Complex Power Research Articles

"Treat male idols as toys": A case study of Chinese self-centered shipper communities

The phenomenon of Chinese idol fans engaging in collective promotional actions has garnered significant academic attention, yet the marginalized community of fans who create secondary works and generate less revenue remains understudied. By examining a self-centered shipper community that rejects industry exploitation and instead seeks pleasure through creative fan art, I address the multifaceted ways in which fans engage with media and the complex power dynamics between fans and the idol industry.

The output improvement of droplet-based nanogenerators through enhanced coupling displacement and conducting currents

The advent of triboelectric nanogenerator (TENG) preludes the high entropy energy harvesting and is gradually applied to droplet energy harvesting. However, the mainstream research concentrates on exploring the solid-liquid contact electrification (CE) and electrostatic induction (EI) processes, ignoring the possibility of charge extraction from water. As a result, the droplet-based nanogenerator (DNG) is limited to small alternating-current (AC) output, which also asks for complex power management. This study reveals that the displacement current is not the sole driving force during the power generation of DNG, and the contribution of the conducting current is equally important. Here, the existence of conducting charge is confirmed by a top-electrode-only DNG, and the charge transfer processes in traditional DNGs are re-examined. Finally, a three-electrode total current DNG is introduced to enhance the coupling of displacement current and conducting current. The charge extraction efficiency is then improved by more than one order of magnitude (charge extraction from single droplet is increased from 1 to 27.3 nC) and retains the direct-current (DC) and high voltage (up to 2315 V) output characteristics.

Temperature dependence of complex permeability and power losses for Mn–Zn ferrites

Temperature dependence of complex permeability and power losses for Mn–Zn ferrites

A novel fault localization method for asymmetric phase‐to‐ground faults on onshore wind farm collector lines without phasor measurement

AbstractExisting fault localization methods for power lines are not applicable to the collector line in onshore wind farms due to the dense access of complex power supplies. Therefore, a fault localization method based on equivalent zero‐sequence injection current (ZIC) is proposed for asymmetric phase‐to‐ground faults on collector lines. Firstly, the idea of load shifting is adopted to generalize the theory of equivalent injection current (EIC) to multi‐terminal and meshed lines. Then, considering features of the zero‐sequence network of wind farms, the EIC theory on two‐terminal and multi‐terminal lines is applied to zero‐sequence networks, and thus faulty areas, faulty sub‐lines, and fault distance can be determined. Attractively, the element correlation in node impedance matrices of zero‐sequence networks is derived from the network branch‐addition process, and the numerical characteristic of matrix elements is further revealed from the particularity of wind farms, based on which root‐mean‐square (RMS) calculation of ZIC can be improved to enable the localization without phasor measurement. Verification results prove that this proposed method can accurately realize the fault localization independent of wind turbine generators with a few measurement points, and the applicability of collector lines with line branches is also considered; additionally, only voltage RMS needs to be measured.

Optimizing Automatic Voltage Regulation: A Deep Reinforcement Learning Approach

This paper presents a novel approach to enhance the performance of Automatic Voltage Regulator (AVR) systems in power systems using Deep Reinforcement Learning (DRL). The AVR plays a critical role in maintaining voltage stability and ensuring reliable power delivery. However, conventional control strategies, such as PID controllers, have limitations in handling complex and nonlinear power system dynamics. In this study, the application of DRL techniques is explored, particularly the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm, to AVR control. This algorithm offers the advantage of handling continuous action spaces and enable the controller to learn optimal control policies directly from the system's state information. The results show that the DRL approach outperforms the traditional PID and Neural Network-based control approaches, with the shortest time response and the best voltage regulation performance. The use of DRL in AVR system control shows promising potential for improving the efficiency and accuracy of power system control. This research provides insights into the advantages of DRL for process control and highlights its potential for future applications in power system control.

