Application of parallel particle swarm optimization on power system state estimation

Jeong, Hae‐Kwon; Lee, Hwa-Seok; Park, June-Ho

doi:10.1109/td-asia.2009.5356917

Cited by 15 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the context of computation in power transmission, it has been explored to obtain improvement in speed of computation. The authors in [42] have achieved 4.8 times faster than normal PSO for transmission system state estimation. Because of the similarity of the structure of the equations and between the inter relationship between variables it is possible to achieve similar results based on the application of parallel PSO for the given network in this study.…”

Section: B Discussionmentioning

confidence: 99%

Three-Phase State Estimation Using Hybrid Particle Swarm Optimization

Nanchian

Majumdar

Pal

2017

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

This paper proposes a method for three phase state estimation in power distribution network including on-load tap changers (OLTC) for voltage control. The OLTC tap positions are essentially discrete variables from the state estimation (SE) point of view. Estimation of these variables in SE presents a formidable challenge. The proposed methodology combines discrete and continuous state variables (voltage magnitudes, angles and tap positions). A hybrid particle swarm optimization (HPSO) is applied to obtain the solution. The method is tested on standard IEEE 13-bus and 123-bus unbalanced test system models. The proposed algorithm accurately estimates the network bus voltage magnitudes and angles and discrete tap values. The HPSO based tap estimation provides more accurate estimation of losses in the network which helps in fair allocation of cost of losses in arriving at overall cost of the electricity.

show abstract

Section: B Discussionmentioning

confidence: 99%

Three-Phase State Estimation Using Hybrid Particle Swarm Optimization

Nanchian

Majumdar

Pal

2017

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…With less than four references per application, PHC technologies have been used in dynamic state estimation [94,200,201], power system planning [236,237], TSCOPF [230,231], power system reliability [238,239], electromagnetic transient simulation [91,92], dynamic models [2,216], short circuit analysis [235], security constrained economic dispatch [242], probabilistic power flow [224], and hydro thermal scheduling [243]. In ref.…”

Section: Other Applicationsmentioning

confidence: 99%

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

et al. 2021

View full text Add to dashboard Cite

The power system expansion and the integration of technologies, such as renewable generation, distributed generation, high voltage direct current, and energy storage, have made power system simulation challenging in multiple applications. The current computing platforms employed for planning, operation, studies, visualization, and the analysis of power systems are reaching their operational limit since the complexity and size of modern power systems results in long simulation times and high computational demand. Time reductions in simulation and analysis lead to the better and further optimized performance of power systems. Heterogeneous computing—where different processing units interact—has shown that power system applications can take advantage of the unique strengths of each type of processing unit, such as central processing units, graphics processing units, and field-programmable gate arrays interacting in on-premise or cloud environments. Parallel Heterogeneous Computing appears as an alternative to reduce simulation times by optimizing multitask execution in parallel computing architectures with different processing units working together. This paper presents a review of Parallel Heterogeneous Computing techniques, how these techniques have been applied in a wide variety of power system applications, how they help reduce the computational time of modern power system simulation and analysis, and the current tendency regarding each application. We present a wide variety of approaches classified by technique and application.

show abstract

“…Em Kandasamy;Hussain (2015), os autores propõem um modelo matemático enquadrado na programação linear inteira, com o objetivo de monitorar os SEPs através de um índice de observabilidade, utilizando unidades de medição fasorial (do inglês: PMU). Também em Jeong et al (2009), os autores utilizam PMUs para monitorar os SEPs utilizando a programação linear inteira binária.…”

Section: Introductionunclassified

Alocação de medidores visando à Estimação de Estados na operação dos sistemas elétricos de potência

Machado¹,

Zvietcovich²,

Baracho³

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

This work illustrates the implementation of the Variable Neighborhood Search (VNS) metaheuristic to solve the problem of allocation of monitors in Electric Power Systems (SEP). This problem is framed in the mathematical programming and solved in the literature by non-exact technique. The monitors have different reference channels (voltage, active and reactive power) registering effective values, which are used in the SEP State Estimation (EE). For this, the EE concepts are used by the Weighted Least Squares method. The methodology was implemented step by step, as well as their own strategies in the application of this problem, with the goal to find better results. The objective is to find the minimum number of monitors and the configuration with this minimum numbers of monitors that generates the smallest error between estimated and measured values. To test the methodology used, two IEEE networks, the 14-bar and the 30-bar network, found satisfactory results.

show abstract

Application of parallel particle swarm optimization on power system state estimation

Cited by 15 publications

References 10 publications

Three-Phase State Estimation Using Hybrid Particle Swarm Optimization

Three-Phase State Estimation Using Hybrid Particle Swarm Optimization

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

Alocação de medidores visando à Estimação de Estados na operação dos sistemas elétricos de potência

Contact Info

Product

Resources

About