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Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High conversion ratio dc-dc converters have received significant attention in renewable energy systems, primarily due to their necessary high-gain characteristics. This research proposes a high step-up ratio full-bridge resonant cascaded (FBRC) dc-dc converter designed for use in photovoltaics (PV), fuel cells (FC), electric vehicles (EV), and other low-voltage output energy sectors to achieve high voltage gain. This converter contains a full-bridge cell with a boost input inductor, a diode-capacitor cascaded stage that replaces the transformer as a voltage multiplier and an inductor-capacitor (LC) parallel-series resonant network across the FB terminal. One of the strategic features of the converter is its high voltage step-up characteristic combined with lower duty cycle operation that limits the maximum current through the active devices, making it particularly suitable for systems that generate low output voltage. In addition, zero-voltage switching (ZVS) is achieved during the turn-off and turn-on operation of the FB switches from 25% to full load, thereby lessening the switching losses. Moreover, the diminished necessity for passive components and the decreased voltage stress on both active and passive devices lead to the use of smaller and more cost-effective components. The theoretical analysis of the proposed converter is validated using a 500 W laboratory-scale prototype wherein high-performance SiC-based MOSFETs have been utilized as switching devices. It offers reduced ripples, with input current ripple at 5% and output voltage ripple at 0.76%. When the load is 400 W and 60 V as the input voltage, the maximum efficiency is found 95.8% at 400 V output voltage. The proposed dc-dc converter, with its high voltage gain and reduced component stress, shows significant promise for application in renewable energy systems. [ABSTRACT FROM AUTHOR]

A grid-forming voltage source converter with an integrated step-down transformer could be a promising solution for supplying low-voltage alternating current loads from a medium-voltage direct current supply. However, it may require a control system that gathers feedback signals from both the primary and secondary sides of the transformer, which in turn complicates the derivation of a standard form linear model. The absence of such a model complicates control tuning, as well as the assessment of dynamics and stability of the converter system. The objective of this paper is to address this gap in knowledge. For the case study, a conventional H-bridge converter with a step-down transformer and an α β -frame dual-loop grid-forming controller is considered. Initially, comprehensive guidelines on deriving a standard form linear model for this converter system are presented. Then, the impact of controlling the VSC in a d q frame and the changes in the transformer vector group on the small-signal model of the VSC are analyzed. The aspects of control tuning are also discussed in detail, and the model's accuracy and efficacy are validated both theoretically and through control hardware-in-the-loop (C-HIL) tests using a Typhoon HIL setup. [ABSTRACT FROM AUTHOR]

Global energy and environmental issues are becoming increasingly problematic, and the vibration and noise problem of 110 kV transformers, which are the most widely distributed, have attracted widespread attention from both inside and outside the industry. DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers. To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias, a multi-field coupling model of a 110 kV transformer was established using the finite element method. The electromagnetic, vibration, and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC, harmonic, and DC power sources. It was found that a DC bias can cause significant distortions in the magnetic flux density, force, and displacement distributions of the core and winding. The contributions of the DC bias effect to the core and winding are different at Kdc=0.85. At this point, the core approached saturation, and the increase in the core force and displacement slowed. However, the saturation of the core increased the leakage flux, and the stress and displacement of the winding increased faster. The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics. When the DC bias coefficient was 1.25, the noise sound pressure level reached 73.6 dB. [ABSTRACT FROM AUTHOR]

