State space modeling of an offshore wind power plant with an MMC-HVDC connection for an eigenvalue-based stability analysis

IEEE Access

Published On 2022/8/4

Large offshore wind power plants (OWPPs) installed far from the coastline are emerging to benefit from the strong and steady wind resources available at these locations. The high-voltage direct-current (HVDC) transmission system based on the modular multilevel converter (MMC) is the most appropriate solution to transmit the produced energy to the onshore grid, in a way that a complex power-electronic-based electrical system is formed at the OWPP. Undesired interactions can occur between the MMC-based HVDC station, the several wind-turbine (WT) converters, and the passive elements of the offshore grid. To guarantee a safe and reliable operation of the OWPP, a small-signal analysis must be performed in advance to predict possible unstable situations and their root causes, in a way as to take corrective measures to avoid them. In this paper, a state-space model of an OWPP is developed adopting a recently …

Journal

IEEE Access

Published On

2022/8/4

Volume

10

Page

82844-82869

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Other Articles from authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Electronics

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions using domain-adaptive multitask learning (MTL) with long-term regularization. First, features extracted from partial charging curves are utilized for short-term state of health predictions. Then, the long-term degradation trajectory is directly predicted by recursively using the predicted features within the multitask framework, enhancing the model integrity and lowering the complexity. Then, domain adaptation (DA) is adopted to reduce the discrepancies between different working conditions. Additionally, a long-term regularization is introduced to address the shortcoming that arises when the model is extrapolated recursively for future health predictions. Thus, the short …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE/ASME Transactions on Mechatronics

Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling

Owing to the nonnegligible impacts of temperature on the safety, performance, and lifespan of lithium-ion batteries, it is essential to regulate battery temperature to an optimal range. Temperature monitoring plays a fundamental role in battery thermal management, yet it is still challenged by limited onboard temperature sensors, particularly in large-scale battery applications. As such, developing sensorless temperature estimation is of paramount importance to acquiring the temperature information of each cell in a battery system. This article proposes an estimation approach to obtain the cell temperature by taking advantage of the electrothermal coupling effect of batteries. An electrothermal coupled model, which captures the interactions between the electrical and the thermal dynamics, is established, parameterized, and experimentally validated. A closed-loop observer is then designed based on this coupled model …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries

The State of Health (SOH) estimation for automotive batteries is currently assessed with different techniques which may involve long testing procedure or require costly hardware to be implemented. This paper aims at contributing to this domain by exploiting the response of a lead-acid battery with respect to a short-term current profile using an Artificial Neural Network (ANN) classifier for SOH estimation. The method is applicable onboard the vehicle and no additional instrumentation is required on the retained vehicle. The design and validation of a SOH method with a short-term current profile using Artificial Intelligence (AI) in lead-acid batteries, which are commonly used in heavy-duty vehicles for cranking and cabin systems, are presented. The paper validates the considered approach with experimental data, which are representative of actual vehicle operations. In detail, the paper describes the retained …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

arXiv preprint arXiv:2402.07777

Novel Low-Complexity Model Development for Li-ion Cells Using Online Impedance Measurement

Modeling of Li-ion cells is used in battery management systems (BMS) to determine key states such as state-of-charge (SoC), state-of-health (SoH), etc. Accurate models are also useful in developing a cell-level digital-twin that can be used for protection and diagnostics in the BMS. In this paper, a low-complexity model development is proposed based on the equivalent circuit model (ECM) of the Li-ion cells. The proposed approach uses online impedance measurement at discrete frequencies to derive the ECM that matches closely with the results from the electro-impedance spectroscopy (EIS). The proposed method is suitable to be implemented in a microcontroller with low-computational power, typically used in BMS. Practical design guidelines are proposed to ensure fast and accurate model development. Using the proposed method to enhance the functions of a typical automotive BMS is described. Experimental validation is performed using large prismatic cells and small-capacity cylindrical cells. Root-mean-square error (RMSE) of less than 3\% is observed for a wide variation of operating conditions.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Informatics

A Battery Digital Twin From Laboratory Data Using Wavelet Analysis and Neural Networks

