Power Change Rate Research Articles

A multi-task spatio-temporal fusion network for offshore wind power ramp events forecasting

With the accelerated development of offshore wind farms, the impact of Wind Power Ramp Events (WPRE) on power systems has become more pronounced. Accurate prediction of WPRE is essential for mitigating their adverse effects on the grid. However, current studies on offshore WPRE are limited, especially regarding their spatio-temporal correlations and variability across large wind farm clusters. To address this, a novel forecasting approach using a multi-task spatio-temporal fusion network is presented. This method improves WPRE prediction by integrating spatial and temporal data and utilizing multitask learning to characterize ramp features. Firstly, an index system for WPRE characterization is developed, including metrics such as power change rate and duration. Based on this, an X-means classification method for WPRE is established. Secondly, a spatio-temporal encoder combining graph convolutional networks and temporal attention mechanisms has been proposed to model the spatiotemporal dependencies of meteorological changes in offshore wind farms. Finally, a multitask learning framework has been introduced to predict WPRE characteristic indices and occurrence probabilities. This approach enhances feature extraction efficiency through the joint feedback of multiple related tasks, thereby improving prediction accuracy. A case study using data from five offshore wind farms in Eastern China validates the efficacy of the proposed method.

Online energy management strategy for ammonia-hydrogen hybrid electric vehicles harnessing deep reinforcement learning

Powertrain electrification and fuel decarbonization play pivotal roles in the pursuit of carbon peak and carbon neutrality within the realm of transportation. Ammonia and hydrogen are essential carbon-neutral fuels suited for deployment in heavy-duty, long-haul transportation applications. The technological complementarity of ammonia and hydrogen can be realized in the current context by combining various energy conversion devices. Energy management strategy (EMS) is one of the critical technologies in hybrid systems. In this study, a novel EMS based on deep reinforcement learning (DRL) is proposed for a heavy-duty automotive hybrid system containing an ammonia-hydrogen internal combustion engine (ICE), a fuel cell system (FCS), an ammonia electrolysis cell (AEC), and Li-ion batteries (LB). To address the protracted training times and convergence challenges intrinsic to DRL, a fuzzy logic control (FLC) is introduced to provide expert demonstrations during the DRL agent's learning process. The incorporation of FLC not only facilitates the convergence speed of the DRL algorithm, but also strikes a good balance between the operation efficiency improvement and battery SOC maintenance. The simulation results show that the proposed EMS improves average efficiency by 2 % with a modest 0.75 % SOC reduction across four operational scenarios compared to the FLC-based EMS. After thorough validation of the proposed EMS, a parametric study is conducted to examine three key parameters: charge mode threshold, FCS power change rate limit, and hybrid mode threshold. The study aims to offer valuable insights for formulating the EMS of the ammonia-hydrogen hybrid system.

Power Advance Prediction to Improve the Energy Utilization Efficiency of Motor-Driven System Considering Multilink Time-Delay

In the motor-driven system, the utilization of a supercapacitor energy storage unit contributes to energy saving. However, the multi-link time-delay in the system makes the supercapacitor unable to immediately track and respond to the sharp change of motor power. This will cause the large DC bus voltage fluctuation, the peak current at the power grid side, and unnecessary energy loss. In this paper, a power advance prediction control strategy considering multi-link time-delay is proposed. The multi-link time-delay mechanism in the system is analyzed, in particular for the different conditions of motor power change. Afterward, with the motor speed, torque, and the inverter acceleration/deceleration rate obtained in advance, the motor steady power and power change rate are predicted. In the dynamic process, by adjusting the tracking rate in real time, the motor reference power function is derived. The reference current of the supercapacitor is given online, so as to match the power between the motor and the energy storage unit. Both simulation and experimental results verify the rationality and validity of the proposed control strategy. In the dynamic process, the system stability and energy utilization are successfully improved in comparison with multi-parameter collaborative power prediction control and double closed-loop control.

