Power Operation Research Articles

Violent peace: effigies and the marking of the Armistice in Britain

ABSTRACT This article examines how effigies were used in Britain after the signing of the Armistice in November 1918. Effigies of Kaiser Wilhelm, the Crown Prince and members of the German High Command were paraded through the streets of villages, towns and cities as part of celebrations for the cessation of hostilities. These figures were physically and verbally abused, hanged on gibbets, displayed from shop windows, placed on trial and ‘executed’ by hanging and public burning. The violence that these effigies were subjected to is significant as it indicates the way the brutality of the war became part of civic life but it also demonstrates how civilians returned public spaces to public life. The war brought greater control of civic society by the operation of government to mobilise individuals and communities for the war effort. The carnivalesque celebrations that marked the end of the war was a means of acknowledging victory but also formed a means of dissent against the operation of power. Whilst these moments were fleeting and some harked back to traditional forms of celebration, the use of effigies transformed wartime spaces back into public ownership.

“When Father Christmas Is the Gaslighter”

The article is written with, by, and for (m)others whose children have been labelled as having “special educational needs” (SEN). The term (m)others is used to pay attention to the continued impact of the gendered nature of care for (disabled) children at the level of the individual, but also to recognize gender as a social construct, and the many ways of being a (m)other (Anderson). The broad aim is to explore the ways in which special education systems across the global North construct (m)others of disabled children as “mad.” This discussion is timely given the high levels of conflict between parents/carers and global North special education systems in contemporary times. The article explores “madness” as a mechanism of social control produced in special education systems by paying close attention to “intimate encounters” between (m)others and practitioners that occur day-to-day in (special) education settings. The developing analysis is shaped by the concept of “gaslighting,” which offers a useful framework both for understanding deeply affecting and effecting “intimate encounters” between (m)others and practitioners, and for exposing the operations of power in special education systems. The conclusion reflects on what new understandings of (m)others’ madness have been revealed and how they have the potential to (re)shape practice.

Non‐Fused Star‐Shape Giant Trimer Electron Acceptors for Organic Solar Cells with Efficiency over 19 %

AbstractOrganic solar cells (OSCs) based on giant molecular acceptors (GMAs) have attracted extensive attention due to their excellent power conversion efficiency (PCE) and operation stability. However, the large conjugated plane of GMAs poses great challenges in regulating the solubility, over‐size aggregation and yield, which in turn further constrains their development in commercial products. Herein, we employ a non‐fused skeleton strategy to develop novel non‐fused star‐shape trimers (3BTT6F and 3BTT6Cl) for improving device performance. Single‐bond linkage can break the rigid planarity to form a 3D architecture, generating multidimensional charge transfer pathways. Importantly, the non‐fused skeleton strategy can not only significantly improve solubility and synthesis yield, but also effectively suppress molecular excessive aggregation. Consequently, due to the optimized film‐forming process and charge dynamics, 3BTT6F‐based binary device obtains a high PCE of 17.52 %, which is significantly higher than the reported fully fused trimers. Excitingly, 3BTT6F‐based ternary device even obtains a top‐level PCE of 19.26 %. Furthermore, the non‐fused star‐shape configuration also endows these acceptors with enhanced intermolecular interaction in the active layer, demonstrating excellent operational stability. Our work emphasizes the potential of non‐fused star‐shape trimers, providing a new pathway for achieving highly efficient and stable OSCs.

Annihilation Electrochemiluminescence Triggered by Bipolar Electrochemistry

AbstractBipolar electrochemistry (BE) combined with electrochemiluminescence (ECL) has gained considerable attention as a versatile and powerful analytical technique operating in a wireless manner. However, only co‐reactant ECL has been reported so far when using a BE setup. In this work, the generation of annihilation ECL at the anodic extremity of a bipolar electrode (BPE) is demonstrated in two different spatial arrangements of the electrodes. The reported approach is based on a synergetic effect between the asymmetric electroactivity induced across the BPE, which produces different redox states of [Ru(bpy)3]2+, and the electro‐migration mechanism of the formed ionic species, allowing the localization and concentration of the ECL emission. The presented approach demonstrating annihilation ECL via BE, paves the way for the design of easy and straightforward light‐emitting platforms for multiple applications.

