Optimization of Hybrid Wind and Solar Renewable Energy System by Iteration Method

Geleta, Diriba Kajela; Manshahia, Mukhdeep Singh

doi:10.1007/978-3-030-00979-3_10

Cited by 11 publications

(30 citation statements)

References 12 publications

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“…The proposed method can be applied in the interdisciplinary fields for future research directions. Some of these are as follows: modeling and optimization of flexible manufacturing systems, 54 visual analytics solution for scheduling processing phases, 55 the number of out‐of‐cash ATMs and duration of out‐of‐cash status, 56 optimization of hybrid wind and solar renewable energy system, 57 optimization of parquetting of the concentrator of photovoltaic thermal module, 58 verifying the gaming strategy of self‐learning game, 59 optimization of the process of anaerobic bioconversion of liquid organic wastes, 60 efficiency optimization of indoor air disinfection by radiation exposure for poultry breeding 61 …”

Section: Discussionmentioning

confidence: 99%

An inverse model‐based multiobjective estimation of distribution algorithm using Random‐Forest variable importance methods

Gholamnezhad

Broumandnia

Seydi

2020

Computational Intelligence

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Most existing methods of multiobjective estimation of distributed algorithms apply the estimation of distribution of the Pareto-solution on the decision space during the search and little work has proposed on making a regression-model for representing the final solution set.Some inverse-model-based approaches were reported, such as inversed-model of multiobjective evolutionary algorithm (IM-MOEA), where an inverse functional mapping from Pareto-Front to Pareto-solution is constructed on nondominated solutions based on Gaussian process and random grouping technique. But some of the effective inverse models, during this process, may be removed. This paper proposes an inversed-model based on random forest framework. The main idea is to apply the process of random forest variable importance that determines some of the best assignment of decision variables (x n ) to objective functions (f m ) for constructing Gaussian process in inversed-models that map all nondominated solutions from the objective space to the decision space. In this work, three approaches have been used: classical permutation, Naïve testing approach, and novel permutation variable importance. The proposed algorithm has been tested on the benchmark test suite for evolutionary algorithms [modi-

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Section: Discussionmentioning

confidence: 99%

An inverse model‐based multiobjective estimation of distribution algorithm using Random‐Forest variable importance methods

Gholamnezhad

Broumandnia

Seydi

2020

Computational Intelligence

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“…Again also, Zong 7 improved these data by including all necessary data to make it whole dataset for other researchers to apply it with different methods. Moreover, Geleta et al 6 have improved these data by current factors of inflation rate and energy demand due to fit the values with its current states. The reason behind the researchers focus on this data to optimize the stated hybrid problem is to show the efficiency of the algorithm (GSA) over the employed conventional method.…”

Section: Numerical Datasetsmentioning

confidence: 99%

“…Moreover, the power demand data given in Table 6 are multiplied by 1.3%, global average energy demand increment value of the mentioned year. Since, these values are originally recorded from instrument and timely updated; the researchers applied this numerical data in this article as stated in Geleta et al 6 Table 5 shows, the values of the updated input numerical values of the decision variables which used to get the optimal value of the defined hybrid renewable energy system by applying nature inspired algorithm (GSA). Here the values mentioned in Zong 7 which subjected to purchase, for instance solar panel price, wind turbine price, cost of battery, and cost of backup generator are updated by the inflation rate and energy demand rate factors mentioned above.…”

Section: Numerical Datasetsmentioning

confidence: 99%

“…As mentioned, fluctuation of their power output can disturb the need and supply unless certain backup is scheduled. Practicing the use of hybrids of renewable energy technology can manage limitations appear from standalone power source 6,8 . Most rural areas of sub‐Saharan African countries, central and south eastern Asia countries have less access to electric power due to either financial constraint or difficult geographical location.…”

Section: Introductionmentioning

confidence: 99%

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Gravitational search algorithm‐based optimization of hybrid wind and solar renewable energy system

Geleta

Manshahia

2020

Computational Intelligence

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Due to the issue of environmental protection coupled with high energy demand, there was an initiation for exploration of different renewable energy sources. This article aims to optimize the total annual cost of hybrids of wind and solar renewable energy system to satisfy the predesigned load. Minimization of the total annual cost of the system by determining appropriate numbers of the components, so that the desired load can be economically and reliably satisfied under the given constraints. Gravitational Search Algorithm (GSA) was employed for the optimization process. GSA is a recently proposed metaheuristic algorithm which is based on Newton's universal gravitational law of gravity and mass interactions. It uses stochastic rules to escape local optima and find the global optimal solutions. MATLAB codes were designed for the developed fitness function and employed algorithm. The proposed methodology was run for the fitness function through the code and the results were discussed. The result was compared with the results of Particle Swarm Optimization (PSO) and also shown that: GSA has some advantage over PSO algorithm. Even though, the algorithm has several parameters to be adjusted, it is strong in both local and global optimal searches.

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“…Soft computing approaches like fuzzy logic, artificial neural network, genetic algorithm, etc., have established a substantial concentration because of its potential of being a very effectual and competent design optimization technique for solving various nonlinear problems, where mathematical models are not available. Soft computing deals with approximate models and furnishes resolution to complex problems [16,17]. An ANFIS based on hybrid soft computing method has been developed and modeled in Simulink.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Adaptive Neural Network-Based Controller for Improving the Conversion Efficiency of Solar Photovoltaic Module

Deepti¹,

Srihari²,

Manjunathachari

2021

J. Inst. Eng. India Ser. B

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This research article proposes an intelligent artificial neural network-based fuzzy logic controller developed for tracking the exact maximum power point (MPP) under varying conditions of temperature and irradiance. The hybrid soft computing method combines learning abilities of an adaptive artificial neural network (ANN) and incremental conductance algorithm (INC)based fuzzy logic controller (FLC) to handle uncertain data. The adaptive neural fuzzy inference system (ANFIS) is trained with the dataset generated for output voltage, current and all possible varying conditions to estimate the duty cycle of converter as maximum power point capturing device. ANFIS is developed in Takagi-Sugeno inference system, and the rule set is optimized to 49 rules. The error tolerance range is considered as 12% to replicate the hardware characteristics. Mamdani inference system is chosen for implementing INC principle-based FLC. The objective of the proposed method is to identify maximum operating point under varying temperature and irradiance and operate the system to improve the performance. The proposed model is implemented in MATLAB/Simulink environment.

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Optimization of Hybrid Wind and Solar Renewable Energy System by Iteration Method

Cited by 11 publications

References 12 publications

An inverse model‐based multiobjective estimation of distribution algorithm using Random‐Forest variable importance methods

An inverse model‐based multiobjective estimation of distribution algorithm using Random‐Forest variable importance methods

Gravitational search algorithm‐based optimization of hybrid wind and solar renewable energy system

Implementation of an Adaptive Neural Network-Based Controller for Improving the Conversion Efficiency of Solar Photovoltaic Module

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