Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.634475
Title: Optimization approaches for parameter estimation and Maximum Power Point Tracking (MPPT) of photovoltaic systems
Author: Ma, Jieming
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2014
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Optimization techniques are widely applied in various engineering areas, such as modeling, identification, optimization, prediction, forecasting and control of complex systems. This thesis presents the novel optimization methods that are used to control Photovoltaic (PV) generation systems. PV power systems are electrical power systems energized by PV modules or cells. This thesis starts with the introduction of PV modeling methods, on which our research is based. Parameter estimation is used to extract the parameters of the PV models characterizing the utilized PV devices. To improve efficiency and accuracy, we proposed sequential Cuckoo Search (CS) and Parallel Particle Swarm Optimization (PPSO) methods to extract the parameters for different PV electrical models. Simulation results show the CS has a faster convergence rate than the traditional Genetic Algorithm (GA), Pattern Search (PS) and Particle Swarm Optimization (PSO) in sequential processing. The PPSO, with an accurate estimation capability, can reduce at least 50% of the elapsed time for an Intel i7 quad-core processor. A major challenge in the utilization of PV generation is posed by its non linear Current-Voltage (I-V ) relations, which result in the unique Maximum Power Point (MPP) varying with different atmospheric conditions. Maximum Power Point Tracking (MPPT) is a technique employed to gain maximum power available from PV devices. It tracks operating voltage corresponding to the MPP and constrains the operating point at the MPP. A novel model-based two-stage MPPT strategy is proposed in this thesis to combine the offline maximum power point estimation using the Weightless Swarm Algorithm (WSA) with an online Adaptive Perturb & Observe (APO) method. In addition, an Approximate Single Diode Model (ASDM) is developed for the fast evaluations of the output power. The feasibility of the proposed method is verified in an MPPT system implemented with a Single-Ended Primary-Inductor Converter (SEPIC). Simulation results show the proposed MPPT method is capable of locating the operating point to the MPP under various environmental conditions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.634475  DOI: Not available
Share: