Voltage stability assessment: An approach with expanded Newton Raphson -Sydel

Eidiani, Mostafa; Zeynal, Hossein; Zadeh, Alimorad Khajeh; Mansoorzadeh, Shahriar; Nor, Khalil Md

doi:10.1109/peoco.2011.5970424

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…Here, the static ATC of the grid described in section 2 is computed. The outcome matched the time consumed by six different methods, 1‐standard CPF [3], 2‐CPF–GMRES [4], 3‐expanded NRS [2], 4‐ STAC [9], 5‐ Holomorphic embedding load flow (HELF) [29], and 6‐ the newly proposed method (SATC‐DE).…”

Section: Numeric Resultsmentioning

confidence: 87%

“…For years, Continuation Power Flow (CPF) has been employed to evaluate SATC, and has been improved using the general minimum residual method (GMRES) to speed up voltage stability computation [2, 3]. In this regard, the classic Newton‐Raphson (NR) can also be substituted with Newton‐Raphson‐Seydel (NRS) method for a quicker and more precise calculation of voltage stability and SATC [4–6].…”

Section: Introductionmentioning

confidence: 99%

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An efficient differential equation load flow method to assess dynamic available transfer capability with wind farms

Eidiani

2021

IET Renewable Power Gen

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Differential equation load flow (DELF) is an innovative approach to convert a load flow model to a fictitious dynamic system. DELF solves the entire time‐domain analysis of a dynamic system trajectory rather than solving load flow equations repeatedly in a set of conditions. The static available transfer capability (ATC) determination algorithm is specified through a method called the approximation of the path of the minimum distance method. In this method, instead of a repeated power flow, only three system operating points are used. An estimation of the potential energy boundary surface was also employed to determine the transient stability in an attempt to correct previous methods. A combination of static method and transient stability led to a combined approach to dynamic ATC computation. This paper proposes a truly effective and precise method to assess Dynamic ATC using the differential equation load flow considering the first contingency (n − 1) situations and the intact system condition. For various multilateral and bilateral transactions, this method was successfully implemented on 39, 118‐ and 300‐bus IEEE, 145‐bus Iowa‐State, the west of Iran (1153‐bus), and Mashhad MV distribution network (4438‐buses) systems. The results showed a better suitability, a higher speed and accuracy than other dynamic ATC methods.

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Section: Numeric Resultsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

An efficient differential equation load flow method to assess dynamic available transfer capability with wind farms

Eidiani

2021

IET Renewable Power Gen

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“…Algorithms based on the latter begin with static methods as their upper limits. The former includes continuation power flow (CPF) [26], Newton‐Raphson‐Seydel (NRS) method [27] and minimum distance (MD) methods [28]. DELF (differential equation load flow) is one of the new static methods [29].…”

Section: Introductionmentioning

confidence: 99%

A rapid state estimation method for calculating transmission capacity despite cyber security concerns

Eidiani

2023

IET Generation Trans & Dist

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The present paper simulates cyberattacks in DIgSILENT for energy management and state estimation. Assuming hackers have adequate information to alter input data, they can alter the power market and the available transmission capability (ATC). Detection programs may fail if false information is coordinated in the presence of error information. The minimum number of network measurements before the state estimation program converges is 54% of the total measurements. In the new method, weighted least squares improve the state estimation speed. The new method can also improve the speed of ATC. In addition to identifying the cyberattack, the algorithm in the present paper can be used to identify the target of the attack. It also uses complete and correct information and even incorrect measurements to report state estimator errors. The present paper shows that ATC values follow the inverse pattern of load and loss. A real sample network is presented at the end of the paper to test the suggested method.

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“…Model-driven power flow calculation enhances the knowledge and understanding of the model and improves visualization. Traditional power flow calculation methods [5][6][7][8] obtain voltage magnitude and phase angle by constructing differential equations [9,10] and solving them with iterative numerical methods; [11] considers DC, establishes a unified steady-state nonideal model, and proposes a power flow calculation model that retains the nonlinear algorithm; [12] used the decoupled linear power flow (DLPF) method to effectively calculate the voltage magnitude, but sacrificed the accuracy of the voltage phase angle calculation; [13] proposes a combination of Newton-Raphson-Seder and downslope algorithms with a combination of static and transient stability; [14] considers integrated energy sources and constructs a power flow calculation model based on a refined thermal network. With the development of measurement devices and storage equipment, the data-driven [15] power flow method was developed, and the nonlinear power flow model was linearized [16][17][18][19] to speed up the power flow calculation.…”

Section: Introductionmentioning

confidence: 99%

Distribution network power flow calculation based on the BPNN optimized by GA‐ADAM

Liu,

Feng,

et al. 2023

The Journal of Engineering

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Power system operation and control are based on power flow calculations. In order to solve the uncertainty of the increasing penetration of renewable energy, the voltage fluctuation at the load point increases in the distribution network, and the inaccuracy of the power flow calculation due to the insufficient power flow data collection capability of the traditional power system. In this paper, a data‐driven power flow analysis model is proposed, a back propagation neural network combined with genetic algorithm (GA) and adaptive moment estimation (ADAM) optimization algorithm model is constructed to analyze the power flow calculation method of distribution networks under stochasticity. Firstly, the power flow initial value information, topology characteristics, and power factor index are introduced to construct a training set, and the mapping relationship between bus voltage and power is fully explored by training the regression model. Second, the GA‐ADAM algorithm is used to optimize the initial values and weight parameters of the model. Finally, it is verified based on IEEE‐33 bus distribution model, and the model is used for power flow calculation, and compared with other methods through each relevant error evaluation indicators. The results show that the model constructed in this paper has small error indicators and high accuracy, which improves the efficiency and accuracy of power flow calculation.

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Voltage stability assessment: An approach with expanded Newton Raphson -Sydel

Cited by 20 publications

References 14 publications

An efficient differential equation load flow method to assess dynamic available transfer capability with wind farms

An efficient differential equation load flow method to assess dynamic available transfer capability with wind farms

A rapid state estimation method for calculating transmission capacity despite cyber security concerns

Distribution network power flow calculation based on the BPNN optimized by GA‐ADAM

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