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Linear LAV-based state estimation integrating hybrid SCADA/PMU measurements

机译:基于线性的LAV的状态估计整合混合体SCADA / PMU测量

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摘要

The accuracy of power system state estimation (PSSE), its robustness against bad data and the speed of its algorithm are crucial to economic and secure system operation. On the other hand, observability and redundancy considerations mandate PSSE to take advantage of traditional supervisory control and data acquisition (SCADA) measurements along with available phasor measurement unit (PMU) measurements. This set of hybrid PMU/SCADA inputs has traditionally made the problem formulation non-linear, and hence time-consuming to solve due to the iterative process of solution. This study addresses the foregoing challenges by proposing a novel linear least-absolute-value (LAV) estimation, without the need for an initial guess of the system state. The linearity of the proposed PSSE formulation is guaranteed regardless of whether PMU-only, SCADA-only or hybrid SCADA/PMU measurements are utilised. This facilitates the fast and non-iterative solution of the LAV estimation of system state based on linear programming. The LAV estimator outperforms the weighted-least-squares estimator in dealing with erroneous measurements, by automatically rejecting bad data of any size. An extensive number of simulation studies carried out on test systems of different sizes confirm the superiorities of the proposed method in comparison with other existing PSSE methods.
机译:电力系统状态估计(PSSE)的准确性,其对不良数据的鲁棒性和其算法的速度对经济和安全系统运行至关重要。另一方面,可观察性和冗余注意事项要求PSSE利用传统的监督控制和数据采集(SCADA)测量以及可用的相量测量单元(PMU)测量。这组混合PMU / SCADA输入传统上,传统上是由于解决方案的迭代过程而产生的问题配方,并且因此耗时。本研究通过提出新的线性最少绝对值(LAV)估计来解决前述挑战,而无需初始猜测系统状态。无论仅使用PMU,仅使用SCADA或混合体SCA / PMU测量,都保证了所提出的PSSE制剂的线性。这有助于基于线性规划的系统状态的稳压和不迭代解。 LAV估计器通过自动拒绝任何尺寸的不良数据来处理错误的测量来优越加权 - 最小二乘估计器。在不同尺寸的测试系统上进行了广泛的模拟研究,确认了与其他现有PSSE方法相比的所提出的方法的优势。

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