Model Of Load Research Articles

Research on peak load shifting for hybrid energy system with wind power and energy storage based on situation awareness

The uncertainty of wind power and load fluctuations can elevate the peaking pressure on the power grid and influence the optimization strategy for peak load shifting. Additionally, there is a need to explore the trade-off and dynamic adjustment between economic considerations and the effectiveness of peak load shifting strategies. In this paper, based on the situation awareness theory, an optimization model on peak load shifting is proposed for a hybrid energy system with wind power and energy storage unit. First, in the situation perception stage, simulations that incorporate stochastic volatility are executed for renewable energy outputs and electric loads, leading to the establishment of uncertainty models for both wind power and electric load. During the situation comprehension stage, an optimization model with multi-objective is proposed to simulate the hybrid system situation, in which the mutual constraints and games are considered between peak load shifting effects and system economy. Then, in the stage of situation presentation and situation orientation, by setting quantitative indicators to analyze and judge the system situation, the problem of changing uncertainty from the weights of various attributes in multi-objective decision-making is transformed into the prediction problem of the adjustable factor, and a hybrid network model is proposed to realize the interval prediction of the adjustable factor, further balancing the goals of load leveling and system economy. Example simulations demonstrate that the proposed optimization model for peak load shifting can effectively reduce the peak-valley difference ratio of the net load by over 39.08 %, thereby smoothing the overall net load fluctuation. In the face of uncertainty, the model shows minimal impact on the peak-valley difference ratio of the net load, experiencing only 3.91 % and 7.13 % changes in response to wind power and load fluctuations, respectively. These findings underscore the model's sensitivity to different levels of uncertainty in both wind power and load scenarios. The MAML-LSTM-FC model can clarify the selection range of the adjustable factor according to the requirements of system quantitative indexes, thereby achieving dynamic coordination between the effects of peak load shifting and economics.

A Neuro-Fuzzy Logic Based Machine Learning Model for Electrical Load Forecasting

One of the critical aspects of meeting the demands of the energy worldwide is the accurate estimate of electrical loads typically connected to inter connected power systems. However, it is a completely non-trivial challenge as the variables which govern the electrical load are extremely fluctuating in nature with high amount of randomness and non-correlation thereby making the forecasting problem difficult in terms of the accuracy. The work presented in this paper is based on the ANFIS regression process. The essence of the algorithm lies in the fact that the proposed approach uses the errors in each iteration as an exogenous input to the system and hence is used to attains faster convergence with lesser error margin for the system. Moreover, the discrete wavelet transform is used as the decimating and smoothening filter. The performance of the proposed system is evaluated in terms of the accuracy of the system and the percentage error. It has been shown that the proposed work clearly outperforms the baseline approach without the DWT filtering thereby attain a higher degree of accuracy in the forecasting approach. Keywords:- Machine Learning, ANFIS,, Load Forecasting, Regression Learning, Mean Absolute Percentage Error.

Decoupled average model-based sliding mode current control of LC-filtered inverters in rotating frame

Recently, LC-filtered inverter has been a trendy research object towards the efficient exploitation and use of sustainable energy sources. The nested control scheme of the system includes a voltage control loop and a current one, which can be developed on the rotating frame. The conventional PI controller is employed for the outer loop voltage controller and it performs effectively. However, the PI current controller introduces delay to the system and overshoot occurs frequently due to the influence of nonlinear load. This paper proposes a novel method of decoupled average model-based sliding mode current control performing faster response and better tracking performance of the desired current. Besides, the comprehensive model of the nonlinear load is applied to examine the robustness of the proposed control scheme. This method is developed on the average model, fixed-frequency PWM can therefore be used to modulate the inverter. The novel method is compared with a conventional PI current controller. Offline simulation on MATLAB/Simulink and real-time simulation using Opal-RT experimental platform prove the advantages of the proposed current controller considering critical scenarios with the nonlinear load.

Real-time energy management strategy for flexible traction power supply system

Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases.

