Output Load Research Articles

THE RESONANT AMPLIFIER OF THE ACTIVE ELECTRICAL POWER WITH ADDITIONAL VOLTAGE SOURCE. SUGGESTIONS, ANALYSIS, NUMERICAL ESTIMATES

The problem of increasing energy saving in industry leads to the need to develop new technical devices. The paper presents and analyzes the circuit of the resonance amplifier of active electric power, evaluates the characteristics of electromagnetic processes for the fundamental substantiation of the operation of real devices based on this circuit. Mathematical analysis of electromagnetic processes taking place was carried out using strict methods of the theory of electric circuits. The advantages of this scheme in comparison with similar previous proposals are shown. The device includes four active-reactive circuits that are inductively connected. The first of them is an input circuit with a harmonic signal source that needs to be amplified. The second circuit generates enhanced reactive power in the "voltage resonance" mode. The third circuit with an additional voltage source provides reactive power output from the second circuit in the "resonance of currents" mode without adverse effects on amplification processes. The fourth circuit, inductively connected to the third circuit, contains an output load - a resistor that simulates the allocation of active power. On the basis of the analysis and numerical evaluation of the characteristics of the processes taking place in the proposed scheme of the resonance amplifier of active electric power, its basic efficiency is shown. The calculations of the experimental model made it possible to formulate recommendations for the selection of circuit elements of a working resonant amplifier with a high efficiency for low-impedance output loads. The considered real parameters of the device make it possible to minimize the dissipation of the energy of converted electrical signals and to increase the amplification factor of the electric power of harmonic currents and voltage by choosing the element base. The obtained results can be considered as practical recommendations for the creation of real devices for amplifying active electric power for use in various industries.

Lyapunov-Based Control Strategy for a Single-Input Dual-Output Three-Level DC/DC Converter

This paper proposes a robust control strategy using direct Lyapunov strategy (DLS) for a single-input dual-output three-level dc-dc converter (SIDO-TLC) while the output loads and voltage references are disturbed. The first proceeding is to concurrently focus on the current and voltage dynamics of the converter buck and boost segments for exerting the related variable errors to the DLS-based function. Accordingly, through a comprehensive analysis based on the outcome of this proceeding, the PI controller coefficients allotted to the current reference design process are enabled to reach their maximum robustness capabilities against any output voltage variation. Moreover, the converter reference current tracking-based assessment leads to several smooth hyperbolic curves with their unique geometric properties. Consequently, the alteration trend of these curves facilitates the Lyapunov coefficients to be adjusted commensurate with the dynamic operation of the converter while converging to zero steady-state errors. Using a TMS320F28335 digital signal processor (DSP), disparate experimental results are conducted on the SIDO-TLC to verify the accurate and robust performance of the proposed control strategy.

A Nonlinear Load Current Feedforward Strategy for the Charge-Controlled LLC Converter and Its Digital Implementation to Improve the Dynamic Response

Compared with the traditional single voltage loop control strategy in LLC converters, the charge-controlled LLC converter can significantly improve its dynamic response due to its model having the first-order feature. Generally, a load current feedforward method could improve the converter's dynamic response. However, for the LLC converter, it is almost impractical due to the unclear relationship between the load current and the control variable. To build this relationship, a mathematical expression between the load current change and the control variable is derived that can precisely offset the load disturbance and achieve faster dynamic response. The derived relationship is nonlinear, related to both input and output voltages and switching frequency, and is suitable for digital implementation. To further analyze the dynamic response, the output impedances with and without the load current feedforward control are established and analyzed, which visually demonstrates the dynamic improvement in the time domain through the calculation of the output impedance and load change spectrum in the frequency domain. Finally, a 120W LLC prototype with nominal 48V input and 12V output is built to verify the nonlinear load current feedforward control and its analysis.

