Nonlinear Least Squares Estimation for Parameters of Mixed Weibull Distributions by Using Particle Swarm Optimization

Lu, Zhong; Dong, Li; Zhou, Jia

doi:10.1109/access.2019.2915279

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…In [99], the capacity constraint was used to form linear inequality constraint for the objective function in OPF. Heuristic methods have been investigated for these problems, such as particle swarm optimization [100], ANN and GA [101]. One of the main concerns of heuristic methods is the uncertainty of the optimality and heavy computation load if a large number of iterations are required for convergence.…”

Section: Constraint Managementmentioning

confidence: 99%

Review on Distribution Network Optimization under Uncertainty

Liao

2019

Energies

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With the increase of renewable energy in electricity generation and increased engagement from demand sides, distribution network planning and operation face great challenges in the provision of stable, secure and dedicated service under a high level of uncertainty in network behaviors. Distribution network planning and operation, at the same time, also benefit from the changes of current and future distribution networks in terms of the availability of increased resources, diversity, smartness, controllability and flexibility of the distribution networks. This paper reviews the critical optimization problems faced by distribution planning and operation, including how to cope with these changes, how to integrate an optimization process in a problem-solving framework to efficiently search for optimal strategy and how to optimize sources and flexibilities properly in order to achieve cost-effective operation and provide quality of services as required, among other factors. This paper also discusses the approaches to reduce the heavy computation load when solving large-scale network optimization problems, for instance by integrating the prior knowledge of network configuration in optimization search space. A number of optimization techniques have been reviewed and discussed in the paper. This paper also discusses the changes, challenges and opportunities in future distribution networks, analyzes the possible problems that will be faced by future network planning and operations and discusses the potential strategies to solve these optimization problems.Keywords: distribution network optimization; flexibility exchange; demand side management; network planning and operation IntroductionDistribution networks face great challenges with the changes in current and future distribution networks, such as the inclusion of more green energy, installation of more controllable power electronic devices, differentiated power quality requirements from different customers and increased active engagement from customer sides. To provide stable and greener electricity and meet the requirements from various stakeholders, the network should properly plan and utilize the available network resources to meet the constraints, improve quality of services and reduce the operating cost. Proper planning/operation strategies enable the cost-effective running of the network and improved customer experience in using electricity or participating in network operation/management. Distribution planning and operation problems (such as the integration of more renewable energy, the utilization of flexibility resources and customer engagement for various purposes, etc.) can be tackled with appropriate definition of optimization problems and the use of properly tailored optimization techniques. The research on distribution system optimization can be broadly divided into two categories:(1) Planning: With the global trend of using more renewable energy to reduce emission, one of the challenges in distribution system planning nowadays is to integrate more distrib...

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Section: Constraint Managementmentioning

confidence: 99%

Review on Distribution Network Optimization under Uncertainty

Liao

2019

Energies

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“…In recent years, the cuckoo search (CS) algorithm has been extensively applied in numerical optimization [20,21] and multi-objective optimization [22,23], among other domains. The CS algorithm is widely employed in diverse scenarios to search for robust solutions with fast convergence.…”

Section: Introductionmentioning

confidence: 99%

Application of the Improved Cuckoo Algorithm in Differential Equations

Sun

2024

Mathematics

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To address the drawbacks of the slow convergence speed and lack of individual information exchange in the cuckoo search (CS) algorithm, this study proposes an improved cuckoo search algorithm based on a sharing mechanism (ICSABOSM). The enhanced algorithm reinforces information sharing among individuals through the utilization of a sharing mechanism. Additionally, new search strategies are introduced in both the global and local searches of the CS. The results from numerical experiments on four standard test functions indicate that the improved algorithm outperforms the original CS in terms of search capability and performance. Building upon the improved algorithm, this paper introduces a numerical solution approach for differential equations involving the coupling of function approximation and intelligent algorithms. By constructing an approximate function using Fourier series to satisfy the conditions of the given differential equation and boundary conditions with minimal error, the proposed method minimizes errors while satisfying the differential equation and boundary conditions. The problem of solving the differential equation is then transformed into an optimization problem with the coefficients of the approximate function as variables. Furthermore, the improved cuckoo search algorithm is employed to solve this optimization problem. The specific steps of applying the improved algorithm to solve differential equations are illustrated through examples. The research outcomes broaden the application scope of the cuckoo optimization algorithm and provide a new perspective for solving differential equations.

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“…It is very difficult to solve the parameters of the complex Weibull model, so many scholars have researched how to fit the Weibull model with higher accuracy [3][4] . And some methods for solving parameters are proposed, such as maximum likelihood estimation, moment estimation, graphical method, etc [5][6][7][8] . The above research results make the complex Weibull distribution model gradually widely used.…”

Section: Introductionmentioning

confidence: 99%

Research on Reliability of CNC Machine Tools Based on Sectional Model Involving Two Weibull Distributions

Chen

Wang

Zhang

2023

J. Phys.: Conf. Ser.

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To study the failure distribution rule of CNC machine tools and accurately fit the distribution model of failure time, the field test failure data from a series of CNC machine tools are analyzed in this paper. A sectional model involving two Weibull distributions is used to fit the failure time of CNC machine tools. The D-test method is used to examine the fit of the model. By comparing the goodness of fit with the ordinary Weibull distribution model, it is finally verified that the sectional model involving two Weibull distributions is more consistent with the failure data distribution rule of the CNC machine tools.

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Nonlinear Least Squares Estimation for Parameters of Mixed Weibull Distributions by Using Particle Swarm Optimization

Cited by 12 publications

References 25 publications

Review on Distribution Network Optimization under Uncertainty

Review on Distribution Network Optimization under Uncertainty

Application of the Improved Cuckoo Algorithm in Differential Equations

Research on Reliability of CNC Machine Tools Based on Sectional Model Involving Two Weibull Distributions

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