Experimental and numerical evaluation for drum dynamic reliability under extremely complex working conditions

Coal mining machine drums are prone to damage and malfunction under extremely complex working conditions, which seriously affects the efficiency and safety of coal production. In this paper, based on the theory of coal rock cutting and virtual simulation technology, finite element models of drum cutting coal rock were established and then verified by physical experiments. Through simulation analysis, the dynamic reliability of the drum was studied from three aspects: load, stress and wear, and a mathematical model of drum load was established with respect to the traction speed and drum rotation speed; based on the orthogonal test, the optimal working parameters to improve the wear resistance of the drum were derived. The results of the study found that when the traction speed increases, the load on the drum increases, and when the drum rotation speed increases, the load on the drum decreases; when the traction speed is increased from 2 to 6 m/min, the stress on the pick body under different rotation speeds increases to different degrees, with an average increase rate of 27.394%; when the drum rotation speed is 90 r/min, the traction speed is 3 m/min, and the coal loading mode is projectile loading, the wear depth of the picks and spiral blades is relatively small. The research method and results of this paper can provide a reference for the selection of the drum working parameters.

RECSAE: An interactive model of relevance cognitive load, spatial memory, ADHD and EEG for special educators and mental health professionals

The cognitive load (CL) increases as the brain is confronted with high-volume audiovisual and other multimodal information from the external environment. Spatial memory is a broad term for recording and recovery of this information. It has three dimensions, such as sensory memory, which is responsible for information capture and preprocessing, working memory, which is responsible for information processing and output generation, and long-term memory, which is responsible for registration/encoding and retrieval/decoding of what has been learned after the information processing. Cognitive training for long-term memory formation is a complex task that is conducted by analysing the "relevance" of the information. Relevance refers to the degree of meaningfulness of the input information based on logic, beliefs and perceptions. A high degree of relevance promotes the firing of the reentrant circuit for capturing similar information to create memory in the brain. An electroencephalogram (EEG) measures brain activities by using electrodes to deliver waveform signals through the scalp at a particular duration. This article proposes an interactive RECSAE model for the formation of relevant long-term memory through CL training and attempts to corroborate it with the EEG signals. The model is finally explained and correlated with Attention Deficit Hyperactive Disorder (ADHD) as a real-world use case. The proposed model is generic and would help psychologists, neurophysiologists, EEG researchers and special educators to evaluate CL training and memory dysfunctions by using EEG waves.

Theoretical research on carbon emission factors of distributed smart distribution network under high proportion of renewable energy access

In the context of the “dual carbon” goal, a high proportion of renewable energy connected to the grid to replace traditional high-carbon units has become a general trend. Taking the high proportion of renewable energy power system as the research object, the medium and long term planning of the installed capacity of source, grid, load and storage of power system is carried out considering the two dimensions of investment and operation maintenance, and the optimization model of source, grid, load and storage of power system is established. A low-carbon dispatching model considering carbon emission and demand response of distribution network is also proposed. Firstly, considering the characteristics of distribution network receiving power from the main network, the carbon emission model of distribution network based on carbon emission flow theory is constructed, and the low-carbon index of distribution network is proposed. Secondly, the improved dynamic carbon emission factor of distribution network is proposed, which is used as a guide signal to provide reference for low-carbon electricity consumption of distribution network users. According to the characteristics of users in the distribution network, the industrial demand response model aiming at low dispatch cost and low carbon and the residential demand response model aiming at electricity consumption satisfaction and low carbon satisfaction are constructed respectively.

Hybrid model for microgrid short term load forecasting based on machine learning

Short-term load forecasting (STLF) is crucial for microgrid (MG) operators to optimize energy generation and storage schedules based on anticipated load variations. Accurately predicting peak demand periods enables operators to ensure sufficient power supply while minimizing reliance on expensive backup sources, resulting in cost savings, and improved overall system efficiency. Residential MG power demand is highly dynamic due to external factors like residents’ lifestyles, behaviors, and weather responses, leading to significant irregularity and management challenges. To deal with these challenges, we propose a hybrid STLF model that combines Artificial Neural Networks (ANN) and Fuzzy Logic (FL), referred to as the Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was trained and tested using real power consumption data. We evaluated the performance of the ANFIS model by comparing it with another ANN model using three evaluation metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The trained ANFIS model achieved an MAPE of 8.7528% and an RMSE of 0.4752kw, while the ANN model achieved an MAPE of 8.8123% and an RMSE of 0.4816kw. These results confirm the accuracy of the hybrid ANFIS model compared to ANN. The ANFIS model demonstrated its ability to capture complex and nonlinear relationships between various factors affecting load demand, making it suitable for handling the dynamic nature of MG load.