Short-term power load comparison based on time series and neural networks considering multiple features

In order to achieve a certain balance between power supply and production demand, as well as to ensure the operation of social machinery and multiple markets, accurate power load forecasting is indispensable. A load prediction method based on the Convolutional Neural Network (CNN) - the Long Short-Term Memory (LSTM) and the CNN-gated Recurrent Unit (GRU) is proposed. The multi-feature load forecasting is constructed by the date factor, the weather factor, the load factor and the electricity price factor. The advantage of CNN in feature extraction of the data set is used to establish a high-dimensional relationship with load, optimize the input LSTM and GRU network model, train each group of the neural network model, and output load prediction value. Combining the load data of a certain area with its meteorological factors, the CNN-LSTM and CNN-GRU methods are tested and compared with the single-network models of Back Propagation (BP), LSTM and GRU. By comparing it with other popular algorithms, it is proved that the presented model has superior computational efficiency and prediction accuracy.

A W-Band Transmitter for Automotive Radar Sensors in 28-nm FD-SOI CMOS

This article presents a 77-GHz transmitter (TX) in a 28-nm fully depleted silicon-on-insulator CMOS technology for automotive radar applications. The circuit has been designed to simultaneously fulfill both short-/medium-range (S/MR) and long-range (LR) operations. It exploits an LO frequency doubling architecture, which allows preventing voltage-controlled oscillator (VCO) pulling effects for improved phase noise (PN) performance. The proposed TX comprises a 38-GHz VCO driving a frequency doubler and then a current-combining two-path power amplifier. The VCO takes advantage of a novel tank architecture to cope S/MR and LR requirements with an effective and silicon area-saving solution. The proposed TX also features a feedback loop that allows properly setting the power delivered to the output load. It exhibits a 5-GHz bandwidth, ranging from 76 to 81 GHz, with a PN performance as low as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 93 dBc/Hz at a 1-MHz offset frequency from a 77-GHz carrier. It is also able to deliver a maximum output power as high as 17.5 dBm with a dynamic control range of around 13 dB. The overall power consumption is 351 mW.

A Fast Transient Adaptive On-Time Controlled BUCK Converter with Dual Modulation.

This paper proposed a fully integrated adaptive on-time (AOT) controlled buck converter with fast load transient. An adaptive on-time generator is presented to stabilize the output frequency. To enhance the light load efficiency, the converter could transfer from the pulse width modulation (PWM) to pulse skip modulation (PSM) as the load current decreases. The buck converter can switch between these two modulation modes adaptively with the assistance of a zero current detection circuit. Implemented in the TSMC 0.18 µm BCD (BiCMOS/DMOS) process, the proposed buck converter works with an input voltage ranging from 5.5 to 15 V, an output voltage ranging from 0.5 to 5 V, and an output load ranging up to 5 A. The experimental results show that based on the dual modulation adaptive on-time controlled mode, the transient recovery time from light to heavy load and from heavy load to light load is 13 µs and 15 µs, respectively. An overshot voltage of 57 mV and an undershot voltage of 53 mV are also achieved.

A full-wave HTS flux pump using a feedback control system

Transformer-rectifier flux pumps are DC superconducting power supplies capable of charging superconducting magnets to high currents and stored magnetic energies. Here, we demonstrate a full-wave superconducting flux pump assembled from high-temperature superconducting (HTS) wire that utilizes superconducting switches controlled by applied magnetic field. A negative DC offset occurs in the superconducting secondary of the circuit during operation which is related to the output load current. A feedback control system is proposed and demonstrated to account for the negative DC offset. Increasing the primary current proportional to the load current during operation allowed for the maximum output of the flux pump to be increased from 35 A to more than 275 A. These results are reproduced using a coupled electrical- and magnetic–circuit model formulated in the MATLAB Simulink® package.

