PSS parameters values finding using SMVSDFT objective function and a new technique for multi-objective function in a multi-machine power system

Purpose The purpose of this study/paper is to integrate distributed generation optimally in power system using plant propagation algorithm. Distributed generation is a growing concept in the field of electricity generation. It mainly comprises small generation units installed at calculated points of a power system network. The challenge of optimal allocation and sizing of DG is of utmost importance. Design/methodology/approach Plant propagation algorithm and particle swarm optimisation techniques have been implemented where a weighting factor-based multi-objective function is minimised. The objective is to cut down real losses and to improve the voltage profile of the system. Findings The results obtained using plant propagation algorithm technique for IEEE 33-bus systems are compared to those attained using particle swarm optimisation technique. The paper deals with the optimisation of weighting factor-based objective function, which counterpoises the losses and improves the voltage profile of the system and, therefore, helps to deliver the best outcomes. Originality/value This paper fulfils an identified need to study the multi-objective optimisation techniques for integration of distributed generation in the concerned power system network. The paper proposes a novel plant-propagation-algorithm-based technique in appropriate allocation and sizing of distributed generation unit.

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Development of a Data-Partition Technique for Gradient-Based Optimization Methods in History Matching

SPE Journal ◽

10.2118/130473-pa ◽

2011 ◽

Vol 16 (03) ◽

pp. 582-593 ◽

Cited By ~ 2

Author(s):

D.Y.. Y. Ding

Keyword(s):

Objective Function ◽

History Matching ◽

Optimization Methods ◽

New Technique ◽

Large Field ◽

Data Partition ◽

Geostatistical Models ◽

Gradient Based ◽

A New Technique ◽

Influential Parameters

Summary Assisted history matching is now widely used to constrain reservoir models. However, history matching is a complex inverse problem, and it is always a big challenge to history match large fields with a large number of parameters. In this paper, we present a new technique for the gradient-based optimization methods to improve history matching for large fields. This new technique is based on data partition for the gradient calculations. In history matching, the objective function can be split into local components, and a local component generally depends on fewer influential parameters. On the basis of this decomposition, we can propose a perturbation design, which allows us to calculate all derivatives of the objective function with only a few perturbations. This method is particularly interesting for regional and well-level history matching, and it is also suitable to match geostatistical models by introducing numerous local parameters. This new technique makes history matching with a large number of parameters (large field) tractable.

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