Photovoltaic Emulation System and Maximum Power Point Tracking Algorithm Under Partial Shading Conditions

In this thesis, a novel photovoltaic (PV) emulator and the state-of-art learning–based real-time hybrid maximum power point tracking (MPPT) algorithms have been presented. Real-time research on PV systems is a challenging task because it requires a precise PV emulator that can faithfully reproduce the nonlinear properties of a PV array. The prime objective of the constructed emulator based on integration of unilluminated solar panels with external current sources is to overcome the constraints such as the need for wide surrounding space, high installation cost, and lack of control over the environmental conditions. In addition, the proposed PV emulator is able to simulate the electrical characteristics of the PV system under uniform irradiation as well as partially shading conditions (PSC). Moreover, the application of MPPT technology in PV systems under PSC conditions is challenging. Under complex environmental conditions, the power-voltage (P-V) characteristic curve of a PV system is likely to contain both local global maximum power points (LMPPs) and global maximum power points (GMPP). The MPPT algorithm applied to a PV system should have minimal steady-state oscillations to reduce power losses while accurately searching for the GMPP. The proposed MPPT algorithms resolved the drawbacks of the conventional MPPT method that have poor transient response, high continuous steady-state oscillation, and inefficient tracking performance of maximum power point voltage in the presence of partial shading. The intended algorithms have been verified using MATLAB/Simulink and the proposed PV emulator by applying comparative analysis with the traditional MPPT algorithms. In addition, the performance of the proposed MPPT algorithms and control scheme is validated experimentally with the implementation of MATLAB/Simulink/Stateflow on dSPACE Real-Time-Interface (RTI) 1007 processor board and DS2004 A/D and CP4002 Digital I/O boards. The results indicate that the algorithm is effective in reducing power losses and faster in tracking the speed of the maximum power point with less oscillation under partial shading conditions. In addition, excellent dynamic characteristics of the proposed emulator have been proven to be an ideal tool for testing PV inverters and various maximum power point tracking (MPPT) algorithms for commercial applications and university studies

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Energy storage for complementary services in grid-tied PV systems

The continuous increase in penetration of renewable-based power plants together with the intermittent and variable nature of those natural resources have made grid stability issues a major concern, imposing limitations to higher penetration rates. Energy Storage Systems (ESS) have arise as an enabling technology capable of providing PV/ESS configurations with additional capabilities, as such as ancillary or complementary services. This work presents a complete analysis of three difierent complementary services (Maximum Power Ramp Rate limitations, Power Clipping and Peak Shaving). Additionally two different PV/ESS configurations are analysed. For that purpose, three different power converter interfaces between PV and ESS were tested. The results obtained from those tests, showing the performance of the aforementioned complementary services, are presented in this thesis. Moreover, the experimental validation of a PV/ESS, which consists of a full bridge based partial power converter as power interface between PV system and ESS, is also presented in this document. This document also includes two different ESS sizing strategies, each for an specific complementary service. These sizing strategies rely on a prediction of a year of PV power generation obtained from annual measurements of irradiance and temperature. In both cases, the resulting power prediction is contrasted against a desired power profile

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

Energy storage for complementary services in grid-tied PV systems

The continuous increase in penetration of renewable-based power plants together with the intermittent and variable nature of those natural resources have made grid stability issues a major concern, imposing limitations to higher penetration rates. Energy Storage Systems (ESS) have arise as an enabling technology capable of providing PV/ESS configurations with additional capabilities, as such as ancillary or complementary services. This work presents a complete analysis of three difierent complementary services (Maximum Power Ramp Rate limitations, Power Clipping and Peak Shaving). Additionally two different PV/ESS configurations are analysed. For that purpose, three different power converter interfaces between PV and ESS were tested. The results obtained from those tests, showing the performance of the aforementioned complementary services, are presented in this thesis. Moreover, the experimental validation of a PV/ESS, which consists of a full bridge based partial power converter as power interface between PV system and ESS, is also presented in this document. This document also includes two different ESS sizing strategies, each for an specific complementary service. These sizing strategies rely on a prediction of a year of PV power generation obtained from annual measurements of irradiance and temperature. In both cases, the resulting power prediction is contrasted against a desired power profile