Cultural coyote optimization algorithm applied to a heavy duty gas turbine operation

Pierezan, Juliano; Maidl, Gabriel; Yamao, Eduardo Massashi; Coelho, Leandro dos Santos; Mariani, Viviana Cocco

doi:10.1016/j.enconman.2019.111932

Cited by 84 publications

(36 citation statements)

References 51 publications

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“…In recent years, COA has been widely applied for optimization problems in many different fields. e authors in [32] have applied COA to minimize the gas consumption of turbines in combined cycle power plants in Brazil. e proposed solution fully satisfied the physical limits of the turbine and pollution emission regulations.…”

Section: Complexitymentioning

confidence: 99%

“…e second improvement can be seen in Algorithm 1. Here, the number of pairs of close solutions is symbolized by Count and determined by using a pseudocode while the maximum number of pairs of close solutions and the ratio can be, respectively, symbolized by Count max and R, which are obtained by equations (32) and (33). As indicated by equation 33, R is within 0 and 1, and its value is directly proportional to the number of pairs of close solutions.…”

Section: 4mentioning

confidence: 99%

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Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Nguyen

Pham²,

Kien

et al. 2020

Complexity

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This paper proposes an improved coyote optimization algorithm (ICOA) for optimizing the location and sizing of solar photovoltaic distribution generation units (PVDGUs) in radial distribution systems. In the considered problem, four single objectives consisting of total power losses, capacity of all PVDGUs, voltage profile index, and harmonic distortions are minimized independently while satisfying branch current limits, voltage limits, and harmonic distortion limits exactly and simultaneously. The performance of the proposed ICOA method has been improved significantly since two improvements were carried out on the two new solution generations of the conventional coyote optimization algorithm (COA). By finding four single objectives from two IEEE distribution power systems with 33 buses and 69 buses, the impact of each proposed improvement and two proposed improvements on the real performance of ICOA has been investigated. ICOA was superior to COA in terms of capability of finding higher quality solutions, more stable search ability, and faster convergence speed. Furthermore, we have also applied five other metaheuristic algorithms consisting of biogeography-based optimization (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSO), sunflower optimization (SFO), and salp swarm algorithm (SSA) for dealing with the same problem and evaluating further performance of ICOA. The result comparisons have also indicated the outstanding performance of ICOA because it could find much better results than these methods, especially SFO, SSA, and GA. Consequently, the proposed ICOA is a very effective method for finding the optimal location and capacity of PVDGUs in radial distribution power systems.

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Section: Complexitymentioning

confidence: 99%

Section: 4mentioning

confidence: 99%

Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Nguyen

Pham²,

Kien

et al. 2020

Complexity

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“…OCOA could improve the result over these six methods significantly once OCOA has found a better average fitness function for many test cases, especially for hybrid benchmark functions with high dimensions, which were representatives of complicated optimization problems. Recently, OCOA has been successfully applied for different optimization problems in these studies [40][41][42][43]. In [40], OCOA has been applied for finding main parameters of three-diode based photovoltaic modules under different environment conditions consisting of the change of temperature and the change of irradiation.…”

mentioning

confidence: 99%

“…Thanks to the high performance of OCOA, the three-diode photovoltaic model could be established and it could work as effectively as other commercial Photovoltaic modules in the market. In [41], OCOA has been applied for reducing gas consumption of turbines in combined cycle power plants in Brazil while satisfying pollutant emission regulations and physical limitations of turbines. OCOA has been evaluated to be more effective than ABCA, BSA, SaDE, GWOA, PSO and the Symbiotic Organisms Search (SOS).…”

mentioning

confidence: 99%

“…Although OCOA has been applied successfully for different problems, its real performance is still not demonstrated persuasively. Among the studies in [39][40][41][42][43], only optimal reactive power dispatch problem in [43] is a real challenge for evaluating the effectiveness of OCOA. The result comparisons in [43] indicated that OCOA could not reach good results as other methods, even the success rate of OCOA was very low.…”

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confidence: 99%

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Optimal Power Flow for Transmission Power Networks Using a Novel Metaheuristic Algorithm

Cao

Đại

et al. 2019

Energies

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In the paper, a modified coyote optimization algorithm (MCOA) is proposed for finding highly effective solutions for the optimal power flow (OPF) problem. In the OPF problem, total active power losses in all transmission lines and total electric generation cost of all available thermal units are considered to be reduced as much as possible meanwhile all constraints of transmission power systems such as generation and voltage limits of generators, generation limits of capacitors, secondary voltage limits of transformers, and limit of transmission lines are required to be exactly satisfied. MCOA is an improved version of the original coyote optimization algorithm (OCOA) with two modifications in two new solution generation techniques and one modification in the solution exchange technique. As compared to OCOA, the proposed MCOA has high contributions as follows: (i) finding more promising optimal solutions with a faster manner, (ii) shortening computation steps, and (iii) reaching higher success rate. Three IEEE transmission power networks are used for comparing MCOA with OCOA and other existing conventional methods, improved versions of these conventional methods, and hybrid methods. About the constraint handling ability, the success rate of MCOA is, respectively, 100%, 96%, and 52% meanwhile those of OCOA is, respectively, 88%, 74%, and 16%. About the obtained solutions, the improvement level of MCOA over OCOA can be up to 30.21% whereas the improvement level over other existing methods is up to 43.88%. Furthermore, these two methods are also executed for determining the best location of a photovoltaic system (PVS) with rated power of 2.0 MW in an IEEE 30-bus system. As a result, MCOA can reduce fuel cost and power loss by 0.5% and 24.36%. Therefore, MCOA can be recommended to be a powerful method for optimal power flow study on transmission power networks with considering the presence of renewable energies.

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A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

Yousif,

Ismail,

Al‐Bazi

2024

Advcd Theory and Sims

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In gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators.

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Cultural coyote optimization algorithm applied to a heavy duty gas turbine operation

Cited by 84 publications

References 51 publications

Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Optimal Power Flow for Transmission Power Networks Using a Novel Metaheuristic Algorithm

A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

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