Wind-Solar-Thermal Power Coupling System in The Power Market Environment Benefit Distribution Studies

With the continuous increase in the proportion of new energy, China’s power system will further increase the capacity and flexibility of new energy consumption capacity and flexibility in the future. The combined operation of renewable energy and thermal power can improve the utilization rate of renewable energy and the economy of thermal power operation to a certain extent, and is one of the effective ways to solve the problem of curtailment of wind and solar power. Based on the combing of the market mode of wind-solar-thermal coupling system, this paper discusses the game relationship between wind power, photovoltaic power and thermal power, and proposes the operation strategy of wind-solar-thermal power coupling system under the spot market; Then, with the goal of maximizing economic benefits, a two-stage stochastic optimization model was constructed in the coupled operation mode and the independent operation mode, respectively, and the contribution of the alliance members in the day-ahead market, flexibility contribution and environmental cost contribution was weighed, and the revenue distribution method between renewable energy and thermal power was proposed by introducing the market contribution index; Finally, based on the simulation and verification of a local power grid in Liaoning Province, the results show that the proposed joint operation strategy effectively improves the overall revenue level, and proposes a gain cooperative revenue distribution strategy with computational efficiency, which fully considers the contribution of each entity to the alliance for fair distribution, and is conducive to guiding the coordinated development of new energy and thermal power.

GVAF: generalized, flexible filtering software for annotated variant files.

In the rapidly advancing field of genomics, many tools have been developed to interpret genetic variants using next-generation sequencing (NGS) data. However, these tools often produce annotated variant files in different formats, which require specific software or programming skills to filter and analyze. To provide a filtering tool that can be used with diverse variant annotation tools without requiring specific software or programming skills. We developed Germline Variant Annotation and Filtering (GVAF), a command-line software tool that can handle annotated variant files in any table-shaped format. GVAF offers powerful filtering operations without the need for additional software or programming expertise. Built on the Java framework and bash scripts, it provides extensive features, including flexible filtering rules, recognition of genotype-related fields from variant call format (VCF) files, and customizable result generation. GVAF also integrates easily into existing data analysis pipelines. Compared to other tools, GVAF offers a broader range of functionalities, making it more flexible and intuitive for managing annotated variant files. This GVAF software and online manual is publicly available at https://www.sysbiolab.org/gvaf for academic users and is designed to streamline the variant interpretation process, aiding researchers in producing meaningful results.

Improved YOLOv7 Electric Work Safety Belt Hook Suspension State Recognition Algorithm Based on Decoupled Head

Safety is the eternal theme of power systems. In view of problems such as time-consuming and poor real-time performance in the correct use of seat belt hooks by manual supervision operators in the process of power operation, this paper proposes an improved YOLOv7 seat belt hook suspension state recognition algorithm. Firstly, the feature extraction part of the YOLOv7 backbone network is improved, and the M-Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (M-SPPCSPC) feature extraction module is constructed to replace the Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC) module of the backbone network, which reduces the amount of computation and improves the detection speed of the backbone network while keeping the sensory field of the backbone network unchanged. Second, a decoupled head, which realizes the confidence and regression frames separately, is introduced to alleviate the negative impact of the conflict between the classification and regression tasks, consequently improving the network detection accuracy and accelerating the network convergence. Ultimately, a dynamic non-monotonic focusing mechanism is introduced in the output layer, and the Wise Intersection over Union (WioU) loss function is used to reduce the competitiveness of high-quality anchor frames while reducing the harmful gradient generated by low-quality anchor frames, which ultimately improves the overall performance of the detection network. The experimental results show that the mean Average Precision (mAP@0.5) value of the improved network reaches 81.2%, which is 7.4% higher than that of the original YOLOv7, therefore achieving better detection results for multiple-state recognition of hooks.