Data-driven method for estimating emission factors of multiple pollutants from excavators based on portable emission measurement system and online driving characteristic identification

This study aims to explore the factors that influence the emission characteristics of multiple pollutants from non-road mobile machinery (NRMM) under real-world conditions and to establish a data-driven method for calculating accurate emission factors. This research focused on NRMM excavators meeting the third-stage emission standards and identified the actual work characteristics of 108 excavators in different scenarios based on a self-developed testing system for 368,000 h. Additionally, a portable emission testing system (PEMS) was used to study the instantaneous emission characteristics under different driving styles and modes for 10 EC210 excavators with the largest engineering construction inventory. The results showed that the average time proportions of idling, working, and moving modes for excavators were 21 %, 66 %, and 13 %, respectively. The results also revealed that the instantaneous emission rates of multiple pollutants varied significantly under different driving styles and modes. Driving style affected the hydraulic pump power change rate through hydraulic pilot pressure, and engine load surge caused turbocharger response delay and in-cylinder combustion deterioration, which affected pollutant emissions. Driving mode affected the emission characteristics of idling, high-speed idling, moving, and working modes of excavators through the external characteristics corresponding to the engine speed gear set. The data-driven method for calculating emission factors differed from the traditional method for most indicators to varying degrees. In terms of fuel-based emission factors (EFfs), except for the EFfNOx indicator, which was 7.859 % higher than the traditional method, the other three indicators were significantly lower than the traditional method. In terms of power-based emission factors (EFps), except for EFpPM and EFpPN, the other two indicators were much higher than the traditional method. EFpCO and EFpNOx were 7.93 % and 20.332 % higher than the traditional method, respectively. It is recommended to use the data-driven method based on the actual driving data distribution to provide scientific support for accurately establishing the emission inventory.

The Synergistic Effect between Precipitation and Temperature for the NDVI in Northern China from 2000 to 2018

Based on monthly precipitation (P), temperature (T) data, and remote sensing images collected from March 2000 to February 2019, this article was constructed to reveal the synergistic effect between P and T for the NDVI in northern China qualitatively and quantitatively by using a one-variable linear regression, the coefficient of variation, multivariate correlation coefficients, and a geodetector. The results show that the NDVI in the study area decreased from 2000 to 2012, increased from 2013 to 2018, decreased in the west, and increased in the east of Northern China. Overall, the NDVI, P, and the average maximum temperature (Tmax) had the strongest multivariate correlations (approximately 43.4% of the total study area passed the 95% confidence level significance test), followed by the average temperature (Tave) and average minimum temperature (Tmin). The explanatory power of the synergistic effect between P and Tmax for the NDVI was the strongest, with the value of explanatory power varying from 0.41 to 0.81, followed by Tave and Tmin. Spatially, the explanatory power of the synergistic effect between P and T for the NDVI was strengthened overall in the study area from northwest to southeast. The annual change rate of the explanatory power showed that the overall explanatory power between P and T for the NDVI in the study area was weakened in the central area and strengthened in the east and the west. Specifically, the synergistic effect between P and T on the NDVI was weakened in both Shaanxi and Ningxia Huizu Zizhiqu, while the opposite occurred in Xinjiang Uygul Zizhiqu, Qinghai, and another five provinces in the eastern part of the study area.

A design of ultra-short-term power prediction algorithm driven by wind turbine operation and maintenance data for LSTM-SA neural network

Due to factors such as meteorology and geography, the generated power of wind turbines fluctuates frequently. In this way, power changes should be predicted in grid connection to take control measures in time. In this paper, an operation and maintenance data-driven LSTM-SA (long short-term memory with self-attention) prediction algorithm is designed to predict the ultra-short-term power of wind turbines. First, the wind turbine operation and maintenance data, including wind speed, blade deflection angle, yaw angle, humidity, and temperature, are subjected to feature selection by using the Pearson correlation coefficient method and the Lasso algorithm, thereby establishing the correlation between wind speed, blade deflection angle, and out power. Then, full-connect neural network is trained to establish a mapping model of wind speed, blade deflection angle, and out power. The power change rate k is calculated by the derivative of output power to wind speed. Finally, based on the historical power data and the power change rate k, the LSTM neural network power prediction model is trained to calculate the output power prediction value. In order to increase the training efficiency and reduce the delay, the self-attention mechanism is used to optimize the hidden layer of the LSTM model. The test results show that, compared with similar prediction algorithms, this algorithm has higher prediction accuracy, faster convergence speed, and better stability, which can solve the problem of accurately predicting ultra-short-term power when wind power training data is inadequate.

