Parameter Optimization Framework Research Articles

Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

AbstractIn‐situ observations of hydrodynamics and suspended sediment concentrations (SSCs) were conducted on an abandoned lobe in the northern part of the modern Yellow River Delta, China. The SSC record at the site is found to be the superposition of a general trend (fast increase and slow decrease cycle) caused by storm waves (SubSSC1) and relatively smaller fluctuations caused by tidal currents (SubSSC2). Physically, this indicates that storm waves eroded the bottom sediments while tidal currents then re‐suspended and advected the suspended sediments in the study area. To further obtain the suspended sediment transport parameters, first, SubSSC1 is modeled with significant wave height which incorporates a “memory curve” to consider the remaining impacts of historical waves. It is detected that waves in the past 75 hr still influence the present SSC which is reasonable because 75 hr is roughly the typical duration of a normal storm. Second, SubSSC2 is modeled with tidal excursion and trigonometric functions with measured periodicities. Finally, some sediment transport parameters, for example, the background SSC, the horizontal SSC gradient, the tidal constituents that advect it, and their relative time lags are optimized from the best fits of the measured and modeled SSC time series. The proposed framework for model construction and parameter optimization can be extended to other sea areas for inferring sediment transport parameters from field SSC time series at a specific station.

Research on optimization of key thermal parameters of the secondary loop of PWR based on improved BP neural network

The simulation and optimization time of traditional secondary loop thermal parameters is relatively long, and the computational cost is generally high. To solve this problem, we construct an accurate model of the full-power steady-state operation of the secondary loop of the PWR, and use meta-learning combined with Bayesian optimization to realize the adaptive optimization of BP neural network hyperparameters. The effectiveness of the method is verified by designing comparative experiments by taking thermal efficiency as the optimization goal and high-pressure heater end difference and other parameters as the optimization variables, and using the improved BP neural network to optimize the accurate model. The results show that the improved secondary loop thermal parameter prediction and optimization framework can quickly and accurately predict the thermal efficiency and optimize the thermal parameters, and has good generalization ability, which lays a foundation for improving the thermal efficiency of nuclear power plants.

Adaptive Force Field Parameter Optimization for Expanding Reaction Simulations within Wide-Ranged Temperature.

Large-scale and long-term simulation of chemical reactions are key research topics in computational chemistry. However, there are still difficulties in simulating high-temperature reactions, such as polymer thermal decomposition. Herein, we introduce an adaptive potential parameter optimization framework designed to automatically fine-tune parameters, and the application of it to optimize ReaxFF parameters enhances the accuracy of chemical reaction simulations conducted at experimental temperatures. To achieve this, we leverage the power of Random Forests and interpretable machine learning techniques that enable the identification and selection of parameters that exert a substantial influence on the target attribute. By training deep neural network (NN) models, we established optimized parameter associations with reference properties. We train deep neural network (NN) models to establish the relationship between the optimized parameters and reference properties. We employ a Genetic Algorithm (GA) to utilize the surrogate NN model and the quantum mechanical targets to speed up the search for the optimal parameters. Our simulation results of resin pyrolysis show that the adaptive optimized ReaxFF can predict the peak temperature more accurately and obtain reasonable product composition under conditions that more closely resemble experimental scenarios. This work facilitates advances in force field parameter optimization for more accurate and universal reaction simulations.