Lithium-ion (Li-ion) batteries are the preferred choice for energy storage applications. Li-ion performances degrade with time and usage, leading to a decreased total charge capacity and to an increased internal resistance. In this article, the wavelet analysis is used to filter the voltage and current signals of the battery to estimate the internal complex impedance as a function of state of charge (SoC) and state of health (SoH). The collected data are then used to synthesize a battery digital twin (BDT). This BDT outputs a realistic voltage signal as a function of SoC and SoH inputs. The BDT is based on feedforward neural networks trained to simulate the complex internal impedance and the open-circuit voltage generator. The effectiveness of the proposed method is verified on the dataset from the prognostics data repository of NASA.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industry Applications

Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework

Machine Learning is widely studied in battery state of health (SOH) estimation due to its advantage in establishing the non-linear mapping between measurements and SOH. However, the requirement of a big dataset and the lack of robustness limit the practical application, especially in small sample learning. To tackle these challenges, an optimal ensemble framework called BaggELM (bagging extreme learning machine) is proposed for battery SOH estimation. Specifically, the required dataset is reduced by optimizing the input voltage and the hyperparameters of the BaggELM algorithm. Moreover, a statistical post-processing method is used to aggregate multiple ELMs, and the final estimate is determined by the maximum probability density value. As a result, the effects of random parameterization of ELM and the training data size on SOH estimation are suppressed, thus improving the robustness and accuracy of …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Intelligent Cell Balancing Control for Lithium-Ion Battery Packs

This study introduces a balancing control strategy that employs an Artificial Neural Network (ANN) to ensure State of Charge (SOC) balance across lithium-ion (Li-ion) battery packs, consistent with the framework of smart battery packs. The model targets a battery pack consisting of cells with diverse characteristics, reflecting real-world heterogeneous conditions. A fundamental aspect of this approach is the ability to bypass individual cells optimally. This key feature stops current flow to and from the cell, allowing it to rest and cool off while avoiding charging or discharging cycles. The implementation of ANN enables adaptive and dynamic management of SOC, which is essential for optimizing performance and extending the lifespan of battery packs. The results demonstrate the effectiveness of the proposed ANN-based balancing strategy in SOC balancing, demonstrating its potential as a critical solution in enhancing battery management systems for electric vehicles.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Grid Impedance Shaping for Grid-Forming Inverters: A Soft Actor-Critic Deep Reinforcement Learning Algorithm

This paper proposed an advanced method for adjusting grid impedance in grid-forming inverters, utilizing the Soft Actor-Critic Deep Reinforcement Learning (SAC-DRL) algorithm. The approach contains a flexible strategy for controlling virtual impedance, supported by an equivalent grid impedance estimator. This facilitates accurate modifications of virtual impedance based on the grid’s X/R ratio and the converter’s power capacity, aiming to optimize power flow and maintain grid stability. A unique feature of this methodology is the division of virtual reactance into two segments: one adhering to standard control protocols and the other designated for precision enhancement via the SAC-DRL method. This strategy introduces a layer of intelligence to the system, strengthening its resilience against fluctuations in grid impedance. Experimental validations, executed on a laboratory setup, verify the robustness of this approach, highlighting its potential to significantly improve intelligent power grid management practices.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Electric vehicle battery charging strategy

As a key enabler for transportation electrification and a contributor toward the net-zero carbon future, battery plays a pivotal role in determining the energy management performance of electric vehicles. Technical challenges facing the development of advanced automotive battery charging arise from various contradictory objectives, immeasurable internal states, and hard constraints. This chapter presents a critical introduction to the state-of-the-art charging strategies for the electric vehicle battery and their key enabling technologies. Specifically, battery charging solutions for electric vehicles are first classified and discussed. Then, the battery models on which these solutions rest are stated, the related charging frameworks are summarized, and the advantages and drawbacks of the adopted technologies are discussed. Suggestions for overcoming the limitations of the discussed charging strategies are proposed …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities

Transportation electrification is a promising solution to meet the ever-rising energy demand and realize sustainable development. Lithium-ion batteries, being the most predominant energy storage devices, directly affect the safety, comfort, driving range, and reliability of many electric mobilities. Nevertheless, thermal-related issues of batteries such as potential thermal runaway, performance degradation at low temperatures, and accelerated aging still hinder the wider adoption of electric mobilities. To ensure safe, efficient, and reliable operations of lithium-ion batteries, monitoring their thermal states is critical to safety protection, performance optimization, as well as prognostics, and health management. Given insufficient onboard temperature sensors and their inability to measure battery internal temperature, accurate and timely temperature estimation is of particular importance to thermal state monitoring. Toward …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Reliability Engineering & System Safety

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide the predictions of battery knee, lifetime, state of health degradation, and aging rate variations, as well as the assessment of battery health. Early information is used to predict knee slope and other life-related information via deep multi-task learning, where the convolutional-long-short-term memory-bayesian neural network is proposed. The structure is also used for online state of health and degradation rate predictions for the detection of accelerating aging. The two probabilistic predicted boundaries identify the accelerating aging regions for battery health assessment. To avoid wrong and premature alarms, the empirical model is used for data preprocessing and …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Fractional-order control techniques for renewable energy and energy-storage-integrated power systems: A review

The worldwide energy revolution has accelerated the utilization of demand-side manageable energy systems such as wind turbines, photovoltaic panels, electric vehicles, and energy storage systems in order to deal with the growing energy crisis and greenhouse emissions. The control system of renewable energy units and energy storage systems has a high effect on their performance and absolutely on the efficiency of the total power network. Classical controllers are based on integer-order differentiation and integration, while the fractional-order controller has tremendous potential to change the order for better modeling and controlling the system. This paper presents a comprehensive review of the energy system of renewable energy units and energy storage devices. Various papers are evaluated, and their methods and results are presented. Moreover, the mathematical fundamentals of the fractional-order method are mentioned, and the various studies are categorized based on different parameters. Various definitions for fractional-order calculus are also explained using their mathematical formula. Different studies and numerical evaluations present appropriate efficiency and accuracy of the fractional-order techniques for estimating, controlling, and improving the performance of energy systems in various operational conditions so that the average error of the fractional-order methods is considerably lower than other ones.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Vehicular Technology

Battery states monitoring for electric vehicles based on transferred multi-task learning

State/temperature monitoring is one of the key requirements of battery management systems that facilitates efficient and intelligent management to ensure the safe operation of batteries in electrified transportation. This paper proposes an online end-to-end state monitoring method based on transferred multi-task learning. Measurement data is directly used for sharing information generation with the convolutional neural network. Then, the multiple task-specific layers are added for state/temperature monitoring. The transfer learning strategy is designed to improve accuracy further under various application scenarios. Experiments under different working profiles, temperatures, and aging conditions are conducted to evaluate the method, which covers the wide usage ranges in electric vehicles. Comparisons with several benchmarks illustrate the superiority of the proposed method with better accuracy and …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Battery aging behavior evaluation under variable and constant temperatures with real loading profiles

Studying and analyzing battery aging behavior is crucial in battery health prognostic and management. This paper conducts novel and comprehensive experiments to evaluate battery aging under variable external stresses, including different dynamic load profiles and variable environmental temperatures. Respond analysis in the time and frequency domain is performed to account for the different aging rates under different current loadings, where the statistic calculation and fast Fourier transform are used for the analysis. The empirical model is used to fit the fade curve for the comparisons between constant and variable temperatures. The capacity decrease and internal resistance increase are extracted to evaluate capacity and power fade, respectively. The experimental results show that the urban dynamic operating conditions help to prolong the service life compared to the constant current aging case. In contrast …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Small Signal Model of Modular Multilevel Converter with Power Synchronization Control

Power synchronization control (PSC) is one of the popular control schemes in grid-forming control-based converters because it simulates the grid support capability of conventional synchronous generators. However, prior research is based on two-level converters which do not have complex internal circuits, and whether PSC can be directly applied to the modular multilevel converter (MMC) topology since MMC has sub-module capacitor voltage ripples and inherent second harmonic circulating current algorithm, has not been analyzed. This paper establishes the small signal model of MMC with PSC considering the MMC internal dynamic and circulating current suppression control (CCSC). The power oscillation phenomenon when grid short-circuit ratio (SCR) increases is also demonstrated with the closed-loop system eigenvalues calculation and verified with the experimental results.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Hyperparameter optimization in bagging-based ELM algorithm for lithium-ion battery state of health estimation

