Model Predictive Method Research Articles

Research on a Resource Modeling and Power Prediction Method Based on Virtual Aggregation

Distributed resources at a grid’s end cannot upload operational power data to local centers due to data transmission and privacy issues. This leaves the centers with incomplete information, thus impacting decision making. This paper presents a virtual aggregation-based model for such scenarios. We define four virtual aggregate types based on resource response characteristics. Using characteristic coefficients, we identify these aggregates’ categories and proportions from bus power. To address blind source separation in single-channel power signals, we apply the Ensemble Empirical Mode Decomposition-Fast Independent Component Analysis (EEMD-FastICA) method. This helps extract and analyze bus power, thereby deriving power curves for different aggregates. Moreover, we use a graph convolutional network to explore how factors like date, time, weather, and pricing intertwine with aggregate power. We develop a predictive model with an advanced SpatioTemporal Graph Convolutional Network (STGCN), thus facilitating proactive power forecasting for virtual aggregates. Case studies show our method’s efficacy in extracting power curves under limited information, with the STGCN ensuring accurate, forward-looking predictions.

Automatic Modeling Prediction Method of Nitrogen Content in Maize Leaves Based on Machine Vision and CNN

Existing maize production is grappling with the hurdles of not applying nitrogen fertilizer accurately due to subpar detection accuracy and responsiveness. This situation presents a significant challenge, as it has the potential to impact the optimal yield of maize and ultimately, the profit margins associated with its cultivation. In this study, an automatic modeling prediction method for nitrogen content in maize leaves was proposed based on machine vision and convolutional neural network. We developed a program designed to streamline the image preprocessing workflow. This program can process multiple images in batches, automatically carrying out the necessary preprocessing steps. Additionally, it integrates an automated training and modeling system that correlates the images with nitrogen content values. The primary objective of this program is to enhance the accuracy of the models by leveraging a larger dataset of image samples. Secondly, the fully connected layer of the convolutional neural network was reconstructed to transform the optimization goal from classification based on 0–1 tags into regression prediction, so that the model can output numerical values of nitrogen content. Furthermore, the prediction model of nitrogen content in maize leaves was gained by training many samples, and samples were collected in three key additional fertilizing stages throughout the growth period of maize (i.e., jointing stage, bell mouth stage, and tasseling stage). In addition, the proposed method was compared with the spectral detection method under full-wave band and characteristic wavelengths. It was verified that our machine vision and CNN (Convolutional Neural Network)-based method offers a high prediction accuracy rate that is not only consistently better—by approximately 5% to 45%—than spectral detection approaches but also features the benefits of easy operation and low cost. This technology can significantly contribute to the implementation of more precise fertilization practices in maize production, leading to potential yield optimization and increased profitability.

A Model Predictive Control-Based Voltage Optimization Method for Highway Transportation Power Supply Networks With Soft Open Points

A Model Predictive Control-Based Voltage Optimization Method for Highway Transportation Power Supply Networks With Soft Open Points

Research on Traffic Accident Risk Prediction Method Based on Spatial and Visual Semantics

Predicting traffic accidents involves analyzing historical data, determining the relevant factors affecting the occurrence of traffic accidents, and predicting the likelihood of future traffic accidents. Most of the previous studies used statistical methods or single deep learning network model prediction methods while ignoring the visual effects of the city landscape on the drivers and the zero-inflation problem, resulting in poor prediction performance. Therefore, this paper constructs a city traffic accident risk prediction model that incorporates spatial and visual effects on drivers. The improved STGCN model is used in the model, a CNN and GRU replace the origin space–time convolution layer, two layers of a GCN are added to extract the city landscape similarity of different regions, and a BN layer is added to solve the gradient explosion problem. Finally, the features extracted from the time–space correlation module, the city landscape similarity module and the spatial correlation module are fused. The model is trained with the self-made Chicago dataset and compared with the existing network model. The comparison experiment proves that the prediction effect of the model in both the full time period and the high-frequency time period is better than that of the existing model. The ablation experiment proves that the city landscape similarity module added in this paper performs well in the high-frequency area.

Parameter Optimization and Performance Research: Radial Inflow Turbine in Ocean Thermal Energy Conversion

Combining one-dimensional parameter optimization and three-dimensional modeling optimization, a 30 kW radial inflow turbine for ocean thermal energy conversion was designed. In this paper, the effects of blade tip clearance, blade number, twist angle, and wheel–diameter ratio on the radial inflow turbine were analyzed. The results show that the model prediction method based on 3D numerical simulation data can effectively complete secondary optimization of the radial turbine rotor. The prediction model can be used to directly obtain the optimal modeling parameter of the rotor. The tip clearance, blade number, twist angle, wheel–diameter ratio, and shaft efficiency were found to be 0.273 mm, 16, 43.378°, 0.241, and 88.467%, respectively. The optimized shaft efficiency of the turbine was found to be 2.239% higher than the one-dimensional design result, which is of great significance in reducing the system’s power generation costs and promoting the application of this approach in engineering power generation using ocean thermal energy.

