Optimal Tuning for Linear and Nonlinear Parameters of Power System Stabilizers in Hybrid System Modeling
This paper focuses on the systematic optimal tuning of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. As the PSS consists of both linear parameters (such as the gain and time constant) and nonsmooth nonlinear parameters...
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| Veröffentlicht in: | IEEE transactions on industry applications 2009-01, Vol.45 (1), p.87-97 |
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| creator | Seung-Mook Baek Jung-Wook Park Hiskens, I.A. |
| description | This paper focuses on the systematic optimal tuning of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. As the PSS consists of both linear parameters (such as the gain and time constant) and nonsmooth nonlinear parameters (such as saturation limits of the PSS), two methods are applied for the optimal tuning of all parameters. One is to use the optimization technique based on the Hessian matrix estimated by the feedforward neural network, which identifies the first-order derivatives obtained by the trajectory sensitivities, for the nonlinear parameters. Moreover, the other is to use the eigenvalue analysis for the linear parameters. The performances of parameters optimized by the proposed method are evaluated by the case studies based on time-domain simulation and real-time hardware implementation. |
| doi_str_mv | 10.1109/TIA.2008.2009478 |
| format | Article |
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As the PSS consists of both linear parameters (such as the gain and time constant) and nonsmooth nonlinear parameters (such as saturation limits of the PSS), two methods are applied for the optimal tuning of all parameters. One is to use the optimization technique based on the Hessian matrix estimated by the feedforward neural network, which identifies the first-order derivatives obtained by the trajectory sensitivities, for the nonlinear parameters. Moreover, the other is to use the eigenvalue analysis for the linear parameters. The performances of parameters optimized by the proposed method are evaluated by the case studies based on time-domain simulation and real-time hardware implementation.</description><identifier>ISSN: 0093-9994</identifier><identifier>EISSN: 1939-9367</identifier><identifier>DOI: 10.1109/TIA.2008.2009478</identifier><identifier>CODEN: ITIACR</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Damping ; Eigenvalue analysis ; Eigenvalues and eigenfunctions ; feedforward neural network (FFNN) ; Feedforward neural networks ; Hardware ; Hessian matrix estimation ; Hybrid power systems ; hybrid system ; Neural networks ; nonlinearities ; Optimization methods ; parameter optimization ; Performance evaluation ; Power system modeling ; power system stabilizer (PSS) ; Time domain analysis ; trajectory sensitivities</subject><ispartof>IEEE transactions on industry applications, 2009-01, Vol.45 (1), p.87-97</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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As the PSS consists of both linear parameters (such as the gain and time constant) and nonsmooth nonlinear parameters (such as saturation limits of the PSS), two methods are applied for the optimal tuning of all parameters. One is to use the optimization technique based on the Hessian matrix estimated by the feedforward neural network, which identifies the first-order derivatives obtained by the trajectory sensitivities, for the nonlinear parameters. Moreover, the other is to use the eigenvalue analysis for the linear parameters. The performances of parameters optimized by the proposed method are evaluated by the case studies based on time-domain simulation and real-time hardware implementation.</description><subject>Damping</subject><subject>Eigenvalue analysis</subject><subject>Eigenvalues and eigenfunctions</subject><subject>feedforward neural network (FFNN)</subject><subject>Feedforward neural networks</subject><subject>Hardware</subject><subject>Hessian matrix estimation</subject><subject>Hybrid power systems</subject><subject>hybrid system</subject><subject>Neural networks</subject><subject>nonlinearities</subject><subject>Optimization methods</subject><subject>parameter optimization</subject><subject>Performance evaluation</subject><subject>Power system modeling</subject><subject>power system stabilizer (PSS)</subject><subject>Time domain analysis</subject><subject>trajectory sensitivities</subject><issn>0093-9994</issn><issn>1939-9367</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFkUFPHDEMhSPUSmxp75W4RFx6miWZZJL4iBAUpKUgsT1HyYwHgmYnSzIrtPz6ZrXAoRcutmx_fpL9CPnJ2ZxzBqfL67N5zZjZBZDaHJAZBwEVCKW_kFlpigoA5CH5lvMTY1w2XM7I4-16Cis30OVmDOMD7WOiizCiS9SNHf0Tx2Ff3bnkVjhhyjT29C6-YKL32zzhit5PzochvO5mYaRXW59C9z68iR0WiYfv5Gvvhow_3vIR-Xt5sTy_qha3v6_PzxZVKxo5VZ7XpkPkUnrpOqW94jXva90CAhgNne6EUKL2YJRXfdt7Jz3vBCintKyZOCK_9rrrFJ83mCe7CrnFYXAjxk22RmktDW_qT0kthao5gCrkyX_kU9yksZxhTaOAcSFNgdgealPMOWFv16l8Nm0tZ3ZnkS0W2Z1F9s2isnK8XwmI-IFL3ehyj_gHchqMbw</recordid><startdate>200901</startdate><enddate>200901</enddate><creator>Seung-Mook Baek</creator><creator>Jung-Wook Park</creator><creator>Hiskens, I.A.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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As the PSS consists of both linear parameters (such as the gain and time constant) and nonsmooth nonlinear parameters (such as saturation limits of the PSS), two methods are applied for the optimal tuning of all parameters. One is to use the optimization technique based on the Hessian matrix estimated by the feedforward neural network, which identifies the first-order derivatives obtained by the trajectory sensitivities, for the nonlinear parameters. Moreover, the other is to use the eigenvalue analysis for the linear parameters. The performances of parameters optimized by the proposed method are evaluated by the case studies based on time-domain simulation and real-time hardware implementation.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIA.2008.2009478</doi><tpages>11</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Damping Eigenvalue analysis Eigenvalues and eigenfunctions feedforward neural network (FFNN) Feedforward neural networks Hardware Hessian matrix estimation Hybrid power systems hybrid system Neural networks nonlinearities Optimization methods parameter optimization Performance evaluation Power system modeling power system stabilizer (PSS) Time domain analysis trajectory sensitivities |
| title | Optimal Tuning for Linear and Nonlinear Parameters of Power System Stabilizers in Hybrid System Modeling |
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