Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

Ehteram, Mohammad; Banadkooki, Fatemeh Barzegari; Fai, Chow Ming; Moslemzadeh, Mohsen; Sapitang, Michelle; Ahmed, Ali Najah; Irwan, Dani; El-Shafie, Ahmed

doi:10.1016/j.egyr.2021.06.008

Cited by 20 publications

(2 citation statements)

References 42 publications

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“…This method has proven very useful for overcoming obstacles to the efficient utilization of systems with many reservoirs. To effectively manage water resources, multi-reservoir systems require an integrated operation of numerous reservoirs that are connected (Ehteram et al, 2021). The purpose is to determine, for a certain horizon of time, the appropriate release policy for each reservoir, taking into account such factors as water supply needs, flood prevention, and ecological constraints .…”

Section: Background Of the Studymentioning

confidence: 99%

Modified Particle Swarm Optimization for the Optimum Use of Multi-Reservoir Systems: MRP Complex, Chhattisgarh

Verma,

Sahu,

Prasad

et al. 2023

J ENVIRON INFORM LET

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The effective operation of reservoirs has been a key concern among the water resources management authorities as a consequence of global water shortages and decreased surface water runoff owing to increasing water demand and the effect of climate change. Water shortages, economic constraints, and environmental concerns have all contributed to the optimization of operations becoming increasingly replaced by new technologies. Optimization operation of water resources system is a challenging issue because of its nonlinearity, multiple constraints, and several dimensions. To address the challenges associated with the operation of the multipurpose Mahanadi reservoir in Chhattisgarh, India. The present study utilizes the application of modified particle swarm optimization (MPSO). The present study was conducted to evaluate the performance of the particle swarm optimization (PSO) model in comparison to the current operating reservoir system, to minimize the sum of squared deviations in downstream release and demand. The average percentage changes in reliability (100.31%), resilience (85.02%), sustainability (24.54%), as well as vulnerability reduced up to 69.54%. The model also had the lowest error parameters in the system such as (RMSE = 1.3892, MAPE = 0.1003, NMSE = 0.1025, MAE = 38.6689, and MSE = 1.9299), despite having the highest R2, i.e., 0.8974. When applied to the Ravishankar Sagar reservoir, MPSO yields optimal, worst, average, and standard deviation (SD) values of 0.45, 0.56, 0.51, and 0.038, respectively. In terms of optimizing the release and storage rates, MPSO performed consistently better than the PSO and other metaheuristics reviewed from the literature during the study. Therefore, MPSO is advantageous in the search for the optimal reservoir operation policy because it is easy to implement, requires less functional evaluations, and quickly tracks global optimum. Hence this study provides significant evidence that MPSO can be used to effectively solve real optimization challenges.

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Section: Background Of the Studymentioning

confidence: 99%

Modified Particle Swarm Optimization for the Optimum Use of Multi-Reservoir Systems: MRP Complex, Chhattisgarh

Verma,

Sahu,

Prasad

et al. 2023

J ENVIRON INFORM LET

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“…SOA is a new meta-heuristic algorithm, proposed by Gaurav Dhiman and Vijay Kumar in 2018 [29][30][31]. The principle of SOA is based on the migration and foraging behavior of seagulls.…”

Section: Soamentioning

confidence: 99%

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

Zhu

Liu

et al. 2022

Electronics

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As vital equipment in high-speed train power supply systems, the failure of onboard traction transformers affect the safe and stable operation of the trains. To diagnose faults in onboard traction transformers, this paper proposes a hybrid optimization method based on quickly and accurately using support vector machines (SVMs) as fault diagnosis systems for onboard traction transformers, which can accurately locate and analyze faults. Considering the limitations of traditional transformers for identifying faults, this study used kernel principal component analysis (KPCA) to analyze the feature quantity of dissolved gas analysis (DGA) data, electrical test data, and oil quality test data. The improved seagull optimization algorithm (ISOA) was used to optimize the SVM, and a Henon chaotic map was introduced to initialize the population. Combined with differential evolution (DE) based on the adaptive formula, the foraging formula of the seagull optimization algorithm (SOA) was improved to increase the diversity of the algorithm and enhance its ability to find the optimal parameters of SVM, which made the simulation results more accurate. Finally, the KPCA–ADESOA–SVM model was constructed and applied to fault diagnosis for the traction transformer. The example analysis compared the diagnosis results of the proposed diagnosis model with those of the traditional diagnosis model, showing further optimization of the feature quantity and improvements in the diagnosis accuracy. This proves that the proposed diagnosis model has high generalization performance and can effectively increase the fault diagnosis accuracy and speed of traction transformers.

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Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates

Ehteram

Graf

Ahmed

et al. 2022

Stoch Environ Res Risk Assess

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Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

Cited by 20 publications

References 42 publications

Modified Particle Swarm Optimization for the Optimum Use of Multi-Reservoir Systems: MRP Complex, Chhattisgarh

Modified Particle Swarm Optimization for the Optimum Use of Multi-Reservoir Systems: MRP Complex, Chhattisgarh

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates

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