基于机器学习优化换流站调相机稳态无功的方法
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TM721、TM342

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国网江西省电力有限公司重大科技项目(52182020008J)


Method of Optimizing Steady Reactive Power of Converter Station Synchronous Condenser Based on Machine Learning
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    摘要:

    提高换流母线电压的暂态稳定对保障直流输电安全运行具有重要作用。在研究调相机与交流滤波器稳态输出无功协调对换流母线电压暂态稳定性影响的基础上,采用基于多二元表描述的电压暂态稳定裕度指标和支持向量机,提出了基于换流站母线电压和稳态无功协调状态预测母线电压暂态稳定裕度的回归建模方法,进一步利用回归模型,提出了提高换流母线电压暂态稳定裕度和降低系统网损的调相机与交流滤波器稳态无功协调优化方法。最后,利用雅中直流馈入江西电网的电力系统分析综合程序PSASP(power system analysis software package,PSASP)计算分析模型,验证了所提无功协调优化方法提升南昌换流站母线电压暂态稳定性的有效性。仿真结果表明优化方法能够充分挖掘调相机基于动态无功支撑提高换流母线电压对严重故障扰动保持暂态稳定的能力,同时减少交流滤波器的投运数量。

    Abstract:

    Improving the transient stability of the converter bus voltage plays an important role in ensuring the safe operation of DC power transmission. On the basis of the study of the impact of the steady-state output reactive power coordination between synchronous condenser and AC filter on the transient stability of the converter bus voltage, the regression modeling method for predicting bus voltage transient stability margin based on bus voltage and steady-state reactive power coordination state of the converter station is proposed by using voltage transient stability margin index based on multi-binary table description and support vector machine. Further, using the regression model, a coordinated optimization method of the steady-state reactive power of synchronous condenser and AC filter to improve the transient stability margin of the converter bus voltage and reduce the system network loss is proposed. Finally, the effectiveness of the proposed machine learning-based reactive power coordination optimization method to enhance the bus voltage transient stability of Nanchang converter station is verified using the PSASP computational analysis model of Yazhong DC feeding into Jiangxi grid. Simulation results show that the optimization method can fully excavate the ability of the synchronous condenser to improve the transient stability of the converter bus voltage against severe fault disturbances based on dynamic reactive power support, while reducing the number of AC filters put into operation.

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高云超,陈波,肖劲松,等. 基于机器学习优化换流站调相机稳态无功的方法[J]. 科学技术与工程, 2023, 23(14): 6045-6053.
Gao Yunchao, Chen Bo, Xiao Jinsong, et al. Method of Optimizing Steady Reactive Power of Converter Station Synchronous Condenser Based on Machine Learning[J]. Science Technology and Engineering,2023,23(14):6045-6053.

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  • 收稿日期:2022-09-29
  • 最后修改日期:2023-05-16
  • 录用日期:2022-12-02
  • 在线发布日期: 2023-06-01
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