Application of improved support vector machine model in fault diagnosis and prediction of power transformers

AbstractPower transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K‐means algorithm into the model and constructs a high‐precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.

A Two-Stage Power Distribution Scheme of Multiple Wind Farms Participating in Primary Frequency Regulation

With the increasing penetration rate of renewable energy power generation (RPG) in power system, improving the frequency regulation ability of RPG has become a general trend. The premise of effective participation of RPG in primary frequency regulation (PFR) is to reasonably design the power distribution scheme for RPG devices. In order to effectively evaluate the frequency support capability of wind power generation system, the system frequency response (SFR) model under the joint action of synchronous generators, wind farms (WF) and low frequency load shedding device is established in this paper. According to the time-domain solution of this mathematical model, the influence of the output power of the WF on SFR is mathematically explained. At the same time, the influence of time delay on SFR is further demonstrated. In order to maximize the release of the rotor kinetic energy of the wind turbine and ensure the safe operation of the wind turbine when designing the power distribution scheme, an accurate prediction method of wind turbine rotor speed based on piecewise fitting and Bernoulli equation is proposed. Based on the established SFR model and rotor speed prediction method, a two-stage power distribution scheme for multiple WFs during PFR is proposed, which comprehensively considers the SFR requirements and different power output capabilities of multiple WFs. The effectiveness and superiority of the proposed power distribution scheme are validated with revised IEEE 39-bus test system and revised PG&E 69-bus test system.

Model Predictive Control of Switched Reluctance Machines for Suppressing Torque and Source Current Ripples Under Bus Voltage Fluctuation

This article presents a novel model predictive method for torque ripple and source current ripple suppression in switched reluctance machines (SRMs) when the bus voltage fluctuates. Based on the measured rotor position, phase current, and phase voltage, the states, including bus voltage, capacitor current, and phase flux-linkage, are calculated for the preparation of the prediction. On this basis, the machine torque and inverter current for all the possible switching states are predicted to construct the cost function. The operating state with the minimum value of cost function is considered as the optimal switch signal and applied to the control power converter, thus achieving the effect of simultaneous reduction of torque ripple and source current ripple. Experimental results show the proposed method has a significant effect on the suppression of both torque ripple and source current ripple even with bus voltage fluctuation, which verifies the effectiveness of the proposed method. The influence of the key factors, such prediction horizon, parametric variation, and weight coefficient, on the machine performance are further investigated. The proposed method is easy to be implemented in the control logic as well as robust to bus voltage fluctuations, enabling to promote the competitiveness of SRMs in electric vehicle application.

Predicting of elderly population structure and density by a novel grey fractional-order model with theta residual optimization: a case study of Shanghai City, China

Background Accurately predicting the future development trend of population aging is conducive to accelerating the development of the elderly care industry. This study constructed a combined optimization grey prediction model to predict the structure and density of elderly population.MethodsIn this paper, a GT-FGM model is proposed, which combines Theta residual optimization with fractional-order accumulation operator. Fractional-order accumulation can effectively weaken the randomness of the original data sequence. Meanwhile, Theta residual optimization can adjust parameter by minimizing the mean absolute error. And the population statistics of Shanghai city from 2006 to 2020 were selected for prediction analysis. By comparing with the other traditional grey prediction methods, three representative error indexes (MAE, MAPE, RMSE) were conducting for error analysis.ResultsCompared with the FGM model, GM (1,1) model, Verhulst model, Logistic model, SES and other classical prediction methods, the GT-FGM model shows significant forecasting advantages, and its multi-step rolling prediction accuracy is superior to other prediction methods. The results show that the elderly population density in nine districts in Shanghai will exceed 0.5 by 2030, among which Huangpu District has the highest elderly population density, reaching 0.6825. There has been a steady increase in the elderly population over the age of 60.ConclusionsThe GT-FGM model can improve the prediction accuracy effectively. The elderly population in Shanghai shows a steady growth trend on the whole, and the differences between districts are obvious. The government should build a modern pension industry system according to the aging degree of the population in each region, and promote the balanced development of each region.

Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance

A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.