An 11 nW, ＋0.34 °C/−0.38 °C inaccuracy self-biased CMOS temperature sensor at sub-thermal drain voltage

This paper presents a low-power, high-accuracy self-biased full CMOS temperature sensor based on sub-threshold currents at sub-thermal drain voltage. The sensor achieves high accuracy and minimal corner dependence by generating sub-threshold current ratios using NMOS transistors of different sizes operating at sub-thermal drain voltage. The proposed self-biased all-CMOS temperature sensing architecture enhances sensitivity by up to seven times and improves linearity. The overall stability under temperature fluctuations is significantly enhanced by utilizing a substrate diode structure that maintains constant current variation. Additionally, a high-threshold comparator with a fast response compresses the oscillator reset voltage difference, enabling ultra-low power operation. Timing logic control is employed to discard unstable cycle outputs, thereby reducing errors and achieving high-accuracy outputs. Operating at 1 V, the circuit consumes only 11 nW at 27 °C in a 180 nm CMOS process. It achieves a peak-to-peak inaccuracy of ＋0.34 °C/−0.38 °C from −10 to 100 °C after two-point calibration, with a resolution of 40 mK and a resolution FoM as low as 3.7 pJK2.

A game-based power system planning approach considering real options and coordination of all types of participants

With the ongoing advancement of global power marketization, the increasing diversity of stakeholders in the power market and their interactions have a significant impact on power system planning and operation. Traditional methods employ game theory to describe the interactions among market participants. However, these approaches fail to include all relevant participant types and overlook the long-term uncertainty value of planning schemes. Therefore, this paper proposes a game theory-based power system planning approach considering real options (RO) and coordination of all types of participants. First, the dynamic gaming interaction among supply-transmission-demand is studied, and the independent system operator is integrated into the game planning framework based on the overall perspective of power market operation. Moreover, the new power supply, grid planning, and operation of distributed generation (DG) are taken as decision variables based on the RO theory, and a game decision model of all participants in the power system planning considering the investment uncertainty is constructed. Finally, the iterative search algorithm is employed to solve the model, and the framework is validated based on the IEEE 30-bus system. The results indicate that through collaborative planning involving various market participants, Generation Companies and Transmission Companies expand their investments to maximize profits, while large power consumers reduce their investment in DG and instead increase their direct power purchases to lower costs. Moreover, these participants are inclined to pursue projects with higher asset value volatility, which further enhances their profit. By integrating RO theory with game theory, the proposed approach establishes a comprehensive planning framework that effectively addresses investment uncertainty. This integration enables market participants to make more optimal decisions, ultimately leading to an enhancement in the long-term economic performance of the power system.

Optimal resilience-based restoration plan for disrupted interdependent infrastructure networks under uncertainty

The functioning of vital services in a society depends heavily on the proper and continuous operation of critical infrastructure networks, including water, transportation, power, natural gas, and telecommunication. Such networks are essential for maintaining the economy, security, and overall quality of life. However, since these critical networks are interdependent; hence, they are susceptible to various types of disruptions caused by natural disasters, failures, or malicious activities, leading to varying degrees of performance impacts that could directly affect people's daily lives. Thus, a disruption to one network can quickly spread to others, resulting in significant impacts on daily life. Therefore, restoring such disrupted networks in a timely manner is crucial to ensure a prompt recovery and minimize the impact on daily life. In this work, we consider the problem of restoring a set of interdependent infrastructure networks following a disruptive event. We study two essential factors that can accelerate the recovery process: (i) the restoration facilities location, which are considered as dispatching points for restoration crews, and (ii) the routes of the restoration crews within the interdependent networks. Accordingly, we formulate two optimization models using mixed integer programming. The first model, restoration facility location model, finds the optimal location for restoration facilities within interdependent infrastructure networks considering uncertainty in disruptions. The second model, restoration crew routing model, determine the optimal routes for restoration crews within the interdependent infrastructure networks considering: (i) travelled distance, (ii) restoration time required for disrupted network components, and (iii) different importance of network components. The two optimization models are solved through generated interdependent power and water networks to demonstrate their effectiveness.