A technical feasibility study of a liquid carbon dioxide energy storage system: Integrated component design and off-design performance analysis

Liquid carbon dioxide (CO2) energy storage (LCES) system is emerging as a promising solution for high energy storage density and smooth power fluctuations. This paper investigates the design and off-design performances of a LCES system under different operation strategies to reveal the coupling matching regulation mechanism of the charging and discharging processes. An off-design performance prediction model is developed based on preliminary designs of turbomachinery components and heat exchangers to evaluate the feasibility of the LCES system. The performances of the charging and discharging processes are analyzed under different load levels with two operation strategies: constant pressure charging and constant pressure discharging (CP-CP) operation strategy, and constant pressure charging and sliding pressure discharging (CP-SP) operation strategy. Results show that the LCES system has a round trip efficiency of 61.83% and an energy storage density of 21.92 kW·h·m−3 under the rated condition. As the input load level increases from 80% to 120%, the maximum charging time of the system decreases from 5.15 h to 3.36 h under the constant pressure operation strategy. When the output load level increases from 70% to 120%, the maximum discharging time of the system decreases from 5.48 h to 3.31 h under the constant pressure operation strategy and from 5.60 h to 3.30 h under the sliding pressure operation strategy. Besides, the round trip efficiency of the LCES system increases first and then decreases as both input and output load levels rise, while the energy storage density of the system follows a parabolic curve only with an increase in the output load levels. For better system performance, the CP-SP operation strategy and CP-CP operation strategy are more suitable for the system with output load levels of 70–100% and 100–120%, respectively. These achievements will provide a valuable reference for the stable operation of the LCES under off-design conditions.

Spatial and temporal distribution characteristics and prediction analysis of nitrogen and phosphorus surface source pollution in Shandong Province under the climate and land use changes

The aim of this study was to explore the characteristics of non-point source pollution of nitrogen (N) and phosphorus (P) under the background of climate and land use in Shandong Province. First, using the InVEST NDR module in the model, N and P non-point source pollution in 2010 and 2020 in Shandong Province were simulated; then, based on precipitation data under three different global climate models (MRI-ESM-0, GFDL-ESM4, and Ec-Earth3) and two Shared Socioeconomic Pathways (SSP245 and SSP585), land use data under two Shared Socioeconomic Pathways (SSP245 and SSP585) were used to simulate and predict the non-point source pollution of N and P in Shandong Province in 2030. The results showed the following: (1) On the time scale, the output load and total output of N and P decreased during 2010–2020, while the output load and total output of N and P increased during 2020–2030. (2) On the spatial scale, the spatial distribution of N and P output loads in 2010, 2020, and 2030 is roughly the same, being “low in the northwest and high in the southeast”. (3) Different climate scenarios have a great influence on N and P output load and total output, and the N and P pollution in the SSP585 scenario is more serious. The total output of N and P did not change much in different climate models, while the spatial distribution of the output load of N and P varied significantly, indicating that different climate models had a greater impact on the spatial distribution of the output load of N and P. (4) The overall cold hot spot pattern of nitrogen and phosphorus pollution in Shandong Province is stable, basically showing a “band + cluster + scatter” distribution pattern; the hot spot area in the central and southern region of Shandong Province changes little regardless of the model, the northwest is basically a cold spot area, and the nitrogen and phosphorus hot spot area under the SSP245 scenario in Ec-Earth3 model had the least amount of change. According to research results, combined with the actual situation of Shandong Province, it is hoped that it can provide theoretical basis for the prevention and control of non-point source pollution in Shandong Province in the future.

H‐infinity loop shaping control of wireless power transfer system based on generalized state space averaging model

SummaryThe transmission distance and the output load of wireless power transfer (WPT) system are particularly prone to change, which results in higher requirements for the robustness and dynamic performance of the control. In order to reduce the influence of these characteristics, an H∞ loop shaping controller based on generalized state space averaging (GSSA) method is proposed in this paper. Based on the GSSA model, an appropriate compensation function is constructed for the WPT system to the target loop transfer function. The nominal coprime factorization of WPT system is carried out, and on this basis, H∞ controller is achieved by Riccati equation to minimize the infinite norm from disturbance to error output. The designed controller meets the requirements of robust. Furthermore, the obtained high‐order controller is discretized by a reasonable discretization method, and the details and limitations of its practical application are discussed based on the hardware computational capacity. The experimental results show that the proposed controller has better robustness and dynamic performance and flexibility than the mixed sensitivity H∞ controller and the Z‐N PI controller.

