Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization
Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unl...
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description | Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration. |
doi_str_mv | 10.3390/su15064751 |
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The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su15064751</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Air-turbines ; Alternative energy sources ; Blackouts ; Case studies ; Climate change ; Controllers ; Cost function ; Damping ; Dynamic stability ; Dynamical systems ; Electric generators ; Electricity ; Energy resources ; Energy sources ; Error analysis ; Evolutionary computation ; Generators ; Global warming ; Induction generators ; Management ; Mathematical optimization ; Objective function ; Optimization ; Optimization techniques ; Oscillation ; Oscillations ; Power plants ; Process parameters ; Renewable resources ; Simulation ; Sustainability ; Systems stability ; Thermal power ; Thermal power plants ; Turbines ; Wind power</subject><ispartof>Sustainability, 2023-03, Vol.15 (6), p.4751</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-d43d4e94c3167dc7f81144006bbfc21c303e461b20f3aa62547d8fb6f28a4b9e3</citedby><cites>FETCH-LOGICAL-c368t-d43d4e94c3167dc7f81144006bbfc21c303e461b20f3aa62547d8fb6f28a4b9e3</cites><orcidid>0000-0003-0148-5014 ; 0000-0001-5700-9776 ; 0000-0003-2458-7179 ; 0000-0001-5903-5257 ; 0000-0002-3803-229X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Feleke, Solomon</creatorcontrib><creatorcontrib>Satish, Raavi</creatorcontrib><creatorcontrib>Pydi, Balamurali</creatorcontrib><creatorcontrib>Anteneh, Degarege</creatorcontrib><creatorcontrib>Abdelaziz, Almoataz Y</creatorcontrib><creatorcontrib>El-Shahat, Adel</creatorcontrib><title>Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization</title><title>Sustainability</title><description>Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration.</description><subject>Air-turbines</subject><subject>Alternative energy sources</subject><subject>Blackouts</subject><subject>Case studies</subject><subject>Climate change</subject><subject>Controllers</subject><subject>Cost function</subject><subject>Damping</subject><subject>Dynamic stability</subject><subject>Dynamical systems</subject><subject>Electric generators</subject><subject>Electricity</subject><subject>Energy resources</subject><subject>Energy sources</subject><subject>Error analysis</subject><subject>Evolutionary computation</subject><subject>Generators</subject><subject>Global warming</subject><subject>Induction generators</subject><subject>Management</subject><subject>Mathematical optimization</subject><subject>Objective function</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>Oscillation</subject><subject>Oscillations</subject><subject>Power plants</subject><subject>Process parameters</subject><subject>Renewable resources</subject><subject>Simulation</subject><subject>Sustainability</subject><subject>Systems stability</subject><subject>Thermal power</subject><subject>Thermal power plants</subject><subject>Turbines</subject><subject>Wind power</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpVkd9LwzAQx4soOOZe_AsCPilsJk2ato9jvxwMJm7ik5Q0TWZG29QkZc6_3mwTdHcPdxyf7x3HNwhuERxgnMJH26IIUhJH6CLohDBGfQQjePmvvw561m6hD4xRimgneB-zqlH1BmgJpkZ8tqLme8DqAjzrnTBgtbdOVGBpuSpL5pSuLdgp9wHG0_kMvCkPrluTq1ocReMJWDZOVer7yN4EV5KVVvR-azd4nU7Wo6f-Yjmbj4aLPsc0cf2C4IKIlHCMaFzwWCYIEQIhzXPJQ8QxxIJQlIdQYsZoGJG4SGROZZgwkqcCd4O7097GaP-CddlWt6b2J7MwTlGMkySinhqcqA0rRaZqqZ1h3GchKsV1LaTy82FMcIopoQfB_ZnAM058uQ1rrc3mq5dz9uHEcqOtNUJmjVEVM_sMwexgT_ZnD_4B4Vl_tw</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Feleke, Solomon</creator><creator>Satish, Raavi</creator><creator>Pydi, Balamurali</creator><creator>Anteneh, Degarege</creator><creator>Abdelaziz, Almoataz Y</creator><creator>El-Shahat, Adel</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0003-0148-5014</orcidid><orcidid>https://orcid.org/0000-0001-5700-9776</orcidid><orcidid>https://orcid.org/0000-0003-2458-7179</orcidid><orcidid>https://orcid.org/0000-0001-5903-5257</orcidid><orcidid>https://orcid.org/0000-0002-3803-229X</orcidid></search><sort><creationdate>20230301</creationdate><title>Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization</title><author>Feleke, Solomon ; Satish, Raavi ; Pydi, Balamurali ; Anteneh, Degarege ; Abdelaziz, Almoataz Y ; El-Shahat, Adel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-d43d4e94c3167dc7f81144006bbfc21c303e461b20f3aa62547d8fb6f28a4b9e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Air-turbines</topic><topic>Alternative energy sources</topic><topic>Blackouts</topic><topic>Case studies</topic><topic>Climate change</topic><topic>Controllers</topic><topic>Cost function</topic><topic>Damping</topic><topic>Dynamic stability</topic><topic>Dynamical systems</topic><topic>Electric generators</topic><topic>Electricity</topic><topic>Energy resources</topic><topic>Energy sources</topic><topic>Error analysis</topic><topic>Evolutionary computation</topic><topic>Generators</topic><topic>Global warming</topic><topic>Induction generators</topic><topic>Management</topic><topic>Mathematical optimization</topic><topic>Objective function</topic><topic>Optimization</topic><topic>Optimization techniques</topic><topic>Oscillation</topic><topic>Oscillations</topic><topic>Power plants</topic><topic>Process parameters</topic><topic>Renewable resources</topic><topic>Simulation</topic><topic>Sustainability</topic><topic>Systems stability</topic><topic>Thermal power</topic><topic>Thermal power plants</topic><topic>Turbines</topic><topic>Wind power</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Feleke, Solomon</creatorcontrib><creatorcontrib>Satish, Raavi</creatorcontrib><creatorcontrib>Pydi, Balamurali</creatorcontrib><creatorcontrib>Anteneh, Degarege</creatorcontrib><creatorcontrib>Abdelaziz, Almoataz Y</creatorcontrib><creatorcontrib>El-Shahat, Adel</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Feleke, Solomon</au><au>Satish, Raavi</au><au>Pydi, Balamurali</au><au>Anteneh, Degarege</au><au>Abdelaziz, Almoataz Y</au><au>El-Shahat, Adel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization</atitle><jtitle>Sustainability</jtitle><date>2023-03-01</date><risdate>2023</risdate><volume>15</volume><issue>6</issue><spage>4751</spage><pages>4751-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su15064751</doi><orcidid>https://orcid.org/0000-0003-0148-5014</orcidid><orcidid>https://orcid.org/0000-0001-5700-9776</orcidid><orcidid>https://orcid.org/0000-0003-2458-7179</orcidid><orcidid>https://orcid.org/0000-0001-5903-5257</orcidid><orcidid>https://orcid.org/0000-0002-3803-229X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Air-turbines Alternative energy sources Blackouts Case studies Climate change Controllers Cost function Damping Dynamic stability Dynamical systems Electric generators Electricity Energy resources Energy sources Error analysis Evolutionary computation Generators Global warming Induction generators Management Mathematical optimization Objective function Optimization Optimization techniques Oscillation Oscillations Power plants Process parameters Renewable resources Simulation Sustainability Systems stability Thermal power Thermal power plants Turbines Wind power |
title | Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization |
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