Economic and Reliable Preventive Maintenance Scheduling in Power Systems by Using Binary Crow Search Algorithm

El‐Ela, Adel A. Abou; El‐Sehiemy, Ragab A.; Shaheen, Abdullah M.; Shalaby, Ayman S.

doi:10.4028/www.scientific.net/jera.56.182

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…For heuristic optimization algorithms comparison, statistical results of a number of runs are recorded. In ordre to investigate the performance of the optimization algorithms of PSO, CSA [37,38], AO [39], and BES, they are run for 50 separate runs and the statistical results of best, mean, worst, standard deviation (STD), and standard error (STE) are compared as recoreded in Tables 5 and 6 for Zafaranh and Shark El-Ouinate sites, respectively. The 5000 runs number is executed and fitness values are recorded for each considered algorithm.…”

Section: Goodness Of Fitmentioning

confidence: 99%

Assessment of Wind Energy based on Optimal Weibull Parameters Estimation using Bald Eagle Search Algorithm: Case Studies from Egypt

El‐Ela

El‐Sehiemy

Shaheen

et al. 2023

J. Electr. Eng. Technol.

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As the wind speed is intermittent and unpredictable, statistical distribution approaches have been used to describe wind dates. The Weibull distribution with two parameters is thought to be the most accurate way for modeling wind data. This study seeks wind energy assessment via searching for optimal parameter estimation of the Weibull distribution. For this target, several analytical and heuristic methods are investigated. The analytical methods such as maximum likelihood method, moment method, energy pattern factor method (EPFM), and empirical method (EM) are used to find these optimal parameters. Also, these parameters are obtained by four heuristic optimization algorithms called particle swarm, crow search, aquila optimizer, and bald eagle search optimizers. The simulation results of analytical and heuristics are assessed together to identify the best probability density function (PDF) of wind data. In addition, these competitive models are submitted to find the most appropriate model to represent wind energy production. In all methods, the error between actual and estimated wind energy density is computed as the target fitness function. The simulation tests are carried out based on per year real data that are collected from Zafaranah and Shark El-Ouinate sites in Egypt. Also, different indicators of fitness properties are assessed such as the root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and wind production deviation (WPD). The simulation results declare that the proposed bald eagle search optimization algorithm offers greater accuracy than other analytical and heuristic algorithms in estimating the Weibull parameters. Besides, statistical analysis of the compared methods demonstrates the high stability of the BES algorithm. Moreover, the BES algorithm presents the fastest convergence compared to the others. Furthermore, different models are analyzed to deduce the nonlinear relationship between the wind output power and the regarding speed where the error of wind energy density between actual and estimated is greatly minimized using the cubic model at least values of statistical indicators.

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Section: Goodness Of Fitmentioning

confidence: 99%

Assessment of Wind Energy based on Optimal Weibull Parameters Estimation using Bald Eagle Search Algorithm: Case Studies from Egypt

El‐Ela

El‐Sehiemy

Shaheen

et al. 2023

J. Electr. Eng. Technol.

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“…By maintaining the global diversity of the population, variation ensures global search power. On the other hand, the termination principle outputs and terminates the algorithm after the results it produces meet certain principle requirements [22]. The simple process of variation is shown in Figure 2.…”

Section: Multipopulation Collaborative Genetic Algorithm Based On Col...mentioning

confidence: 99%

A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP

Shang

2023

Journal of Applied Mathematics

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Flexible job-shop scheduling problem (FJSP) is a new research hotspot in the field of production scheduling. To solve the multiobjective FJSP problem, the production of flexible job shop can run normally and quickly. This research takes into account various characteristics of FJSP problems, such as the need to ensure the continuity and stability of processing, the existence of multiple objectives in the whole process, and the constant complexity of changes. It starts with deep learning neural networks and genetic algorithms. Long short-term memory (LSTM) and convolutional neural networks (CNN) are combined in deep learning neural networks. The new improved algorithm is based on the combination of deep learning neural networks LSTM and CNN with genetic algorithm (GA), namely, CNN-LSTM-GA algorithm. Simulation results showed that the accuracy of the CNN-LSTM-GA algorithm was between 85.2% and 95.3% in the test set. In the verification set, the minimum accuracy of the CNN-LSTM-GA algorithm was 84.6%, both of which were higher than the maximum accuracy of the other two algorithms. In the FJSP simulation experiment, the AUC value of the CNN-LSTM-GA algorithm was 0.92. After 40 iterations, the F1 value of the CNN-LSTM-GA algorithm remained above 0.8, which was significantly higher than the other two algorithms. CNN-LSTM-GA is superior to the other two algorithms in terms of prediction accuracy and overall performance of FJSP. It is more suitable for solving the discrete manufacturing job scheduling problem with FJSP characteristics. This study significantly raises the utilisation rate of the assembly shop’s equipment, optimises the scheduling of FJSP, and fully utilises each processing device’s versatile characteristics, which are quite useful for the production processes of domestic vehicle manufacturing companies.

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“…Also, CSA is a one of modern optimization tools which is based on crow’s intelligence in storing and retrieving its food in hiding locations 43 . This algorithm is used for the first time to solve the GEP problem, but it has been applied for solving several optimization problems such as economic dispatch 44 , unit commitment 45 , preventive maintenance scheduling 46 , capacitor allocation in distribution systems 47 , and optimal power flow problem 48 , 49 .…”

Section: Honey Badger Algorithm For Reliability Constrained Dynamic Gepmentioning

confidence: 99%

Reliability constrained dynamic generation expansion planning using honey badger algorithm

Abou El Ela,

El-Sehiemy,

Shaheen

et al. 2023

Sci Rep

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Generation expansion planning (GEP) is a complex, highly constrained, non-linear, discrete and dynamic optimization task aimed at determining the optimum generation technology mix of the best expansion alternative for long-term planning horizon. This paper presents a new framework to study the GEP in a multi-stage horizon with reliability constrained. GEP problem is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over planning horizon. Added to that, the spinning reserve, fuel mix ratio and reliability in terms of Loss of Load Probability are maintained. Moreover, to decrease the GEP problem search space and reduce the computational time, some modifications are proposed such as the Virtual mapping procedure, penalty factor approach, and the modified of intelligent initial population generation. For solving the proposed reliability constrained GEP problem, a novel honey badger algorithm (HBA) is developed. It is a meta-heuristic search algorithm inspired from the intelligent foraging behavior of honey badger to reach its prey. In HBA, the dynamic search behavior of honey badger with digging and honey finding approaches is formulated into exploration and exploitation phases. Added to that, several modern meta-heuristic optimization algorithms are employed which are crow search algorithm, aquila optimizer, bald eagle search and particle swarm optimization. These algorithms are applied, in a comparative manner, for three test case studies for 6-year, 12-year, and 24-year of short- and long-term planning horizon having five types of candidate units. The obtained results by all these proposed algorithms are compared and validated the effectiveness and superiority of the HBA over the other applied algorithms.

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Economic and Reliable Preventive Maintenance Scheduling in Power Systems by Using Binary Crow Search Algorithm

Cited by 3 publications

References 44 publications

Assessment of Wind Energy based on Optimal Weibull Parameters Estimation using Bald Eagle Search Algorithm: Case Studies from Egypt

Assessment of Wind Energy based on Optimal Weibull Parameters Estimation using Bald Eagle Search Algorithm: Case Studies from Egypt

A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP

Reliability constrained dynamic generation expansion planning using honey badger algorithm

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