Grid converters are the key player in renewable energy integration. The high penetration of renewable energy systems is calling for new more stringent grid requirements. As a consequence, the grid ...converters should be able to exhibit advanced functions like: dynamic control of active and reactive power, operation within a wide range of voltage and frequency, voltage ride-through capability, reactive current injection during faults, grid services support.This book explains the topologies, modulation and control of grid converters for both photovoltaic and wind power applications. In addition to power electronics, this book focuses on the specific applications in photovoltaic wind power systems where grid condition is an essential factor.With a review of the most recent grid requirements for photovoltaic and wind power systems, the book discusses these other relevant issues:modern grid inverter topologies for photovoltaic and wind turbines islanding detection methods for photovoltaic systems synchronization techniques based on second order generalized integrators (SOGI) advanced synchronization techniques with robust operation under grid unbalance condition grid filter design and active damping techniques power control under grid fault conditions, considering both positive and negative sequences Grid Converters for Photovoltaic and Wind Power Systemsis intended as a coursebook for graduated students with a background in electrical engineering and also for professionals in the evolving renewable energy industry. For people from academia interested in adopting the course, a set of slides is available for download from the website. www.wiley.com/go/grid_converters
<p><i>Design, Control and Application of Modular Multilevel Converters for HVDC Transmission Systems&nbsp;</i>is a comprehensive guide to semiconductor technologies applicable ...for MMC design, component sizing control, modulation, and application of the MMC technology for HVDC transmission.</p> <p>Separated into three distinct parts, the first offers an overview of MMC technology, including information on converter component sizing, Control and Communication, Protection and Fault Management, and Generic Modelling and Simulation. The second covers the applications of MMC in offshore WPP, including planning, technical and economic requirements and optimization options, fault management, dynamic and transient stability. Finally, the third chapter explores the applications of MMC in HVDC transmission and Multi Terminal configurations, including Supergrids.</p> <p>Key features:</p> <ul> <li>Unique coverage of the offshore application and optimization of MMC-HVDC schemes for the export of offshore wind energy to the mainland.</li> <li>Comprehensive explanation of MMC application in HVDC and MTDC transmission technology.</li> <li>Detailed description of MMC components, control and modulation, different modeling approaches, converter dynamics under steady-state and fault contingencies including application and housing of MMC in HVDC schemes for onshore and offshore.</li> <li>Analysis of DC fault detection and protection technologies, system studies required for the integration of HVDC terminals to offshore wind power plants, and commissioning procedures for onshore and offshore HVDC terminals.</li> <li>A set of self-explanatory simulation models for HVDC test cases is available to download from the companion website.</li> </ul> <p>This book provides essential reading for graduate students and researchers, as well as field engineers and professionals who require an in-depth understanding of MMC technology.</p> <div>&nbsp;</div>
This paper presents a new multiresonant frequency-adaptive synchronization method for grid-connected power converters that allows estimating not only the positive- and negative-sequence components of ...the power signal at the fundamental frequency but also other sequence components at other harmonic frequencies. The proposed system is called MSOGI-FLL since it is based on both a harmonic decoupling network consisting of multiple second-order generalized integrators (MSOGIs) and a frequency-locked loop (FLL), which makes the system frequency adaptive. In this paper, the MSOGI-FLL is analyzed for single- and three-phase applications, deducing some key expressions regarding its stability and tuning. Moreover, the performance of the MSOGI-FLL is evaluated by both simulations and experiments to show its capability for detecting different harmonic components in a highly polluted grid scenario.
Battery energy storage system expands the flexibility of the electricity grid, which facilitates the extensive usage of renewable energies in industrial applications. In order to ensure the ...techno-economical reliability of the battery energy storage system, managing the lifespan of each battery is critical. In this paper, a novel evolutionary framework is proposed to estimate the Lithium-ion battery state of health, which uniformly optimizes the two key processes of establishing a data driven estimator. The features in the degradation process of a battery are conveniently measured by a group of current pulses, which last only few seconds. The proposed evolutionary framework selects the most efficient combination of the short-term features from the current pulse test, and guarantees an optimal training process simultaneously. A hybrid encoding technology is applied to mix the feature extraction and the parameters of support vector regression in one chromosome. With the benefit of the proposed evolutionary framework, the battery state of health is estimated by using support vector regression and genetic algorithm in a more efficient way. A mission profile corresponding to batteries providing the primary frequency regulation service to the power system is used to cycle two Lithium-ion batteries for the validation of the proposed method.
•Short term features from current pulse tests are utilized.•The feature extraction and the SVR parameters are optimized simultaneously.•A hybrid encoding method is used to mix the parameters and the features.•LiFePO4/c batteries are aged with mission profile providing the PFR service.•Five-folder cross validation is used to verify the performance of the estimator.
Battery State of Health (SOH) is critical for the reliable operation of the grid-connected battery energy storage systems. During the long-term Lithium-ion (Li-ion) battery degradation, large amounts ...of data can be recorded. Unfortunately, massive raw data are naturally with different qualities, which makes it difficult to guarantee the superior performance of one unified and powerful data driven estimator. Thus, this paper proposes a novel ensemble learning framework to estimate the battery SOH, which can boost the performance of the data driven SOH estimation through a well-designed integration of the weak learners. Moreover, the short-term current pulses, which are convenient to be obtained from real applications, act as the deterioration feature for SOH estimation. To establish the weak learners with good diversity and accuracy, support vector regression is chosen to utilize the measurement from a specific condition. A Self-adaptive Differential Evolution (SaDE) algorithm is used to effectively integrate the weak learners, which can avoid the trial and error procedure on choosing the trial vector generation strategy and the related parameters in the traditional differential evolution. For the validation of the proposed method, two LiFePO4/C batteries are cycling under a mission profile providing the primary frequency regulation service to the grid.
•A novel optimized ensemble learning method is proposed for Li-ion battery SOH estimation.•Short term features from current pulse tests are utilized.•The integration of each weak learner is optimized by the self-adaptive differential evolution algorithm.•LiFePO4/C batteries are aged with the mission profile providing the primary frequency regulation service to the grid.
Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen ...the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.
This paper analyzes and compares three transformerless photovoltaic inverter topologies for three-phase grid connection with the main focus on the safety issues that result from the lack of galvanic ...isolation. A common-mode model, valid at frequencies lower than 50 kHz, is adopted to study the leakage current paths. The model is validated by both simulation and experimental results. These will be used to compare the selected topologies, and to explain the influence of system unbalance and the neutral conductor inductance on the leakage current. It will be demonstrated that the later has a crucial influence. Finally, a comparison of the selected topologies is carried out, based on the adopted modulation, connection of the neutral and its inductance, effects of unbalance conditions, component ratings, output voltage levels, and filter size.
Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery ...Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method.
•The SOH estimation accuracy is improved in an optimization manner.•The optimal multiple voltage ranges are automatically selected by NSGA-II and grid search.•Various solutions at different battery charging stages are provided for SOH estimation.•Three NMC-based batteries are aged for 360 days to validate the proposed method.
There is a strong trend in the photovoltaic inverter technology to use transformerless topologies in order to acquire higher efficiencies combining with very low ground leakage current. In this ...paper, a new topology, based on the H-bridge with a new ac bypass circuit consisting of a diode rectifier and a switch with clamping to the dc midpoint, is proposed. The topology is simulated and experimentally validated, and a comparison with other existing topologies is performed. High conversion efficiency and low leakage current are demonstrated.
As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are ...an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.
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