Investigation in improving the Cs-free negative hydrogen ion production with short-pulse low power in the afterglow of pulse-power-modulated plasma sources

Abstract Pulse plasma ion sources have been shown to be capable of achieving high negative hydrogen ion densities ( n H − ) or currents. The fast cooling of the electrons and reduction in the electron density (n e) provide favorable plasma conditions to improve the H− production, with a high density ratio of negative hydrogen ions to electrons during the pulse-off period. The purpose of this research is to further improve the performance of volume-produced H− ion sources based on the pulse power modulation. In this study, a time-modulated negative hydrogen plasma sustained by 10 kHz pulse power at the typical gas pressure of 0.3 Pa was numerically investigated using a global model. The model is compared with measurements obtained in a pulsed radio frequency ion source and shows a reasonable agreement. A steplike low-high power strategy in the active glow period and a short-pulse low power in part of the afterglow period are employed to mitigate overshoot of the electron temperature (T e) in the initial stage of the pulse period and to optimize H− production in the afterglow, respectively. The model predicts that a small-magnitude, short-duration low power operation in the afterglow increases n H − and decreases n e from the onset of the low power until the beginning of the next high power pulse, due to a modest temporary increase in the T e (less than 2 eV). It facilitates dissociative electron attachments and thus H− formation. The reduction in the n e indicates that positive ions lost to the walls cannot be compensated by weak ionization. H− production with a low number of co-extracted electrons can be optimized by adjusting the magnitude and duration of the low power in the afterglow.

The Phenomenon of Difficult Enforcement in the Circuit Trial System in China from the Perspective of Power Operation

The phenomenon of "difficulty in enforcement" is prevalent in the judicial practice of China and poses similar challenges to circuit trials. This paper, based on Su Li's premise of the shifting local power dynamics in rural China, utilizes Foucault's theory of micro-power and Geertz's theory of local knowledge to explore how these misalignments in local power relationships affect the enforcement work in circuit trials in China, and provides suggestions on how to alleviate this issue. In the power relationship of enforcement within circuit trials, the relatively weaker state power struggles to support the compulsory execution of judgments, while the relatively stronger individual power resists the enforcement of judgments. This leads to a reliance on the recognition and cooperation of the judgment debtor, as well as the assistance of local power as a third party. However, the lack of local knowledge among judges makes it difficult for "legally sound but unreasonable" judgments to gain the recognition and cooperation of the parties involved, resulting in challenges to enforcement. Therefore, it is necessary to strengthen cooperation with local powers and other state agencies, seeking a multi-faceted approach.

A Surrogate Model Approach to Predict Operations and Maintenance Cost for Combined Cycle Power Plant

Abstract In the transition toward carbon neutrality by 2050, renewable energy sources are gaining prominence. The variability of renewable energy poses challenges for power grid stability. Gas turbine combined cycle (GTCC) power plants play a crucial role in grid stabilization, especially during peak loads. In GTCC power generation, operations and maintenance (O&M) cost is the next most important after fuel and initial investment cost, and it is necessary to predict O&M cost according to changes in the operating environment for efficient and stable operation of GTCC power plant. However, various uncertainties exist when estimating O&M costs because actual design information is limitedly disclosed due to maintenance contracts between operators and manufacturers, and it is difficult to obtain accurate variables according to the operation condition. In this paper, the correlation was analyzed considering the uncertainty of the factors affecting the O&M cost of the GTCC power plant through sensitivity analysis, and the main factors were defined. In addition, a surrogate model was proposed that can quickly calculate fixed and variable O&M cost according to changing operating scenarios such as shortening startup time, enhancing ramp rate, or lower minimum load. Through the case study, short-term and long-term O&M costs can be estimated in the operating environment of the GTCC power plant currently being operated. Additionally, the model facilitates cost projections for new hybrid GTCC facilities (combining gas turbines with energy storage systems), aiding long-term investment planning.

Extrinsic goals benefit capitalism but not well-being. Rethinking the economy's goal for a healthier future.