Artificial neural networks are widely studied for the state of health (SOH) estimation of Lithium-ion batteries because they can recognize global features from the raw data and are able to cope with multi-dimensional data. But the performance of the model depends to some extent on the selection of the hyperparameters, which remain constant during model training. To improve the generalization performance as well as accuracy, an ensemble learning framework is proposed for battery SOH estimation, where multiple extreme learning machines are trained combined with bagging technology. The numbers of bags and neurons of the base model are then tuned by five commonly used hyperparameter optimization methods. Moreover, the SOH value with maximum probability density is selected as the output estimate to further improve the estimation accuracy. Finally, experimental results on both NMC and LPF batteries …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Ieee Transactions on Industrial Electronics

Lithium-ion battery lifetime extension with positive pulsed current charging

Some studies verified that the pulsed current charging technique could extend the battery lifetime and improve the charging performance of lithium-ion batteries. However, some researchers are skeptical of this opinion because their studies have not found any advantages of pulsed current charging over conventional constant current (CC) charging. Positive pulsed current (PPC), the most common pulsed current mode, was selected for the investigation in this work. The effect of the PPC with various parameters, including the duty cycle, amplitude, and frequency, on the performance and lifetime of lithium-ion batteries, are investigated by experiments. According to the experimental results, the charging speed, charging capacity, and maximum rising temperature are mainly determined by the duty cycle and amplitude of the PPC. The battery lifetime with PPC under the frequency range from 0.05 Hz to 2 kHz has been …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Energy Conversion and Management

Spatial–temporal data-driven full driving cycle prediction for optimal energy management of battery/supercapacitor electric vehicles

For multi-energy storage vehicles, the performance of online predictive energy management strategies largely relies on the length and effective utilization of predictive information. In this context, this paper proposes a novel velocity prediction method for the full driving cycle of electric vehicles based on the spatial–temporal commuting data, then the predicted velocity is applied to predictive energy management in electric vehicles with battery/supercapacitor hybrid energy storage system. Firstly, an one-year real-world commuting data set is collected on a Chinese arterial road with 10 intersections, 225 records are classified into 79 categories. Then, a real-time two-stage full driving cycle prediction method is proposed, where a medium-term prediction based on a long–short term memory (LSTM) network and a long-term prediction generated by a spatial–temporal interpolation method (STIM) are spliced for each …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects

With the advent of sustainable and clean energy transitions, lithium-ion batteries have become one of the most important energy storage sources for many applications. Battery management is of utmost importance for the safe, efficient, and long-lasting operation of lithium-ion batteries. However, the frequently changing load and operating conditions, the different cell chemistries and formats, and the complicated degradation patterns pose challenges for traditional battery management. The data-driven solutions that have emerged in recent years offer great opportunities to uncover the underlying data mapping within a battery system. In particular, transfer learning improves the performance of data-driven strategies by transferring existing knowledge from different but related domains, and if properly applied, would be a promising approach for smarter battery management. To this end, this paper presents a systematic …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Review of grid stability assessment based on AI and a new concept of converter-dominated power system state of stability assessment

Artificial intelligence (AI) has been increasingly used for power system stability assessment due to its fast evaluation speed compared to conventional time-domain methods. This article reviews four types of classic grid stability assessment methods based on AI in the recent literature first, where different AI algorithms from the literature are summarized and compared. Moreover, as the number of power converters using grid forming control intensively increases in the modern system, the influence of the converter parameters on grid stability needs to be investigated. In this context, the concept of the converter-dominated power system state of stability (CDPS-SOS) assessment based on AI is qualitatively discussed. The CDPS-SOS assessment can reveal the system stability margin by considering the converter control parameters and grid bus voltages. Overall, this article aims to give an overview of AI-based stability …

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Simon X. Yang

Simon X. Yang

University of Guelph

IEEE Access

The Optimal Global Path Planning of Mobile Robot Based on Improved Hybrid Adaptive Genetic Algorithm in Different Tasks and Complex Road Environments

