, is the key apparatus of energy transfer and control when PV orother DG is connected to power system. In this paper, switch function is used toanalysis the direct current (DC) side current fault features of grid-connected inverterwhen short circuit or open circuit occurs in the inner power switch tube, orasymmetric short-circuit happens at the outlet of the grid-connected AC side.Changing regularity of the base wave and second harmonics magnitude in DC sidecurrent under different faults is deeply studied. After the above work, a new inverter protection scheme is proposed which adds base wave over current protection andsecond harmonics over current protection to the existing inverter protection device.The new scheme needs no extra hard ware cost and can reflect all kinds of faults ingrid-connected inverter comprehensively, agilely and quickly.
     Accurate prediction of the PV source generated power in a specified period hasfundamental significance for the power network planning, economic dispatch,electricity trade and optimal combination of DG operation and management. In thispaper, influence of weather elements to generated power is analyzed. Furthermore, PVgeneration power forecasting model based on BP neural network and recurrent neuralnetwork (RNN) is proposed, and the design methods of neural network nodes numberin each layer, model assessment, learning algorithm and other aspects are introduced.On the basis, considering the problem that randomness of neural network initialweights and threshold values, slow convergent ability, and local optimum, theglobally convergent ability and strong robustness of chaos adaptive particle swarmoptimization (CAPSO) algorithm are used in the initial design of RNN, andgenerating power forecasting model based on the combination of CAPSO algorithmand RNN is proposed. Forecasting results of the former three models are analyzed andcompared respectively.
     More and more PV or other DG sources have connected to power grid, whichbring certain effect to the operation stability of power system. Aiming to theout-of-step problem exists in PV and other DG grid-connected system, in this paper,the difference between inverter-based distributed generation out-of-step andtraditional rotating generation out-of-step is analyzed, and the apparent impedanceangle deviation of common coupling point is used to judge the out-of-step condition.Accordingly, a new out-of-step protection theory based on apparent impedance angledeviation is proposed for DG grid-connected system. Simulation results suggest thatthe proposed out-of-step criterion has a clear principle and is insensitive to theinfluence of three-phase short circuit.
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