Modeling of Ship DC Power Grid and Research on Secondary Control Strategy

Compared to alternating current (AC) grids, direct current (DC) grids are becoming more and more popular. A power distribution approach is suggested in order to solve the issue of uneven power distribution of distributed generation (DG) in a ship DC microgrid. Power control is carried out using a tracking differentiator (TD), while the output power change rate is not greater than the maximum power ramp rate permitted by the battery, and state-of-charge balance is attained quickly. The proposed strategy also reduces the communication pressure on the power grid. A distributed hierarchical control model of a DC microgrid based on a consensus algorithm is created in order to validate the suggested methodology. The simulation results demonstrate that the established model is capable of simulating the DC microgrid accurately, that the states of charge values of the five batteries gradually converge under the adjustment of the secondary strategy, and that the suggested strategy is reasonable and efficient.

Innovation in Power Maneuvering Mode for NPP Hanhikivi with WWER- 1200 reactor

The possible innovative methods of maneuvering are investigated on the example of NPP Hanhikivi with WWER-1200 reactor in a frame of AES-2006 project. Stationary fuel loading analysis was performed for the for the most significant graph of daily manoeuvring (100-50-100)% Nnom with the rate of power change 1-5 % Nnom per minute, that are the European Utility Requirements. The improvement is ensured by maintaining a constant average coolant temperature in the core "Tav = const" (by changing the pressure in SGs) and a constant boron content in the primary coolant "CB = const". The change of power and Xenon concentration during power manoeuvring is compensated by special movement of the special chosen grey CRs in the core instead of CB change. CB is changed in usual way only for fuel burnup compensation during reactor campaign. The mode "Tav = const" is normally used for the control of power of PWRs in wide diapason and it reduces the amount of radioactive primary water discharges and the mechanical fatigue of the RCS components. Implementation of both – main mode "Tav = const" and auxiliary mode "CB = const" leads to positive effect of synergy. The mode "Tav = const" facilitates the implementation of the mode "CB = const" very much, and they together allow to completely eliminate the production of liquid waste during maneuvering and ensure load following practically for the full length of reactor campaign. Presence of large CRs quantity (121 pieces) in the WWER-1200 allows using part of them as grey CRs without safety violation due to small decrease of EP efficiency. The efficiency of grey CRs is 2-3 times less than of usual black CRs that allows more softly maintain criticality, AO and power peaking factors in their acceptable diapasons at CB = const. Analysis is performed with Russian 3D code BIPR-7 only for neutronics aspects without considering strength cyclic characteristics of the equipment and nuclear fuel.

Research on the prospects of applying socio-demographic data for the analysis of demand management potential

Today, the most developed countries are rapidly increasing the volume of electricity generation based on renewable energy sources, which primarily includes an increase in the share of generation at solar and wind power plants. The increase in the share of their generation leads to problems with maintaining the balance of generation and load. The reason for this is the stochastic nature of the generation of solar and wind power plants. There are a significant number of possibilities to compensate for this problem: maintaining the balance at the expense of existing conventional power plants with a high rate of power change, installation of energy storage at generation sites, shutting down some generation capacity at renewable energy sources, selling or buying energy in neighbouring countries, and demand management. This article focuses on demand management, specifically on the use of the concept of prosumers for this purpose. As process participants in electric power systems, solicitors are also capable of functioning in today's conventional electric systems, provided they gain additional market operations capabilities, but the benefits of the Smart Grid greatly enhance their effectiveness. We considered the potential of attracting Smart Grid players to align load schedules and maintain the balance of active power in the power system. For this purpose, we considered methods and tools for analysing the modes of operation of conventional consumers and solenoid meters. However, to obtain real consumption schedules of a residential area or even an individual house is a difficult task, which requires a significant amount of coordination and at the same time provides information only about individual facilities. That is why the analysis of the modes of operation of requestors is based on the study of load schedules of ordinary consumers based on the software for generating load schedules, taking into account the socio-demographic characteristics of households and international experience in demand management.