The nonlinear resonance between the saturated inductance of a transformer and capacitance of the switching device in a substation induces overvoltages and overcurrents. The voltage transformer experience 2–4 pu overvoltages due to the different modes of nonlinear resonance. Though many overvoltage suppression circuits are being proposed, the transformer does not have a single suppression circuit against all modes. A memristor-based suppression circuit has been proposed already for the fundamental (periodic) mode of ferroresonance. In this paper, an aperiodic mode of ferroresonance has been addressed with same memristive circuit along with meminductive circuit. A subharmonic mode of ferroresonance overvoltage of 3.13 pu has been induced in a (400 kV/ √ 3 )/(110 V/ √ 3 ) CVT model according to IEC 61869 -5 standards. The primary current got increased from 23.3 mA to 1.56 A and has subharmonic frequency components. A meminductor circuit is proposed as a suppression circuit against ferroresonance for a Substation CVT. A gas discharge lamp T8 Philips 18 W with low-frequency magnetic ballast emulated peculiar pinched hysteretic characteristics of a memristor. The relation between charge and flux termed as memristance of the memristor circuit is used to mitigate the nonlinear resonance oscillations. A mutator circuit for mitigation requirement has been connected to the lamp model to emulate meminductor characteristics. The mitigation performance shows that the meminductor circuit dominantly recovers the secondary voltage of CVT in 0.188 s with a reduction of 1/3rd, 1/5th, and 1/7th harmonic components by 35.4%, 78.15%, and 90.41%, respectively. [ABSTRACT FROM AUTHOR]

Electrical transformers are generally designed to operate from sinusoidal sources. However, increased use of nonlinear loads, such as electric motor drives, lighting, fluorescent lamps, rectifiers, and computers, which generate high values of harmonics, makes this transformer operate in the nonlinear region. This paper discusses and focuses on the effect of the characteristics of a single-phase transformer when it is operated from a non-sinusoidal source with a non-linear load. An experimental 1 kVA single-phase transformer with a voltage of 220V and frequency of 50Hz with linear and nonlinear loads was considered. The results of this investigation show the losses increased by 25% when the transformer was fed from a sinusoidal power supply and the load was non-linear. In addition, the losses and THD for the current reached 40% and 12.4%, respectively, at nearfull load when the transformer was fed from a non-sinusoidal power supply and non-linear loads. The correlation between total harmonic distortion (THD) and these types of power supplies and loads was illustrated. The results showed that increasing the current harmonic distortion will lead to increased transformer losses and thus reduce their life expectancy. [ABSTRACT FROM AUTHOR]

To calculate the losses of distribution feeders, this paper uses an iterative method that is limited to restricted measurements. The approach presented in this paper uses bill data in addition to output information from a very small number of real-time measurements located on the secondary side of distribution transformers. This method attempts to estimate the load of distribution transformers injected into LV feeders. Energy losses for LV feeders are evaluated by first estimating the power and periodic energy injected to each of the LV feeders and then subtracting the total consumption bills from these estimated values. By using this method, the amount of energy loss is estimated. In this article, a new method called iterative power factor adjustment method is considered as a potential method for estimating losses. The power factor can be increased by repeatedly using evolutionary algorithms and including capacitors in the system. In order to reduce system losses and increase network effectiveness. In this paper, a new method for examining and evaluating Non-Technical Losses (NTL) is proposed. This method considers load estimation and limited measurement to place high priority feeders. [ABSTRACT FROM AUTHOR]

This study addresses inherent challenges stemming from uncertainty associated with the integration of wind energy into the electricity market. A novel approach is proposed to leverage the capabilities of dynamic transformers to optimize the utilization of uncertain wind power output, thereby enhancing financial investment efficiency for wind power stakeholders. The flexible combination of wind turbines (WTB), transmission transformers (TTS), and Energy Storage Systems (ESS) can actively reserve or provision electricity. Electricity generation control is based on optimal analysis results using linear integer programming algorithms that consider temperature fluctuations, lifespan of transformers, and electricity market prices. Maximizing the dynamic transformer's efficiency as proposed and optimizing revenue and costs from the fluctuating wind power output significantly improves financial performance metrics when investing in wind farm projects. Financial figures highlighted in the paper emphasize notable benefits, particularly for wind farm expansion projects. The potential return on investment ratio is expected to increase up to 5.64 times compared to conventional wind farm investment scenarios, with an improvement to increase from 4.4% to 24.8%. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and purpose Methods Results Conclusions Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi‐automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time.An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three‐phase system, using the average current measured across all three phases, a 480 V reference voltage for both machines, and vendor‐provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC’), smoothed by convolution with a window function (sLDC’), and used to detect transitions between different system modes including (in descending power levels): scan, prepared‐to‐scan, idle, low‐power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1‐month LDC, for each nonproductive system mode (i.e., prepared‐to‐scan, idle, lower‐power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data).The validation revealed differences in mean power values <1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2 kW and the total energy consumed for 1 month ranged from 11 106 to 14 466 kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722 kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9 kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends.A semi‐automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. The methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners. [ABSTRACT FROM AUTHOR]

