Control of Modular Multilevel Converters for Grid Integration of Full-Scale Wind Energy Conversion Systems

The growing demand for wind power generation has pushed the capacity of wind turbines towards MW power levels. Higher capacity of the wind turbines necessitates operation of the generators and power electronic conversion systems at higher voltage/power levels. The power electronic conversion system of a wind energy conversion system (WECS) needs to meet the stringent requirements in terms of reliability, efficiency, scalability and ease of maintenance, power quality, and dv/dt stress on the generator/transformer. Although the multilevel converters including the neutral point clamped (NPC) converter and the active NPC converter meet most of the requirements, they fall short in reliability and scalability. Motivated by modularity/scalability feature of the modular multilevel converter (MMC), this research is to enable the MMC to meet all of the stringent requirements of the WECS by addressing their unique control challenges. This research presents systematic modeling and control of the MMC to enable it to be a potential converter topology for grid integration of full-scale WECSs. Based on the developed models, appropriate control systems for control of circulating current and capacitor voltages under fixed- and variable-frequency operations are proposed. Using the developed MMC models, a gradient-based cosimulation algorithm to optimize the gains of the developed control systems, is proposed. Performance/effectiveness of the developed models and the proposed control systems for the back-to-back MMC-based WECS are evaluated/verified based on simulations studies in the PSCAD/EMTDC software environment and experimental case studies on a laboratory-scale hardware prototype

Wind energy-harvesting technologies and recent research progresses in wind farm control models

In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems’ safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment

Artificial Intelligence-based Control Techniques for HVDC Systems

The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PD

Biogeography-Based Optimization for Combinatorial Problems and Complex Systems

Biogeography-based optimization (BBO) is a heuristic evolutionary algorithm that has shown good performance on many problems. In this dissertation, three problem1s 1 are researched for BBO: convergence speed and optimal solution convergence of BBO,1 1BBO application to combinatorial problems, and BBO application to complex systems. The first problem is to analyze BBO from two perspectives: how the components of BBO affect its convergence speed and the reason that BBO converges to the optimal solution. For the first perspective, which is convergence speed, we analyze the two essential components of BBO -- population construction and information sharing. For the second perspective, a mathematical BBO model is built to theoretically prove why BBO is capable of reaching the global optimum for any problem. In the second problem addressed by the dissertation, BBO is applied to combinatorial problems. Our research includes the study of migration, local search, population initialization, and greedy methods for combinatorial problems. We conduct a series of simulations based on four benchmarks, the sizes of which vary from small to extra large. The simulation results indicate that when combined with other techniques, the performance of BBO can be significantly improved. Also, a BBO graphical user interface (GUI) is created for combinatorial problems, which is an intuitive way to experiment with BBO algorithms, including hybrid BBO algorithms. The third and final problem addressed in this dissertation is the optimization of complex systems. We invent a new algorithm for complex system optimization based on BBO, which is called BBO/complex. Four real world problems are used to test BBO/Complex and compare with other complex system optimization algorithms, and we obtain encouraging results from BBO/Complex. Then, a Markov model is created for BBO/Complex. Simulation results are provided to confirm the mode

High Step-Up/Down Transformerless Multilevel Converter for Renewable Energy Applications

This thesis focuses on a high step-up/down transformerless dc-dc modular multilevel converter (MMC) that would be applicable to dc power systems. The design achieves high voltage ratios for interfacing renewable energy sources such as photovoltaic and line interactive Uninterruptible Power System (UPS) systems. The circuit topology provides for high step-up/down dc-dc conversion ratios using an MMC approach operating in resonant mode in order to improve overall efficiency. This topology operates to step-up the input voltage with 1:10 or larger conversion ratio. As a bidirectional converter, it also provides step-down capability at the same voltage ratio (10:1 or greater). The MMC circuit system consists of an upper and lower set of cells. The number of the upper cells is N, and the number of the lower cells is M. Phase-shift pulse width modulation (PS-PWM) is used to control voltage and power flow. PS-PWM with high duty cycle is generated to ensure that all the capacitors are connected except for one of them, which is out of the connection. A MATLAB/Simulink™ and LTspice simulations for the proposed topology are presented. Moreover, PV and UPS systems with the proposed topology are simulated using MATLAB/Simulink™. In photovoltaic application systems, a closed loop control system is represented for voltage regulation in case there is a change in the input voltage. In UPS application, closed loop controllers for charging and discharging batteries are presented

A Robust Maximum Power Point Tracking Control Method for a PEM Fuel Cell Power System

Taking into account the limited capability of proton exchange membrane fuel cells (PEMFCs) to produce energy, it is mandatory to provide solutions, in which an efficient power produced by PEMFCs can be attained. The maximum power point tracker (MPPT) plays a considerable role in the performance improvement of the PEMFCs. Conventional MPPT algorithms showed good performances due to their simplicity and easy implementation. However, oscillations around the maximum power point and inefficiency in the case of rapid change in operating conditions are their main drawbacks. To this end, a new MPPT scheme based on a current reference estimator is presented. The main goal of this work is to keep the PEMFCs functioning at an efficient power point. This goal is achieved using the backstepping technique, which drives the DC-DC boost converter inserted between the PEMFC and the load. The stability of the proposed algorithm is demonstrated by means of Lyapunov analysis. To verify the ability of the proposed method, an extensive simulation test is executed in a Matlab-Simulink (TM) environment. Compared with the well-known proportional-integral (PI) controller, results indicate that the proposed backstepping technique offers rapid and adequate converging to the operating power point.The authors are very grateful to the UPV/EHU for its support through the projects PPGA18/04 and to the Basque Government for its support through the project ETORTEK KK-2017/00033. The authors would also like to thank the Tunisian Government for its support through the research unit UR11ES82

Digital Twin Techniques for Power Electronics-Based Energy Conversion Systems : A Survey of Concepts, Application Scenarios, Future Challenges, and Trends

The steady increase in energy demands has led to ever-increasing “energy generation.” This, coupled with the need for higher efficiency, flexibility, and reliability, has boosted the use of power electronics in power and energy systems. Therefore, power electronics-based energy conversion systems (PEECSs) have become prominent in power generation, power transmission, and end user applications. Given the relevance of such systems, and by considering their trend of digitalization, it is crucial to establish digital and intelligent PEECSs. To this end, digital twins (DTs) can be adopted, as they integrate many cuttingedge information techniques to realize the life cycle management of complex systems by constructing real-time mappings of them. In this article, existing DT techniques for PEECSs are reviewed. The concept, system layers, and key technologies of DTs are described first. Some application cases of DTs are then elaborated. Finally, future trends and challenges of DTs are discussed to provide a valuable reference for subsequent research.acceptedVersionPeer reviewe