In-situ retention of nitrogen, phosphorus in agricultural drainage and soil nutrients by biochar at different temperatures and the effects on soil microbial response

This study conducted a two-year experiment to investigate the impacts of biochar with various temperatures (350 °C, 500 °C, and 650 °C), on the reduction of pollutants in agricultural runoff and the enhancement of soil fertility. The results showed that the biochar significantly reduced the concentrations of total nitrogen and total phosphorus in farmland runoff. Moreover, higher-temperature biochar demonstrated greater efficacy in decreasing pollutants in farmland drainage. Treatment with RB650 resulted in a reduction of the total nitrogen and total phosphorus output load by 29.31–30.67 % and 21.92–25.21 %, respectively, compared to RB350. Furthermore, biochar exhibited substantial enhancements in soil fertility. This was supported by heightened soil organic matter content, increased availability of nutrients, and a noteworthy (P < 0.05) upsurge in pH, organic matter, total nitrogen, and total phosphorus content observed in the second year following the application of biochar. Biochar has the potential to enhance soil enzyme activity and affect microbial community composition, thereby facilitating nutrient cycling. The findings illustrated the regenerative and recyclable characteristics of biochar's adsorption activity throughout crop growth. This process enables sustained improvement in soil nutrient retention capacity and fertility. Thus, it emphasizes the potential of biochar as an in-situ model for nutrient retention and recycling, offering an effective approach to mitigate agricultural non-point source (NPS) pollution and enhance soil fertility.

A Low Power Low Inrush Current LDO with Different Techniques for PSR and Stability Improvement

One of the most popular power management regulators is the low drop-out voltage regulator (LDO). LDOs have different specifications such as the power supply rejection (PSR) over different frequencies, stability over different load ranges, inrush current spike flows through the input supply, and power consumption. In this work, we present a low power low inrush current LDO design with different techniques for PSR and stability improvement across different frequencies. The LDO presented in this work is a low-power and small area LDO but achieves a high PSR over a wide range of frequencies. The LDO is designed in 65 nm CMOS technology and achieves a PSR better than 80 dB up to 30 MHz for an output load current of 25 mA using an output load capacitor of 4 µF. The design can be used in capless/capped LDOs with wide load current ranges as high as 200 mA and load capacitor range from 1 nF to 12 µF with inrush current improvement by more than 2×. The presented LDO consumes a zero-load quiescent current of 10 µA and its area of 180 µm × 180 µm.

Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy

Rational design and advanced energy management considering multiple uncertainties are imperative for the superior integrated energy system (IES). This work proposed a novel two-layer stochastic multiple scenario optimization framework for the collaborative optimization of capacity and operation of IES. To improve the accuracy of probability density estimation, the improved kernel density estimation (KDE) was employed to obtain the probability density distributions of wind speed, sunlight and multi-demands. Then the scenario sets were generated by Latin hypercube sampling (LHS) simulation and self-organization map (SOM) clustering. To decouple the output, efficiency and part load factor of devices during operation, the following optimal contribution rate (FOCR) strategy was proposed, which could actively adjust the output ratio of energy conversion devices to realize the flexible energy supply. The developed optimization methodology was used for a case study in an office building. The results indicate that the improved KDE for probability distribution estimation of uncertainties achieves the accuracy percentage enhancement, for the average value, of 37.1% and 49.6% for root-mean-square error (RMSE), respectively, compared with the conventional KDE and parametric model. Considering energy saving, economy and environmental indicators, the performance of the FOCR strategy is superior to those of the three traditional strategies.

