| Author: | Huang, Huazhang |
| Title: | Probabilistic small signal stability analysis for power systems with plug-in electric vehicle and wind power integration |
| Degree: | Ph.D. |
| Year: | 2013 |
| Subject: | Wind energy conversion systems -- Stability. Electric power systems. Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Electrical Engineering |
| Pages: | xx, 124 p. : ill. ; 30 cm. |
| Language: | English |
| Abstract: | Small signal stability analysis (SSSA) is one of the most significant tools for evaluating the rotor angle stability issue of power system operation and control, especially in interconnected power networks. Due to the rapid increase of renewable energy sources, especially wind power, integration of these new kinds of generations result in new impacts on dynamic stability of power system; these effects need to be considered and estimated by SSSA more carefully. The mainstreaming devices adapted in wind farms are fixed-speed induction generator (FSIG) and double-fed induction generators (DFIG) based wind energy conversion systems (WECS), which have controllability by a bidirectional converter, thus providing flexible operation. This thesis is devoted to study the damping performance of power system impacted by WECS and also damping enhancements from WECS. As the mechanical torque oscillations from wind turbine model were not taken into account before, impacts from wind shear and tower shadow are focused upon and relevant estimations are made first. A damping enhancement with neural network and adaptive fuzzy control theory is proposed to improve the operational performance with such oscillations. By taking advantage of the controllable type WECS, a power oscillation damper (POD) has been implemented in the decoupled control system of DFIG. Parameters of the POD are optimized by a widely used algorithm, which with shorter optimization time can provide higher quality solutions, i.e. the modified particle swarm optimizer (MPSO). Its performance has been evaluated by eigenvalues of electro-mechanical oscillation modes in the system from SSSA and also transient simulations for observing the power angle differences and electrical power torque of synchronous generators. However, wind power is acknowledged as a stochastic source. The power output of a wind farm is undispatchable and difficult to be completely and accurately predicted. An analytical method based probability theory for probabilistic SSSA is proposed in this thesis for accounting for the variation of wind farm power output when designing damping controllers for DFIG-based WECS. The coordination with other damping controllers in the system such as power system stabilizers (PSS) for synchronous generators also has been studied. Case study results confirm that damping control scheme by the proposed method can consistently enhance system stability with synchronous machines and DFIG-based WECSs, under a wide range of operating conditions of DFIG. Recently, with the fast development of plug-in electric vehicles (PEVs) and large rating chargers allocated in charging stations, the potential storage capability of battery based PEVs is planned to work in coordination with wind farms. The joint effects of these two new establishments on rotor angle stability are intended to be studied. Similar to wind power, PEV is also a kind of stochastic source. It is significant to consider stochastic effects of both on power system stability. In this thesis, a simulative method with high efficiency, quasi Monte-Carlo (QMC) based probabilistic SSSA method is proposed to evaluate the damping impacts. Negative damping effect found in the system, from PEVs, is proven by both the study of different scenarios and individual analysis. Compared to commonly using simulative method Monte Carlo simulations (MCS), QMC can provide a more reliable result with a shorter computational time. |
| Rights: | All rights reserved |
| Access: | open access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b26158875.pdf | For All Users | 2.25 MB | Adobe PDF | View/Open |
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