2,299 research outputs found

    Robust multi-machine power system stabilizer design using bio-inspired optimization techniques and their comparison

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    DATA AVAILABILITY : Data will be made available on request.This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO.https://www.elsevier.com/locate/ijepeshj2024Electrical, Electronic and Computer EngineeringSDG-07:Affordable and clean energ

    A novel model reference adaptive control approach investigation for power electronic converter applications

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    This paper demonstrates the viability and effectiveness of a novel adaptive control approach applied to power electronic converters. A methodology based on the formulation of a Lyapunov-based approach is showcased to represent the operation of a new adaptive controller for the regulation of two power converter topologies (Buck and Boost). The models of the Buck and Boost converter topologies include the parasitic parameters that represent the non-ideal components. The basic idea of the control approach is to demonstrate adaptive stabilization for the proposed non-linear system. The most important design specification to stabilize the system is to track the reference trajectory in such a way that the error on the output variable converges asymptotically to zero. This adaptation mechanism is explicitly designed so that the asymptotic stability of the equilibrium condition is guaranteed according to the Lyapunov theorem and sensitivity theory. The details of the design algorithm are explained in the paper. The proposed control approach has been compared to other Lyapunov-based control techniques proposed in literature for the same non-ideal converters. The results show that the proposed controller provides better level of robustness and performance than the other wellestablished Lyapunov based controllers. To verify the effectiveness of the controller in real time, a test bench has been set up with prototypes of both converters and the controller has been implemented using the Arduino microcontroller and the control system driven through the Matlab/Simulink platform

    Grain boundaries in polycrystalline materials for energy applications : First principles modeling and electron microscopy

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    Polycrystalline materials are ubiquitous in technology, and grain boundaries have long been known to affect materials properties and performance. First principles materials modeling and electron microscopy methods are powerful and highly complementary for investigating the atomic scale structure and properties of grain boundaries. In this review, we provide an introduction to key concepts and approaches for investigating grain boundaries using these methods. We also provide a number of case studies providing examples of their application to understand the impact of grain boundaries for a range of energy materials. Most of the materials presented are of interest for photovoltaic and photoelectrochemical applications and so we include a more in depth discussion of how modeling and electron microscopy can be employed to understand the impact of grain boundaries on the behavior of photoexcited electrons and holes (including carrier transport and recombination). However, we also include discussion of materials relevant to rechargeable batteries as another important class of materials for energy applications. We conclude the review with a discussion of outstanding challenges in the field and the exciting prospects for progress in the coming years

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications

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    Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified

    Comparative analysis of energy transfer mechanisms for neural implants

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    As neural implant technologies advance rapidly, a nuanced understanding of their powering mechanisms becomes indispensable, especially given the long-term biocompatibility risks like oxidative stress and inflammation, which can be aggravated by recurrent surgeries, including battery replacements. This review delves into a comprehensive analysis, starting with biocompatibility considerations for both energy storage units and transfer methods. The review focuses on four main mechanisms for powering neural implants: Electromagnetic, Acoustic, Optical, and Direct Connection to the Body. Among these, Electromagnetic Methods include techniques such as Near-Field Communication (RF). Acoustic methods using high-frequency ultrasound offer advantages in power transmission efficiency and multi-node interrogation capabilities. Optical methods, although still in early development, show promising energy transmission efficiencies using Near-Infrared (NIR) light while avoiding electromagnetic interference. Direct connections, while efficient, pose substantial safety risks, including infection and micromotion disturbances within neural tissue. The review employs key metrics such as specific absorption rate (SAR) and energy transfer efficiency for a nuanced evaluation of these methods. It also discusses recent innovations like the Sectored-Multi Ring Ultrasonic Transducer (S-MRUT), Stentrode, and Neural Dust. Ultimately, this review aims to help researchers, clinicians, and engineers better understand the challenges of and potentially create new solutions for powering neural implants