Power transformers are the most important component in power system. Exposing these transformers to the harmonic distortions causes additional heat losses, insulation stress, decrease in lifetime of insulation, and reduced power factor with decrease in efficiency of the system. The lifespan of distribution transformers is influenced by the fragility of power quality in power networks. Harmonic mitigation filters with robust control technique are required to reduce the harmonic effects on power transformer. Traditionally, synchronous reference frame (DQ) is employed to control the shunt active power filter (APF) for mitigation of harmonics in power transformers. DQ control of Shunt APF lacks merits of fast response, delayed operation due to phased lock loop under abnormal grid conditions leading to insufficient harmonic elimination. A developed DQ method based on detecting the positive and negative sequence components is proposed to precisely control the shunt APF for reliable operation of power transformer. This detection technique improves the response time, mitigate the harmonics effecting the operation of transformer and overall power factor. The proposed control system is evaluated under different abnormal operating scenarios and compared with traditional DQ method. The results and analysis confirm the efficacy of the developed DQ method in improving the power transformer performance. [ABSTRACT FROM AUTHOR]

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot. [ABSTRACT FROM AUTHOR]

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model's training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages. [ABSTRACT FROM AUTHOR]

As an important part of automotive shock absorber, the columnar parts in automotive shock absorber will inevitably have machining defects during the process, which will not only degrade the performance of the parts, but also degrade or even fail the performance of the final shock absorber after assembly. Yolov5, as a target detection algorithm, has received much attention due to its high accuracy and fast operation speed. However, the algorithm faces some challenges when applied in a practical industrial environment. In this paper, improvement measures are proposed to address the limitations of sample collection and the high speed of pipeline recognition in industrial environments. The network model is optimized and designed. Firstly, the ASPP module is replaced by the SPP module thus improving the viewability throughout the process providing recognition accuracy. Secondly, the Conv and C3 layers of Yolov5s are replaced by Transformer to obtain higher recognition accuracy. By improving and optimizing the above methods, we can better cope with the improvement of detection accuracy under small sample conditions. Experiments show that the method can significantly improve the detection accuracy and operation speed of Yolov5s under the hardware condition of lower computing power, which is more suitable for industrial scenario application scenarios. [ABSTRACT FROM AUTHOR]

In a wide range of current changes, it is difficult for current transformer to carry out steady energy extraction, because the excitation characteristics of current transformer are interfered by load impedance. To solve this problem, a dynamic impedance control strategy of voltage source converter is proposed. The method dynamically and continuously adjusts the secondary impedance according to the different states of the primary current, which not only meets the requirement of load power consumption under the condition of low current, but also reduces the influence of the magnetic saturation problem of the core. The principle of energy collection is analyzed, and the equivalent model of current transformer and circuit is established. The relationship between excitation characteristics and impedance parameters is discussed empirically-, which lays a foundation for the construction of the final control strategy. The experimental results show that, first, without capacitive impedance control, the effective value of ct induced voltage is 37.313V, and the corresponding energy collection power is 1.39W. Under the capacitive impedance control, the RMS value of ct induced voltage increases to 72.868V and the energy collection power reaches 5.31W. It can be seen that when the capacitive reactance state is changed, the RMS value of induced voltage and energy collection power are significantly different. Second, the output current of the strong current generator is adjusted to 70a, and when the current increases, the system enters the magnetic saturation state. At this time, the positive and negative half waves of the voltage and current on the CT secondary side produce high amplitude spike pulse waves, which is very dangerous to the insulation of the CT winding and the safety of the load equipment. Under the same conditions, after the induction impedance control is added to the CT core, the magnetic saturation state is relieved, and the waveform changes from peak wave to sine wave, the energy taking system returns to normal working state, the DC voltage output is normal, and the system power supply is stable. Third, the output current of the strong current generator continues to increase to 80a, with the increase of the primary current, the system waveform state remains stable, and the induced voltage and DC output voltage remain constant near the preset reference value. It can be concluded that the magnetic induction intensity of the core will be positively affected by the capacitive impedance operation, and the energy supply effect of the transformer will be increased. The operating characteristics of inductance impedance can reduce the magnetic induction intensity of the iron core, thus inhibiting the saturation of the iron core under high current operating conditions and maintaining stable power output. [ABSTRACT FROM AUTHOR]

