Multiobjective Control of Power Plants Using Particle Swatm Optimization Techniques
| Format: | jou |
|---|---|
| Terbitan: |
POLBAN
, 2007
|
| Subjects: |
| ctrlnum |
ai-jbptppolban-gdl-jou-2007-1jinsheo2k-2511 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title>Multiobjective Control of Power Plants Using Particle Swatm Optimization Techniques</title><subject/><subject>Genetic algorithm (GA), multiobjective optimization, particle swarm optimization (P50), power plant control, pressure set point scheduling</subject><publisher>POLBAN</publisher><date>2007</date><language>Bahasa Indonesia</language><type>Other:jou</type><identifier>jbptppolban-gdl-jou-2007-1jinsheo2k-2511</identifier><description>Multiobjective optimal power plant operation requires an optimal mapping between unit load demand and pressure set point in a fossil fuel power unit (FFPU). In general, the optimization problem with varying unit load demand cannot be solved using a fixed nonlinear mapping. This paper presents a modern heuristic method, particle swarm optimization (PSO), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem, where the objective functions are given with preferences. This optimization procedure is used to design the reference governor for the control system. This approach provides the means to specify optimal set points for controllers under a diversity of operating scenarios. Variations of the P50 technique, hybrid P50, evolutionary P50, and constriction factor approach are applied to the FFPU, and the comparison is made among the P50 techniques and genetic algorithm. </description><recordID>ai-jbptppolban-gdl-jou-2007-1jinsheo2k-2511</recordID></dc>
|
| format |
Other:jou Other |
| title |
Multiobjective Control of Power Plants Using Particle Swatm Optimization Techniques |
| publisher |
POLBAN |
| publishDate |
2007 |
| topic |
Genetic algorithm (GA) multiobjective optimization particle swarm optimization (P50) power plant control pressure set point scheduling |
| contents |
Multiobjective optimal power plant operation requires an optimal mapping between unit load demand and pressure set point in a fossil fuel power unit (FFPU). In general, the optimization problem with varying unit load demand cannot be solved using a fixed nonlinear mapping. This paper presents a modern heuristic method, particle swarm optimization (PSO), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem, where the objective functions are given with preferences. This optimization procedure is used to design the reference governor for the control system. This approach provides the means to specify optimal set points for controllers under a diversity of operating scenarios. Variations of the P50 technique, hybrid P50, evolutionary P50, and constriction factor approach are applied to the FFPU, and the comparison is made among the P50 techniques and genetic algorithm. |
| id |
IOS17601.ai-jbptppolban-gdl-jou-2007-1jinsheo2k-2511 |
| institution |
Politeknik Negeri Bandung |
| institution_id |
2033 |
| institution_type |
library:university library |
| library |
UPT Perpustakaan Politeknik Negeri Bandung |
| library_id |
1640 |
| collection |
Repository Polban |
| repository_id |
17601 |
| city |
BANDUNG BARAT |
| province |
JAWA BARAT |
| repoId |
IOS17601 |
| first_indexed |
2022-08-31T03:15:46Z |
| last_indexed |
2022-08-31T03:15:46Z |
| recordtype |
dc |
| _version_ |
1742648020649902080 |
| score |
17.538404 |