Manual and software-based measurements of treatment zone parameters and characteristics in children with slow and fast axial elongation in orthokeratology.

To compare the treatment zone (TZ) measurements obtained using manual and software-based methods in orthokeratology (ortho-k) subjects and explore the TZ characteristics of children with slow and fast axial elongation after ortho-k. Data from 69 subjects (aged 7 to <13years old), who participated in three 24-month longitudinal orthokeratology studies, showing fast (>0.27mm, n=38) and slow (<0.09mm, n=31) axial elongation, were retrieved. The TZ after ortho-k was defined as the central flattened area enclosed by points with no refractive power change. TZ parameters, including decentration, size, width of the peripheral steepened zone (PSZ), central and peripheral refractive power changes and peripheral rate of power change, were determined manually and using python-based software. TZ parameters were compared between measurement methods and between groups. Almost all TZ parameters measured manually and with the aid of software were significantly different (p<0.05). Differences in decentration, size and the PSZ width were not clinically significant, but differences (0.45 to 0.92 D) in refractive power change in the PSZ were significant, although intraclass coefficients (0.95 to 0.98) indicated excellent agreement between methods. Significantly greater TZ decentration, smaller TZ size and greater inferior rate of power change (relative to the TZ centre) were observed in slow progressors using both methods, suggesting a potential role of TZ in regulating myopia progression in ortho-k. TZ measurements using manual and software-based methods differed significantly and cannot be used interchangeably. The combination of TZ decentration, TZ size and peripheral rate of power change may affect myopia control effect in ortho-k.

Model predictive and fuzzy logic controllers for reactor power control at Reaktor TRIGA PUSPATI

The Reaktor TRIGA PUSPATI (RTP) rely extensively on a core power control system for control reactivity to fulfilment the fundamental safety function for a nuclear reactor. It is technically challenging to operate within tight multiple parameter constraints and keep the core power output stable. At present, the power tracking performance of the system could be considered unsatisfactory which produces a relatively long settling time and high control effort. Hence, a study of a model predictive control (MPC) strategy by integrating the current Power Change Rate Constraint (PCRC) using fuzzy logic which is part of the core power control design is conducted. In this paper, the MPC design based on mathematical models of the reactor core included point kinetics model, thermal-hydraulic model, reactivity model and dynamic rod position model help to enhance core power control. The power tracking performance of the proposed control method and previous Feedback Control Algorithm-Fuzzy PCRC is compared via computer simulation with different power range operations. Overall, the results show the MPC-Fuzzy PCRC strategy provides a greater level of operational safety and optimum operation of the TRIGA reactor.

Co-optimization method of speed planning and energy management for fuel cell vehicles through signalized intersections

Network of vehicles technology can help vehicles obtain more traffic information during their journey. In this paper, a co-optimization method of speed planning and energy management is proposed for fuel cell vehicles (FCVs) while passing through multiple continuous signalized intersections. Based on the off-line dynamic programming (DP) algorithm and the signal phase and timing (SPaT), the constant-speed hydrogen consumption map and the weighted orientation graph of the FCV are established. Afterwards, a bi-level DP method is introduced to solve the speed planning problem online. In the meantime, a modified alternating direction method of multipliers (ADMM) algorithm, which includes a cyclic constraint inspection strategy to constrain the power change rate of fuel cell system (FCS), is used to solve the energy management problem. The simulation results demonstrate that the proposed method can generate an optimal speed reference trajectories for FCV through 4 and 8 signalized intersections in 3 s and 5 s respectively, which is practically implementable on real vehicles. Simulation results show that our proposed method is able to achieve similar fuel saving performance with off-line DP but a in real time framework, which is over 20% better than the improved intelligent driving model (IDM) with ADMM. • Co-optimization method of speed planning and energy management is investigated. • A bi-level DP method is presented to solve the speed planning problem. • The modified ADMM is applied to energy management system of fuel cell vehicles.