Moving Target

Abstract Several computer-aided design (CAD) and product lifecycle management (PLM) platforms have already added generative capabilities. But AI can do more than help engineers automatically update geometric constraints or scale models for different manufacturing loads. When engineering experts look beyond augmenting the average design engineer’s creative capabilities, they can envision a future where the entire engineering and manufacturing ecosystem is turned on its head.

Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet.

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

Avaliação probabilística da carga variável de projeto para garagens brasileiras de veículos leves

Abstract Design loads for parking garages should be reviewed every few years due to changes in fleet characteristics and the impact of design loads on carbon emissions by the built environment. Specifically for Brazil, the authors are unaware of existing studies to justify the design values and corresponding exceedance probabilities stated in NBR 6120:2019 - Design Loads for Structures. In this paper, a simplified probabilistic model for live loads in parking garages is presented. A set of updated statistics characterizing the gross curb weight of the fleet in circulation was obtained based on technical specifications and sales reports between 2003 and 2022. These statistics and the probabilistic load model are employed to derive the equivalent uniform design load corresponding to a 30% probability of being exceeded in 50 years, according to the definition stated in NBR 6120:2019 and NBR 8681:2003. The results provide support for a significant reduction of the current design load for light-duty vehicle parking garages, from the current 3.0 kN/m2 to at least 2.5 kN/m2. Such a reduction has a significant impact on new building construction costs and carbon emissions and would not compromise the structural safety of parking garages built in Brazil.

Daily Load Forecasting and Data-Driven Strategies for Steel Industry Based on Random Forest Modeling

Abstract As a large power consumer, the iron and steel industry urgently needs to improve productivity, reduce energy consumption, and save costs by revolutionizing energy management. In this study, we design a power demand management system for the iron and steel industry, and around the load management module, we propose a data-driven strategy based on daily load situational awareness and introduce the random forest model into daily load forecasting in the iron and steel industry. At the same time, the projection principle is applied to improve the traditional gray correlation similar day selection algorithm, and a combination method of daily load forecasting based on the gray projection improved random forest algorithm is proposed. The electric load data of the iron and steel industry in a specific region is utilized as an experimental sample to investigate the model’s forecasting performance and the impact of the data-driven strategy. The model for daily load forecasting in this paper has an average relative error of 1.18%, which is better than other models. The application of the data-driven strategy brought about 6.99% and 6.69% reductions in load demand and basic electricity cost. The data-driven strategy for the steel industry based on the Random Forest model can predict electric loads more accurately and reduce energy costs, as shown by the results.

Improved quantum artificial bee colony algorithm-optimized artificial intelligence models for suspended sediment load predicting

Improved quantum artificial bee colony algorithm-optimized artificial intelligence models for suspended sediment load predicting

Analysis of the Deformation and Dynamics of High-Speed Railway Bridge Under the Coupling Effect of Shield Tunneling and Train Load

In order to study the dynamic response of high-speed railway bridge and its deformation law under the coupling effect of vibration load and shield tunneling, a coupling model of shield tunneling and train load is established based on the actual case of tunneling under an adjacent bridge. The deformation characteristics and dynamic response of the bridge are investigated by analyzing the deformation under different tunneling conditions and train running speeds. The results show that the maximum disturbance of the original stress field around the bridge is caused when the shield penetrates to the near side of the bridge structure, at which time, the damping effect of the ground and bridge system on the vibration load is weakened, thus intensifying the dynamic response of the bridge system, and the additional deformation caused by the vibration load is the largest; the presence of train loads during the shield excavation slightly attenuates the differential settlement of the bridge, but increases the cumulative settlement of the bridge, in addition, the additional deformation of the bridge will increase with the increase of the train running speed; the additional deformation caused by the train load within 2[Formula: see text]m of the shield crossing on both sides of the bridge is large, so the construction should be avoided as much as possible when the train is running in this construction section.