An Environmental Objects Feature Detection Algorithm Based on Image Detection in the Power Transmission System

Background:: The image processing technology can be adopted in the power transmission incident detection system, when it is combined with artificial intelligence and machine learning academic knowledge of power transmission surveillance video analysis system, it can automatically detect abnormal objects in the power transmission system. It can quickly and accurately detect abnormal objects, known as environmental object feature detection necessary for the safety of power transmission system. Objective:: In order to improve the object detection ability of the power transmission system, it adopts artificial intelligence and machine learning knowledge in the power transmission surveillance system and it can automatically detect abnormal events or objects. Methods:: Compared with differential binary target detection technique, it proposes a new adaptive background subtraction threshold algorithm to adapt the complex power transmission monitoring environment. Meanwhile, it takes special recognition algorithm for power transmission monitoring environment to ensure the accuracy and stability of the detection system. Results:: The proposed method can be used in the monitoring system and the accuracy and stability of the detection system have been verified through experiments. Conclusion:: Through adopting the special recognition algorithm for power transmission monitoring environment, it can ensure the accuracy and stability of the detection system, in the detection experiments, it can analyze the objects more easily than before.

A variegated GWO algorithm implementation in emerging power systems optimization problems

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm.

Gas extraction governmentality: The depoliticization of Groningen's extractive territorialization

The Dutch Northeastern province of Groningen has attracted national and international attention for being situated on top of Europe's biggest gas field. Decades of gas extraction have caused human-made gasquakes that have become highly politicized as they have resulted in the damage of thousands of houses, messy compensation policies, unsafe living situations, an intense situation of social and psychological desperation, and deep political distrust. An issue that has, however, been mostly absent from political debates is the fact that the gas extraction has also caused land subsidence with major implications for the area's water systems. In this article, we investigate the depoliticizing governmentality techniques of Groningen's extractive territorialization. In doing so, we differentiate how the same governmentality techniques have mostly depoliticized gas extraction-land subsidence while they have failed to depoliticize the gasquakes. We nuance the complex power dynamics (i.e., simultaneous de- and re-politicization), socio-material transformation processes (i.e., land subsidence and gasquakes), and institutional dynamics (i.e., public water management and private compensation organizations) evoked by extractive transformations. We argue that the extractive alliance's land subsidence-governmentalities reflect a subtle re-ordering of territorial control. Yet, we also highlight the limits, contestations, and contingency of extractive governmentalization. We unravel how an understanding of power as simultaneously unintentional and intentional informs our analysis. We show how the extractive alliance benefits from the interplay of power strategies, yet not without being strongly contested by local inhabitants and social movements.

Diagnostic reactive astrogliosis biomarkers in Alzheimer´s disease continuum

AbstractBackgroundThe Alzheimer’s disease (AD) biomarker field is moving rapidly. Creating a platform where multi‐PET tracer approaches and various fluid biomarkers with discriminative power in the early pathological stages could provide deeper insight into the role of reactive astrogliosis in the AD continuum. Accumulating data indicates that reactive astrogliosis may play a critical role in the early stages and, perhaps, precedes other pathological hallmarks of AD. Different PET tracers for imaging reactive astrogliosis are now available along with plasma GFAP showing a direct correlation with increased brain amyloid‐PET. We also propose that alpha‐7‐nicotinic acetylcholine receptors (α7nAChRs) might be early modulators of reactive astrogliosis and Aβ‐pathology in AD. New α7nAChRs‐PET‐tracers might be promising future new biomarkers of reactive astrogliosis with potential early diagnostic properties.MethodTranslational molecular imaging studies including in vivo multimodal PET investigations ([11C]‐deprenyl, [11C]‐PIB, [18F]‐Flutemematol, [18F]‐FDG, [18F]‐RO‐948), MRI, plasma GFAP analyses in control, presymptomatic ADAD, MCI to AD dementia and in vitro PET tracer binding studies (deprenyl, SMBT‐1, and BU99008) in postmortem brains using small/large section‐autoradiography and tissue‐homogenates.ResultWe hypothesize from our in vivo PET/in vitro brain imaging translational/clinical studies a ‘first and second wave of reactive astrogliosis’ with distinct close‐loop relationships with other pathological biomarkers across AD continuum. Validation of newly developed PET‐tracers BU99008 and SMBT‐1 for mapping and visualizing reactive astrogliosis in the brain demonstrated multiple binding properties in AD brain and highlighted the crucial need of multi‐PET tracer approach for understanding the complexity of reactive astrogliosis in AD. We have observed a significant positive correlation between plasma GFAP levels and [18F]‐flutemetamol binding in patients undergoing memory assessment. In presymtomatic ADAD patients, high [11C] deprenyl binding is observed earlier in the disease evolution than increase in plasma GFAP levels following a divergent direction. This clearly suggests plasma GFAP as a later biomarker than [11C]‐deprenyl in AD trajectory, possibly reflecting different types of reactive astrocytes at different stages of AD.ConclusionIt’s an exciting time for the AD field with several new biomarkers/PET‐tracers for reactive astrogliosis, which will improve our understanding of underlying astrocytic complexity and add power to the existing diagnostic criteria in AD.

