Multi-Objective Optimal STATCOM Allocation for Voltage Sag Mitigation

Voltage sags mitigation has become a significant concern for utilities due to the pressure from both customers and regulators recently. This paper proposes a novel multi-objective optimization formulation for STATCOM allocation problem to mitigate voltage sags. The allocation of STATCOM is formulate...

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Bibliographic Details
Published in:IEEE transactions on power delivery Vol. 35; no. 3; pp. 1410 - 1422
Main Authors: Liu, Yang, Xiao, Xian-Yong, Zhang, Xiao-Ping, Wang, Ying
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Allocations
Automatic voltage control
Cost analysis
Decision making
Evolutionary algorithms
Frequency variation
Genetic algorithms
Mathematical model
multi-objective evolutionary algorithm (MOEA)
multi-objective optimization
Multiple objective analysis
Nonlinear programming
Optimization
Pareto optimization
Pareto optimum
Power quality
Power system stability
Regulators
Resource management
static synchronous compensators (STATCOM)
Utilities
voltage sag profile
Voltage sags
ISSN:0885-8977, 1937-4208
Online Access:Get full text
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Summary:Voltage sags mitigation has become a significant concern for utilities due to the pressure from both customers and regulators recently. This paper proposes a novel multi-objective optimization formulation for STATCOM allocation problem to mitigate voltage sags. The allocation of STATCOM is formulated as a multi-objective optimization problem (MOP) with two conflicting objectives including the voltage sag performance and annual investment costs of STATCOM. To analytically evaluate the voltage sag performance in the optimization process, a mathematical voltage sag profile calculation model of power system with multiple STATCOMs is proposed and solved by nonlinear programming method. System Average RMS Variation Frequency index-X index is chosen to quantify the voltage sag performance for optimization. Sensitivity-based candidate bus selection is also performed as an essential step to reduce problem size. An improved multi-objective evolutionary algorithm (MOEA) called SPEA2+SDE (Strength Pareto Evolutionary Algorithm with shifting density estimation) is utilized to solve the proposed MOP model, so a set of Pareto optimal STATCOM allocations for voltage sag mitigation can be derived demonstrating the trade-off between two objectives and better supporting the decision-making. IEEE 30-bus system and IEEE 118-bus system are tested to demonstrate the effectiveness and feasibility of proposed method.
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ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2019.2947715