Reduced order H ∞ TCSC controller &amp; PSO optimized fuzzy PSS design in mitigating small signal oscillations in a wide range

Tayal, Vijay Kumar; Lather, J. S.

doi:10.1016/j.ijepes.2014.12.033

Cited by 25 publications

(11 citation statements)

References 22 publications

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“…At present, the selection of optimal parameters based on SVM is usually achieved from a large number of experiments. On the basis of SPP‐SVM model, we propose an automatic approach to search for the optimal working parameters of kernel function automatically using an improved PSO algorithm. The optimal C and γ are automatically calculated.…”

Section: Proposed Methods Of Spp‐svmmentioning

confidence: 99%

An accurate traffic classification model based on support vector machines

Cao

Fang

et al. 2017

Int. J. Network Mgmt

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SummaryNetwork traffic classification is a fundamental research topic on high-performance network protocol design and network operation management. Compared with other state-of-the-art studies done on the network traffic classification, machine learning (ML) methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines (SVMs) based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real-time accurate SVM training model named SPP-SVM is proposed. An SPP-SVM is deducted from the scaling dataset and employs principal component analysis (PCA) to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP-SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP-SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two-and multi-class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP-SVM's two-and multi-class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.

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Section: Proposed Methods Of Spp‐svmmentioning

confidence: 99%

An accurate traffic classification model based on support vector machines

Cao

Fang

et al. 2017

Int. J. Network Mgmt

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“…For further analysis, another operating condition with the reactive power (Q) increased to 0.62 pu is considered (detailed data of the modified nominal system in terms of the Heffron‐Phillips model and the TCSC parameters ( K p , K v , K q ) are in Tayal and Lather) with the same TCSC controller designed for Q = 0.015 pu. The active power is maintained at 1.0 pu.…”

Section: Simulation Of Examplementioning

confidence: 99%

“…Furthermore, the active power (P) is varied in steps of 0.1 pu within a range of 1.0 to 0.1 pu, with Q raised at 0.62 pu. For these operating conditions, the Heffron‐Phillips model coefficients and the TCSC parameters are considered as in Tayal and Lather . The step response performances of the TCSC controller are shown in Table and Figure .…”

Section: Simulation Of Examplementioning

confidence: 99%

A frequency domain model-based design of PSS and TCSC controller for damping the small signal oscillations in the power system

Salgotra

Pan

2018

Int Trans Electr Energ Syst

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Summary For improvement of the operation and control of the power system, the flexible AC transmission system devices are significantly used. This paper proposes a model‐based frequency response matching technique for designing a lead‐lag power system stabilizer and a thyristor controlled series capacitor controller to improve the small signal stability. The method relies on development of an appropriate reference model, based on the desired specifications that can be attainable by the system dynamics. The unknown controller parameters are obtained by the frequency response matching between the closed‐loop control system and the reference model. The efficacy of the proposed controllers is examined on an unstable test power system taken from the literature. Improvement of the damping factor and the other performance indices shows robustness and effectiveness of the proposed controllers for stabilizing the power system oscillations in comparison with some design methods prevalent in the literature. It is illustrated through rigorous simulation that the proposed controller works successfully for stabilizing the power system over a wide unstable operating conditions and perturbed system models.

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“…It is developed on the behavior of bird and fish flock movement. This algorithm is used by PSO to solve the optimization problems [17][18][19][20][21]. Each single solution is a particle in the multi-dimensional search space.…”

Section: Synthesis Of Pso Fuzzy Power System Stabilizer (Psofpss)mentioning

confidence: 99%

PSO based robust Fuzzy power system stabilizer design for single machine infinite bus system

Tayal

Lather

2015

2015 Annual IEEE India Conference (INDICON)

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A robust PSO optimized fuzzy logic power system stabilizer (PSOFPSS) is proposed in this paper. To damp out the low frequency oscillations and small signal stability enhancement the Fuzzy PSS (FPSS) is added with the excitation system. The single machine infinite bus (SMIB) system is analyzed with Fuzzy PSS (FPSS) and with PSOFPSS. The rotor angular speed deviation and acceleration are considered as inputs to the fuzzy logic controller. The effectiveness of the FPSS is compared with the PSOFPSS. The simulation results obtained over a number of operating conditions indicate the promising performance of PSOFPSS over the FPSS.

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Reduced order H ∞ TCSC controller & PSO optimized fuzzy PSS design in mitigating small signal oscillations in a wide range

Cited by 25 publications

References 22 publications

An accurate traffic classification model based on support vector machines

An accurate traffic classification model based on support vector machines

A frequency domain model-based design of PSS and TCSC controller for damping the small signal oscillations in the power system

PSO based robust Fuzzy power system stabilizer design for single machine infinite bus system

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