Multi-objective hydropower station operation using an improved cuckoo search algorithm

Meng, Xuejiao; Wang, Xuebin; Wang, Yimin

doi:10.1016/j.energy.2018.11.096

Cited by 77 publications

(33 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CS is a nature-inspired algorithm developed based on the evolution of cuckoo birds [17,18]. Cuckoos typically lay their fertilized eggs in other cuckoos' nests, hoping that their offspring will be raised by other cuckoos.…”

Section: Cuckoo Searchmentioning

confidence: 99%

Commercial Vehicle Ride Comfort Optimization Based on Intelligent Algorithms and Nonlinear Damping

Chen

Wang³

et al. 2019

Shock and Vibration

View full text Add to dashboard Cite

The method chosen to conduct vehicle dynamic modeling has a significant impact on the evaluation and optimization of ride comfort. This paper summarizes the current modeling methods of ride comfort and their limitations. Then, models based on nonlinear damping and equivalent damping and the multibody dynamic model are developed and simulated in Matlab/Simulink and Adams/Car. The driver seat responses from these models are compared, showing that the accuracy of the ride comfort model based on nonlinear damping is higher than the one based on equivalent damping. To improve the reliability of ride comfort optimization and analysis, a ride comfort optimization method based on nonlinear damping and intelligent algorithms is proposed. The sum of the frequency-weighted RMS of the driver seat acceleration, the RMS of dynamic tyre load, and suspension working space is taken as the objective function in this article, using nonlinear damping coefficients and stiffness of suspension as design variables. By applying the particle swarm optimization (PSO), cuckoo search (CS), dividing rectangles (DIRECT), and genetic algorithm (GA), a set of optimal solutions are obtained. The method efficiency is verified through a comparison between frequency-weighted RMS before and after optimization. Results show that the frequency-weighted RMS of driver seat acceleration, RMS values of the suspension working space of the front and rear axles, and RMS values of the dynamic tyre load of front and rear wheels are decreased by an average of 27.4%, 21.6%, 25.0%, 19.3%, and 22.3%, respectively. The developed model is studied in a pilot commercial vehicle, and the results show that the optimization method proposed in this paper is more practical and features improvement over previous models.

show abstract

Section: Cuckoo Searchmentioning

confidence: 99%

Commercial Vehicle Ride Comfort Optimization Based on Intelligent Algorithms and Nonlinear Damping

Chen

Wang³

et al. 2019

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Amongst these algorithms, CS is a comparatively new one, initially introduced by Yang and Deb [22]. Due to some attractive features like good balance between the local search and global search, simplicity, and efficiency [23,24], the CS algorithm has been successfully applied to many optimization problems in various fields with promising results [25][26][27][28][29], including the parameter optimization problems in modeling manufacturing processes such as parameter estimation of a common empirical model for the temperature of cutting tools [30], estimation of soft-sensing model parameters for fermentation processes [31], and parameter identification of a neural network model for the electron beam welding process [32]. Besides, some researches have revealed that compared with PSO, GA, and some other intelligent optimization algorithms, CS is potentially far more efficient [30,33,34].…”

Section: Introductionmentioning

confidence: 99%

Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

Yang

Zhang

2020

Neural Comput & Applic

View full text Add to dashboard Cite

This article presents a hybrid model for predicting the temperature of molten steel in a ladle furnace (LF). Unique to the proposed hybrid prediction model is that its neural network-based empirical part is trained in an indirect way since the target outputs of this part are unavailable. A modified cuckoo search (CS) algorithm is used to optimize the parameters in the empirical part. The search of each individual in the traditional CS is normally performed independently, which may limit the algorithm’s search capability. To address this, a modified CS, information interaction-enhanced CS (IICS), is proposed in this article to enhance the interaction of search information between individuals and thereby the search capability of the algorithm. The performance of the proposed IICS algorithm is first verified by testing on two benchmark sets (including 16 classical benchmark functions and 29 CEC 2017 benchmark functions) and then used in optimizing the parameters in the empirical part of the proposed hybrid prediction model. The proposed hybrid model is applied to actual production data from a 300 t LF at Baoshan Iron & Steel Co. Ltd, one of China's most famous integrated iron and steel enterprises, and the results show that the proposed hybrid prediction model is effective with comparatively high accuracy.

show abstract

“…Li [16] thought that the contradiction between the minimum flood loss and the maximum storage level at the end of the period depended on the initial water level of the reservoir, but did not give a detailed explanation. Meng [17] proposed that for the two objectives of power generation and water supply, the relationship between the two is not always competitive during the research period, and he found out that the competition relationship decreases with probability of water supply increases. Huang [18] pointed out that asymmetry trade-offs existed among power generation, water supply, and ecological objectives in reservoir operation.…”

Section: Introductionmentioning

confidence: 99%

Competitive Relationship between Flood Control and Power Generation with Flood Season Division: A Case Study in Downstream Jinsha River Cascade Reservoirs

Yao

Dong

Jia

et al. 2019

Water

View full text Add to dashboard Cite

The lower reaches of Jinsha River host the richest hydropower energy sources in China. With the construction of Wudongde and Baihetan, the multi-objective optimization for cascade reservoirs (along with Xiluodu and Xiangjia Dam) in the lower reaches of Jinsha River will create significant benefits. This paper focuses on the competitive relationship between flood control and power generation, and attaches attention to the measurement of different objective functions and their competitive relationship. With observations of the flood in 1974, 1981, and 1985, a 100-year return period flood with peak-3d volume pair as different inputs for the optimal model is approached by NSGA-II. Different flood seasons divided by flood feature is applied to figure out specific competitive relationship. The results can be concluded as the following: (1) Strong competitive relationship mainly occurs in pre-flood season. (2) Whether it shows a strong competitive relationship depends on the amount of discharge. If the turbine is set to full capacity, power generation is fulfilled certainly, which means that there exists a weak competitive relationship between multi-objectives. (3) The different processes of floods have an effect on the duration of a competitive relationship. A flood with a late peak causes the extension of strong competition in the pre-flood season, which lends itself to a strong competition relationship in the post-flood season. (4) The intensity of competition in the pre-flood season is higher than that in the post-flood season because it has a larger range.

show abstract

Multi-objective hydropower station operation using an improved cuckoo search algorithm

Cited by 77 publications

References 47 publications

Commercial Vehicle Ride Comfort Optimization Based on Intelligent Algorithms and Nonlinear Damping

Commercial Vehicle Ride Comfort Optimization Based on Intelligent Algorithms and Nonlinear Damping

Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

Competitive Relationship between Flood Control and Power Generation with Flood Season Division: A Case Study in Downstream Jinsha River Cascade Reservoirs

Contact Info

Product

Resources

About