The dramatic rise in non-communicable diseases around the world but notably in high-income countries like the UK is a manifestation of a global economic system-capitalism-that prioritizes wealth over health. A decade ago, the former WHO Director-General, Margaret Chan highlighted how 'efforts to prevent non-communicable diseases go against the business interests of powerful economic operators' [United Nations. (2013) Take Action for the Sustainable Development Goals. https://www.un.org/sustainabledevelopment/sustainable-development-goals/ (last accessed 16 February 2024)]. While there is a growing literature on how politics and economics influence population health-for better or worse-less attention has been given to exploring how economic systems like capitalism influence people's psychological well-being. To fill this gap, the following article examines how the continued pursuit of economic growth under capitalism (neoliberal free-market forms especially) impacts well-being through challenging basic psychological needs for security, autonomy, competence and relatedness. In doing so, I hope to shed important light on the sources and possible solutions to our growing health and social problems, and stimulate a conversation on how to achieve a healthier future for us all.

Using SAR imagery to extract flash flood sediment deposition area in the northern Loess Plateau

The lack of hydrology data brings challenges to the accurate simulation of the inundation range of flash floods using hydrodynamic models. In addition, the water bodies of flash floods often disappear quickly, which makes it difficult for remote sensing satellites to extract flash flood inundated areas without standing water. There is currently no research using flood sediment deposition areas to characterize flash floods in areas without water accumulation. To address this research gap, we developed a method based on Google Earth Engine (GEE) that uses Sentinel-1 SAR imagery to automatically extract flash flood sediment deposition areas. This method, termed Power Operation of Different Polarization Combination (PODPC), differs from other feature extraction methods in that it utilizes not only the original Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarizations and their simple combinations but also employs power operations and threshold techniques to extrac t sediment deposition areas. We explored the advanced features of PODPC in terms of extraction accuracy, threshold segmentation effect, and sediment deposition area distribution. The results demonstrate that the histograms generated by PODPC exhibit clearer bimodal characteristics, facilitating better threshold selection. VV3, (VV + VH)6, and (VV × VH)3 produced fewer false deposition areas, achieving an overall accuracy of 92 % ∼ 93 %. Compared to commonly used methods such as VV, VH, and VV + VH, PODPC increased the overall accuracy by an average of 23 %. (VV-VH)n and (VV/VH)n are are considered unsuitable for the extraction of sediment deposition areas. The extracted sediment deposition area can serve as indicator of flash flood severity and as validation and input data for flash flood models, enhancing the scientific assessment of flash flood disasters.

Multi-step prediction of offshore wind power based on Transformer network and Huber loss

In the context of the burgeoning expansion of renewable energy sources, the precise prediction of offshore wind power assumes a pivotal role in safeguarding the reliability, economic viability, and sustainable progression of offshore wind farms. The present study introduces a novel methodology for offshore wind power prediction, predicated upon the synergy of the Transformer network and Huber loss function. Empirical validation is conducted utilizing authentic data from a European offshore wind farm. The resulting analyses delineate a discernible superiority of the Transformer network over classical LSTM and GRU models in capturing the intricate long-term dependencies intrinsic to the time series. Furthermore, the inclusion of the Huber loss function effectively mitigates the challenges posed by the high volatility often characteristic of offshore wind power data. The study also demonstrates the beneficial integration of autoencoder reconstruction for denoising and slime mould optimization algorithm to augment prediction performance. Distinctively diverging from traditional single-step prediction paradigms, the multi-step prediction model constructed within this research offers a more comprehensive and precise prediction of wind power. Such an innovative approach represents a valuable contribution to the field, with tangible implications for the dependable operation and future advancement of wind power.

