Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement With Graph-Computing-Based Power Flow Calculation

Shi, Qingxin; Yuan, Chen; Feng, Wei; Dai, Renchang; Wang, Zhiwei; Li, Fangxing

doi:10.1109/access.2019.2949507

Cited by 7 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The graph-based approach showed significant enhancement over speedup over a conventional MIP solution method on a Tigergraph v2.51 database. Similar applications of noSQL were explored in other power system studies [196,197,198].…”

Section: State Of the Artmentioning

confidence: 94%

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

Al-Shafei¹,

Zareipour²,

Cao³

2022

Preprint

View full text Add to dashboard Cite

show abstract

Section: State Of the Artmentioning

confidence: 94%

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

Al-Shafei¹,

Zareipour²,

Cao³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…[17] - [19]. In recent years, graph computing has also been extended to solve power system problems such as distribution network reconfiguration, parallel power flow calculation, and real-time EMS development [20]- [22]. With the development of a graph database (GDB) and graph models for parallel computing, more complex problems can be entirely solved using graph-based methods and achieve better overall computing performance [23] - [25].…”

Section: Introductionmentioning

confidence: 99%

Graph Computing Based Distributed Parallel Power Flow for AC/DC Systems with Improved Initial Estimate

Feng

Yuan

Shi

et al. 2021

Journal of Modern Power Systems and Clean Energy

Self Cite

View full text Add to dashboard Cite

The sequential method is easy to integrate with existing large-scale alternating current (AC) power flow solvers and is therefore a common approach for solving the power flow of AC/direct current (DC) hybrid systems. In this paper, a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is developed. The proposed approach is capable of speeding up the entire computation process without compromising the accuracy of result. First, the AC/DC network is intuitively represented by a graph and stored in a graph database (GDB) to expedite data processing. Considering the interconnection of AC grids via high-voltage direct current (HVDC) links, the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow analysis. For each area, the fast-decoupled power flow (FDPF) is employed with node-based parallel computing (NPC) and hierarchical parallel computing (HPC) to quickly identify system states. Furthermore, to reduce the alternate iterations in the sequential method, a new decoupled approach is utilized to achieve a good initial estimate for the Newton-Raphson method. With the improved initial estimate, the sequential method can converge in fewer iterations. Consequently, the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power system. The performance is verified on standard IEEE 300-bus system, extended large-scale systems, and a practical 11119-bus system in China.

show abstract

“…Some considerations were made recently to improve scalability of some methods in the literature. like in [18][19][20], where the focus is large systems scalability and reduced computational burden by using parallel and high performance computing. System partitioning was made using graph methods to facilitate parallel computation using fast-decoupled power flow method.…”

Section: Introductionmentioning

confidence: 99%

Sequential network‐flow based power‐flow method for hybrid power systems

Mohamed

El-Shatshat

2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

This paper presents a sequential network-flow graph-based method for a steady-state power flow solution in hybrid multi-terminal power systems. The proposed method is a unique and novel one, which differs from other established methods that involve the use of modified versions of classical power flow methods. The proposed method formulates a power flow problem as a maximum network-flow problem and solves it using a pushrelabel max-flow algorithm. The solution procedure solves AC and DC parts sequentially, while accounting for voltage source converter losses using a generalised converter model. The proposed flow-Augmenting method solves the power flow problem using matrix vector multiplication in its most abstract form, and it is independent of system parameters and network configuration. The proposed formulation is computationally efficient, as it is based on matrix vector multiplication, and is scalable, because the formulation works as a graph-based method, which, inherently, allows for parallel computation for added computational speed. Further, unlike previously reported methods, the proposed method does not rely on Jacobian matrix formulation or any matrix inversion. This proves to be a strong advantage for the proposed method, as a significant reduction in computational time is observed, as a result. The proposed method is validated on 5-bus hybrid system and CIGRE B4 DC system.

show abstract

Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement With Graph-Computing-Based Power Flow Calculation

Cited by 7 publications

References 33 publications

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

Graph Computing Based Distributed Parallel Power Flow for AC/DC Systems with Improved Initial Estimate

Sequential network‐flow based power‐flow method for hybrid power systems

Contact Info

Product

Resources

About