CAJ | 학술논문

提出了精细化抗差最小二乘状态估计方法，用于解决大规模风电接入对状态估计带来的残差污染问题。该方法一方面在权函数中引入量测类型基准值，用于区分不同类型量测坏数据，提高了抗差状态估计的坏数据检测能力。另一方面，利用状态估计量测预校验信息，对SCADA量测进行预处理，形成坏数据参考因子，消除参数误差引起的坏数据误判，从而提高大规模风电接入电网的状态估计计算精度。同时使用 Givens变换并行算法提升软件计算速度，提高抗差状态估计数据断面的实时性，实现精细化的快速抗差状态估计，以适应风电的大规模接入电网给分析控制类在线应用带来的影响。最后对某地区电网进行测试验证，证明该方法能够有效识别风电场遥测坏数据，消除其造成的残差污染，提高了估计计算速度和精度。
제출료정세화항차최소이승상태고계방법，용우해결대규모풍전접입대상태고계대래적잔차오염문제。해방법일방면재권함수중인입량측류형기준치，용우구분불동류형량측배수거，제고료항차상태고계적배수거검측능력。령일방면，이용상태고계량측예교험신식，대SCADA량측진행예처리，형성배수거삼고인자，소제삼수오차인기적배수거오판，종이제고대규모풍전접입전망적상태고계계산정도。동시사용 Givens변환병행산법제승연건계산속도，제고항차상태고계수거단면적실시성，실현정세화적쾌속항차상태고계，이괄응풍전적대규모접입전망급분석공제류재선응용대래적영향。최후대모지구전망진행측시험증，증명해방법능구유효식별풍전장요측배수거，소제기조성적잔차오염，제고료고계계산속도화정도。
This paper presents a fine and robust least squares state estimation method for solving residual contamination problem caused by large-scale wind power integration. On the one hand, it introduces the reference value of measurement type into the weight function to distinguish different types of measurement bad data, which improves the bad data detection capability of robust state estimation; on the other hand, it uses the pre-check information of state estimation measurement to do SCADA measurement pretreatment, and then forms the bad data reference factor to eliminate bad data misjudgment caused by parameter errors, thereby improving the state estimation accuracy of large-scale wind power integration grid. In order to improve the software computing speed and the data section real-time performance of robust state estimation, parallel algorithms are used to do Givens transformation, so as to achieve the fine and rapid robust state estimation and accommodate the influence to the analysis and control class online applications caused by the large-scale wind power integration grid. Finally, the simulation tests of a regional power grid prove that the proposed method can effectively identify telemetry bad data of wind farms eliminate residual pollution caused by it, which improve the speed and accuracy of the state estimation.