CPES - Indexed Articles in Conferences

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    PV Inverter Fault Classification using Machine Learning and Clarke Transformation
    ( 2023) Ricardo Jorge Bessa ; Ana Silva ; 4882 ; 9079
    In a photovoltaic power plant (PVPP), the DC-AC converter (inverter) is one of the components most prone to faults. Even though they are key equipment in such installations, their fault detection techniques are not as much explored as PV module-related issues, for instance. In that sense, this paper is motivated to find novel tools for detection focused on the inverter, employing machine learning (ML) algorithms trained using a hybrid dataset. The hybrid dataset is composed of real and synthetic data for fault-free and faulty conditions. A dataset is built based on fault-free data from the PVPP and faulty data generated by a digital twin (DT). The combination DT and ML is employed using a Clarke/space vector representation of the inverter electrical variables, thus resulting in a novel feature engineering method to extract the most relevant features that can properly represent the operating condition of the PVPP. The solution that was developed can classify multiple operation conditions of the inverter with high accuracy. © 2023 IEEE.
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    Evaluation of the uncertainties used to perform flow security assessment: A real case study
    ( 2019) Helena Vasconcelos ; Carla Silva Gonçalves ; Meirinhos,J ; Omont,N ; Pitto,A ; Ceresa,G ; 3348 ; 6595
    In this paper, a validation framework is proposed to evaluate the quality of uncertainty forecasts, when used to perform branch flow security assessment. The consistency between probabilistic forecasts and observations and the sharpness of the uncertainty forecasts is verified with advanced metrics widely used in weather prediction. The evaluation is completed by assessing the added value of exploiting uncertainty forecasts over the TSO current practices of using deterministic forecasts. For electric power industry, this proposed validation framework provides a way to compare the performance among alternative uncertainty models, when used to perform security assessment in power systems. The quality of the proposed metrics is illustrated and validated on historical data of the French transmission system. © 2019 IEEE.
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    Analysis of consumer-centric market models in the Brazilian context
    ( 2020) Barbosa,PHP ; Dias,B ; Tiago André Soares ; 6611
    In recent years, the large deployment of distributed energy resources (DERs) in low voltage networks is changing the traditional approach to power systems. This massive change is pushing towards new solutions to improve energy trading in low voltage networks. Consumer-centric options, such as full peer-to-peer (P2P) and energy community markets (CM) are seen as viable options to increase the active participation of end-users in the electricity markets. This work studies the full P2P and CM market approaches applied to the actual regulatory framework in Brazil, evaluating and comparing both approaches to be potentially applied in Brazil. A case study based on a typical Brazilian neighborhood is designed, allowing to assess the behavior of consumers and prosumers in both markets. The results show the economic viability of both models, considering the social welfare and the penetration of distributed generation in the system. An important conclusion under the current regulatory framework is that the full P2P can have greater benefits over the CM, as long as the distributed generation is enough to confer near self-sufficiency to the peer's demand. © 2020 IEEE.
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    Transações peer-to-peer de energia elétrica considerando as restrições da rede de eletricidade [Not available in English]
    ( 2021) Botelho,DF ; Tiago André Soares ; Peters Barbosa,PH ; Dias,BH ; de Oliveira,LW ; Moraes,CA ; 6611
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    Prosumer-centric P2P energy market under network constraints with TDF’s penalization
    ( 2021) Botelho,D ; Peters,P ; de Oliveira,L ; Dias,B ; Tiago André Soares ; Moraes,C ; 6611