A novel comparison of gauss-seidel and newton- raphson methods for load flow analysis

Chatterjee, Sreemoyee; Mandal, Suprovab

doi:10.1109/icpedc.2017.8081050

Cited by 29 publications

(16 citation statements)

References 5 publications

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“…However, reaching this result requires a certain amount of computer performance, and the fact is that the performance computers of distribution network operators are limited. So, to enable operators to manage large distribution networks too, it is necessary to use numerically stable algorithms that can solve the AC-power-flow problem with a minimal number of operations when developing software for the management of distribution networks [5][6][7][8][9][10][11]. The system of the AC-power-flow equations is, therefore, a system of nonlinear equations.…”

Section: Ac-power-flow Problem Formulation and Methods For Solutionmentioning

confidence: 99%

See 1 more Smart Citation

Steady-State Analysis of Electrical Networks in Pandapower Software: Computational Performances of Newton–Raphson, Newton–Raphson with Iwamoto Multiplier, and Gauss–Seidel Methods

et al. 2022

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At the core of every system for the efficient control of the network steady-state operation is the AC-power-flow problem solver. For local distribution networks to continue to operate effectively, it is necessary to use the most powerful and numerically stable AC-power-flow problem solvers within the software that controls the power flows in these networks. This communication presents the results of analyses of the computational performance and stability of three methods for solving the AC-power-flow problem. Specifically, this communication compares the robustness and speed of execution of the Gauss–Seidel (G–S), Newton–Raphson (N–R), and Newton–Raphson method with Iwamoto multipliers (N–R–I), which were tested in open-source pandapower software using a meshed electrical network model of various topologies. The test results show that the pandapower implementations of the N–R method and the N–R–I method are significantly more robust and faster than the G–S method, regardless of the network topology. In addition, a generalized Python interface between the pandapower and the SciPy package was implemented and tested, and results show that the hybrid Powell, Levenberg–Marquardt, and Krylov methods, a quasilinearization algorithm, and the continuous Newton method can sometimes achieve better results than the classical N–R method.

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Section: Ac-power-flow Problem Formulation and Methods For Solutionmentioning

confidence: 99%

“…Since the AC-power-flow equations are nonlinear, it is necessary to use an iterative numerical method to find the values of system unknowns [9].…”

Section: Ac-power-flow Problem Formulation and Methods For Solutionmentioning

confidence: 99%

Steady-State Analysis of Electrical Networks in Pandapower Software: Computational Performances of Newton–Raphson, Newton–Raphson with Iwamoto Multiplier, and Gauss–Seidel Methods

et al. 2022

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“…The load flow analysis of the system, which includes computation of its line and node parameters in terms of voltage, current, power etc., is done using the Newton-Raphson (NR) method of power flow analysis [17], to have accurate analysis with simple control.…”

Section: ) Load Flow Analysis -Newton Raphson Methodsmentioning

confidence: 99%

Machine Learning-Based Predictive Techno-Economic Analysis of Power System

Queen

Jayakumar

Deepika³

et al. 2021

IEEE Access

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This paper proposes a predictive techno-economic analysis in terms of voltage stability and cost using regression-based machine learning (ML) models and effectiveness of the analysis is validated. Predictive analysis of a power system is proposed to address the need for faster and accurate analyses that would aid in the operation and control of modern power system. Several methods of analyses including metaheuristic optimization algorithms, artificial intelligence techniques and machine learning algorithms are being developed and used. Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources (solar and wind) and reactive power compensative device (STATCOM) are proposed and developed with features that include hour of the day, solar irradiation, wind velocity, dynamic grid price and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. The data feasibility tests and output database generation is performed using MATLAB. Linear and higher order polynomial regression models are developed for the 8760hr database using Python 3.0 in JupyterLab and a best-fit predictive ML model is identified by analysing the coefficients of determination. The voltage stability and cost predictive ML models were tested for a 24hr input profile. The results obtained and the comparison with the expected values are furnished. Prediction of the outputs for the test data validate the accuracy of the developed model.

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“…Computationally, these steps are illustrated in Figure 3 covered a transformation of the SQOF into programming sequences of the hybrid system. Moreover, the system is also evaluated using the Newton Raphson method for determining balanced energy performances as referred to in [31]. Furthermore, Figure 3 is also used to guide the procedures and hierarchies for optimizing the SQOF.…”

Section: Optimizing Proceduresmentioning

confidence: 99%

Optimization of Power Balance Transaction Based on Renewable Energy Sources Using Artificial Salmon Tracking Algorithm for Modeling the Interconnected Grid Development

Afandi¹,

Aripriharta²,

Rahmawati³

2019

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

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Since environmental requirements penetrate engineering processes to keep global warming and to reduce pollutant emissions, the system operation should be designed based on environmentally approach friendly. Operationally, the system processes are supported by energy suppliers to meet continuously energy transaction while clean and green energies are also targeted to keep the environmental conditions. In other word, the renewable energy source becomes an opportunity integration of a potential natural source inclusion into conventional energy producers. Technically, the energy balance is also important to optimize to get an optimal power portion during the operating period. These works are prioritized to search the balanced combination of the integrated energy mix composition. The works also present the novel computational intelligence to find out the energy portion using Artificial Salmon Tracking Algorithm. By considering technical requirements and tested on the integrated renewable energy plants, this algorithm is applied to optimize the economic power production. The works show dynamically the total power for feeding the energy consumption through local loads. The power production is also balanced in various combination portions of energy sources in accordance with the power demand as similar as discharged pollutants. Moreover, the computation has been obtained clearly for optimal solutions within 24 hours using a proposed algorithm.

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A novel comparison of gauss-seidel and newton- raphson methods for load flow analysis

Cited by 29 publications

References 5 publications

Steady-State Analysis of Electrical Networks in Pandapower Software: Computational Performances of Newton–Raphson, Newton–Raphson with Iwamoto Multiplier, and Gauss–Seidel Methods

Steady-State Analysis of Electrical Networks in Pandapower Software: Computational Performances of Newton–Raphson, Newton–Raphson with Iwamoto Multiplier, and Gauss–Seidel Methods

Machine Learning-Based Predictive Techno-Economic Analysis of Power System

Optimization of Power Balance Transaction Based on Renewable Energy Sources Using Artificial Salmon Tracking Algorithm for Modeling the Interconnected Grid Development

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