Generalized model for dynamics and stability of milling of titanium alloys by integrating process damping, multiple modes and multiple delays

Machining dynamics modelling and chatter stability prediction are important for milling of titanium alloys to improve machining productivity and surface quality. Since titanium alloys are typically machined at low spindle speeds, process damping plays an important role in dynamics and stability of milling of titanium alloys. What is more, multiple modes and multiple delays, resulting from the complexity of milling systems and processes, significantly affect chatter stability of milling. However, there is a lack of comprehensive dynamical models capable of studying how these three factors influence chatter stability. To address this gap, this paper proposes a generalized dynamic model for milling of titanium alloys considering process damping, multiple modes and multiple delays. A time-domain chatter prediction method related to the generalized dynamic model is proposed by extending the Gaussian quadrature-based method. Experiments are conducted on multi-mode cutting systems using both uniform and variable pitch cutters to validate the proposed dynamic model and chatter prediction method. The impacts of multiple modes and multiple delays on chatter stability are investigated considering the presence of process damping. Experimental validation and simulation show that in order to accurately predict chatter stability of milling of titanium alloys at low spindle speed, all dynamic modes should be taken into account whether the modes are well separated or not. Even if the low-frequency modes are much more rigid than the high-frequency modes, low-frequency modes may be dominant in milling of titanium alloys and should be considered in the prediction procedure of chatter stability. In addition, it is observed in both simulations and experiments that variable pitch angles can improve the chatter stability limit in the process damping zone.

Micro–macro approach of anisotropic damage: A semi-analytical constitutive model of porous cracked rock

A semi-analytical constitutive model of a porous cracked rock is proposed based on the micro–macro approach within a thermodynamic framework. The representative volume element (RVE) of a porous cracked rock is divided into two subproblems, P1 and P2. The porosity evolution equation of a spherical pore is proposed for P1, whereas P2 is an inelastic problem containing numerous anisotropic penny-shaped cracks. First, the relationship between microscopic and macroscopic strains is established through homogenization. This relationship is introduced into the macroscopic free energy equation. A crack propagation criterion based on the Griffith release rate is proposed, and the evolution equations of the shear and tensile crack densities are proposed based on the surface energy function. Subsequently, the Gaussian numerical integration method is used to obtain a semi-analytical explicit expression of the macroscopic stiffness tensor. Finally, based on the proposed crack propagation criterion, a strength-prediction method is developed. A series of uniaxial and triaxial experimental data for different rocks from other references is used to validate the proposed constitutive model and strength prediction method. The results obtained using the proposed model agree well with the experimental curves obtained from uniaxial tension and triaxial compression tests performed on different rocks. The macroscopic mechanical behavior and evolution of microscopic parameters for different rocks with an increase in confining pressure are effectively demonstrated by the proposed model. Strength prediction method can accurately describe the failure strength of different rocks. Furthermore, a sensibility analysis of model parameters revealed that initial porosity e0 and dilation coefficient ζ have a deteriorating effect on the failure strength, and material constant η has an enhancing effect on the failure strength.

Cubature Kalman Filters Model Predictive Static Programming Guidance Method with Impact Time and Angle Constraints Considering Modeling Errors

This paper proposes a CKF-MPSP guidance method for hitting stationary targets with impact time and angle constraints for missiles in the presence of modeling errors. This innovative guidance scheme is composed of three parts: First, the model predictive static programming (MPSP) algorithm is used to design a nominal guidance method that simultaneously satisfies impact time and angle constraints. Second, the cubature Kalman filter (CKF) is introduced to estimate values of the influence of the inevitable modeling errors. Finally, a one-step compensation scheme is proposed to eliminate the modeling errors’ influence. The proposed method uses a real missile dynamics model, instead of a simplified one with a constant-velocity assumption, and eliminates the effects of modeling errors with the compensation scheme; thus, it is more practical. Simulations in the presence of modeling errors are conducted, and the results illustrate that the CKF-MPSP guidance method can reach the target with a high accuracy of impact time and angles, which demonstrates the high precision and strong robustness of the method.

Real-Time Average Junction Temperature Estimation for Multichip IGBT Modules With Low Computational Cost

The junction temperature of the insulated gate bipolar transistor (IGBT) modules is a vital parameter for the reliability of the power electronic system. For effective thermal management, it is necessary to estimate junction temperature in real time. However, existing thermal models have limitations to achieve fast temperature prediction for multichip IGBT modules. In this study, we investigate the real-time junction temperature prediction for multichip IGBT modules with low computational cost. According to the power module' structure, an average 2D thermal model with physical significance is proposed to predict the virtual junction temperature for multichip IGBT modules, which can be extracted from finite element model based on the least square method. To further improve the computational efficiency, a temperature predictor based on the staggered cycle-by-cycle calculation method is proposed and its prediction error under various extreme operation conditions is analyzed by enumeration method. Finally, the comprehensive experimental results verify the effectiveness of the proposed average thermal model and junction temperature prediction method.

