Three phase power system state estimation

Adamu, Hussaini Ali (2023). Three phase power system state estimation. University of Birmingham. Ph.D.

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Abstract

The Weighted Least Squares (WLS) technique has been used for data filtering for several years due to its feasibility, accuracy and computational stability. However, despite its merit there has been a lot of advancement recently in the literature to improve the method, in areas associated with errors caused by bias values called outliers, using robust techniques to improve the accuracy of the state estimates.
In addition, most of this research are presented in single-phase by assuming a balance 3-phase power system, while less emphasis is led on unbalance 3-phase power systems. Chapters 3 and 4 have introduced the detail modelling of unbalanced 3-phase systems in both load flow and state estimation with standard benchmark results as a foundation.
To overcome the problems of outliers, a new method called the Modified Fair Maximum Likelihood Estimator (MFM-E) is introduced in chapter 5. Its three-function equations were presented: Objective function, Influence function and the weight function. Additionally, its weight function was used in a robust Iteratively Reweighted Least Squares (IRLS) algorithm to improve the state estimates feasibility, accuracy and computational efficiency. The method was examined with two other methods by simulations conducted on an IEEE node-13 test system, and has shown to be the best.
Similarly, to solve the problem caused by outliers, another novel method, known as the Modified Welsch Maximum Likelihood Estimator (MWM-E) has been presented in chapter 6. Also, its Objective function, Influence function and weight function have been introduced. The method was shown to have better feasibility and estimation accuracy, as compared to other methods by virtue of simulation results conducted on an IEEE 13-node test system in MATLAB, using the robust IRLS technique. Again, it is highlighted that the new method has the best time response in terms of computational efficiency.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):