Investigation of UPFC performance under system uncertainties

Hemmati, Reza; Nikzad, Mehdi; Farahani, Shoorangiz Shams Shamsabad

doi:10.1016/j.ijepes.2012.05.031

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…If the variation of one variable affects the other variables, there is a correlation among them and these variables are depending on each other, too. 21 The correlation coefficient among uncertain input variables x and y is as (14).…”

Section: Modeling Of Correlations Among Uncertain Input Variables Usi...mentioning

confidence: 99%

Data clustering based probabilistic UPFC allocation for improving power system reliability considering correlated uncertain variables

Rezaeian‐Marjani

Nazarpour

Galvani

2021

Int Trans Electr Energ Syst

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Summary Flexible alternating‐current transmission system (FACTS) devices as power electronic‐based technologies have been developed to improve the performance of power systems. This paper proposes a probabilistic framework for optimal allocation of unified power flow controller (UPFC) as one of the most beneficial FACTS devices to increase the reliability of the power system. For this purpose, the expected power not served (EPNS) is considered as a reliability index that is obtained based on the summation of required load shedding in all buses during single contingencies, including transmission lines and generators outage. Power demands and wind speed are considered as uncertain input variables. In addition, the correlations among these variables are included in the proposed study method. The well‐known particle swarm optimization (PSO) algorithm is used to optimize the proposed objective function. Also, the firefly algorithm (FA) is implemented to determine the total amount of load shedding for the calculation of EPNS, considering each possible generated solution by the PSO. The k‐means based data clustering method (DCM) is used for probabilistic evaluation of the problem, for the first time. Also, the correlations among uncertain input variables are modeled with the Cholesky decomposition method. Extraction of statistical information of UPFC parameters for improving power system reliability in a probabilistic framework is one of the most important achievements of the proposed method. This information is very important in deciding on the UPFC sizing. In order to evaluate the effectiveness of the proposed method, the IEEE 14‐bus and 30‐bus test systems have been used.

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Section: Modeling Of Correlations Among Uncertain Input Variables Usi...mentioning

confidence: 99%

Data clustering based probabilistic UPFC allocation for improving power system reliability considering correlated uncertain variables

Rezaeian‐Marjani

Nazarpour

Galvani

2021

Int Trans Electr Energ Syst

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“…The presented controller effectively damps out the oscillations without any PSS in the system. Hemmati et al [49] have implemented quantitative feedback theory (QFT) and μ‐synthesis for designing UPFC controller for studying robustness analysis under parametric uncertainties. A Lyapunov theory with finite‐time convergence and chattering‐free characteristics based UPFC controller for damping power system oscillations is presented by Shotorbani et al [50].…”

Section: Control Approaches In Multimachine Systemsmentioning

confidence: 99%

Comprehensive review on enhancement of stability in multimachine power system with conventional and distributed generations

Paital

Ray

Mohanty

2018

IET Renewable Power Generation

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A comprehensive review on stability analysis in multimachine power system is presented in this study. The increasing demand of power has led to the expansion of power system and complexity in design as well as operation. This threatens to deteriorate the stability and reliability in the power network. However, with the advances in semiconductor and power electronic control technology, various flexible AC transmission systems (FACTS) are designed to enhance the power system stability. Different power system configurations with the incorporation of conventional and distributed generations (DGs) are proposed whose stability is being tested in the presence of the FACTS controllers. The optimal damping of power system oscillations under transient operating conditions are studied in presence of HVAC and HVDC links with conventional as well as DG systems and energy storage options like battery, supercapacitors and so on. Different control approaches such as classical control, robust control, wide-area control along with soft computing approaches like neural networks, fuzzy logic control, adaptive neurofuzzy inference system, genetic algorithm, particle swarm optimisation and so on are broadly discussed. A comparative analysis of different methodologies is presented to establish a quick survey of the proposed solutions for stability enhancement.

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“…A solving method for the comprehensive problem, including unit commitment and UPFC allocation considering the uncertainty of wind power generation (WPG), is proposed in [22]. Robust control methods are exploited to design the UPFC controllers under parametric uncertainties of the system in [23]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic multi‐objective optimization method for interline power flow controller (IPFC) allocation in power systems

Rezaeian‐Marjani

Ebrahimi

Tousi

2022

IET Generation Trans & Dist

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The interconnections of power systems are extended to improve operating conditions and increase their adequacy and security. Furthermore, with the increasing penetration of renewable energy sources such as wind turbines (WTs), probabilistic assessment of these systems' performance is very important, especially in risk management, bidding strategies, and operational decisions. Interline power flow controller (IPFC) is one of the flexible AC transmission system (FACTS) devices, which can increase power transfer capability and maximize the use of the existing transmission network. In the structure of the IPFC, there are two converters whose settings should be determined optimally to get the maximum benefit from it. This paper introduces a probabilistic multi-objective optimization method for the allocation of the IPFC to reduce the active power losses and improve the power flow index (PFI) of the lines with considering the IPFC cost using the multiobjective particle swarm optimization (MOPSO) algorithm. The uncertainties are taken to account in loads and wind speed of WTs. Also, the k-means-based data clustering method (DCM) is used for the probabilistic assessment of this problem for the first time, and its performance is compared with the Monte Carlo simulation (MCS) method. The efficiency of the proposed approach is investigated on the IEEE 30-bus test system.

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Investigation of UPFC performance under system uncertainties

Cited by 8 publications

References 13 publications

Data clustering based probabilistic UPFC allocation for improving power system reliability considering correlated uncertain variables

Data clustering based probabilistic UPFC allocation for improving power system reliability considering correlated uncertain variables

Comprehensive review on enhancement of stability in multimachine power system with conventional and distributed generations

Probabilistic multi‐objective optimization method for interline power flow controller (IPFC) allocation in power systems

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