University of Twente Student Theses


Lightweight online Kalman-filter-based sensitivity estimator for distribution grids utilizing phasor measurements

Sterenborg, N.E. (2023) Lightweight online Kalman-filter-based sensitivity estimator for distribution grids utilizing phasor measurements.

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Abstract:In recent years, the energy transition is posing new challenges for the Dutch electricity grid and its operation. The rapid increase in the number of small scale renewable energy sources (RESs) such as photovoltaics (PVs) has a significant effect on the electricity network. Under specific scenarios, the power generation from these devices can be so large that the grid capacity is exceeded which necessitates advanced control solutions. In residential areas, the lack of synchronisation between peak consumption and peak production of electrical energy can result in high currents flowing back into the grid, which in turn leads to an increase in local voltage levels. Since the structure of the electricity grid was not designed with these current in mind, this increase in voltage can exceed grid codes. The problem of an increasing voltage becomes worse with physical distance to the transformer, which may lead to unfair situations where prosumers further away from the transformer can generate less power compared to closer prosumers. Fair control mechanisms for this problem are an active area of research, and often require information on the surrounding low voltage (LV) grid. As this data is frequently unavailable, algorithms that can estimate it become essential. This thesis introduces a lightweight sensitivity estimator designed for the implementation of fair curtailment algorithms. The estimator’s primary objective is to estimate grid parameters based on phasor measurements, utilizing GPS-synchronized, real-time data. The design prioritizes computational efficiency to minimize implementation costs. To achieve this, a Kalman filter approach is employed, ensuring that the estimator operates autonomously without user intervention. The performance of the estimator is compared to a benchmark method, and ground truth data. Using the sensitivity estimation, the voltage prediction is only up to 38mV RMS more compared to the ground truth voltage prediction. With this marginal increase in prediction error, this estimator presents a viable solution for applications like PV inverters, where user interaction cannot be relied upon.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:30 exact sciences in general, 50 technical science in general, 53 electrotechnology
Programme:Electrical Engineering MSc (60353)
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