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Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm
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| Zeitschriftentitel: | Energy Science & Engineering |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Energy Science & Engineering, 9, 2021, 3, S. 330-342 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
|
| Schlagwörter: |
| author_facet |
Li, Xia Cui, Tao Huang, Kun Ma, Xin Li, Xia Cui, Tao Huang, Kun Ma, Xin |
|---|---|
| author |
Li, Xia Cui, Tao Huang, Kun Ma, Xin |
| spellingShingle |
Li, Xia Cui, Tao Huang, Kun Ma, Xin Energy Science & Engineering Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm General Energy Safety, Risk, Reliability and Quality |
| author_sort |
li, xia |
| spelling |
Li, Xia Cui, Tao Huang, Kun Ma, Xin 2050-0505 2050-0505 Wiley General Energy Safety, Risk, Reliability and Quality http://dx.doi.org/10.1002/ese3.821 <jats:title>Abstract</jats:title><jats:p>Compressor stations, which usually consist of multiple compressors in parallel, are installed to power natural gas travel in pipelines. Compressor station optimization, which should be expressed as a mixed integer nonlinear programming (MINLP) problem, makes economic sense for the entire gas transmission system. However, it has often been simplified as a nonlinear programming (NLP) or mixed integer linear programming (MILP) problem in previous research. Most of existing solutions are based on discretization and a genetic algorithm (GA). This paper addresses the general MINLP problem for compressor station optimization without simplification; a novel hybrid intelligent algorithm is proposed to solve this problem. The proposed algorithm, DWOA, leverages advantages of the whale optimization algorithm (WOA) and differential evolution (DE). The proposed algorithm can balance exploration and exploitation to find the global optimal solution. An approach to handling constraints is also presented, where the original problem model is reformulated to be continuous by expanding the flow rate range of the compressor. A case study is performed to illustrate the performance of this approach. Results show that the continuous reformulated model is easier to solve, and DWOA produces a satisfactory solution that differs from theoretical results by only 1.61%. In addition, DWOA demonstrates better accuracy and stability than WOA, DE, and DE‐WOA, another hybrid algorithm. Therefore, this solution has the potential to promote comprehensive compressor station optimization.</jats:p> Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm Energy Science & Engineering |
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| title |
Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
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Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_full |
Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_fullStr |
Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_full_unstemmed |
Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_short |
Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_sort |
optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| topic |
General Energy Safety, Risk, Reliability and Quality |
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http://dx.doi.org/10.1002/ese3.821 |
| publishDate |
2021 |
| physical |
330-342 |
| description |
<jats:title>Abstract</jats:title><jats:p>Compressor stations, which usually consist of multiple compressors in parallel, are installed to power natural gas travel in pipelines. Compressor station optimization, which should be expressed as a mixed integer nonlinear programming (MINLP) problem, makes economic sense for the entire gas transmission system. However, it has often been simplified as a nonlinear programming (NLP) or mixed integer linear programming (MILP) problem in previous research. Most of existing solutions are based on discretization and a genetic algorithm (GA). This paper addresses the general MINLP problem for compressor station optimization without simplification; a novel hybrid intelligent algorithm is proposed to solve this problem. The proposed algorithm, DWOA, leverages advantages of the whale optimization algorithm (WOA) and differential evolution (DE). The proposed algorithm can balance exploration and exploitation to find the global optimal solution. An approach to handling constraints is also presented, where the original problem model is reformulated to be continuous by expanding the flow rate range of the compressor. A case study is performed to illustrate the performance of this approach. Results show that the continuous reformulated model is easier to solve, and DWOA produces a satisfactory solution that differs from theoretical results by only 1.61%. In addition, DWOA demonstrates better accuracy and stability than WOA, DE, and DE‐WOA, another hybrid algorithm. Therefore, this solution has the potential to promote comprehensive compressor station optimization.</jats:p> |
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| author | Li, Xia, Cui, Tao, Huang, Kun, Ma, Xin |
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| description | <jats:title>Abstract</jats:title><jats:p>Compressor stations, which usually consist of multiple compressors in parallel, are installed to power natural gas travel in pipelines. Compressor station optimization, which should be expressed as a mixed integer nonlinear programming (MINLP) problem, makes economic sense for the entire gas transmission system. However, it has often been simplified as a nonlinear programming (NLP) or mixed integer linear programming (MILP) problem in previous research. Most of existing solutions are based on discretization and a genetic algorithm (GA). This paper addresses the general MINLP problem for compressor station optimization without simplification; a novel hybrid intelligent algorithm is proposed to solve this problem. The proposed algorithm, DWOA, leverages advantages of the whale optimization algorithm (WOA) and differential evolution (DE). The proposed algorithm can balance exploration and exploitation to find the global optimal solution. An approach to handling constraints is also presented, where the original problem model is reformulated to be continuous by expanding the flow rate range of the compressor. A case study is performed to illustrate the performance of this approach. Results show that the continuous reformulated model is easier to solve, and DWOA produces a satisfactory solution that differs from theoretical results by only 1.61%. In addition, DWOA demonstrates better accuracy and stability than WOA, DE, and DE‐WOA, another hybrid algorithm. Therefore, this solution has the potential to promote comprehensive compressor station optimization.</jats:p> |
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| spelling | Li, Xia Cui, Tao Huang, Kun Ma, Xin 2050-0505 2050-0505 Wiley General Energy Safety, Risk, Reliability and Quality http://dx.doi.org/10.1002/ese3.821 <jats:title>Abstract</jats:title><jats:p>Compressor stations, which usually consist of multiple compressors in parallel, are installed to power natural gas travel in pipelines. Compressor station optimization, which should be expressed as a mixed integer nonlinear programming (MINLP) problem, makes economic sense for the entire gas transmission system. However, it has often been simplified as a nonlinear programming (NLP) or mixed integer linear programming (MILP) problem in previous research. Most of existing solutions are based on discretization and a genetic algorithm (GA). This paper addresses the general MINLP problem for compressor station optimization without simplification; a novel hybrid intelligent algorithm is proposed to solve this problem. The proposed algorithm, DWOA, leverages advantages of the whale optimization algorithm (WOA) and differential evolution (DE). The proposed algorithm can balance exploration and exploitation to find the global optimal solution. An approach to handling constraints is also presented, where the original problem model is reformulated to be continuous by expanding the flow rate range of the compressor. A case study is performed to illustrate the performance of this approach. Results show that the continuous reformulated model is easier to solve, and DWOA produces a satisfactory solution that differs from theoretical results by only 1.61%. In addition, DWOA demonstrates better accuracy and stability than WOA, DE, and DE‐WOA, another hybrid algorithm. Therefore, this solution has the potential to promote comprehensive compressor station optimization.</jats:p> Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm Energy Science & Engineering |
| spellingShingle | Li, Xia, Cui, Tao, Huang, Kun, Ma, Xin, Energy Science & Engineering, Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm, General Energy, Safety, Risk, Reliability and Quality |
| title | Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_full | Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_fullStr | Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_full_unstemmed | Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_short | Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_sort | optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| title_unstemmed | Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm |
| topic | General Energy, Safety, Risk, Reliability and Quality |
| url | http://dx.doi.org/10.1002/ese3.821 |