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Research On The Configuration And Setting Optimization Strategy Of Power System Under-voltage Load Shedding

Posted on:2017-03-17Degree:MasterType:Thesis
Country:ChinaCandidate:Z L ChenFull Text:PDF
GTID:2272330482487123Subject:Electrical engineering
Abstract/Summary:PDF Full Text Request
Power system under voltage load shedding is an effective measure to ensure system voltage stability and prevent voltage collapse. However, the under voltage load shedding configuration scheme and the setting method remain to be improved, optimization and coordination strategy remain to be further studied. The exsiting configuration scheme is designed usually based on the experience of engineering staff, and has strong subjectivity; The exsiting setting scheme, in which the under voltage thresholds, the amount of load shed and action time are set independently from one another. This setting method which separates the various parameters has a great workload and lacks a more scientific approach. The setting scheme would remain unchanged for a long time after it has been determined, which cannot adapt to the complex and changeable power network; The traditional under voltage load shedding doesn’t use the load information enough, so that the economic loss due to load shedding is great and the voltage recovery effect is not good; It also lacks effective coordination among the load shedding buses, which leads to the erroneous shedding and excessive shedding easily. With the development of China’s electric power system in this information age, based on the shared electric power system information, the systematic configuration scheme, the scientific setting scheme, the simple and effective optimization and coordination scheme of under voltage load shedding will be possible now, and the study is significant for ensuring system voltage stability and preventing the system voltage collapse.Firstly, this paper proposes a systematic configuration scheme based on wide area information. The scheme creates a bus participation factor and a bus importance factor for each load bus based on wide area information and calculates its shedding risk value. The load bus with the smallest shedding risk value is chosen as the location for the under voltage load shedding, thus forming the under voltage load shedding systematic configuration scheme.Secondly, this paper proposes an under voltage load shedding adaptive setting method based on the station area information. This method gets the correct PV curve of the load bus in the current power system network topology based on the station area information, then using the relationship between voltage and power of PV curve to determine the under voltage thresholds and the amount of load shed, considering the slope of the PV curve to determine the action time, forming under voltage load shedding scientific setting scheme.Thirdly, this paper proposes under voltage load shedding optimized algorithm based on the load information. The effect of five load information factors to the load shedding scheme is analysed, these five factors are the importance of load, the load rate, unit outage economic losses, the load’s active power and reactive power characteristics. Using fuzzy synthetic judgment, taking the five factors as the index to evaluate every load line of the under voltage load shedding location substation, the load line that has the poorest evaluation will be shedding first, forming a simple and effective under voltage load shedding optimized scheme.Finally, this paper proposes the under voltage load shedding coordination strategy based on multi-agent theory. Based on the basic theory of multi-agent, the two-level under voltage load shedding multi-agent system structure is put forward, using the game theory to realize agents’coordination. The station level agents form the station decision algorithm by seeking perfect Bayesian and the region level agent forms the coordination algorithm by seeking Nash equilibrium, and together they form under voltage load shedding coordination strategy.
Keywords/Search Tags:under voltage load shedding, information sharing, risk analysis, PV curve, fuzzy synthetic judgment, Multi-agent, game theory
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