State Estimation in Active Distribution Systems: Comparison Between Weighted Least Squares and Extended Kalman Filter Algorithms

Master Thesis


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Distributed Generation units (DGs) have been continuously deployed since the early 1990s in distribution power systems to mitigate greenhouse gas emissions and climate change. Therefore, distribution systems, which were designed initially as passive networks, with little monitoring, are evolving into active networks, forcing Distribution System Operators to implement newer control applications in Distribution Management Systems (DMS). Distribution System State Estimation (DSSE) is a crucial component in DMS. DSSE is the process of obtaining the nodal voltage magnitude and its respective phasor angle in real-time by utilizing available recorded measurements and the parameters from the network topology. The state estimator's outputs are then used to realize appropriate monitoring and control for the distribution networks. Potential applications include optimal voltage and VAR control, DERs dispatch, islanding operation, and fault detection and location. Transmission System State Estimation (TSSE) has been developed and applied since the late 1960s. However, TSSE methods are not directly transferable to distribution networks (DNs) since they differ in their design, topology and operation. DNs have distinctively shorter lines with higher Resistance to Reactance ratio, and single-phase and unbalanced threephase circuits and loads. Importantly, scarcity of measurements in DNs present tremendous challenges in DSSE. Despite these challenges, pioneering work in DSSE commenced in the 1990s. The motivation of this research is to extend this work by comparing the performance of the Weighted Least Square (WLS) and the Extended Kalman Filter (EKF) state estimation algorithms in active DNs. This dissertation develops and tests the performance of active distribution system state estimation WLS and EKF algorithms that consider the integration of DGs in standard IEEEbus test feeders. An important contribution of this dissertation consists in the validation of the theoretical state estimation findings via the use of real-life data. The ADRESCONCEPT project data were used to simulate real-time measurements, while the Particle Swarm Optimization algorithm was used to place the DGs and PMUs on the best possible nodes of the selected test system. This dissertation starts by reviewing the state-of-the-art in DSSE, and provides the measurement and process model of the WLS and the EKF algorithms. Then, it illustrates the analytical formulation of the two algorithms as a function of the input measurements. Finally, a case studies on modified IEEE-33 bus and IEEE-69 bus test feeders were carried out on MATLAB/OpenDSS software, and numerical evaluation and results are presented. The EKF algorithm out-performs the WLS algorithm with an average RMS error of 0.00020588 to 0.00025168. Similarly, EKF converges in 3 iterations, while WLS converges in 4. Preceding this dissertation some of the research findings were published and presented in the 2019 and 2020 Southern Africa Universities Power Engineering Conferences and the 2020 Power Africa Conference.