Coordinated Tuning of a Group of Static Var Compensators Using Multi-Objective Genetic Algorithm
The optimal coordinated tuning of a group of Static Var Compensators (SVC), in steady state, allows the Power Electric Systems (PES) to operate close to their overload limits, maintaining the voltage stability in several operating conditions. The mentioned tuning problem was considered as a Multi-objective Optimization Problem (MOP) with three objectives to optimize: the financial investment for acquiring the set of compensators, the maximum voltage deviation and total active power loss. The Genetic Algorithm (GA), which belongs to the group of Evolutionary Algorithms, was utilized and adapted for MOP, obtaining a Multi-Objective GA (MOGA). The parameters to be adjusted in each compensator are: the reference voltage and the minimum and maximum reactive power injected to the system. In this work, the number of compensators and their locations were calculated using the Q-V sensitivity curve, from the Load Flow algorithm, based on Newton-Raphson method. The proposed coordinated tuning method will be validated considering an example of PES, where was located and tuned a specific set of compensators. Time simulations were made for dynamic performing the steady state coordinated tuning.
| Main Authors: | , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Centro Latinoamericano de Estudios en Informática
2011
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| Online Access: | http://www.scielo.edu.uy/scielo.php?script=sci_arttext&pid=S0717-50002011000100006 |
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| Summary: | The optimal coordinated tuning of a group of Static Var Compensators (SVC), in steady state, allows the Power Electric Systems (PES) to operate close to their overload limits, maintaining the voltage stability in several operating conditions. The mentioned tuning problem was considered as a Multi-objective Optimization Problem (MOP) with three objectives to optimize: the financial investment for acquiring the set of compensators, the maximum voltage deviation and total active power loss. The Genetic Algorithm (GA), which belongs to the group of Evolutionary Algorithms, was utilized and adapted for MOP, obtaining a Multi-Objective GA (MOGA). The parameters to be adjusted in each compensator are: the reference voltage and the minimum and maximum reactive power injected to the system. In this work, the number of compensators and their locations were calculated using the Q-V sensitivity curve, from the Load Flow algorithm, based on Newton-Raphson method. The proposed coordinated tuning method will be validated considering an example of PES, where was located and tuned a specific set of compensators. Time simulations were made for dynamic performing the steady state coordinated tuning. |
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