Metro stray currents flowing into transformer-neutral points cause the high neutral DC and a transformer to operate in the DC bias state.Because neutral DC caused by stray current varies with time, the neutral DC value cannot be used as the only characteristic indicator to evaluate the DC bias risk level. Thus, unified characteristic extraction and assessment methods are proposed to evaluate the DC bias risk of a transformer caused by stray current, considering the signals of transformer-neutral DC and vibration. In the characteristic extraction method, the primary characteristics are obtained by comparing the magnitude and frequency distributions of transformer-neutral DC and vibration with and without metro stray current invasion. By analyzing the correlation coefficients, the final characteristics are obtained by clustering the primary characteristics with high correlation. Then, the magnitude and frequency characteristics are extracted and used as indicators to evaluate the DC bias risk. Moreover, to avoid the influence of manual experience on indicator weights, the entropy weight method (EWM) is used to establish the assessment model. Finally, the proposed methods are applied based on the neutral DC and vibration test data of a certain transformer. The results show that the characteristic indicators can be extracted, and the transformer DC bias risk can be evaluated by using the proposed methods. [ABSTRACT FROM AUTHOR]

Bit-depth expansion is a technique for reconstructing a highbit image by predicting the missing bits in a low-bit image. With the development of high-bit monitors, corresponding high-bit images are needed to maximize their performance. However, many image data are still in 8-bit format. It is a complicated task to accurately recover lost information by expanding the bit depth while distinguishing between false contours and edges in real images. In this study, we propose a novel bit-depth expansion method using a Swin Transformerbased network with Channel Attention Layers (CALs). This network achieves high-performance bit-depth expansion by utilizing not only spatial features, which is an advantage of the Swin Transformer-based network, but also the correlation between channels obtained by CALs. Experimental results show that the proposed method outperforms conventional methods. [ABSTRACT FROM AUTHOR]

A temporary frequency response test and measurement error prediction method of direct current voltage transformer (DCTV) based on artificial intelligence (AI) is proposed. Firstly, the frequency characteristic of direct current (DC) side voltage of DCTV is analyzed. On this basis, a DCTV transient Frequency Response testing method based on transient alternating current (AC) & DC superposition was developed. Then, the method of voltage sudden change and phase correction is used to achieve transient process DCTV response time testing. Finally, the ant colony optimization (ACO) algorithm was improved by combining an adaptive inertia weight improvement strategy, achieving accurate prediction of the Measurement Error of DCTV. The proposed AI based DCTV transient Frequency Response testing and Measurement Error prediction method were compared and analyzed with the other three methods through simulation experiments. Compared to the other three comparison methods, the maximum transformation error in the evaluation indicators of mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) decreased by 0.006, 0.0119, and 0.0085, respectively, while the maximum phase error decreased by 0.2794, 0.3004, and 0.2823, respectively. [ABSTRACT FROM AUTHOR]

