电力系统低模型耦合智能状态估计
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1.中国南方电网电力调度控制中心;2.湖南大学电气与信息工程学院;3.南京南瑞继保电气有限公司

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适应电力市场的状态估计研究与功能开发


Smart Power System State Estimation with Low Model Coupling
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Affiliation:

1.Power Dispatching Control Center, China Southern Power Grid;2.College of Electrical and Information Engineering, Hunan University;3.NARI RELAYS Electric Co., Ltd. Nanjing

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    摘要:

    在传统电力系统状态估计中,状态修正方程的迭代步长一般选取固定值,从应用效果看,该方法常因数据质量低、网络条件复杂而不能有效收敛。为解决该问题并提高状态估计的适配性,本文对经典逻辑函数进行重构,找到了在图像上与状态估计高质量数值迭代具有天然适配性的母函数,并将其作为步长控制因子;设定母函数的形状控制参数并使部分参数可通过“迭代效果”进行实时微调,母函数会自动进行状态估计适应性调整;此外,引入权因子函数,使算法在迭代过程中执行变权操作,可降低不良数据的影响。因所提方法的步长调整机制直接由迭代效果控制,无需电网数学模型的直接“介入”,相对于传统采用解析方法调整步长的策略,该方法具有对模型耦合性较低、可移植性强的特点。基于IEEE30节点系统的研究显示,在量测出现不良数据和网络准病态、病态的条件下,本文所提方法的数值稳定性、运算效率和估计质量均明显优于传统固定步长方法。

    Abstract:

    In traditional power system state estimation (PS-SE), the iterative step size of state correction equation is generally fixed. But considering the low data quality and the complex network condition in practice, it is difficult that the convergency, estimation quality, and computation efficiency can reach the satisfactory state simultaneously by adopting fixed step iteration strategy. To solve this problem, we find a variant of classical logistic function (we call it as the generating function) which can intrinsically match the iteration demand of PS-SE, it is adopted as the step size adjusting factor. By “installing” some control parameters on the generating function and adjusting part of them by the feedback of the iteration performance in real time, generating function can be auto-adjusted in the iteration process to satisfy PS-SE’s demand. In addition, a weight factor function is introduced to auto-adjust the state variables’ weights in iteration period, it has good performance on suppressing bad data. The proposed step adjusting mechanism is directly controlled by the iteration performance, this means it couples loosen with the network mathematical model, so it is a model low-dependent PS-SE algorism with good portability. Based on IEEE30 node system, it is found that the proposed algorithm is superior to the one of adopting fixed step strategy in terms of convergence, computation efficiency and estimated quality when the measurement has bad data and the power system is under quasi ill-conditioned and ill-conditioned.

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  • 收稿日期:2020-10-09
  • 最后修改日期:2021-03-29
  • 录用日期:2021-04-14
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