    A Literature Review of Fault Diagnosis Based on Ensemble Learning

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    The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

    A Current-Source Modular Converter for Large-Scale Photovoltaic Systems

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    The world is shifting toward renewable energy sources (RESs) to generate clean energy and mitigate the stress of global warming caused by CO2 emissions in recent decades. Among several RES types, large-scale photovoltaic (LSPV) plants are a promising source for meeting ambitious clean energy targets and being part of power generation. With the progress of high-power modular inverters, new opportunities have arisen to integrate them into LSPV systems connected to medium-voltage (MV) grids to obtain high efficiency and reliability, better system flexibility, and improved electrical safety compared with string or central inverters. This thesis presents and implements a new current source three-phase modular inverter (TPMI) based on a novel dual-isolated SEPIC/CUK (DISC) converter. The TPMI is designed with a single power processing stage comprised of seriesconnected DISC submodules (SMs) to deliver MV into the utility grid. It outperforms conventional high-power inverters in terms of modularity, scalability, galvanic isolation compliance, and distributed maximum power point tracking (MPPT) capabilities. The DISC converter employed as an SM in the proposed TPMI generates bipolar output (i.e., both positive and negative voltages). In addition to having step-up and step-down capabilities with a continuous input current, this converter shares an input side inductor, thereby reducing the number of components. The DISC structure, modulation method, operation, novel state-space model, and parameter design procedure are analysed in details. Then, simulation results are presented to validate the theoretical and analytical analyses of the DISC converter. The proposed TPMI inverter is subsequently integrated into the LSPV grid connection to prove its suitability for such applications. In the theoretical analysis, the advantages of TPMI structure over conventional topologies are discussed. Then, the modulation technique, and operational concept are presented, followed by a dedicated control strategy is implemented by adding a system and SM-level controllers. The system controller is required for the generation of uniform duty ratios for all SMs in order to regulate the power transfer. The SM level controller is introduced to ensure equal current and voltage distribution between SMs and to compensate for minor discrepancies between the various parameters. The entire TPMI system is demonstrated through MATLAB and Simulink simulations, with the objective being to deliver the rated (1 MW) power from the PV modules under normal operation, uniform shading, and partial shading conditions and to match PV generation with the grid’s power demands. A downscaled 3-kW TPMI inverter was developed in the laboratory to validate its feasibility experimentally with its control strategy in different operating conditions. Finally, the TPMI performance is compared with selected current source inverter topologies, which shows that TPMI obtains good efficiency within the context of existing state-of-the-art current source converters. Then, the TPMI structure is modified by redesigning its DISC SMs, which provides several benefits, including a reduction in the number of switch devices operating at high frequency, thus decreasing switching losses, and an increase in efficiency. In this study, a half-cycle modulation (HCM) scheme is developed for the switches, and the operation of a modified DISC SM is analysed. Simulation and experimental results validate the performance of the modified TPMI topology and demonstrate its suitability for LSPV applications. According to the results of the comparison, the maximum power efficiency of the modified TPMI structure is 95.5%, which represents an improvement over the original TPMI structure

    A fast and accurate global maximum power point tracking controller for photovoltaic systems under complex partial shadings

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    The operating conditions of partially shaded photovoltaic (PV) generators created a need to develop highly efficient global maximum power point tracking (GMPPT) methods to increase the PV system performance. This paper proposes a simple, efficient, and fast GMPPT based on fuzzy logic control to reach the point of global maximum power. The approach measures the PV generator current in the areas where it is almost constant to estimate the local maximums powers and extracts the highest among them. The performance of this method is evaluated firstly by simulation versus four well-known recent methods, namely the hybrid particle swarm optimization, modified cuckoo search, scrutinization fast algorithm, and shade-tolerant maximum power point tracking (MPPT) based on current-mode control. Then, experimental verification is conducted to verify the simulation findings. The results confirm that the proposed method exhibits high performance for complex partial irradiances and can be implemented in low-cost calculators
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