Modular multilevel converters with non-sinusoidal ac voltage output can reduce cost and volume in medium-voltage-connected electric vehicle battery charging applications. The use of full-bridge submodules in such converters enables single-stage ac/ac voltage conversion, allowing a medium-voltage grid to be directly connected to a medium-frequency isolation transformer. The application of a square wave voltage at the medium-frequency transformer's single-phase port enhances the converter's efficiency and power density in comparison to a sinusoidal voltage. This paper presents the analysis and modelling of a modular multilevel converter, comparing its operation with sinusoidal and square wave output voltages. A single control scheme for both output voltage waveforms is proposed for the three-phase and single-phase ac currents, circulating currents, and the energy stored in the submodule capacitors. The control strategy of the three-phase and single-phase port currents is verified through simulation and experiments using a scaled-down prototype, thereby validating its suitability for high-power bidirectional battery chargers. [ABSTRACT FROM AUTHOR]

The object of research is a three-phase electricity metering unit, which includes a digital meter and measuring current transformers. The reduction of non-technological energy losses is restrained due to the insufficient accuracy of the accounting of electric energy in distribution power networks under a reduced load current of the metering unit. The possibility of representing the dependence of the relative error of electricity measurement on current values by a fuzzy function at reduced load has been confirmed. The boundaries of such a function are approximated with sufficient accuracy by the sum of two exponents, which is explained by its significant nonlinearity in the range of reduced current. The proposed EMRL software allows to estimate the real consumption and the most possible level of underaccounting based on the array of electricity meter readings. The accuracy of estimating by the EMRL the amount of electricity consumed with a probability of 0.7 can be estimated with a relative error not exceeding 2 %. The probability of psychophysical assessments of the accuracy of EMRL «very good» and «good» is at least 0.833. The trend of a significant decrease in the relative value of underaccounting with an increase in the level of electricity consumption was revealed. With a daily consumption of up to 10 kW·h, the amount of underaccounting can reach 18 %, and with a consumption of more than 20 kW·h, it does not exceed 6 %. The adequacy of the results of estimating the amount of consumed electricity at reduced load using the EMRL was confirmed by experimental data at a significance level of 0.05. The software capabilities allow to increase the accuracy of the accounting of electrical energy in distribution networks with a reduced load current of the metering unit. The program can be used as part of automated systems of commercial electricity metering or advanced metering infrastructure to determine the most possible underaccounting due to the operation of metering units at a reduced load [ABSTRACT FROM AUTHOR]

Models based on transformer architecture have risen to prominence for video captioning. However, most models are only to improve either the encoder or the decoder, because when we improve the encoder and decoder simultaneously, the shortcomings of either side may be amplified. Based on the transformer architecture, we connect a bidirectional decoder and an encoder that integrates fine-grained spatio-temporal features, objects, and relationships between the objects in the video. Experiments show that improvements in the encoder amplify the information leakage of the bidirectional decoder and further produce a worse result. To tackle this problem, we generate pseudo reverse captions and propose a Bidirectional Transformer with Knowledge Graph (BTKG), which integrates the outputs of two encoders into the forward and backward decoders of the bidirectional decoder, respectively. In addition, we make fine-grained improvements on the interior of the different encoders according to four modal features of the video. Experiments on two mainstream benchmark datasets, i.e., MSVD and MSR-VTT, demonstrate the effectiveness of BTKG, which achieves state-of-the-art performance in significant metrics. Moreover, the sentences generated by BTKG contain scene words and modifiers, that are more in line with human language habits. Codes are available on https://github.com/nickchen121/BTKG. [ABSTRACT FROM AUTHOR]