Optimal Extreme Random Forest Ensemble for Active Distribution Network Forecasting-Aided State Estimation Based on Maximum Average Energy Concentration VMD State Decomposition

As the penetration rate of distributed generators (DG) in active distribution networks (ADNs) gradually increases, it is necessary to accurately estimate the operating state of the ADNs to ensure their safe and stable operation. However, the high randomness and volatility of distributed generator output and active loads have increased the difficulty of state estimation. To solve this problem, a method is proposed for forecasting-aided state estimation (FASE) in ADNs, which integrates the optimal extreme random forest based on the maximum average energy concentration (MAEC) and variable mode decomposition (VMD) of states. Firstly, a parameter optimization model based on MAEC is constructed to decompose the state variables of the ADNs into a set of intrinsic mode components using VMD. Then, strongly correlated weather and date features in ADNs state prediction are selected using the multivariate rapid maximum information coefficient (RapidMIC) based on Schmidt orthogonal decomposition. Finally, by combining the set of intrinsic mode functions of the ADNs state, calendar rules, and weather features, an ensemble FASE method based on the extreme random tree (ERT) ensemble for the ADNs based on cubature particle filtering (CPF) is developed. An optimization model based on mean absolute error and root mean square error is established to obtain the optimal integration strategy and final estimation results. Simulation verification is performed on the IEEE 118-bus standard distribution system. The results show that the proposed method achieves higher accuracy compared to other estimation methods, with root mean square errors of 1.4902 × 10−4 for voltage magnitude and 4.8915 × 10−3 for phase angle.

Forecasting Model of Electricity Sales Market Indicators With Distributed New Energy Access

It is difficult for the existing electricity sales market to adapt to the vast amount of distributed new energy access. This article proposes an electricity sales market index prediction model for high proportion distributed new energy access under the cloud-side cooperation architecture. First, an index prediction system is designed based on the cloud edge collaboration architecture. The edge computing center processes regional data nearby to improve prediction efficiency. Second, on the edge side, a K-means clustering algorithm is used to classify the data. Third, the power data, distributed power output data, load data, weather data, holiday information, and electricity price data are obtained. Finally, the ConvLSTM-Adaboost prediction model is built in the cloud center. The ConvLSTM is used as the base learner, and the Adaboost-integrated algorithm is used for serial training. At the same time, the prediction results of each base learner are weighted and integrated to obtain the final power and load prediction results of the electricity sales market. Experiments show that the prediction results of MAE, PMSE, and MAPE of the proposed model for daily electricity are 52.539MW, 56.859MW, and 2.063%, respectively. Not only is this superior to other models, but it provides a better analysis of influencing factors.

High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles

Rooftop photovoltaics (PV) are playing an increasingly important role in building a clean and decarbonized energy system. For such distributed resources, formulating scientific development plans and incentives tailored to local conditions requires a comprehensive potential assessment at high spatial and temporal resolutions. Here, we evaluate the resource volume, power generation potential, economic feasibility, and market returns on electricity sales of rooftop PV in Jiangsu Province, China at hourly and 500-m resolutions by combining remote sensing survey, PV output simulation and load dispatching. The province's annual rooftop PV generation meets approximately 30% of the total social electricity consumption, and the entire region has reached both plant-side and user-side grid parity. Based on the case study, we investigate the suitable development scale of rooftop PV subject to different owners, as well as the impact of grid's system flexibility and energy storage on rooftop PV curtailment. For household use, the installation of a 3-kW rooftop PV is suitable, while for grid power supply, rooftop PV development needs to be sized to accommodate the grid. We find that increasing system flexibility significantly reduces PV curtailment for the same penetration rate, and that the use of energy storage helps increasing PV penetration under the curtailment constraint. This study represents a relatively extreme case of high penetration of variable solar PV generations, high uncertainty of PV outputs, and high variability in user-side loads, providing a reliable reference for rooftop PV development in other China's provinces.

