Particle Swarm Optimization-Based Feature Selection and Parameter Optimization for Power System Disturbances Classification

Ahila, R.; Sadasivam, V.; Manimala, K.

doi:10.1080/08839514.2012.721697

Cited by 19 publications

(5 citation statements)

References 24 publications

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“…One of the main significance of this research, statistical parameters were utilized for optimal feature selection and optimal features are more important than the raw data. The classification accuracy [27]can be evaluated from the following equation.…”

Section: Resultsmentioning

confidence: 99%

Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

Abubakar*,

Shahzad,

Rehman

et al. 2019

IJRTE

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In real power system, Power quality disturbances (PQDs) have become major challenge due to the introduction of renewable energy resources and embedded power systems. In this research, two novel feature extraction methods multi resolution analysis wavelet transform (MRA-WT) and Multiscale singular spectral analysis (MSSA) have been analysed with convolution neural network classifier for the classification of PQDs. Statistical parameters are also applied for the optimal feature selection. MSSA is time-series tool and MRA-WT are applied for feature extraction and 1-dimensional CNN (1-DCNN) is used to classify the single and multiple PQDs. The architecture is built with forward propagation and back propagation is utilized to tune the data. Finally, the results of two selected feature extraction techniques are compared with classification accuracy. The simulation based results explained that MSSA with 1-DCNN has significantly higher classification accuracy under different noisy conditions.

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Section: Resultsmentioning

confidence: 99%

Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

Abubakar*,

Shahzad,

Rehman

et al. 2019

IJRTE

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“…Each point in feature subsets space is a subset of features. Ahila, Sadasivam, and Manimala (2012), proposed an evolutionary algorithm based on particle swarm optimization (PSO) to perform simultaneous feature and model selection. They used a probabilistic neural network as the classifier and PSO as the searching algorithm to explore feature subset space and model parameters.…”

Section: Related Workmentioning

confidence: 99%

Scalable Feature Selection in High-Dimensional Data Based on GRASP

Moshki

Kabiri

Mohebalhojeh

2015

Applied Artificial Intelligence

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Feature selection in high-dimensional data is one of the active areas of research in pattern recognition. Most of the algorithms in this area try to select a subset of features in a way to maximize the accuracy of classification regardless of the number of selected features that affect classification time. In this article, a new method for feature selection algorithm in high-dimensional data is proposed that can control the trade-off between accuracy and classification time. This method is based on a greedy metaheuristic algorithm called greedy randomized adaptive search procedure (GRASP). It uses an extended version of a simulated annealing (SA) algorithm for local search.In this version of SA, new parameters are embedded that allow the algorithm to control the trade-off between accuracy and classification time. Experimental results show supremacy of the proposed method over previous versions of GRASP for feature selection. Also, they show how the trade-off between accuracy and classification time is controllable by the parameters introduced in the proposed method.

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“…Particle swarm optimization was applied in [234,235]. Swarm optimization [236] (Chapters 7 to 9) is a loop which for each individual (i.e., individual particle) in the swarm, evaluates the fitness of that individual, in order to establish whether for each dimension in turn, the particular individual is the best so far.…”

Section: A Survey Of the Application Of Other Techniques: Genetic Fumentioning

confidence: 99%

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Nissan

2019

Designs

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An important aspect of managing a nuclear reactor is how to design refuellings, and from the 1980s to the present different artificial intelligence (AI) techniques have been applied to this problem. A section of the reactor core resembles a symmetrical grid; long fuel assemblies are inserted there, some of them new, some of them partly spent. Rods of “burnable poisons” dangle above, ready to be inserted into the core, in order to stop the reactor. Traditionally, manual design was made by shuffling positions in the grid heuristically, but AI enabled to automatically generate families of candidate configurations, under safety constraints, as well as in order to optimize combustion, with longer cycles of operation between shutdown periods, thus delaying the end-of-cycle point (except in France, where shutdown is on an annual basis, and Canada, where individual fuel assemblies are replaced, with no need for shutdown for rearranging the entire batch). Rule-based expert systems, the first being FUELCON,1 were succeeded by projects combining neural and rule-based processing (a symbolic-to-neural compilation of rules we did not implement), and later on, genetic algorithms in FUELGEN.2 In the literature, one also comes across the application of fuzzy techniques, tabu search, cellular automata and simulated annealing, as well as particle swarms. Safety regulations require simulating the results using a parameter prediction tool; this is done using either nodal algorithms, or neural processing.

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Particle Swarm Optimization-Based Feature Selection and Parameter Optimization for Power System Disturbances Classification

Cited by 19 publications

References 24 publications

Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

Scalable Feature Selection in High-Dimensional Data Based on GRASP

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

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