Second-order wave loads on a floating bridge of T-type cross section

An analytical model for the complete second-order wave loads on a floating bridge of T-type cross section is established within the framework of potential flow theory. The first-order problem is solved using the matched eigenfunction expansion method (MEEM). Meanwhile, the second-order wave loads are divided into two distinct parts which are computed separately. The first part is dependent on the quadratic products of the first-order velocity potential and its derivative. It has been evaluated through two different ways. One is based on a direct pressure integration, i.e., a near-field method. To ensure a high accuracy of the computation, a local expansion is applied to determine the spatial derivative of the first-order velocity potential at a sharp corner. The other is based on the momentum conservation over a control volume. The effect of the control surface location on the computation has been discussed. In addition, the two proposed methods have been applied to both the time-independent wave-load component as well as the second-harmonic component, respectively. The second part of the complete second-order wave loads is due to the second-order velocity potential. It is evaluated based on an indirect method by introducing an auxiliary potential. Detailed numerical investigation is then conducted. The influence law of the sectional geometric parameters on the second-order wave loads is investigated. The numerical results reveal that the cantilever plate imposes an apparent protection effect on the inner web beam. Such effect gets obviously enhanced as the section net width or section height decreases or as the plate width or draft increases.

Scientific justification of the need to improve the system of special physical training of military servants using models of mma and strength fitness loads

The article is devoted to the problem of studying modern ways of improving the system of special physical training of military personnel by modeling the training process using the basic laws of mixed martial arts (MMA) and strength fitness. In the process of analyzing the scientific and methodological literature, the timeliness, expediency and necessity of conducting integral studies on this problem were substantiated, the results of which will allow the development of effective models of training regimes of loads for military personnel, taking into account their individual adaptation reserves of the body and the level of special training, as well as the specifics of the professional activity of that or other subdivision. Development of experimental training models of classes using basic equipment, principles of MMA and strength fitness, as well as carrying out a series of studies using a wide range of physiological, biochemical and morphofunctional methods of monitoring the features of adaptive changes in the body and the level of special training (mastery of the necessary elements of MMA) of military personnel, will make it possible to clearly define the mechanisms for correcting load regime indicators depending on the direction and main tasks of the relevant military units. The work presents an algorithm of experimental research that will allow solving the main tasks and developing a modern concept of the system of special training of military personnel depending on the characteristics of the units.

Low-carbon optimal scheduling for multi-source power systems based on source-load matching under active demand response

To reduce the source-load uncertainty and carbon emission levels of the power system, this study proposes a novel low-carbon economic stochastic optimization scheduling model. Firstly, to partially compensate for the instability of wind power (WP) and photovoltaic power (PV) generation, enhance energy efficiency, and reduce system carbon emissions, the introduction of concentrating solar power (CSP) as one of the power sources on the generation side. Secondly, to optimize load characteristics, a peak–valley time period division method based on fuzzy theory and an active demand response model for electric load using the Logistic fuzzy function are introduced on the load side. Thirdly, taking into account the role of the carbon trading market, a ladder-type carbon trading mechanism (LCTM) is introduced to control carbon emissions and reduce overall economic costs. Finally, considering WP-PV output fluctuations, the degree of matching between power output and load, WP-PV utilization, system economic efficiency, and carbon trading factors, with the objective of maximizing the tracking of the load curve and minimizing comprehensive system economic costs, a multi-objective stochastic optimized scheduling model for power systems is established. A multi-objective Runge–Kutta algorithm based on a population-based parallel search mechanism (PSMORUN) is proposed to solve the active demand response model and the low-carbon stochastic optimization scheduling model. Additionally, constraint repair techniques are employed to handle the complex constraints of the models. To verify the effectiveness of the proposed model, a 10-generator power system, including a WP farm, a PV plant, and a CSP plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the proposed stochastic optimal scheduling model has higher safety, better economy, and lower carbon emissions.

