Short‐circuit level control through a multi‐objective feeder reconfiguration using fault current limiters in the presence of distributed generations

Esmaeili, Abbas; Esmaeili, Saeid; Hojabri, Hossein

doi:10.1049/iet-gtd.2016.0013

Cited by 31 publications

(26 citation statements)

References 30 publications

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“…Related research on this technique is ongoing. These studies generally investigate how much capacity can be installed at various locations to obtain the optimal effect [9][10][11][12][13][14][15][16][17]. The installation of a current-limiting reactor is practiced widely owing to its relatively low cost and short construction period.…”

Section: Introductionmentioning

confidence: 99%

Control Method of Buses and Lines Using Reinforcement Learning for Short Circuit Current Reduction

Han¹

2020

Sustainability

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This paper proposes a reinforcement learning-based approach that optimises bus and line control methods to solve the problem of short circuit currents in power systems. Expansion of power grids leads to concentrated power output and more lines for large-scale transmission, thereby increasing short circuit currents. The short circuit currents must be managed systematically by controlling the buses and lines such as separating, merging, and moving a bus, line, or transformer. However, there are countless possible control schemes in an actual grid. Moreover, to ensure compliance with power system reliability standards, no bus should exceed breaker capacity nor should lines or transformers be overloaded. For this reason, examining and selecting a plan requires extensive time and effort. To solve these problems, this paper introduces reinforcement learning to optimise control methods. By providing appropriate rewards for each control action, a policy is set, and the optimal control method is obtained through a maximising value method. In addition, a technique is presented that systematically defines the bus and line separation measures, limits the range of measures to those with actual power grid applicability, and reduces the optimisation time while increasing the convergence probability and enabling use in actual power grid operation. In the future, this technique will contribute significantly to establishing power grid operation plans based on short circuit currents.

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Section: Introductionmentioning

confidence: 99%

Control Method of Buses and Lines Using Reinforcement Learning for Short Circuit Current Reduction

Han¹

2020

Sustainability

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“…Advanced approach to reduce the short circuit current is related to the network topology reconfiguration. This method has been initially introduced for the optimal power transmission but can be also effectively utilised for short-circuit limitation [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Reduction of Short Circuit Current using DC Transmission and Split-Winding Power Transformer

Krakhmalin

Chivenkov

Vagapov

et al. 2019

2019 54th International Universities Power Engineering Conference (UPEC)

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This paper discusses a new scheme to reduce the level of fault currents in the feeder cable and power transformer windings under short-circuit conditions. The proposed scheme suggests splitting the feeder cable in two cables and connecting it to a network through two rectifier units. Therefore, the split windings of the power transformer are supplied by a six-conductor feeder using unidirectional dc current. Unidirectional magnetic flux produced by the windings are summarised in the transformer core resulting ac flux to ensure a normal transformer operation. The proposed scheme was analysed under short and open-circuit faults. It has been shown that the short-circuit currents in the cable conductors and transformer windings are reduced in 1.41 times in comparison to a conventional scheme.

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“…A RRD pode ser realizada tanto na fase de planejamento como na fase de operação e tem como principal vantagem a melhoria dos índices de desempenho sem a necessidade de instalar novos equipamentos na rede. Na fase de planejamento a RRD é usada para alcançar vários objetivos: maximização de confiabilidade (Brown, 2009); mitigação dos afundamentos de tensão de curta duração (Bahadoorsingh et al, 2007); minimização de perdas resistivas (Tahboub, Pandi e Zeineldin, 2015); controle de nível de curto-circuito (Esmaeili, Esmaeili e Hojabri, 2016) e alocação de geração distribuída (Martins e Borges, 2011). Devido a isso, alguns softwares comerciais incluíram a RRD como ferramenta para auxiliar o planejamento da rede de distribuição de energia (EATON, 2018).…”

Section: Introductionunclassified

Algoritmo de Colônia de Abelhas Artificiais para Alocação de Medidores e Reconfiguração em Redes de Distribuição

Costa

Rodrigues

Raposo

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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This paper proposes a probabilistic technique to carry out simultaneously the reconfiguration (DNR) and meters allocation in distribution networks. A linearized model based on closed analytic formulas is used to evaluate the accuracy of a candidate measurement design for state estimation. The multi-objective optimization technique based on artificial bee colony algorithm (ABC) was used to simultaneously minimize resistive losses, the number of meters and the inaccuracy of the state estimator (SE). In addition, the proposed algorithm considers the effect of correlation between measurements. The results of the tests demonstrated that the bee colony algorithm is able to obtain topologies with significant reductions in losses and measurement designs with acceptable accuracy and a reduced number of meters. Resumo: Este trabalho propõe uma técnica probabilística para realizar simultaneamente a reconfiguração (RRD) e a alocação de medidores em redes de distribuição. Um modelo linearizado baseado em fórmulas analíticas fechadas é usado para avaliar a precisão de um plano de medição candidato para a estimação de estado. A técnica de otimização multiobjetivo baseada no algoritmo de colônia de abelhas artificiais (CAA) foi utilizada para minimizar simultaneamente as perdas resistivas, o número de medidores e a imprecisão do estimador de estado (EE). Além disso, o algoritmo proposto considera o efeito da correlação entre as medições. Os resultados dos testes demonstraram que o algoritmo de colônias de abelhas é capaz de obter topologias com reduções significativas nas perdas e planos de medição com precisão aceitável e número reduzido de medidores.

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Short‐circuit level control through a multi‐objective feeder reconfiguration using fault current limiters in the presence of distributed generations

Cited by 31 publications

References 30 publications

Control Method of Buses and Lines Using Reinforcement Learning for Short Circuit Current Reduction

Control Method of Buses and Lines Using Reinforcement Learning for Short Circuit Current Reduction

Reduction of Short Circuit Current using DC Transmission and Split-Winding Power Transformer

Algoritmo de Colônia de Abelhas Artificiais para Alocação de Medidores e Reconfiguração em Redes de Distribuição

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