Resilient frequency stability of a PV/wind penetrated complex power system with CSA tuned robust Type-2 fuzzy cascade PIF Controller

Resilient frequency stability of a PV/wind penetrated complex power system with CSA tuned robust Type-2 fuzzy cascade PIF Controller

Experimental and Numerical Investigation of the Damping Performance of Metal Additively Manufactured Particle Damper

The additively manufactured particle damper (AMPD) is a novel particle damper fabricated by leaving unfused powder inside the pre-designed structural cavities during the laser powder bed fusion (LPBF) process. It can be applied at high temperatures and a wide range of frequencies without any additional installation space. However, the damping mechanism and performance of AMPD are still unclear due to insufficient experimental and simulation analyses. In this work, the damping capacity of AMPDs with three different unit cell sizes at high frequencies and low vibration amplitudes were investigated experimentally and numerically. The AMPDs were manufactured using LPBD with 316 L stainless steel. The complex power method was used to measure the energy dissipation of the AMPD directly. A discrete element method (DEM) simulation model was developed to visualize the particle motion mode during each vibration process. The computed tomography (CT) pictures were utilized to measure the powder distribution in each AMPD's cavity. Finally, the influence of excitation frequency, excitation amplitude, and cavity size on the damping performance of the AMPD were explained by comparing the experimental and simulation results.

Toward Thinner and Higher Heat Dissipation Advanced Chip Embedded Power Supply Module Packaging

Along with power management complexity, small/thin and high efficiency power supply devices such as DCDC converters and Inverters needs are increasing. In response, several chip-embedded packages have been proposed, but their size/thickness, heat dissipation properties, and cost necessitate the development of further concepts. In this report, we propose novel chip-embedded package that uses Semi-additive metallization with non-prepreg coreless substrate and 300 mm square Panel-level manufacturing for cost competitiveness. In the case of a prepreg substrate, it is difficult to make large thermal vias due to presence of glass cloth and the presence of the substrate core makes thickness reduction difficult. Because of such difficulties, prepreg substrate packages have limited characteristics around thermal performance and thickness. Our proposed structure has excellent heat dissipation and minimized thickness due to using thin non-prepreg coreless substrates that allows for large thermal vias by a laser drill and the thin package. We performed thermal resistance simulations and found that our package improved up to 43% as compared to the prepreg package. And also, our build-up interlayer material has close CTE to Cu-RDL and a low Young’s modulus that is effective in reducing package warpage. As a result of simulation, package warpage after post-curing is just 1/10th of that of the prepreg package, this means highly reliable on board. Developments using Si power die will be reviewed. We believe our proposed new package is also suitable for high performance package utilizing GaN and SiC.

Distributed Power High Dynamic Group Control Technology and Grid Connection Demonstration

Abstract Currently, high penetration distributed power generation group control technology makes it difficult to effectively regulate complex power grids. Therefore, on the ground of the division of distributed photovoltaic power generation clusters, this study proposes a dual-layer control model for distribution networks that integrates the cuckoo particle swarm algorithm and verifies its effectiveness. The experiment showcases that the standard deviations of the research algorithm on the four benchmark functions under the 50 dimension are 2.95e-02, 6.73e+02, 6.48e-04, and 6.45e-06, respectively, most of which are lower than the comparison algorithm. On the basis of connecting to the photovoltaic system, the voltage amplitude fluctuation of the double-layer control model designed through research using control algorithms is relatively small, basically maintaining around 1.05. In addition, after implementing control in practical applications, the actual line loss of the distribution network decreased from the maximum of 15.3% to 8.9%, meeting the line loss requirements of the actual distribution network, indicating the effectiveness of the double-layer control model. Meanwhile, after applying the double-layer control model, the voltage fluctuation of node 42 significantly decreased, and the numerical value remained at 1.05 p.u., achieving a good voltage condition effect. The voltage fluctuation of grid connection in distributed power generation has been significantly improved when applied to the actual grid connection. Overall, the proposed dual-layer control model for the distribution network is effective and practical, which can effectively promote the construction of practical grid connection demonstration projects.