Field load testing of wind turbines based on the relational model of strain vs load

Compared to theoretical calculations, field blade load testing can more accurately obtain actual load characteristics. The main challenge is the field relationship correction of strain vs load. The initial state of the strain sensing installation can be divided into two scenarios. One scenario is to install before the blade is lifted and another is to install online during the service of wind turbines. The key analysis is conducted on the field correction of the relationship of strain vs load, and corresponding correction strategies are constructed. A decoupled calculation model for strains in the edgewise direction induced by the aerodynamic moment and the gravity moment is proposed. Ground and online experiments for blade strain testing have been carried out. Through the combination of strain testing data and SCADA data, some new insights have been gained on strain characteristics. For example, the mapping coefficient of strain vs load induced by the aerodynamic moment varies under different operating conditions, while the mapping coefficient of strain vs load induced by gravitational moment is relatively stable. When the power of the wind turbine is 552 kW, 853 kW, and 1379 kW, the coefficients induced by the aerodynamic moment are 2270 N m/μɛ, 2480 N m/μɛ, and 3224 N m/μɛ, respectively.

Machine learning based prediction model for the pile bearing capacity of saline soils in cold regions

The difficulty in determining the bearing capacity of pile foundations in saline soil environments in cold regions can pose a challenge when developing a bearing capacity prediction model. To address this, the study uses data from the construction of the De Xiang Expressway project in Qinghai Province, China, and considers pile length, pile diameter, corrosion depth, and spalling thickness as influential parameters. We combine Python with ABAQUS to build a finite element model of a single pile in a salt marsh environment with random parameters and develop a database for machine learning. We use the kernel ridge regression (KRR) and multilayer perceptron (MLP) algorithms to establish load-bearing capacity prediction models for vertical and horizontal loads, respectively. The SHAP (SHapley Additive exPlanations) method is employed to perform explanatory analysis on the prediction models, providing insight into influential parameter effects on prediction accuracy. The results demonstrate that the database established through the random parameter finite element method is suitable for machine learning. While the KRR model exhibits good performance in predicting vertical load-bearing capacity, the MLP model performs well in predicting horizontal load-bearing capacity. Moreover, the SHAP method effectively explains the importance of influential parameters on the prediction results and enhances the reliability of the prediction models. The vertical bearing capacity of a saline soil pile foundation is more affected by the depth of salt erosion than the thickness of spalling, while the horizontal bearing capacity is more affected by the thickness of spalling than the depth of salt erosion.

CONCEPTUAL MODEL OF A MICRONЕTWОRK WITH DISTRIBUTED ENERGY RESOURCES

Mіcrоnеtwоrk configurations are considered regarding the form of electricity transmission and distribution. The structures of connected units of dіstrіbutеd еnеrgy rеsоurcеs under different operating modes are described, in which special attention is paid to the primary control elements of dіstrіbutеd еnеrgy rеsоurcеs units for the instantaneous balance of active and reactive power, as well as to dispatch control architectures for long-term energy management. The characteristics of static and dynamic load are studied, the simulation of which is carried out by building physical models of typical loads. The parameters of the system of instantaneous balances of active and reactive power in mіcrоnеtwоrks determined by instantaneous load tracking and load distribution between blocks of distributed energy resources are obtained. The circuit of resistive active filtering of blocks of distributed energy resources for active compensation of distorted loads is given. The presented method effectively suppresses harmonic voltage distortions at the output of blocks of dіstrіbutеd еnеrgy rеsоurcеs. Ref. 10, fig. 2. Keywords: mіcrоnеtwоrk, еlеctrіcіty cоnvеrtеr, dіstrіbutеd еnеrgy rеsоurcе, lоаd dіstrіbutіоn, cоntrоl systеm.