Computation of transmission line parameters for power flow studies

Monteiro, José; Neves, Washington Luiz Araújo; Fernandes, Darlan A.; Souza, Benemar Alencar de; Fernandes, A.B.

doi:10.1109/ccece.2005.1557004

Cited by 2 publications

(2 citation statements)

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“…The equivalent model of the conductor (Figure 1b) contains four electrical elements and is called "canonical π-model". It is commonly used to study wave propagation and power flow analysis [12], [13]. The term Δλl represents the conductor length, while the values of the parameters Rl', Ll', Gl', Cl' depend on the mechanical characteristics of the phase conductor; all these parameters are quantities per unit of length [14].…”

Section: Conductor Modellingmentioning

confidence: 99%

“…For example, 1 2 is the first line input corresponding to the second sample, 1 21 represents the correction for the weight between the first input and the second neuron of the first layer, 21 2 is the error on the second neuron of the first layer corresponding to the second sample. The values of the corrections are calculated through the Q-R decomposition and the same procedure is used for the output layer on the system shown in (12). The weight adjustment is repeated until the error satisfies the stopping criteria indicated in [27].…”

Section: Neural Network Training and Testingmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network-Based Fault Diagnosis of Joints in High Voltage Electrical Lines

Bindi¹,

Aizenberg²,

Belardi³

et al. 2020

Adv. sci. technol. eng. syst. j.

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In this paper a classification system based on a complex-valued neural network is used to evaluate the health state of joints in high voltage overhead transmission lines. The aim of this method is to prevent breakages on the joints through the frequency response measurements obtained at the initial point of the network. The specific advantage of this kind of measure is to be non-intrusive and therefore safer than other approaches, also considering the high voltage nature of the lines. A feedforward multi-layer neural network with multi-valued neurons is used to achieve the goal. The results obtained for power lines characterized by three and four junction regions show that the system is able to identify the health state of each joint, with an accuracy level greater than 90%.

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Section: Conductor Modellingmentioning

confidence: 99%