Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer

Li, Chaoshun; Wang, Wenxiao; Chen, Deshu

doi:10.1016/j.energy.2018.12.213

Cited by 81 publications

(23 citation statements)

References 38 publications

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“…The length of time in each period is set to 1 h in this paper, so the symmetric flow is selected as the study object in the short-term optimal operation problem. Besides, the study results of asymmetric flow can provide guidance for future ultra-short term optimal operation problems or inner-plant economical operations [46].…”

Section: Numerical Simulation Resultsmentioning

confidence: 99%

Short-Term Multi-Objective Optimal Operation of Reservoirs to Maximize the Benefits of Hydropower and Navigation

Jia

Hui

Dong

et al. 2019

Water

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Traditional reservoir operation mainly focuses on economic benefits, while ignoring the impacts on navigation. Thus, the economic operation of reservoirs considering navigational demands is of great significance for improving benefits. A navigation capacity evaluation method (NCEM), which evaluates the navigation capacity considering the influence of flow velocity and water level variation on navigation, is proposed to more effectively evaluate the navigation capacity. Based on two-dimensional hydrodynamic numerical simulation, the NCEM accurately calculates the navigation capacity according to detailed flow velocity and water level changes. In addition, a short-term multi-objective optimal operation model considering the upstream and downstream navigation and power generation is established. Then, the Strength Pareto Evolutionary Algorithm (SPEA2) is used to solve the model. To verify the rationality of the method and model, they are applied to the case study of the Xiangjiaba reservoir. The results demonstrate that the method and model can not only provide a series of operation schemes for decision makers of reservoirs, but also direct the ship to pass safely through the approach channel, implying a certain practical value and significance as a reference for the short-term optimal operation of reservoirs in the future.

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Section: Numerical Simulation Resultsmentioning

confidence: 99%

Short-Term Multi-Objective Optimal Operation of Reservoirs to Maximize the Benefits of Hydropower and Navigation

Jia

Hui

Dong

et al. 2019

Water

Self Cite

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“…Furthermore, the proposed MSCAPSO algorithm can be optimized through better strategies to further improve the convergence speed and global search ability. In addition, the multi-objective optimization that has been widely utilized in the field of controlling [44][45][46][47] could be implemented in fault diagnosis, which is expected to improve the accuracy and reduce the variance of the outputs of the model [48]. Moreover, although the diagnosis experiments considered various load conditions, fault sizes, and locations, the signal to noise ratio of the CWRU bearing data is relatively high, and the conclusions obtained in the experiment may not be very comprehensive.…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Tan

et al. 2019

Entropy

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Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.

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“…Besides, the proposed ASCA algorithm can be optimized through better strategies to further improve the convergence speed and global search ability. Moreover, the multi-objective optimization that has been widely utilized in the field of controlling [48][49][50] could be implemented in fault diagnosis, which is expected to improve the accuracy and reduce the variance of the outputs of the model [51].…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Tan

Wang

et al. 2019

Applied Sciences

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Rolling bearing is of great importance in modern industrial products, the failure of which may result in accidents and economic losses. Therefore, fault diagnosis of rolling bearing is significant and necessary and can enhance the reliability and efficiency of mechanical systems. Therefore, a novel fault diagnosis method for rolling bearing based on semi-supervised clustering and support vector data description (SVDD) with adaptive parameter optimization and improved decision strategy is proposed in this study. First, variational mode decomposition (VMD) was applied to decompose the vibration signals into sets of intrinsic mode functions (IMFs), where the decomposing mode number K was determined by the central frequency observation method. Next, fuzzy entropy (FuzzyEn) values of all IMFs were calculated to construct the feature vectors of different types of faults. Later, training samples were clustered with semi-supervised fuzzy C-means clustering (SSFCM) for fully exploiting the information inside samples, whereupon a small number of labeled samples were able to provide sufficient data distribution information for subsequent SVDD algorithms and improve its recognition ability. Afterwards, SVDD with improved decision strategy (ID-SVDD) that combined with k-nearest neighbor was proposed to establish diagnostic model. Simultaneously, the optimal parameters C and σ for ID-SVDD were searched by the newly proposed sine cosine algorithm improved with adaptive updating strategy (ASCA). Finally, the proposed diagnosis method was applied for engineering application as well as contrastive analysis. The obtained results reveal that the proposed method exhibits the best performance in all evaluation metrics and has advantages over other comparison methods in both precision and stability.

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Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer

Cited by 81 publications

References 38 publications

Short-Term Multi-Objective Optimal Operation of Reservoirs to Maximize the Benefits of Hydropower and Navigation

Short-Term Multi-Objective Optimal Operation of Reservoirs to Maximize the Benefits of Hydropower and Navigation

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

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