Hybrid power system state estimation using Taguchi differential evolution algorithm

Vedik, B.; Chandel, Ashwani Kumar

doi:10.1049/iet-smt.2014.0082

Cited by 33 publications

(11 citation statements)

References 52 publications

(114 reference statements)

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“…DE algorithm is a meta-heuristic optimisation method which replaces the poorest performing population member through mutation, crossover, and selection [31]. There are several types of DE that are reported in the literature.…”

Section: Basic Differential Evolutionmentioning

confidence: 99%

Optimal allocation of plug‐in electric vehicle capacity to produce active, reactive and distorted powers using differential evolution based artificial bee colony algorithm

Farahani

Rashidi

2017

IET Science, Measurement & Technology

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Section: Basic Differential Evolutionmentioning

confidence: 99%

Optimal allocation of plug‐in electric vehicle capacity to produce active, reactive and distorted powers using differential evolution based artificial bee colony algorithm

Farahani

Rashidi

2017

IET Science, Measurement & Technology

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“…Authors have presented TDFA for optimizing of injection moulding process parameters [20]. Taguchi's orthogonal array (TOA) method has been applied for probabilistic load flow studies [21] and state estimation of hybrid power system [22]. To the best of author's knowledge application of TDFA approach for distribution system problem is not found in literature.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal sizing of distributed generation by application of Taguchi desirability function analysis

2019

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Distributed Generation (DG) is modular power generating technology. DG has beneficial impact on tail end voltages, line losses and operating cost. The sizing and location of DG in distribution system is a vital undertaking. In this paper Taguchi Desirability Function Analysis (TDFA) is presented. The remarkable highlight of TDFA is the skillful handling of simultaneous optimization of nearly thousand objectives and its ability to generate multiple optimal solutions. It facilitates the effortless addition of each objective. TDFA is used for finding the optimal size of single as well as multiple DG. Size estimation is carried out for multiple objectives of minimizing the line losses, improving the voltage profile and improving voltage stability index (VSI). The objectives may be minimized, maximized or assigned target values simultaneously. In the scope of this paper more than nine objectives have been optimized simultaneously. TDFA has been implemented to determine optimal size of DG for different load conditions. The proposed approach is tested and verified on IEEE 33-bus and IEEE 85-bus radial distribution system (RDS). Keywords Distributed generation • Radial distribution system • VSI • TDFA List of symbols DG Distributed generation RDS Radial distribution system F No. of input factors l No. of levels FFD Full factorial design df Degree of freedom Y Response R No. of responses x 1, x 2 Input factors ß Regression coefficients ɛ Error term 2FI Two-factor interaction R 2 Coefficient of determination d Desirability function ID Individual desirability CD Composite desirability L i Minimum acceptable value of response Ui Maximum acceptable value of response

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“…Taguchi method (TM) is another optimisation technique which is capable of providing near-optimal solution in less number of experiments [22][23][24][25][26][27]. It is successfully employed in manufacturing process to save millions of dollar by avoiding the revenue loss due to manufacturing defect in products at early stage of the process.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective Taguchi approach for optimal DG integration in distribution systems

Meena

Swarnkar

Gupta

et al. 2017

IET Generation, Transmission & Distribution

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Hybrid power system state estimation using Taguchi differential evolution algorithm

Cited by 33 publications

References 52 publications

Optimal allocation of plug‐in electric vehicle capacity to produce active, reactive and distorted powers using differential evolution based artificial bee colony algorithm

Optimal allocation of plug‐in electric vehicle capacity to produce active, reactive and distorted powers using differential evolution based artificial bee colony algorithm

Multi-objective optimal sizing of distributed generation by application of Taguchi desirability function analysis

Multi‐objective Taguchi approach for optimal DG integration in distribution systems

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