The safety of the electrical energy supply is one of the most critical issues for companies engaged in production. The internal distribution network, which will provide the energy for the production elements, is essential. Power Transformers, which energize the loads fed from the electricity distribution network, are backed up in a way suitable for parallel operation for energy supply security. As a result of this situation, in case of a predictive failure in one of the transformers, energy supply safety is ensured since the other transformer is designed to carry the load alone. When one of these identical transformers is damaged, the relative short-circuit voltage of the new transformer to be integrated into the system may be different. The transformers with different short-circuit voltages are not shared their load equally. For this reason, the total amount of load that they can be loaded, the relative short-circuit voltage that will occur, and the load rates they are loaded change. In this study, a detailed analysis of two transformers with different relative short-circuit voltages and operating in parallel was made, both theoretically and as a result of the application, with the information obtained through the SCADA program. The theoretical values were calculated as a result of the analysis, the SCADA values were compared, and the margin of safety between them was established. It has been shown that the safety margin will protect against overload on a low short-circuit transformer. After the commissioning, the load rating needs to check and it can be regulated in line with the load rates that will occur after commissioning. [ABSTRACT FROM AUTHOR]

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer. [ABSTRACT FROM AUTHOR]

It is of great importance to choose low-cost, high-performance/efficient and operating and protection equipment in accordance with the applicable legislation in electrical facilities. It is necessary to make calculations for the selection of the most suitable cable/line, measuring and protection system equipment. In this study, panel-inverter compatibility calculation, voltage drop, current control, short circuit, lightning risk and protection calculations, grounding resistance and grounding conductor calculations and tilt angle calculation were made for a grid connected photovoltaic system with a power of 0.4 MWe. It has been calculated that the allowable voltage range and maximum powerpoint voltage values of the selected inverter are compatible with the panel system. With voltage drop and current control calculations, copper cable cross section for each inverter output and solar board output were found to be 3x70+35 mm2 and 2(3x185+95) mm2, respectively, and transformer low voltage busbar 60x10 mm2. In order not to deform the low voltage busbars in peak short-circuit current, they should be placed at 10 cm intervals for 80 cm length. According to the transformer low voltage side phase-earth short circuit current, the cross-section of the grounding conductor should be at least 53.97 mm2 copper conductor. In the lightning protection facility of the panels, according to the rolling sphere method, it has been calculated that the air termination rods should be at least 0.84 m above the upper point of the solar panel. The optimum fixed panel tilt angle was found to be 32.08°. The calculations used in the study will benefit the practitioners in the design of grid-connected photovoltaic systems, in the selection of the most suitable electrical equipment with minimum size. [ABSTRACT FROM AUTHOR]

With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy. [ABSTRACT FROM AUTHOR]

The accuracy of voltage transformer (VT) measurements is imperative for the security and reliability of power systems and the equitability of energy transactions. The integration of a substantial number of electric vehicles (EVs) and their charging infrastructures into the grid poses new challenges for VT measurement fidelity, including voltage instabilities and harmonic disruptions. This paper introduces an innovative transformer measurement error prediction model that synthesizes Multivariate Variational Mode Decomposition (MVMD) with a deep learning framework integrating Bidirectional Temporal Convolutional Network and Multi-Head Attention mechanism (BiTCN-MHA). The paper is aimed at enhancing VT measurement accuracy under fluctuating load conditions. Initially, the optimization of parameter selection within the MVMD algorithm enhances the accuracy and interpretability of bi-channel signal decomposition. Subsequently, the model applies the Spearman rank correlation coefficient to extract dominant modal components from both the decomposed load and original ratio error sequences to form the basis for input signal channels in the BiTCN-MHA model. By superimposing predictive components, an effective prediction of future VT measurement error trends can be achieved. This comprehensive approach, accounting for input load correlations and temporal dynamics, facilitates robust predictions of future VT measurement error trends. Computational example analysis of empirical operational VT data shows that, compared to before decomposition, the proposed method reduces the Root-Mean-Square Error (RMSE) by 17.9% and the Mean Absolute Error (MAE) by 23.2%, confirming the method's robustness and superiority in accurately forecasting VT measurement error trends. [ABSTRACT FROM AUTHOR]