Analysis of EEG Characteristics of Drivers and Driving Safety in Undersea Tunnel.

To study the influence of the driving environment of an undersea tunnel on driver EEG (electroencephalography) characteristics and driving safety, a real vehicle experiment was performed in the Qingdao Jiaozhou Bay Tunnel. The experimental data of the drivers’ real vehicle experiment were collected using an illuminance meter, EEG instrument, video recorder and other experimental equipment. The undersea tunnel is divided into different areas, and the distribution law of driving environment characteristics, EEG characteristics and vehicle speed characteristics is analyzed. The correlations between the driving environment characteristics, EEG characteristics and vehicle speed characteristics model the variables that pass the correlation test. The driving safety evaluation model of an undersea tunnel is established, and the driving safety in different areas of the undersea tunnel is evaluated. The results show that there are obvious differences in illumination, EEG power change rate, vehicle speed and other variables in different areas of the undersea tunnel. The driving environment characteristics are highly correlated with the β wave power change rate. The driving safety of different areas of the undersea tunnel from high to low is: upslope area, downslope area, exit area and entrance area. The study will provide a theoretical basis for the safe operation of the undersea tunnel.

Assessment of the Electrical Grid System to Accommodate the First Nuclear Power Plant in Bangladesh

Nuclear energy has been identified as an economical and environmentally clean source of electricity generation in Bangladesh. Nuclear power plants (NPP) are designed to serve as the base-load plant with a lower ramp rate of power change, stringent voltage and frequency margin. Moreover, the nuclear reactor needs a reliable long-term shutdown cooling that consumes huge electricity from the grid under strict voltage and frequency regulations. This paper addresses a modeling approach including the development of a fault-tree model for reliability evaluation of an electric grid system to accommodate an NPP. The analysis of the existing grid system in Bangladesh identified the areas of improvement in which the programs for grid frequency control, modifying governors of the operating plants and reduction of seasonal load variation are the most significant. This study also revealed that the development of a detailed fault tree model is essential to identify the most susceptible elements of the grid system and resources can be allocated accordingly to minimize the risk. The hindrances related to grid development are also discussed in this paper.

An enhanced second-order-consensus-based distributed secondary frequency controller of virtual synchronous generators for isolated AC microgrids

Virtual synchronous generator (VSG) control is a promising control method in microgrid (MG) for the inertia support characteristic. However, the VSG inherits the oscillation feature of synchronous generator (SG), thus the active power and frequency oscillation may be observed when VSG control is applied. In MG, the oscillation may cause the unreasonable dynamic active power sharing among VSGs. Subsequently, the VSGs are prone to overload and even unstable, and the existing works based on consensus control cannot suppress the oscillation effectively. In order to tackle this problem, this paper proposes an enhanced second-order-consensus-based distributed secondary frequency controller by introducing the active power second-order consensus control, and reveals the oscillation suppression capacity of the second-order consensus algorithm in depth. Since there is no specific control requirement for the second-order variables, i.e., the change rate of active power and frequency of MG in steady state, the proposed controller can be simplified by replacing the integral control of second-order variables with the proportional control of first-order variables, i.e., active power and frequency, thus the communication data volume will not increase comparing to the traditional first-order-consensus-based controller. This paper also provides a small-signal model of MG with the proposed controller to evaluate the system dynamic performance and design the parameters. Finally, the simulation results verify the effectiveness of the proposed controller.

