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http://hdl.handle.net/10603/539434
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DC Field | Value | Language |
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dc.coverage.spatial | ||
dc.date.accessioned | 2024-01-15T06:10:49Z | - |
dc.date.available | 2024-01-15T06:10:49Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/539434 | - |
dc.description.abstract | Realtime congestion management is a critical issue in power system operation and control Deregulation renewable penetration and consistent increment in load demand led to overloading or congestion in power transmission lines Traditionally network congestion is cleared by the system operator by rescheduling generators once every 530 min which may be too slow for grids with renewable generation Network congestion can be cleared automatically and immediately by a controller The power system statespace models considered in the existing controller based algorithms for line power flow control are too simple to represent the line power flow dynamics accurately and give optimistic results that are not reliable newlineThis thesis intends to develop an accurate state space model of the power system and design controllers for relieving the congestion as quickly as possible The controller can obtain synchrophasor measurements of line power flow and maintain it at the desired value by controlling some generators and battery energy storage systems BESS in realtime A state space model that relates the governor and BESS set points with line flows considering the de tailed governor turbine model and associated delays was developed in this thesis The same model was used in the model based control technique to manage the congestion A disturbance compensation based Model Predictive Control MPC strategy is proposed to regulate the line power flow at or below the desired value eg thermal limit in realtime by redispatching some generators andor BESSs The MPC technique considers several realistic constraints such as output and rate limits and active power balance in the network newlineAs obtaining a power system model continuously is difficult in todays deregulated power systems an adaptive Neuro Predictive Controller NPC was proposed in this thesis to regulate the line power flow The controller can get the PMU measurements of the line power flows and regulate that by dispatching generators and batteries in realtime. | |
dc.format.extent | xxi, 89 p. | |
dc.language | English | |
dc.relation | ||
dc.rights | university | |
dc.title | Development of Power Flow Contollers for Real Time Congestion Management in Smart Grid | |
dc.title.alternative | ||
dc.creator.researcher | Chakravarthi, Kotakonda | |
dc.subject.keyword | Congestion Management | |
dc.subject.keyword | Controller based congestion management | |
dc.subject.keyword | Engineering | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Engineering Electrical and Electronic | |
dc.subject.keyword | Model Predictive Controller (MPC) | |
dc.subject.keyword | MPC based congestion management | |
dc.subject.keyword | Neuro Predictive Controller | |
dc.subject.keyword | NPC based congestion management | |
dc.subject.keyword | Real time congestion management | |
dc.description.note | ||
dc.contributor.guide | Bhui, Pratyasa | |
dc.publisher.place | Dharwad | |
dc.publisher.university | Indian Institute of Technology Dharwad | |
dc.publisher.institution | Department of Electrical Engineering | |
dc.date.registered | 2019 | |
dc.date.completed | 2023 | |
dc.date.awarded | 2023 | |
dc.format.dimensions | 30 cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Department of Electrical Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 67.27 kB | Adobe PDF | View/Open |
02_prelims pages.pdf | 222.4 kB | Adobe PDF | View/Open | |
03_content.pdf | 45.85 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 45.18 kB | Adobe PDF | View/Open | |
05_chapter 01.pdf | 1.4 MB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 1.23 MB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.68 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 45.99 kB | Adobe PDF | View/Open | |
09_annexures.pdf | 735.07 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 71 kB | Adobe PDF | View/Open |
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