Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem

Krishnamurthy, Senthil; Tzoneva, Raynitchka

doi:10.11591/ijece.v6i5.10259

Cited by 4 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Splitting is based upon software developed by [19] and the simulation model is shown in Figure 5. Performance evaluation of parallel processing for reducing of problem complexity and time is also presented in [20]. The simulation results run on one and four processing units are depicted in Table 10.…”

Section: Estimation Methodsmentioning

confidence: 99%

Granularity analysis of classification and estimation for complex datasets with MOA

Jittawiriyanukoon¹

2019

IJECE

View full text Add to dashboard Cite

<span>Dispersed and unstructured datasets are substantial parameters to realize an exact amount of the required space. Depending upon the size and the data distribution, especially, if the classes are significantly associating, the level of granularity to agree a precise classification of the datasets exceeds. The data complexity is one of the major attributes to govern the proper value of the granularity, as it has a direct impact on the performance. Dataset classification exhibits the vital step in complex data analytics and designs to ensure that dataset is prompt to be efficiently scrutinized. Data collections are always causing missing, noisy and out-of-the-range values. Data analytics which has not been wisely classified for problems as such can induce unreliable outcomes. Hence, classifications for complex data sources help comfort the accuracy of gathered datasets by machine learning algorithms. Dataset complexity and pre-processing time reflect the effectiveness of individual algorithm. Once the complexity of datasets is characterized then comparatively simpler datasets can further investigate with parallelism approach. Speedup performance is measured by the execution of MOA simulation. Our proposed classification approach outperforms and improves granularity level of complex datasets.</span>

show abstract

Section: Estimation Methodsmentioning

confidence: 99%

Granularity analysis of classification and estimation for complex datasets with MOA

Jittawiriyanukoon¹

2019

IJECE

View full text Add to dashboard Cite

show abstract

“…Twenty digital images in the database as listed in Table 2 have been employed by post-process with different embedding algorithms (GLN, ILSVRC and CPVR) in order to recognize the matches. A processing for reducing of state space problem and time is demonstrated in [15].…”

Section: Recognition Modelsmentioning

confidence: 99%

Proposed algorithm for image classification using regression-based pre-processing and recognition models

Jittawiriyanukoon¹

2019

IJECE

View full text Add to dashboard Cite

<span>Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.</span>

show abstract

“…Traditional methods for solving MACEED problems include the linear weighted-sum method [4], Jacobian-based algorithm [5], and Lagrange algorithm [6]. Many evolutionary algorithms can also be employed to solve MACEED problems such as the multilayer distributed multi-objective consensus [7], fuzzified squirrel search [8], and multi-objective squirrel search [9] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Data-driven Surrogate-assisted Method for High-dimensional Multi-area Combined Economic/Emission Dispatch

Lin,

Liang,

Pang

et al. 2024

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Multi-area combined economic/emission dispatch (MACEED) problems are generally studied using analytical functions. However, as the scale of power systems increases, existing solutions become time-consuming and may not meet operational constraints. To overcome excessive computational expense in high-dimensional MACEED problems, a novel datadriven surrogate-assisted method is proposed. First, a cosinesimilarity-based deep belief network combined with a backpropagation (DBN + BP) neural network is utilized to replace cost and emission functions. Second, transfer learning is applied with a pretraining and fine-tuning method to improve DBN + BP regression surrogate models, thus realizing fast construction of surrogate models between different regional power systems. Third, a multi-objective antlion optimizer with a novel general single-dimension retention bi-objective optimization policy is proposed to execute MACEED optimization to obtain scheduling decisions. The proposed method not only ensures the convergence, uniformity, and extensibility of the Pareto front, but also greatly reduces the computational time. Finally, a 4-area 40-unit test system with different constraints is employed to demonstrate the effectiveness of the proposed method.Index Terms--Multi-area combined economic/emission dispatch, high-dimensional power system, deep belief network, data driven, transfer learning.Huijun Liang received the B.E. degree in industrial automation from Wuhan University of Hydraulic and Electric Engineering, Wuhan, China, in 2000, the M.E. degree in control theory and control engineering from Wuhan University, Wuhan, China, in 2004, the Ph.D. degree in control theory and control engineering from Shandong University, Jinan, China, in 2020. He is currently working as a Lecturer in Hubei Minzu University, Enshi, China. His research interests include computational intelligence and optimization in power systems.Aokang Pang received the B.E. degree in electrical engineering and its automation from Xi'an Kedagaoxin University, Xi'an, China, in 2021. is currently pursuing the M.E. degree in electrical engineering in Hubei Minzu University, Enshi, China. His current research interests include computational intelligence and its application to economic dispatch of power systems.

show abstract

Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem

Cited by 4 publications

References 27 publications

Granularity analysis of classification and estimation for complex datasets with MOA

Granularity analysis of classification and estimation for complex datasets with MOA

Proposed algorithm for image classification using regression-based pre-processing and recognition models

Data-driven Surrogate-assisted Method for High-dimensional Multi-area Combined Economic/Emission Dispatch

Contact Info

Product

Resources

About