Health Status Prediction of Lithium Battery Based on LSTM Model with Optimization Algorithms

Consider the current status of health (SOH) of lithium batteries, which presents challenging existing issues of accurately predicting and calculating. In this paper, an LSTM model and multi-optimization algorithm were used to estimate the battery health state. Taking advantage of the fast convergence speed and wide global optimization range of the optimization algorithm, optimized the number of layers and neurons in the LSTM model so the LSTM model was established, used to predict the health status of lithium batteries, and compared with the LSTM model prediction method without optimization. The results showed that the error of the battery health prediction model based on the proposed prediction model was less than 3%, the prediction accuracy was higher than the LSTM model without optimization, and the model had better accuracy and stability.

An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids

Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

Prediction and Control in DNA Nanotechnology.

DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.

A combined inverse prediction and experimental analysis for the frozen ground heat transfer in a structure foundation

This paper takes the representative buried structure in permafrost regions, a transmission line tower foundation, as the research object. An inverse prediction is conducted in a scaled-down experimental system mimicking actual heat conduction of the frozen ground in a tower foundation. In permafrost regions, global warming and the heat transfer through the buried structures bring significantly adverse thermal effects on the stability of the infrastructures. In modeling the thermal effects, it has been a challenge to determine the ground surface boundary condition and heat source strength from the buried structures due to the complex climate and environmental conditions. In this work, based on the improved model predictive inverse method with an adaptive strategy, an inverse scheme is successfully implemented to simultaneously identify the time-varying surface temperature and the time-space-dependent heat source representing the buried structures. In this scheme, an adaptive time-varying predictive model is established by the rolling update of the sensitivity response coefficients according to the predicted temperature field to overcome the influence of nonlinear characteristics in the heat transfer process. The inverse method is verified by simulation and experimental data. According to the experimental inversion results, the reconstructed temperature distribution efficiently predicts the thermal state evolution of the permafrost foundation under seasonal freezing and thawing. It is found that, under the experimental conditions, the intensified thawing and freezing are significantly severe, e.g., the increased area ratio of active layer thickness is as high as 28% after building a tower, and the depth of permafrost table ranges from about 14 mm to about 38 mm, which could be detrimental to the stability and safety of the tower foundation. This study will provide valuable guidance for risk assessments or optimizing the design and maintenance of the real tower foundation and similar buried structures.

A novel static model prediction method based on machine learning for Li-ion batteries operated at different temperatures

In electric vehicles (EVs) and microgrid systems, lithium-ion (Li-ion) batteries are the most realistic choice for energy storage and utilization applications. The precise prediction of the static and dynamic properties of the battery is crucial for these applications. Traditional methods use system identification and optimization concepts to predict the static characteristics; these strategies require both higher-order terms and model re-tuning for each operating temperature. In order to create a unique static model of the battery, this work suggests a machine learning (ML)-based prediction method that uses data-driven concepts to develop the prediction model between the input and output variables with more accuracy. In order to create an accurate prediction model based on the given dataset, a complete dataset of the entire process is fed to the training algorithm. In the conventional ML approach, for each operating temperature a prediction model is developed to predict the static model. But in the proposed technique, a unique ML based model is suggested to determine the relationship between the battery's open circuit voltage (OCV) and State of Charge (SoC) at various operating temperatures. To identify the best prediction model, performance metrics such as root mean square (RMSE) and R2 are used. From the results, it is identified that the neural network-based SoC-OCV prediction is the best among the strategies that have been proposed. Since the predicted model is unique, it can be used at any operating temperature without requiring the prediction model to be adjusted. The proposed model can be used to estimate battery parameters and battery state such as SoC in EV applications.

An Improved Geometric Theoretical Model and Throughflow Prediction Method for a CO2 Scroll Compressor of Automotive Air Conditioning System

The scroll-type compressor is the core component of the refrigeration system using the natural refrigerant carbon dioxide. An accurate scroll geometric theoretical model is essential for evaluating and enhancing compressor performance. The present paper proposed and validated an improved geometric theoretical model of a scroll compressor. A vector triangle method-based general piecewise function describes precisely the volume variation of the working chambers, based on which the transient throughflow modeling and performance evaluation of a carbon dioxide scroll compressor were conducted. The mechanisms and influencing factors of the suction precompression and the asymmetry discharging were analyzed. The results indicate that the strength of the suction precompression is mainly influenced by the suction vacuum under different rotating speeds, partly associated with the tangential leakage under low rotating speeds. The increasing initial suction pressure contributes to the linear raising of the pressure difference between suction initial and ending pressures, decreasing the precompression extension slightly. The variation of the discharge throughflow areas of two symmetric discharge chambers dominates the pressure asymmetry, which is gradually eliminated by the overlapping throughflow area. Compared with the circular discharge structure, the waist-shape port reduces the pressure asymmetry degree and shortens its duration.