A Blockchain-Based Intelligent Data Management Platform for Power Grid Applications

With the access of a large number of renewable energy sources in the power system, the increasingly complex power system operations bring considerable safety concerns for data management. While measurement data analytics in the power grid play an important role in different control and alarming applications, smart and reliable data management is required. Blockchain technology is a decentralized database based on cryptography and distributed storage, which is immutable and traceable. This paper analyzes the matching degree between the characteristics of blockchain technology and the demand for measurement data management, showing the advantages of blockchain for power grid data management. In this paper, a data intelligent management platform based on blockchain technology is designed. A standardized interface design of data service is elaborated, and the application scenarios of intelligent data are listed, providing new possibilities for developing advanced applications based on the measurement data.

Deep learning integration optimization of electric energy load forecasting and market price based on the ANN–LSTM–transformer method

Introduction: Power load forecasting and market price analysis have become crucial in the context of complex power energy systems and volatile market prices. Deep learning technology has gained significant attention in time series forecasting, and this article aims to enhance the accuracy and reliability of power load and market price predictions by integrating and optimizing deep learning models.Methods: We propose a deep learning framework that combines artificial neural networks (ANNs), long short-term memory (LSTM), and transformer models to address key challenges in electricity load forecasting and market price prediction. We leverage ANNs for their versatility and use LSTM networks for sequence modeling to generate initial predictions. Additionally, we introduce transformer technology and utilize its self-attention mechanism to capture long-distance dependencies within the data, further enhancing the model’s performance.Results: In our experiments, we validate the proposed framework using multiple public datasets. We compare our method with traditional forecasting approaches and a single-model approach. The results demonstrate that our approach outperforms other methods in predicting power load and market prices. This increased accuracy and reliability in forecasting can be of significant value to decision-makers in the energy sector.Discussion: The integration of deep learning models, including ANN, LSTM, and transformer, offers a powerful solution for addressing the challenges in power load and market price prediction. The ability to capture long-distance dependencies using the transformer's self-attention mechanism improves forecasting accuracy. This research contributes to the field of energy and finance by providing a more reliable framework for decision-makers to make informed choices in a complex and dynamic environment.

Excavating the English-Language Press in the Ottoman Empire (1841–1923) Editors, State Actors, Readers

The English-language press in the Ottoman Empire was long thought near inexistent. While this article acknowledges that most English-language press titles in that country were few compared to the French-Ottoman press and resorted to French at one moment or another in their history, it investigates a body of English-Ottoman serials that proclaimed some connection to Britain or the US over the period 1841–1923 in order to expose the complex and multidirectional power relationships between British/American Levantine editors, British/American diplomatic actors, the Ottoman State, and readers (both in the Ottoman Empire and beyond). What did it mean to publish an English-Ottoman serial? Considering language as a social practice that helps reconcile the local and the global in the context of the foreign language press, this article seeks to understand motivations behind English-language serial publishing in the Ottoman Empire and how a mix of local/global constraints shaped titles like The Levant Herald (1856–1914).

A deep reinforced markov action learning based hybridized energy management strategy for electric vehicle application