Multi-Time Interval Dynamic Optimization Model of New Energy Output Based on Multi-Energy Storage Coordination

In response to the problem of mismatch between new energy output and multi-energy load requirement in multi-energy power systems, this article proposes a dynamic optimization model for new energy output in multiple time intervals based on multi-energy storage coordination. First, considering the energy conversion characteristics of multi-energy storage, the dynamic optimization method of new energy output based on the discrete division of subinterval of scheduling time is studied. Then, considering the cost of adjusting various resources comprehensively, the optimization objective of new energy output is studied, and a model-solving method based on a directed graph topology distributed algorithm is proposed. Finally, simulation verification was conducted, and the simulation results showed that the method proposed in this paper can effectively suppress the new energy fluctuation and reduce peak-shaving costs.

A 180 nA Quiescent Current Digital Control Dual-Mode Buck Converter With a Pulse-Skipping Load Detector for Long-Range Applications

A digitally controlled dual-mode buck converter using low quiescent current is proposed in this work. In particular, the proposed dual pulse-skipping mode adopts a pulse-skipping adaptive on-time (PSAOT) mode for heavy load; and a pulse-skipping asynchronous mode (PSAM) for light and ultra-light loads to achieve a 100,000X wide load range for the Long-range (LoRa) application. Specifically, the pulse-skipping mode selector adopts a digital logic circuit to realize the 180nA low quiescent current. In addition, a low-power dynamic comparator with an adjustable offset is used to accelerate the transient response. The proposed converter is fabricated in a TSMC 350 nm CMOS process and results in an output voltage of 3.3 V from 5V to 4.2 V input voltage range. These results show that the overall system achieves 92.9 % peak eficiency at 8 mA output load and a figure of merit (FoM) of 2.29.

Hybrid physical and data driven modeling for dynamic operation characteristic simulation of wind turbine

Accurate simulation of wind turbines is the key to achieve efficient control of wind turbines and optimal scheduling of wind power systems. Most of the existing methods are physical simulation models, which have heavy computational burden, especially for large-scale wind turbines, and the simulation accuracy is poor under complex dynamic operating conditions. In response, this paper proposes a hybrid physical and data driven model for dynamic operation characteristic simulation of wind turbines. Firstly, a database of dynamic characteristics of wind turbine operation under various wind conditions is constructed, and then the sequence learning (Seq2Seq) model is used to mine the time series characteristics of wind turbine operation parameters. Combining with long short-term memory (LSTM) network, the mapping relationship between various operating parameter time series to establish. On this basis, an attention mechanism is added to the model to learn the dynamic response characteristics of rotor inertia to natural wind process. Through numerical example analysis, the method proposed in this paper can accurately excavate and learn the time sequence dynamic characteristics of wind turbine operating conditions and parameters under various wind conditions, thereby improving the simulation accuracy and calculation efficiency of wind turbine output power and load dynamic characteristics. Taking the actual wind turbine operation data as an example, the simulation accuracy of wind turbine output power reaches 99.8%. Compared with traditional methods, and the simulation accuracy of tower root load reaches 88.5%.

An efficient energy management framework for residential communities based on demand pattern clustering

Intelligently managing energy production and consumption on community basis can enhance the demand side energy efficiency. The fact that the number of energy resources (especially controllable appliances) could be large imposes non-trivial computational burden to community-scale energy management. This paper proposes a community energy management framework that coordinately manages the operations of different kinds of energy resources in a community (i.e., photovoltaic solar power sources, battery energy storage systems (BESSs), and controllable appliances from different households) to minimize the community’s energy cost while sufficiently considering the peak-to-average ratio (PAR) of the community’s load and the occupants’ satisfaction. The proposed framework is with a two-stage design: in the day-ahead stage, multiple “virtual appliances” are formed, where each virtual appliance represents a group of appliances that have similar operational patterns. Based on this, a day-ahead scheduling model is formulated to determine the optimal operation plans of the virtual appliances and other energy resources. The virtual appliances’ schedules are then mapped to actual individual appliances through the developed mapping algorithms. In the real-time operation stage, a receding horizon-based energy management model is developed for correcting the BESS’s operation subjected to the day-ahead plan and real-time solar power output and uncontrollable load. Extensive simulation is conducted to validate the proposed framework based on a real-world dataset.