Effects of the Anger Management Program for Nurses

The purpose of this study was to examine the effects of an anger management program on anger, job stress, psychological well-being, and heart rate variability in clinical nurses. A quasi-experimental study was conducted using a nonequivalent control group, pre-post test design with repeated measures. The participants included 43 nurses assigned to the experimental and control groups. Anger, job stress, psychological well-being, and heart rate variability were evaluated before the intervention, immediately after the completion of the intervention, and four weeks after the end of the intervention. Chi-square test, t-test, Fisher's exact test, and GEE (Generalized Estimating Equations) were used to analyze the data. There were significant differences in the level of anger, state anger, job stress, and psychological well-being between the two groups. The rate of change in the total power (TP) and the high-frequency band (HF) of the experimental group increased immediately after the intervention completion, but that of the control group decreased at the same time. The above results demonstrate that an anger management program for nurses effectively attenuated anger and job stress, improved psychological well-being, and regulated heart rate variability.

Research on Emission Characteristics of Non-road Mobile Machinery Based on Power

This paper takes 3 construction machinery as the research object, uses the portable emission measurer (PEMS) to test the emission in the actual operation of construction machinery. And analyzes the relationship between the operating characteristics of construction machinery and the characteristics of NOx emission and CO emission. The research results show that the NOx emission of construction machinery is positively correlated with power, and the NOx emission change rate is positively correlated with the mechanical power change rate. There is no obvious correlation between the CO emissions of construction machinery and power, and the change rate of CO emissions is positively correlated with the rate of mechanical power change.

Method of Implanting Hardware Trojan Based on EHW in Part of Circuit

In view of the fact that the traditional Hardware Trojan (HT) implanting method will increase the number of logic gates, and the power consumption changes obviously, this paper proposes a method of implanting HT in part of circuit by using evolvable hardware (EHW) technology. Firstly, the hierarchical clustering method is used to decompose the large-scale circuit, and then the EHW technology is used to redesign the decomposed sub circuit to make it have HT function. Finally, the original sub circuit is replaced by the evolved circuit, so as to achieve the effect of implanting hidden HT in part of circuit. The simulation results show that compared with the direct implantation method, the power change rate of the method is reduced by 35%, and the concealment is better.

A multipronged core power control strategy for Reaktor TRIGA PUSPATI

At present, the power tracking performance of nuclear Reaktor TRIGA PUSPATI (RTP) is considered unsatisfactory performance due to relatively long settling time during transient and a chattering noise during steady-state power output. Application of the conventional Feedback Control Algorithm (FCA) as a power control technique is proven to be inadequate to keep the core power output stable and within tight multiple parameter constraints for the safety demand of the RTP. Hence, the present study proposed a multipronged core power control strategy improvement through manipulation of the current Control Rod Selection Algorithm (CRSA), Control Rod Speed Design (CRSD), and Power Change Rate Constraint (PCRC) which are part of the core power control design. In this paper, the profiling and analysis of the multipronged core power control strategy are presented. The model for core power control consists of mathematical models of the reactor core, FCA controller, and a series of multipronged models. The mathematical models of the reactor core are based on the point kinetics model, thermal-hydraulic model, and reactivity model. The reactor model is integrated with the FCA controller and a combination of CRSA-CRSD-PCRC models. The power tracking performance of the proposed control strategy and conventional FCA is compared via computer simulation. Overall, the results show the multipronged FCA offers a wider options for optimum operation of the TRIGA reactor.

Оценка влияния несимметричных режимов на показатели надежности распределительных систем

The power electric distribution systems (PEDS) possess a great dynamics of development. Thanks to this phenomenon in the power electric distribution systems (PEDS) the probability of apparatus of asymmetrical regimes increase monotonously. As a result of this reliability of the functioning of the power electric equipment installed in the electric knots changes. The asymmetrical regimes in the power electric distribution systems (PEDS) accompanied by the short circuit current are a function of a row determinate is a vague factor of probabilistic nature. Coming from it follows that the investigation of the influence of the asymmetrical regimes accompanied by the current of the short circuit on the reliability of the Power Electric Distribution Systems (PEDS) is one of the most important problems of the development the Power Electric Distribution Systems. The short circuit currents influence the structural and functional reliability of distribution networks and at the reliability of electrical equipment installation. The dynamics of changes in the levels of short circuit currents in electrical networks depends on the rate of change of the re-recovering voltage at the short circuit point on the power supply side and on the load side, as well as on the magnitude and rate of power change at the short circuit point on the power source side and on the load side.