Mann-Iteration Process for Power Flow Calculation of Large-Scale Ill-Conditioned Systems: Theoretical Analysis and Numerical Results

Tostado‐Véliz, Marcos; Hasanien, Hany M.; Turky, Rania A.; Alkuhayli, Abdulaziz; Kamel, Salah

doi:10.1109/access.2021.3114969

Cited by 4 publications

(6 citation statements)

References 61 publications

(99 reference statements)

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“…Now, we have considered case3012wp, case3375wp, and case13659pegase, which are available in MATPOWER's database. These cases correspond with snapshots of real cases, which demonstrates that ill-conditioned solvers may appear in real applications and they are even more frequent nowadays [42]. NR fails to solve these systems when a flat start is used, so that they can be categorized as ill-conditioned [13].…”

Section: Ill-conditioned Casesmentioning

confidence: 88%

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Exploiting the S-Iteration Process for Solving Power Flow Problems: Novel Algorithms and Comprehensive Analysis

2021

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In recent studies, the competitiveness of the Newton-S-Iteration-Process (Newton-SIP) techniques to efficiently solve the Power Flow (PF) problems in both well and ill-conditioned systems has been highlighted, concluding that these methods may be suitable for industrial applications. This paper aims to tackle some of the open topics brought for this kind of techniques. Different PF techniques are proposed based on the most recently developed Newton-SIP methods. In addition, convergence analysis and a comparative study of four different Newton-SIP methods PF techniques are presented. To check the features of considered PF techniques, several numerical experiments are carried out. Results show that the considered Newton-SIP techniques can achieve up to an eighth order of convergence and typically are more efficient and robust than the Newton–Raphson (NR) technique. Finally, it is shown that the overall performance of the considered PF techniques is strongly influenced by the values of parameters involved in the iterative procedure.

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Section: Ill-conditioned Casesmentioning

confidence: 88%

“…The value of the index (42) for studied Newton-SIP methods and NR is depicted in Figure 3 for different sizes of the PF state vector (n). From this figure, it can be appreci-ated that Newton-SIP techniques are more efficient than NR.…”

Section: Comparison Of the Efficiency Of Different Iterative Algorithmsmentioning

confidence: 99%

Exploiting the S-Iteration Process for Solving Power Flow Problems: Novel Algorithms and Comprehensive Analysis

2021

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“…It also constitutes highly robust methodology, improving the properties of the Newton-Raphson method. In all cases, the results obtained with the Mann Iteration Process are superior to that obtained using other classical methodologies, being able to efficiently solve various large-scale ill-conditioned systems [13]. The most widely used power flow analysis formulation is the power balance formulation [14].…”

Section: G Mann Iteration Process (Mip) Technique For Ill-conditioned...mentioning

confidence: 96%

“…Therefore, the new state of the art methods of PF analysis is discovered to generally give better results than the classical methods and give the power the room to flow perfectly, fitting the new grid changes like Decentralized Generation (DG) [6]. These State-of-the-Art methods include Direct Matrix-Current Application (DM-CA) and Direct Matrix-Impedance Approximation (DM-IA) [5], Particle Swamp Optimization (PSO) Algorithm for Optimal PF Incorporating Wind Farm [6], Hybrid Firefly and Particle Swarm Optimization (HFPSO) [7], Artificial Neural Networks (ANNs) [8], Quasi-Oppositional Heap-Based Optimization (QOHBO) Technique [9], Three Stage Semi-Implicit Approach (3S-SIA) [10], Mann Iteration Process (MIP) For Ill-Conditioned System [11], Hybrid of Currentbalance and Power-balance formulation using Rectangular Coordinates (HCPB) [12], Modified Gauss-Seidel (MGS) [13], Batched Fast Decoupled Method [14], Newton-Raphson Load Flow Analysis in Power System Networks with STATCOM in New Approach [14] and so on. This paper will therefore, focus on these state-of-the-art techniques.…”

Section: Introductionmentioning

confidence: 99%

A Review of State-of-the-Art Techniques for Power Flow Analysis

SABO

Kanya

SHU'AIBU

et al. 2023

JSTER

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This paper presents a review of the State-of-the-Art Techniques for Power Flow Analysis (PFA) that are newly proposed. However, some of the existing classical methods for the Power Flow Analysis such as Newton-Raphson method, Gauss-Siedel method, and Fast Decoupled Power Flow Technique were also discussed so as to give a background and a wider view of the improvements recorded so far. From the findings, the State-of-the-Art Techniques such as Particle Swamp Optimization Algorithm for optimal Power Flow Incorporating Wind Farms, Hybrid Firefly and Particle Swamp Optimization Algorithm, Mann Iteration Process Technique for III-Conditioned System, and A New Approach Newton-Raphson Load Flow Analysis in Power System Networks with STATCOM have shown superiority over and above the existing classical methods in terms of accuracy, speed of convergence and overall efficiency. Particularly, there are two newly proposed methods for dc grids: Direct Matrix–Current application (DM-CA) and Direct Matrix-Impedance Approximation (DM-IA) methods that stand out with respect to accuracy, convergence and computational effort which means they can be used for planning, optimization and analysis purposes of practical dc grids.

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“…Several advanced algorithms have been developed in the literature to address the challenges faced by PF analysis, including scalability [12][13][14][15][16] and the management of ill-conditioned cases [17][18][19][20][21][22][23] . These studies mostly focus on the advancement of programming techniques and the formulation of mathematical models grounded in classical computation principles.…”

Section: Introductionmentioning

confidence: 99%

Adiabatic Quantum Power Flow

Kaseb,

Moller,

Vergara

et al. 2024

Preprint

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Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and (ii) an adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (iii) a scalability study of the AQPF algorithm; and (iv) an extension of the AQPF algorithm for larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.

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Mann-Iteration Process for Power Flow Calculation of Large-Scale Ill-Conditioned Systems: Theoretical Analysis and Numerical Results

Cited by 4 publications

References 61 publications

Exploiting the S-Iteration Process for Solving Power Flow Problems: Novel Algorithms and Comprehensive Analysis

Exploiting the S-Iteration Process for Solving Power Flow Problems: Novel Algorithms and Comprehensive Analysis

A Review of State-of-the-Art Techniques for Power Flow Analysis

Adiabatic Quantum Power Flow

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