Harmonic state estimation for distribution systems based on optimization models considering daily load profiles

Melo, Igor D.; Pereira, J.L.R.; Ribeiro, Paulo F.; Variz, Abilio Manuel; Oliveira, Bráulio César de

doi:10.1016/j.epsr.2019.01.033

Cited by 54 publications

(33 citation statements)

References 16 publications

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“…The advantage of using SVD for power system state estimation is that a previous observability analysis is not required, which is opposite to the classic methods of state estimation (Melo et al 2019;Eldery et al 2006;Reis et al 2008;Almeida and Kagan 2011a, b). Thus, for a given measurements set, there will always be a solution and, if the power system is not completely observable, the SVD-based method indicates candidate nodes for placing meters in order to make it completely observable.…”

Section: Three-phase State Estimationmentioning

confidence: 99%

“…Also, regarding the three-phase voltage estimation of all distribution systems nodes, currently only a limited number of utilities have adopted this estimation process (Lubkeman et al 2000;Simendic et al 2005;Katic et al 2013;Atanackovic and Dabic 2013). However, the tests in real systems indicate that this is feasible and sufficiently accurate for the purpose of real-time monitoring (Simendic et al 2005;Katic et al 2013;Melo et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the allocation of meters, it can be observed that, initially, the great majority of the works was focused on symmetrical and balanced transmission systems (Marín et al 2003;Eldery et al 2006;Reis et al 2008;Almeida and Kagan 2011a;Ketabi et al 2012;Kazemi et al 2013;Wong et al 2014). More recently, some works are focused on DSs (Melo et al 2019;Kouzelis et al 2015;Bottura et al 2019;Lucimario et al 2016;Shaaban et al 2019;Dehnavi et al 2019;Kempner et al 2014;Gomes et al 2016). In addition, most of the researches consider the allocation only for monitoring short time voltage variations (Eldery et al 2006;Reis et al 2008;Almeida and Kagan 2011b;Ketabi et al 2012;Kazemi et al 2013;Wong et al 2014;Kempner et al 2014;Gomes et al 2016;Ibrahim et al 2014;Martins et al 2019) which results in a low number of PQM to be installed, insufficient to make the grid completely observable during normal operation.…”

Section: Introductionmentioning

confidence: 99%

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Power Quality Monitor Allocation Based on Singular Value Decomposition and Genetic Algorithm

Breda

Vieira

Oleskovicz

2020

J Control Autom Electr Syst

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This paper proposed a novel method for allocating power quality meters with the main objective of ensuring a completely observable power distribution system. The method was designed to establish the quantity, location and type of measurement to be performed (voltage and/or current) for a given distribution system by employing genetic algorithms and the principles of state estimation based on the singular value decomposition. Moreover, a limitation in the number of current measuring channels was inserted in the mathematical formulation as an alternative for reducing costs. The method has been validated by running a three-phase harmonic state estimation using the IEEE 34 and 37 bus distribution test feeders. The results demonstrated the effectiveness of the method for designing power quality monitoring systems in distribution grids, ensuring full observability for state estimation purposes.

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Section: Three-phase State Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power Quality Monitor Allocation Based on Singular Value Decomposition and Genetic Algorithm

Breda

Vieira

Oleskovicz

2020

J Control Autom Electr Syst

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“…T is the number of sample points, S is the source signal matrix and X is the observation signal matrix. By comparing Equations (1) and (2), it can be seen that there is a corresponding relationship between the HSE model and the BSS model: U h (t) corresponds to X(t), Z h corresponds to A, and I h (t) corresponds to S(t). Therefore, the BSS model can be used in HSE applications.…”

Section: Relationship Between Hse Model and Bss Modelmentioning

confidence: 99%

“…With the continuous penetration of new energy, power electronic equipment and non-linear loads, harmonic pollution in power systems is becoming increasingly serious. Harmonic source localization is the premise of harmonic source management and has important significance in the field of power quality [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy

Hong-geng

2020

Entropy

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Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm.

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Voltage stability assessment using modal analysis based on power systems state estimation

2021

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Harmonic state estimation for distribution systems based on optimization models considering daily load profiles

Cited by 54 publications

References 16 publications

Power Quality Monitor Allocation Based on Singular Value Decomposition and Genetic Algorithm

Power Quality Monitor Allocation Based on Singular Value Decomposition and Genetic Algorithm

Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy

Voltage stability assessment using modal analysis based on power systems state estimation

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