In complex environments, mobile robots performing tasks with different hazard levels need to consider different road factors, this paper proposes a functional model correlating task hazard levels with road factors, proposing an innovative Hybrid Adaptive Genetic Algorithm (HAGA). The HAGA integrates an optimized two-optimization (2-opt) operator* with an enhanced Adaptive Genetic Algorithm (AGA) for efficient path planning in diverse tasks and complex road conditions. Firstly, pre-optimize the initial paths is performed by introducing a new domain knowledge-based operator that duplicates paths in the path are deleted to avoid the redundant paths, and then they are divided into the TOP layer and the ordinary layer, the TOP layer is optimized by using the adaptive 2-opt* operator that satisfies the hyperbolic tangent function (TANH), and the crossover and variability of the ordinary layer are optimized by using the …

Luca Vollero

Luca Vollero

Università Campus Bio-Medico di Roma

IEEE Access

A benchmarking on Optofluidic microplastic pattern recognition: A systematic comparison between statistical detection models and ML-based algorithms

Microplastics, small particles of plastic found in the environment, have become an increasingly worrying topic in recent years. This paper compares a statistical detection model to classifiers from various supervised learning paradigms in order to detect microplastics. The objective of this paper is to present a benchmark for detecting microplastics using statistical and machine learning models. The main goal is to assess and compare their performance when the defined parameters deviate from the optimal solution of the respective model. Results are presented in terms of probability error, comparing the performance of the machine learning techniques to the statistical model. The study considers a range of signal-to-noise ratios and a priori event probabilities, focusing on the classifiers’ ability to handle amplitude variability and threshold variation. Results show that as the number of analyzed particles in the flow …

Panagiotis Trakadas

Panagiotis Trakadas

National and Kapodistrian University of Athens

IEEE Access

Federated Learning-Aided Prognostics in the Shipping 4.0: Principles, Workflow, and Use Cases

The next generation of shipping industry, namely Shipping 4.0 will integrate advanced automation and digitization technologies towards revolutionizing the maritime industry. As conventional maintenance practices are often inefficient, costly, and unable to cope with unexpected failures, leading to operational disruptions and safety risks, the need for efficient predictive maintenance (PdM), relying on machine learning (ML) algorithms is of paramount importance. Still, the exchange of training data might raise privacy concerns of the involved stakeholders. Towards this end, federated learning (FL), a decentralized ML approach, enables collaborative model training across multiple distributed edge devices, such as on-board sensors and unmanned vessels and vehicles. In this work, we explore the integration of FL into PdM to support Shipping 4.0 applications, by using real datasets from the maritime sector. More …

Panagiotis Trakadas

Panagiotis Trakadas

National and Kapodistrian University of Athens

IEEE Access

Improving Connectivity in 6G Maritime Communication Networks with UAV Swarms

The deployment of maritime communication networks (MCNs) enables Internet-of-Things (IoT) applications, related to autonomous navigation, offshore facilities and smart ports. Still, the majority of maritime nodes, residing in MCNs lacks reliable connectivity. Towards this end, integrating unmanned aerial vehicles (UAVs) in sixth generation (6G) MCN topologies results in the formation of an aerial segment, complementing shore base stations that may offer insufficient coverage, and satellite communication, characterized by increased delays. In this study, we focus on an MCN where the direct links towards a shore BS are not available, due to excessive fading conditions. For this case, we use a UAV swarm to provide improved wireless connectivity, adopting non-orthogonal multiple access (NOMA) for high resource efficiency. In downlink communication, UAVs take into consideration the desired service rate and the …

Panagiotis Trakadas

Panagiotis Trakadas

National and Kapodistrian University of Athens

IEEE access

Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks

The full deployment of sixth-generation (6G) networks is inextricably connected with a holistic network redesign able to deal with various emerging challenges, such as integration of heterogeneous technologies and devices, as well as support of latency and bandwidth demanding applications. In such a complex environment, resource optimization, and security and privacy enhancement can be quite demanding, due to the vast and diverse data generation endpoints and associated hardware elements. Therefore, efficient data collection mechanisms are needed that can be deployed at any network infrastructure. In this context, the network data analytics function (NWDAF) has already been defined in the fifth-generation (5G) architecture from Release 15 of 3GPP, that can perform data collection from various network functions (NFs). When combined with advanced machine learning (ML) techniques, a full-scale …