In this paper, a dual-active bridge converter based on the utilization of two transformers is presented. The principles of operation, switching strategy, and transmission power characteristics of the proposed converter under normal operation are discussed, comprehensively. Moreover, the RMS current of two transformers with different values of inductances of the inductors that are in series with the transformers; is discussed. The operation of the proposed dual active bridge (DAB) converter under the open-circuit failure of transformers is studied. In addition, the loss distribution of the proposed converter in different powers is investigated. The proposed dual-transformer-based dual-active bridge converter is compared with the presented converters. Finally, the proposed converter with a low-voltage side (VL= 300 V), the switching frequency of power MOSFETs (fs= 50 kHz), and an accurate model of the electric battery at a high-voltage side (VH= 450 V) are simulated to verify the way of charging and discharging the electrical battery with the proposed converter under normal and open-circuit fault of transformers. [ABSTRACT FROM AUTHOR]

The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety. The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light. This often results in a high false alarm rate in complex air transportation environments. The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information. This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model. Dual-wavelength optical sensors, flue gas analyzers, and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy. The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective, which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN (recurrent neural network) and CNN (convolutional neural network). Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information, respectively, which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism. Finally, the output results of the two models are fused through the gate mechanism. The research results show that, compared with the traditional single-feature detection technology, the multi-technology collaborative fire detection method can better capture fire information. Compared with the traditional deep learning model, the multivariate fire prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995. [ABSTRACT FROM AUTHOR]

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This paper was written to demonstrate the value of using nanofluids for cooling high power transformers while also providing current techniques for business and academia. A numerical analysis of the improvement caused by the cooling of a high voltage transformer using nanofluids has been done. A tank with a temperature source inside and a charge of mineral oil-barium titanate nanoparticles is used to study natural-convection. This study investigates the effects of variables on the thermal efficiency of the tank, including the thermal Rayleigh number and volume fraction. The results show that quenching varies with low and high Rayleigh thermal numbers and depends on the volume percentage of used nanoparticles. The effects were illustrated in thermal transfer rate representations as functions of the thermal Rayleigh number (Rat = 103 and 106) and the solid volume particle from the nanoparticles (0% = f < 10%). The findings showed that improving the solid volume particle of the nanoparticles by 10% causes the fluid being utilized to become more effectively conductive, which improves the rate of heat transfer by roughly 10% when compared to the case of the base fluid. [ABSTRACT FROM AUTHOR]

The electrical protection of power networks with fault contribution from inverter-based power sources imposes new application challenges that have to be dealt with by protection engineers. This paper describes the development of a study case model of an HVDC-MMC link for testing the protection behaviour of connected converter transformers. The paper summarises the implementation and validation of the converter control as well as enhancements to provide Fault Ride-Through capability and fast fault current injection as required by the German Technical Connection Rules for HVDC. The grid code standard requires positive- and negative-sequence reactive current injection in the case of grid faults. A Doubled Decoupled Synchronous Reference Frame Phase Locked Loop (DDSRF-PLL) for Vector Current Control (VCC) is implemented. Additionally, a Fault Detection and Fault Ride-Through Reference Generator with a Current Limitation strategy is introduced. Though these techniques are well described in the literature, the DDSRF is improved for current control stability. The relationship between the parameters of the PLL and the control, as well as the behaviour of the protection system, are demonstrated. Grid faults with large voltage dips pose a significant challenge to the stability of the control system. Nevertheless, it is shown that with the developed model, it is possible to make general statements about the protection behaviour in an inverter-based environment. [ABSTRACT FROM AUTHOR]

The mud pump water sealing system (MPWSS) is important in the efficient operation and prolonged service life of the cutter suction dredger's (CSD) mud pump. Considering that the underwater pump operates underwater and the shaft seal water pressure sensor is prone to failure, a hybrid deep learning model MCNN transformer is proposed to predict the underwater pump shaft seal water pressure in the event of sensor failure. This paper uses big data from the dredging project to deeply excavate the relationship between the shaft end sealing water pressure and other construction data by combining experience and artificial intelligence, and then uses multi-scale convolutional neural network (MCNN) to reconstruct the data, highlighting the time series characteristics of the multi-scale data were then input into the transformer model for prediction, and compared with a single MCNN, transformer model and four other neural networks. Finally, the cutter suction dredger "Hua An Long" was selected as an application research case; experimental comparisons were conducted on seven different models to verify the accuracy and applicability of the MCNN-transformer model. [ABSTRACT FROM AUTHOR]

Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,...), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control. [ABSTRACT FROM AUTHOR]

Few-shot semantic segmentation (FSS) is a challenging task that aims to segment new classes in query images with a few annotated support samples. One inherent challenge in FSS is the intra-class variation resulting from the limited availability of support samples and the diversity of query data. Current methods frequently employ prototype-support techniques to tackle this issue. However, it is important to note that a single support prototype shares limited commonalities with query features, and increases the difficulty of accurate segmentation. In this paper, we propose a lightweight and effective framework named Siamese Transformer (SiaT) with a mere 0.68M learnable parameters to enhance commonalities between prototypes and query features. The SiaT framework consists of two key modules: the Siamese Transformer Module (STM) and the Query Activation Module (QAM). The STM integrates two shared Transformer decoders for information propagation to generate two enhanced prototypes. One Transformer decoder facilitates the propagation of target-related information from the query to the original support prototype, while the other propagates foreground information from the support to the initial query prototype. The QAM utilizes the two enhanced prototypes from the STM as support information, engaging with query features through channel-wise allocation and concatenation, termed query activation. Moreover, SiaT showcases competitive performance on the widely used benchmarks PASCAL- 5 i and COCO- 20 i , which demonstrates its effectiveness in addressing the intra-class variation challenge within FSS tasks. [ABSTRACT FROM AUTHOR]

The Ms 6.8 Luding earthquake struck Sichuan province in China on September 5, 2022, triggering many failures of electrical installations in power grids. All high-voltage bushings of transformers in a 500 kV substation were damaged during the earthquake. Subsequent field investigation of the transformers was carried out, and a simulation model was developed to study the failure mechanism using the acceleration time histories recorded by a seismograph station near the substation. An equivalent modeling technique in the simulation model for the rotational stiffness of the flange joint per relevant code was proposed. Both the modal analysis and dynamic amplification analysis were then performed. After that, two types of improved flange configurations were suggested for newly designed and existing transformer bushings, respectively. Their effectiveness on stress optimization was validated. The results reveal that the first eigenfrequency of the transformer is close to the highest spectral peak of the recorded acceleration time histories, indicating obvious amplification effects. The stress distributions of the flange of the simulation model well match with the engineering failure, and the hitch lugs of the connection flanges are the leading cause that results in the cracks. Adopting the flange with stiffeners and adding axial curved shells and new hitch lugs between the flange plates can enhance the bending stiffness of the HV bushing and dramatically reduce the stresses of the flange at the positions where the engineering cracks propagated. Moreover, some instructive suggestions were proposed for assisting the seismic design and retrofitting of transformers. [ABSTRACT FROM AUTHOR]

The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified. [ABSTRACT FROM AUTHOR]

Aiming at the problems of unbalanced inter-modal optimization and inadequate fusion of multimodal features in multimodal sentiment analysis, a multimodal sentiment analysis model combining dynamic gradient mechanism and multi-view co-attention mechanism (DG-MCM) is proposed, which can effectively mine single-modal representation and fully integrate multimodal information. Firstly, the model uses pre-trained model BERT (bidirectional encoder representation from transformers) and stacked long short- term memory (SLSTM) to learn the features of text, audio and video, and proposes a dynamic gradient mechanism. By monitoring the contribution difference and learning speed of each mode, the feature learning of each mode is assisted. Secondly, the features of different modes obtained are fused using the multi-view co-attention mechanism. By projecting every two modes into multiple spaces for interaction, more adequate fusion features are obtained. Finally, fusion features and single-modal features are spliced together for sentiment prediction. Experimental results on CMU-MOSI and CMU-MOSEI datasets show that this model can fully learn information between single mode and different modes, and effectively improve the ac- curacy of multimodal sentiment analysis. [ABSTRACT FROM AUTHOR]