受大规模新能源接入电网的网架特性约束以及新能源装备自身安全限制,高渗透率新能源并网后的静态电压问题逐渐影响到电力系统的安全稳定运行。伴随着电力电子器件的发展,柔性交流输电技术(flexible AC transmission system, FACTS)在调节线路潮流、改善静态电压稳定性方面的研究更加深入。文章探讨计及分布式静态串联补偿器(distributed static series compensator, DSSC)的新能源电网静态电压稳定性,从系统静态电压稳定性方面评估DSSC发挥的潮流控制效能。首先分析DSSC的等效电路及工作原理,并基于DSSC的等效功率注入模型得到潮流计算方程;其次,基于潮流计算方程提出能够反映系统静态电压稳定性的效能评估指标,计算分析接入不同容量新能源对静态电压稳定性的影响;最后通过IEEE 30节点系统对所提方法进行仿真验证,结果表明,DSSC能够有效调节薄弱节点电压,提升新能源电网的安全稳定性。

The issue of static voltage instability in high-permeability new energy sources has increasingly impacted the safe and stable functioning of power systems. This is due to limitations associated with the grid characteristics of large-scale new energy sources as well as inherent security constraints of the new energy equipment. With advancements in power electronic devices, the Flexible AC Transmission System (FACTS) has undergone deeper research for better regulation of power flow and improvement of static voltage stability. In this study, the static voltage stability of a new energy grid, with a focus on the role of a Distributed Static Series Compensator (DSSC), is examined. Furthermore, the efficacy of the DSSC is evaluated in controlling power flow based on its impact on the system's static voltage stability. First, the equivalent circuit and working principle of the DSSC were analyzed, and the power flow equations were obtained based on the equivalent power injection model of the DSSC. Based on the power flow calculation equation, an evaluation index is proposed to reflect the static voltage stability of the power system. Finally, the proposed method is verified using the IEEE-30 node system. The results show that the DSSC can effectively adjust the voltage of weak nodes and improve the safety and stability of the new energy power grid.

随着电力系统的日益复杂，传统静态电压稳定分析方法难以满足精度和速度要求。为此，提出一种数据驱动的静态电压稳定评估方法。首先，通过仿真工具沿时间轨迹捕捉不同时间断面上的系统状态数据，筛选出与系统电压稳定性关联度较高的特征属性，采用静态负荷有功功率裕度对数据进行标签分类。然后，应用集成多个决策树的随机森林分类器模型将原始状态标签数据转化为稳定信息和行为模型，从数据中感知和学习静态电压稳定行为。最后，通过查询随机森林的分类结果，实现对静态电压稳定性的判断。仿真结果表明，该模型可行有效，适用于静态电压稳定态势评估。

With the increasing complexity of power systems, traditional static voltage stability analysis methods face challenges to meet the accuracy and speed of real-time static stability assessment. In this paper, a data-driven static voltage stability evaluation method is proposed. First, the system state data in different time sections are captured along the time trajectory by simulation tools, and the characteristic attributes with high correlation with voltage stability are screened out. The static load active power margin is used to classify the data. Second, a random forest classifier model integrating multiple decision trees is used to transform the original state label data into stable information and behavior models, and the static voltage stability behavior is sensed and learned from the data. Finally, by querying the classification results of the random forest, the judgment of the static voltage stability is achieved. Simulation results show that the model is feasible and suitable for static voltage stability situation assessment.

大规模风电并网以及负荷的随机波动加剧了电网运行的不确定性，为了有效分析新环境下的电网运行特性，提出一种基于风功率分段Copula函数和负荷高斯混合模型的多段线性化概率潮流计算方法。采用分段Copula函数在时间维度上刻画相邻风电场的空间相关性，分析风功率相关性的季节变化。针对实际负荷的不对称、多峰特性，采用改进K-means聚类优化的期望最大化（expectation maximization，EM）算法，准确快速地建立负荷高斯混合模型。在此基础上，采用多段线性化半不变量法进行概率潮流计算，以减小风功率和负荷大范围波动造成的潮流方程线性化误差。对改进的IEEE 14节点系统进行仿真分析，验证了所提方法的准确性、快速性及有效性。

Large-scale integration of wind power into grid and load stochastic volatility increase the uncertainty in power system operation, in order to effectively analyze the system operation features in the new environment, a calculating method based on wind power piecewise Copula and load Gaussian mixture model for multistage linearization probabilistic power flow is proposed. Piecewise Copula is used to establish the spatial correlation model among wind farms on the time dimension considering seasonal variation. For non-normal and multimodal load, expectation maximization （EM） algorithm is used to establish load Gaussian mixture model, and an improved K-means clustering is proposed to optimize EM algorithm, which can simplify the modeling process. On the premise of these models, calculating probabilistic power flow in the method of multistage linearization cumulant method, fully considering the impact of wind power and load fluctuation on the system operation. The accuracy and efficiency of the proposed probabilistic power flow calculation process is verified through the test on modified IEEE 14-bus system.

风电输出的随机波动性对并网系统的实时运行状态会产生影响，优化系统典型运行方式下的风电并网位置和容量对降低系统的静态安全风险、保证运行经济性具有重要作用。文章考虑电网源荷不确定性，以及风速间、负荷间相关性，利用点估计结合Nataf逆变换生成随机相关性样本的方法，研究了风电并网系统考虑随机相关性的点估计概率潮流计算方法。进而以减小电网网损率和节点电压平均偏移为目标，通过把所研究概率潮流嵌入自适应粒子群算法，同时避免点估计方法求解概率潮流对约束条件的偏乐观性，提出了合理规划风电并网位置和容量的优化方法。最后利用IEEE 57节点系统仿真算例验证了所提优化方法的有效性。仿真结果表明了考虑随机相关性对提高优化结果可靠度的必要性。

Due to the random fluctuation of wind power having impact on the real-time operation state of power system， reasonable optimization of both location and capacity of grid-connected wind farm under the classical operation mode should be considered. It is benefitial for both reducing the static safety risk and improving the economic operation of the grid. Considering the uncertainty of both power-source side and load side， the correlation of different wind speed and the correlation of different loads， a probability power flow calculation method considering the correlation of random variables is studied in this paper， which generates the correlation samples by combing point estimation and inverse Nataf transform. Moreover， through embedding the point-estimation based algorithm for probabilistic power flow， which considers the random correlation， into the adaptive particle swarm optimization algorithm， and avoiding the optimism on the constraints caused by using the point estimation method to solve the probabilistic power flow， taking the minimization of both the active power loss rate of the grid and the average deviation of bus voltages as the objective， the method for reasonably planning both the location and capacity of grid-connected wind farm is presented. Finally， the effectiveness of the proposed optimizing method is validated by the simulation carried out on the IEEE 57-node system. At the same time， the simulation result also shows the necessity of improving the reliability of the optimizing result by considering the correlation of the random variables.