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Performance evaluation of two simplified algorithms of WLS power system state estimation

Majdoub M., Boukherouaa J., Cheddadi B., Belfqih A., Sabri O., Haidi T.

Przegląd Elektrotechniczny

2018

R. 94, nr 12

18--23

This paper presents a performance evaluation of two simplified versions of the well-known basic weighted least squares algorithm for power system state estimation. Different simulations are tested on IEEE 14, 30 and 118 bus systems: a comparative study was carried out in order to identify the advantages and disadvantages of each method. The results bring out the interest of WLS2 Algorithm which reduces by half the computation time compared to the basic algorithm with the same reliability and precision.

W artykule zaprezentowano badanie właściwości dwóch uproszczonych wersji algorytmu najmnijeszych kwadratów stosowanego do oceny systemu energetycznego. Przeprowadzono symulacje systemu IEEE z 134, 30 I 118 szynami. W algorytmie WLS2 udaje się zredukować o połowę czas obliczeń.

An Uncertainty Model Of Approximating The Analytical Solution To The Real Case In The Field Of Stress Prediction

Gutiérrez R., Ramírez M., Olmeda E., Díaz V.

Metrology and Measurement Systems

2015

Vol. 22, nr 3

429--442

Deterministic mechanics has been extensively used by engineers as they needed models that could predict the behavior of designed structures and components. However, modern engineering is now shifting to a new approach where the uncertainty analysis of the model inputs enables to obtain more accurate results. This paper presents an application of this new approach in the field of the stress analysis. In this case, a two-dimensional stress elasticity model is compared with the experimental stress results of five different size tubes measured with resistive strain gages. Theoretical and experimental uncertainties have been calculated by means of the Monte Carlo method and a weighted least square algorithm, respectively. The paper proposes that the analytical engineering models have to integrate an uncertainty component considering the uncertainties of the input data and phenomena observed during the test, that are difficult to adapt in the analytical model. The prediction will be thus improved, the theoretical result being much closer to the real case.