Due to the severe lack of fossil fuels and existing environmental pollution, developing Electric Vehicles (EV) powered by clean, new energy is essential to finding a way to address this problem. The hybrid EV powered by fuel cell is the most practical and suitable option in present days. Several researchers have concluded that a key factor in determining an EV's performance is the hybrid energy management system's output performance. Moreover, proper energy management and performance optimization are the most essential solutions to ensure the normal operation of the hybridized system. In the existing studies, several energy management methodologies are developed using a hybridized energy sources, yet it facing some challenges in terms of high cost, increased system designing complexity, stability issues, and unreliable power supply. Therefore, the proposed work intends to implement a novel Energy Management System (EMS) for EVs with the use of advanced learning algorithms. In the proposed topology, the combination of Proton Exchange Membrane Fuel Cell (PEMFC), battery, and Super-Capacitor (SC) have been used as the major energy sources. Then, a high gain DC-DC converter is utilized to boost the regulate output voltage with reduced stress and loss factors. For improving the performance of converter, a recently developed Gorilla Troop Optimization Algorithm (GTOA) is implemented, which provides the best solution to choose the controlling parameters for generating the switching pulses. Moreover, the Deep Reinforced Markov Action Learning (DRLA) is employed to assure an efficient energy management in EV systems. For validation, the simulation and comparative analyses are carried out in this study with the use of numerous parameters. Index termsElectric Vehicle (EV), Proton Exchange Membrane Fuel Cell (PEMFC), Battery, Super-Capacitor (SC), Renewable Energy Sources (RES), High Gain DC-DC Converter, Gorilla Troop Optimization Algorithm (GTOA), and Deep Reinforced Markov Action Learning (DRLA).

‘Living beyond its present means’: World Bank push and local pushback over lowest-cost housing for postcolonial Dakar

ABSTRACT In mid-1972, the World Bank approved its first loan for a sites and services project, selecting Senegal for the location based on the country's prior experience with similar schemes. Through a close reading of documents in the Bank archive, this article explores the serious differences that emerged between the Bank and Senegal in shaping the project, focusing on three issues: determining whether slum clearance or upgrading should be used to manage existing unregulated urban settlements; eliminating government subsidies for moderate-income housing schemes in order to shift investment to sites and services; and setting appropriate standards for the new Bank-sponsored neighbourhood. Moreover, the partners conceived the project quite differently: while the Bank was fixed on the successful implementation of its first sites and services scheme, for Senegal, this project was only one element of a larger vision for Dakar, which reflected the ambitions of the country's first postcolonial president, Léopold Senghor, and was given shape in the 1967 master plan developed by French urban planner Michel Écochard. The article examines the completed project through the contrasting evaluations produced by the project partners, and considers the complex power dynamics of the relationship between the Bank and Senegal as aid lender and recipient.

Fuzzy EKF-Based Intrusion Detection and Accurate State Estimation of Interconnected DC MGs With CPLs

With the advancement of digital technologies in complex and large-scale power systems, the issue of cyber-attack detection has become of paramount importance to maintain system reliability and performance. In this regard, accurate state estimation plays a key role and leads to proper supervisory decision-making. To detect cyber-attacks in direct current microgrids (DC MGs), a new centralized attack detection system based on the Kalman filter and clustering approach is proposed. On the other hand, appearing nonlinear loads such as constant power loads (CPLs) make the overall system behavior nonlinear and conventional linear Kalman filters ineffective. So, in this paper, an extended Kalman filter (EKF) is utilized. By constructing several output vectors and parallel EKFs, local state estimations are achieved. They are then used to detect attacks based on the residuals and localize them based on the fuzzy clustering approach. The local healthy estimations are aggregated to compute global estimations with low noise. The simulation results on a five-area interconnected DC MG subjected to data integrity and denial-of-service (DoS) attacks show that the proposed approach can detect attacks, evaluate how many attacks occur in the case of multiple attacks, localize the attacks, and obtain accurate state estimations.

A Barrier-Certificated Reinforcement Learning Approach for Enhancing Power System Transient Stability

Increasing integration of renewable resources brings more flexibility and poses new challenges to modern power systems, leading to highly nonlinear and complex dynamics. This paper aims to provide a general solution framework to traditional control problems, such as frequency control and voltage control, which attempt to maintain the stability of either synchronous generators-governed or inverter-governed systems when subjected to a disturbance and simultaneously guarantee operational constraints, providing a complete complement to existing works on control design. Building on reinforcement learning (RL) and control barrier functions, the framework includes two subsystems, i.e., a model-free controller and a barrier-certification system, which discover RL-based control actions and sequentially filter them using a barrier certificate to satisfy operational constraints. Calculating a barrier function is generally challenging for a complex power system. This is addressed by representing the barrier function using neural networks (NNs) and data-based approaches. An adaptive method is introduced to certify the neural barrier function that perseveres barrier conditions, which is more compatible with online implementation. The proposed framework synthesizes a stabilizing controller that satisfies predefined safety regions. The effectiveness of the proposed framework is demonstrated via several comparative case studies.