Contributions to the sequence‐decoupling compensation power flow method for distribution system analysis

Fernandes, Thiago Ramos; Ricciardi, Tiago R.; Silva, Rafael S. da; Almeida, Madson C. de

doi:10.1049/iet-gtd.2018.6176

Cited by 11 publications

(5 citation statements)

References 33 publications

(108 reference statements)

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“…Although many studies have successfully solved singularities in large-scale or radial distribution systems, ungrounded conditions are also one of the causes of singularities during NR-based power flow calculations. For instance, in an unbalanced system with unbalanced loads, the zero-sequence current (i.e., the unbalanced current) flows, and singularities often occur when the zero-sequence current cannot be defined in an ungrounded bus (e.g., a delta [D] connection) [22]. In other words, the singularities are sometimes caused by an ungrounded connection of the TR (i.e., the ungrounded wye [Y] or D-connections).…”

Section: B Contributions and Findingsmentioning

confidence: 99%

Singularity Handling for Unbalanced Three-Phase Transformers in Newton-Raphson Power Flow Analyses Using Moore-Penrose Pseudo-Inverse

Jang

Kim

2023

IEEE Access

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Power systems consist of generators, transformers, loads, and distributed power sources interconnected through lines. The reliable operation of the system requires that voltage and current magnitudes, angles, and power flow are within allowable ranges. Several methodologies have been proposed to solve the power flow consisting of nonlinear algebraic equations. Among them, the Newton-Raphson method is widely used due to its simplicity in building the bus admittance matrix, high accuracy, and fast convergence. However, the method has several limitations, such as singularity issues that occur when some or all Jacobian matrix elements become zero. This prevents the power flow calculation from converging since the inversion of the Jacobian matrix cannot be obtained. To address this limitation, this study proposes a novel and robust power flow calculation methodology that can solve singularities for various ill conditions using the Moore-Penrose Pseudo-inverse. This method improves the classical Newton-Raphson method by addressing its shortcomings. The performance of the proposed algorithm was evaluated using various testbeds, including balanced and unbalanced radial systems, a large-scale power grid, and systems with ungrounded transformers. The accuracy of the proposed algorithm was verified by comparing the power flow calculation with DIgSILENT Power Factory. The testbed consisted of modified IEEE 4-, 13-, 37-, and 69bus systems.INDEX TERMS Moore-Penrose Pseudo-inverse, Newton-Raphson method, Power flow analysis, Singularity, Three-phase transformer.

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Section: B Contributions and Findingsmentioning

confidence: 99%

Singularity Handling for Unbalanced Three-Phase Transformers in Newton-Raphson Power Flow Analyses Using Moore-Penrose Pseudo-Inverse

Jang

Kim

2023

IEEE Access

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“…The zero-sequence transmission network could cause illconditioning of the whole calculation process in both Fortescue and phase-coordinates calculations. In the phase-coordinate approach [7], the ill-conditioning problem is typically solved by adding shunt elements at delta windings which are then compensated by fictitious current generators [19]. Another method for treating ill-conditioning is shown in [17], [19] by embedding a methodology for changing critical pivot values during the factorization phase.…”

Section: Zero-sequence Islandsmentioning

confidence: 99%

“…In the phase-coordinate approach [7], the ill-conditioning problem is typically solved by adding shunt elements at delta windings which are then compensated by fictitious current generators [19]. Another method for treating ill-conditioning is shown in [17], [19] by embedding a methodology for changing critical pivot values during the factorization phase. The obtained matrix is indefinity for equality-constrained state estimation, necessitating a special pivoting strategy involving 1 ´1 and 2 ´2 pivots.…”

Section: Zero-sequence Islandsmentioning

confidence: 99%

Parallel Perturbed Gauss-Newton State Estimator Based on Fortescue Transformation for Unbalanced Power Systems

Džafić

2022

Journal of Modern Power Systems and Clean Energy

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This letter extends the complex-variable perturbed Gauss-Newton method to estimate the state of unbalanced power systems by exploiting the Fortescue transformation. It proposes a novel and efficient graph-based way to deal with singularities due to zero-sequence network parts bounded with delta transformer windings and isolated from the ground. The estimator can handle both phasor and complex power measurements. Compared with the standard complex-variable unbalanced state estimator, it achieves better numerical stability and a speed-up of around three times using a sequential implementation and five times using parallel execution.

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“…The analysis of electrical power systems often uses load flow calculation, a fundamental and widely used approach [2][3][4]. Many applications, from expansion planning to network reconfiguration and storage capacity, depend on these calculations [5][6][7]. The classical formulation based on Newton's method is commonly adopted due to its effectiveness in both transmission and distribution systems [8].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network

da Silva,

de Queiroz,

Garbelini

et al. 2024

ASI

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Total real and reactive power losses in electrical power systems are an inevitable phenomenon and occur due to several factors, such as conductor resistance, transformer impedance, line reactance, equipment losses, and phase unbalance. Minimizing them is crucial to the system’s efficiency. In this study, an artificial neural network, specifically a Multi-layer Perceptron, was employed to predict total real and reactive power losses in electrical systems. The network is composed of three layers: an input layer consisting of the variables loading factor, real and reactive power generated on the slack bus, a hidden layer, and an output layer representing the total real and reactive power losses. The training method used was backpropagation, adjusting the weights based on the desired output. The results obtained, using datasets from IEEE systems with 14, 30, and 57 buses, showed satisfactory performance, with a mean squared error of around 10−4 and a coefficient of determination (R2) of 0.998. In validation with 20% of the data that was not part of the training, the network demonstrated effectiveness, with a mean squared error around 10−3. This indicates that the network was able to accurately predict total power losses based on loads, generating estimates close to the desired values.

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Contributions to the sequence‐decoupling compensation power flow method for distribution system analysis

Cited by 11 publications

References 33 publications

Singularity Handling for Unbalanced Three-Phase Transformers in Newton-Raphson Power Flow Analyses Using Moore-Penrose Pseudo-Inverse

Singularity Handling for Unbalanced Three-Phase Transformers in Newton-Raphson Power Flow Analyses Using Moore-Penrose Pseudo-Inverse

Parallel Perturbed Gauss-Newton State Estimator Based on Fortescue Transformation for Unbalanced Power Systems

Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network

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