A novel multiobjective evolutionary algorithm for environmental/economic power dispatch

Abido, M. A.

doi:10.1016/s0378-7796(02)00221-3

Cited by 321 publications

(173 citation statements)

References 23 publications

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“…We have not considered just non-uniform phase and nonuniform spacing here because it is obvious that phaseposition synthesis will yield better results than both of them due to more degrees of freedom. For MOEA/D-DE, the best compromise solution was chosen from the PF using the method described in [20]. The ith objective function f i is represented by a membership function µ i defined as:…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The MO framework attempts to achieve the best compromise among three design objectives: minimizing the Maximum Side Lobe Level (MSLL), Side Band Level (SBL) and the main lobe Beam Width between the First Nulls (BWFN) at the center frequency f 0 . Since unlike single-objective optimization techniques (that finish with a single best solution) the MOEAs return a set of non-dominated solutions (the Pareto optimal set, to be briefly outlined in Section 2), we used a fuzzy membership function based approach [20] to identify the best compromise solutions over each case. In order to validate the MO design method of time-modulated linear arrays, we undertake a twofold comparative study over three significant instantiations of the design problem involving 16, 32, and 64 elements linear array.…”

Section: Introductionmentioning

confidence: 99%

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Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Pal¹,

Das²,

Basak³

2010

PIER B

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Abstract-This article proposes a Multi-objective Optimization (MO) framework for the design of time-modulated linear antenna arrays with ultra low maximum Side Lobe Level (SLL), maximum Side Band Level (SBL) and main lobe Beam Width between the First Nulls (BWFN). In contrast to the existing optimization-based methods that attempt to minimize a weighted sum of SLL, SBL, and BWFN, we treat these as three distinct objectives that are to be achieved simultaneously and use one of the best known MultiObjective Evolutionary Algorithms (MOEAs) of current interest called MOEA/D-DE (Decomposition based MOEA with Differential Evolution operator) to determine the best compromise among these three objectives. Unlike the single-objective approaches, the MO approach provides greater flexibility in the design by yielding a set of equivalent final solutions from which the user can choose one that attains a suitable trade-off margin as per requirements. We compared time-modulated antenna structures with other methods for linear array synthesis such as the excitation method and the phase-position synthesis method on the basis of the approximated Pareto Fronts

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Pal¹,

Das²,

Basak³

2010

PIER B

View full text Add to dashboard Cite

show abstract

“…Also, a penalty function is augmented with the fitness functions to reduce the number of features selected. After convergence to optima using MOEA/D, a fuzzy membership based technique [12] is incorporated to choose only a single feature subset (representing the best compromise solution) out of the Pareto optimal solution set.…”

Section: Algorithm Principlementioning

confidence: 99%

Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

Zhou

Ling

2017

MATEC Web Conf.

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Abstract. Sensorless Drive Diagnosis can be used to assess the process data without the need for additional costintensive sensor technology, and you can understand the synchronous motor and connecting parts of the damaged state. Considering the number of features involved in the process data, it is necessary to perform feature selection and reduce the data dimension in the process of fault detection. In this paper, the MOEA / D algorithm based on multiobjective optimization is used to obtain the weight vector of all the features in the original data set. It is more suitable to classify or make decisions based on these features. In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive, which makes the fault detection and elimination process more targeted.

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“…But this electric power demand is supplied mainly by thermal power plants, which produce harmful gases like Sulfur Oxides SOx, Nitrogen Oxides NOx and Cox [1], [2]. General OPF solution aims on minimum generating cost and emission is not considered.…”

Section: Introductionmentioning

confidence: 99%

Emission Constraint Optimal Power Flow using Differential Evolution

Ravi¹,

Rajan²

2013

IJCA

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Objective of the power systems firm is to achieve the maximum profit and customer goodwill by providing reliable and quality power supply. This power system operation problem is solved by Optimal Power Flow (OPF). It gives requirement of operating states which satisfy the objective of the firm. Thermal power plants are the main source of power generation. Fuel cost of these power plants has to minimize for better profit at the same time it should satisfy system load demand, real, reactive power limit, voltage limit, power transmission limit and other limitations. For generation cost minimization Economic Load Dispatch (ELD) and Optimal Power Flow (OPF) was developed. When cost is the single objective, the power generation may pollute the environment. Thermal electric power could not be generated without pollution but this pollution can be reduced for the sake of good and healthy atmospheric condition. Differential Evolution (DE) algorithm is used in this paper to solve Emission Constraint Optimal Power Flow problem. Standard IEEE 30 bus, power system having 6 thermal power plants, is considered to validate the simulation.

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A novel multiobjective evolutionary algorithm for environmental/economic power dispatch

Cited by 321 publications

References 23 publications

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

Emission Constraint Optimal Power Flow using Differential Evolution

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