An adaptive multi-objective optimization method for optimum design of distribution networks

Mohamadi, Mohamad Reza; Abedini, Moein; Rashidi, Bahram

doi:10.1080/0305215x.2019.1577412

Cited by 12 publications

(3 citation statements)

References 66 publications

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“…Metaheuristic algorithms perform well in various engineering problems. For example, the have been applied in gear system design, cam design, wind turbine blade design, aeronautical equipment and combustion systems [ 12 ]; and in engineering areas such as robotics, data mining, security and many others [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

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Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

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Section: Introductionmentioning

confidence: 99%

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

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“…• DG sizing limits The distributed generators' active and reactive powers must be within limits according to Equations (22) and (23) [51].…”

Section: Equality Constraintsmentioning

confidence: 99%

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

et al. 2020

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Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

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“…Metaheuristic algorithms perform well when applied to various engineering problems. For example, they have been applied to gear system design, cam design, wind turbine blade design, and to the design of aeronautical equipment and combustion systems [17]. Several techniques have been applied to handle vibrations using metaheuristic algorithms to increase efficiency by maximizing the amplitude of the defect frequency [18].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine

Montoya-Santiyanes

Rodríguez-Abreo

Rodriguez

et al. 2022

Sensors

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Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 ∘C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.

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An adaptive multi-objective optimization method for optimum design of distribution networks

Cited by 12 publications

References 66 publications

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine

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