On the Effectiveness of the System of Delegating the Power of Administrative Punishment

The system of "delegating the power of administrative punishment" established in Article 24 of the "Administrative Punishment Law" breaks through the level restriction of the exercise of administrative punishment power and extends the power of administrative punishment to the grass-roots level to a certain extent, which is an important institutional way to improve the predicament of administrative enforcement power at the grass-roots level. However, with the continuous application of this system, the problem of its effectiveness has become increasingly prominent. In terms of the way and content of empowerment, "urgent need of grass-roots management" and "able to effectively undertake" should be used as the judging criteria for the delegation of administrative punishment power, and provincial subjects should establish a differentiated list of administrative punishment power delegation according to relevant provisions. In the operation of power, we should enhance the undertaking ability of the undertaking subject from the perspective of the establishment of law enforcement agencies in townships (streets) and the allocation of law enforcement personnel, so as to ensure the effective operation of power. In terms of system supervision, we should make clear the subject and way of supervision to prevent the abuse of the power of administrative punishment.

Fröhlich Scattering Effects on Electron Mobility in β‐Ga2O3 Power Devices under High Temperature

The impact of Fröhlich scattering on β‐Ga2O3 Schottky barrier diodes (SBDs) electron mobility, particularly at high temperatures, is investigated. This scattering is crucial due to electron–polar optical phonon interactions. Temperature‐dependent I–V characteristics of a β‐Ga2O3 SBD from 300 to 473 K showed significant current reduction due to electron–polar optical phonon scattering, supported by correlation with mobility profiles. Additionally, in situ temperature‐dependent XPS analysis revealed a notable positive shift in core‐level binding energy, attributed to heightened electron–phonon interactions. This study provides crucial insights into carrier transport mechanisms, essential for power device design and operation.

Prediction of outflow temperature of reservoir based on theory-guided machine learning models and optimization of operation for improving outflow temperature

The proportion of hydropower within the energy-resource structure is gradually increasing. However, the construction of reservoirs inevitably leads to ecological and environmental issues, especially the release of low-temperature water in summer and its detrimental effects on fish spawning and reproduction. These issues can be alleviated by optimizing the reservoir operation scheme, but the large amount of time required for predicting the reservoir outflow temperature emerges as the primary constraint. In this study, we considered Pubugou (PBG) Reservoir and proposed an efficient and high-precision prediction method for reservoir outflow temperature based on theory-guided machine learning (TGML) algorithms. This method was applied to the multi-objective optimization of the reservoir operations, and an optimization operation plan was proposed to improve the outflow temperature. The research results indicate that the LightGBM machine learning model shows good predictive performance, with a maximum deviation of no more than 1 °C in predicting the outflow temperature. There is a significant competitive relationship between reservoir power generation and the outflow temperature. Compared with the current operational scheme, the maximum power operation plan results in a 4.4% increase in total power generation but causes the average outflow temperature during the fish spawning period to decrease by 0.1 °C. The total power generation of the improved outflow temperature operation scheme was 0.8% lower than that of the actual scheme, but the average outflow temperature during fish spawning increased by 0.2 °C. Properly increasing the discharge flow from the reservoir and lowering the water level can thus help alleviate the issue of outflow temperature.

Bearing faults classification using novel log energy-based empirical mode decomposition and machine Mel-frequency cepstral coefficients

The accurate diagnosis of faults in bearing components is crucial for the safe and efficient operation of electrical and power drives. These machines generate sound and vibration signals that indicate their operational state. While vibration signals are often utilized for fault diagnosis, they require costly transducers. On the other hand, sound signal transducers are more affordable, but their lower signal-to-noise ratio complicates the differentiation between healthy and faulty bearings. This paper addresses these challenges by introducing a machine sound-based bearing fault diagnosis system. The proposed method employs a novel Log Energy-based Empirical Mode Decomposition and Reconstruction for advanced sound preprocessing. Feature extraction is performed using Machine Mel-frequency Cepstral Coefficients, with feature selection facilitated by a Genetic Algorithm. Classification is achieved through Support Vector Machines. The system demonstrated a high classification accuracy of 99.26% on the SUBF v2.0 dataset, outperforming other diagnostic methods, even in noisy conditions. This approach is particularly suited for industrial applications, offering a reliable solution for preventing downtime and ensuring the reliability of equipment.