Measurements, Models, Systems and Design

531 s.

Hourly Dispatching Wind-Solar Hybrid Power System with Battery-Supercapacitor Hybrid Energy Storage

This dissertation demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a specific dispatching horizon for an entire day utilizing a hybrid energy storage system (HESS) configured by batteries and supercapacitors. Here, wind speed and solar irradiance are predicted one hour ahead of time using a multilayer perceptron Artificial Neural Network (ANN), which exhibits satisfactory performance with good convergence mapping between input and target output data. Furthermore, multiple state of charge (SOC) controllers as a function of energy storage system (ESS) SOC are developed to accurately estimate the grid reference power (PGrid,ref) for each dispatching period. A low pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC), and the cost optimization of the HESS is computed based on the time constant of the LPF through extensive simulations. Besides, the optimum value of depth of discharge for ESS considering both cycling and calendar expenses has been investigated to optimize the life cycle cost of the ESS, which is vital for minimizing the cost of a dispatchable wind-solar power scheme. Finally, the proposed ESS control algorithm is verified by conducting control hardware-in-the loop (CHIL) experiments in a real-time digital simulator (RTDS) platform

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Operational Planning and Optimisation in Active Distribution Systems for Flexible and Resilient Power

The electricity network is undergoing significant changes to cater to environmental-deterioration and fuel-depletion issues. Consequently, an increasing number of renewable resources in the form of distributed generation (DG) are being integrated into medium-voltage distribution networks. The DG integration has created several technical and economic challenges for distribution network operators. The main challenge is basically the problem of managing network voltage profile and congestion which is caused by increasing demand and intermittent DG operations. The result of all of these changes is a paradigm shift in the way distribution networks operate (from passive to active) and are managed that is not limited only to the distribution network operator but actively engages with network users such as demand aggregators, DG owners, and transmission-system operators. This thesis expands knowledge on the active distribution system in three specific areas and attempts to fill the gaps in existing approaches. A comprehensive active network management framework in active distribution systems is developed to allow studies on (i) the flexibility of network topology using modern power flow controllers, (ii) the benefits of centralised thermal electricity storage in achieving the required levels of flexibility and resiliency in an active distribution system, and (iii) system resiliency toward fault occurrence in hybrid AC/DC distribution systems. These works are implemented within the Advanced Interactive Multidimensional Modelling Systems (AIMMS) software to carry out optimisation procedure. Results demonstrate the benefit provided by a range of active distribution system solutions and can guide future distribution-system operators in making practical decisions to operate active distribution systems in cost-effective ways

The Modeling and Advanced Controller Design of Wind, PV and Battery Inverters

Renewable energies such as wind power and solar energy have become alternatives to fossil energy due to the improved energy security and sustainability. This trend leads to the rapid growth of wind and Photovoltaic (PV) farm installations worldwide. Power electronic equipments are commonly employed to interface the renewable energy generation with the grid. The intermittent nature of renewable and the large scale utilization of power electronic devices bring forth numerous challenges to system operation and design. Methods for studying and improving the operation of the interconnection of renewable energy such as wind and PV are proposed in this Ph.D. dissertation.;A multi-objective controller including is proposed for PV inverter to perform voltage flicker suppression, harmonic reduction and unbalance compensation. A novel supervisory control scheme is designed to coordinate PV and battery inverters to provide high quality power to the grid. This proposed control scheme provides a comprehensive solution to both active and reactive power issues caused by the intermittency of PV energy. A novel real-time experimental method for connecting physical PV panel and battery storage is proposed, and the proposed coordinated controller is tested in a Hardware in the Loop (HIL) experimental platform based on Real Time Digital Simulator (RTDS).;This work also explores the operation and controller design of a microgrid consisting of a direct drive wind generator and a battery storage system. A Model Predictive Control (MPC) strategy for the AC-DC-AC converter of wind system is derived and implemented to capture the maximum wind energy as well as provide desired reactive power. The MPC increases the accuracy of maximum wind energy capture as well as minimizes the power oscillations caused by varying wind speed. An advanced supervisory controller is presented and employed to ensure the power balance while regulating the PCC bus voltage within acceptable range in both grid-connected and islanded operation.;The high variability and uncertainty of renewable energies introduces unexpected fast power variation and hence the operation conditions continuously change in distribution networks. A three-layers advanced optimization and intelligent control algorithm for a microgrid with multiple renewable resources is proposed. A Dual Heuristic Programming (DHP) based system control layer is used to ensure the dynamic reliability and voltage stability of the entire microgrid as the system operation condition changes. A local layer maximizes the capability of the Photovoltaic (PV), wind power generators and battery systems, and a Model Predictive Control (MPC) based device layer increases the tracking accuracy of the converter control. The detail design of the proposed SWAPSC scheme are presented and tested on an IEEE 13 node feeder with a PV farm, a wind farm and two battery-based energy storage systems

Enhancing the performance of flexible AC transmission systems (FACTS) by computational intelligence

The thesis studies and analyzes UPFC technology concerns the management of active and reactive power in the power networks to improve the performance aiming to reach the best operation criteria. The contributions of the thesis start with formatting, deriving, coding and programming the network equations required to link UPFC steady-state and dynamic models to the power systems. The thesis derives GA applications on UPFC to achieve real criteria on a real world sub-transmission network. An enhanced GA technique is proposed by enhancing and updating the working phases of the GA including the objective function formulation and computing the fitness using the diversity in the population and selection probability. The simulations and results show the advantages of using the proposed technique. Integrating the results by linking the case studies of the steady-state and the dynamic analysis is achieved. In the dynamic analysis section, a new idea for integrating the GA with ANFIS to be applied on the control action procedure is presented. The main subject of the thesis deals with enhancing the steady-state and dynamics performance of the power grids by Flexible AC Transmission System (FACTS) based on computational intelligence. Control of the electric power system can be achieved by designing the FACTS controller, where the new trends as Artificial Intelligence can be applied to this subject to enhance the characteristics of controller performance. The proposed technique will be applied to solve real problems in a Finnish power grid. The thesis seeks to deal, solve, and enhance performances until the year 2020, where the data used is until the conditions of year 2020. The FACTS device, which will be used in the thesis, is the most promising one, which known as the Unified Power Flow Controller (UPFC). The thesis achieves the optimization of the type, the location and the size of the power and control elements for UPFC to optimize the system performance. The thesis derives the criteria to install the UPFC in an optimal location with optimal parameters and then designs an AI based damping controller for enhancing power system dynamic performance. In this thesis, for every operating point GA is used to search for controllers' parameters, parameters found at certain operating point are different from those found at others. ANFISs are required in this case to recognize the appropriate parameters for each operating point

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc