Active Real-Time SSO Detection for Enhanced System Security
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Inclou dades d'ús des de 2022
Cita com:
hdl:2117/372911
Tipus de documentProjecte Final de Màster Oficial
Data2022-07-01
Condicions d'accésAccés restringit per decisió de l'autor
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Abstract
This thesis addresses the challenge of sub synchronous oscillations (SSO) in the electrical grid. Although there are clear protocols in place for SSO screening, the increasing complexity and wider range of operating conditions in the energy system is increasing the risk of new modes of SSO occurring. For this reason, sophisticated monitoring solutions are needed to enhance system security. In this thesis, a real-time detection tool for early SSO risk identification is developed. In addition, the thesis explores how artificial intelligence can be used to support the development of the
proposed detection tool. The applied methodology to build the detection tool consists of three steps. The first step is to develop a synthetic data set of SSO oscillations in in Matlab Simulink ® and then extract the current waveform which is the power system measurement where SSO seems to manifest itself the most prominently. Then, in the second step, two signal processing algorithms, Fast Fourier Transform (FFT) and Prony’s method, are applied to the synthetically generated SSO waveforms to extract the signals’ features. The methods are implemented in Matlab ®, tested, validated and compared for SSO detection performance. Due to their different merits and draw- backs, both methods are implemented in parallel to provide complementary analysis. In the third and final step of the tool development in this thesis, the data set containing the system operating conditions and the signal features extracted with FFT and Prony’s method is analysed making use of machine learning. Specifically, K-means clustering is applied on the data set in Python. Although the results of this exploratory approach are not conclusive, the data set seems to contain around four distinguishable clusters which can be linked to SSO risk levels. It is, however, recommended to focus on other predictive machine learning applications to further improve the tool’s predictive SSO detection capabilities. The validated performance of the tool developed in this thesis is useful both for SSO detection in itself, but also to potentially train an AI algorithm to replace the intermediate signal processing algorithms with direct SSO classification
MatèriesElectric power system stability -- Mathematical models, Machine learning -- Industrial applications, Migrogrids (Smart power grids) -- Automatic control -- Design and construction, Sistemes de distribució d'energia elèctrica -- Estabilitat -- Models matemàtics, Aprenentatge automàtic -- Aplicacions industrials, Microxarxes (Xarxes elèctriques intel·ligents) -- Control automàtic -- Disseny i construcció
TitulacióMÀSTER UNIVERSITARI EN ENGINYERIA DE L'ENERGIA (Pla 2013)
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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final-thesis.pdf | 3,724Mb | Accés restringit | ||
p1-p2-merged.pdf | 3,735Mb | Accés restringit |