Inferring in-air gestures in complex indoor environment with less supervision

People have high demands for comfort and technology in indoor environments. Gestures, as a natural and friendly human computer interaction (HCI) method, have received widespread attention and have been the subject of many research studies. Traditional approaches are based on wearable devices and cameras, which can be cumbersome to operate and infringe upon users’ privacy. Millimeter-wave (mmWave) radar avoids these problems by detecting gestures in a noninvasive manner. However, it encounters practical challenges in complex indoor environments, such as dynamic disturbance from surroundings, variable usage conditions and diverse gesture patterns, which conventionally require considerable manual effort to address. In this paper, we attempt to minimize human supervision and propose a noninvasive gesture recognition method named RaGe that involves a commercial mmWave indoor radar. First, a parameter optimization framework considering gesture prior constraints is proposed for radar configuration, which functions to weaken the disturbance from surroundings. Second, we alleviate data shortages in variable usage conditions and achieve low-cost data augmentation by applying affine transformations. Third, we combine deformable convolution operations with an unsupervised attention mechanism, thus exploring the intrinsic features involved in diverse gesture patterns. Experimental results show that RaGe is able to recognize 7 gestures with 99.3% accuracy and less human supervision, surpassing the state-of-the-art methods in comparative experiments.

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

A Quality Prediction and Parameter Optimization Approach for Turbine Blade Multistage Manufacturing

As core components of an aircraft engine, turbine blades are featured by complex curved surface with strict precision requirements in multistage manufacturing processes (MMPs). Hence, reliable quality prediction and parameter optimization strategy on MMPs have long been in great demand. However, most techniques focus on the precision improving of single-stage single-product manufacturing systems; these improvements are difficult to implement in MMPs with coordinate transformations among multiple machines. In this article, we propose a noninvasive feature-based quality prediction and parameter optimization framework for high-precision MMPs. In particular, rather than retrofitting sensors onto machinery after installation, this study presents an executable and noninvasive solution by using coordinate measuring machine (CMM) data as input features for prediction. Based on features of different stages of machining, a sequential multistep deep learning architecture is developed to predict the final product quality. A preheating optimization algorithm, modified from a local gradient search method, is used for the optimization of key initial machining features, which results are further improved by the L-BFGS method, thus increasing the final production quality. To estimate the effectiveness of the proposed framework, experiments are carried out on two series of aviation turbine blades with complex curved surfaces from the Wuxi Turbine Blade Company Ltd. China. CMM data at key stages (i.e., blade root milling and comprehensive accurate milling) are used as inputs to predict the final geometrical errors. Comparison results verify that the proposed framework achieves the smallest root mean square error at 0.030 mm and 0.032 mm for two blades, respectively.

Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning

Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.

Multi-objective robust parameter optimization using the extended and weighted k-means (EWK-means) clustering in laser powder bed fusion (LPBF)

Metal additive manufacturing (AM) technology, especially laser powder bed fusion (LPBF), has received abundant interest from industries and the research community. Process optimization methods have thus multiplied to improve the overall quality of the final parts. However, little attention has been given to the quality repeatability issue. This paper proposes a novel multi-objective robust parameter optimization framework to explore optimal process parameters with respect to relative density and dimensional accuracy of LPBF-fabricated parts. Specifically, a modified k-means clustering, named the Extended and Weighted K-means (EWK-means), was constructed to simultaneously optimize the mean and the variance of the multiple responses. Experiments were conducted to verify the effectiveness of the proposed optimization framework. In addition, the effects of the process parameters, environment-related parameters, and physical properties on the hardness of the parts were analyzed using several machine learning models. The results showed that the proposed method achieved a set of optimal process parameters with better quality and satisfactory variability in the printed parts compared with other robust parameter optimization methods.

Optimization-Based Design and Control of Dynamic Systems

Dynamic systems such as robots, autonomous vehicles, and process plants require careful design and control to achieve optimal performance. This paper presents an integrated framework for the simultaneous optimization of system design parameters and control policies. The underlying mathematical formulation utilizes optimization techniques to find the best system configuration and control strategy according to specified objectives. These objectives may include metrics such as tracking error, energy consumption, safety margins, and cost. Both model-based and data-driven techniques are explored for learning the system dynamics and synthesizing optimal controllers. The optimization algorithms leveraged include gradient-based methods, evolutionary algorithms, and reinforcement learning. The benefits of the joint optimization approach are demonstrated through case studies on representative dynamic systems. Results show that the integrated design and control framework outperforms sequential optimization, leading to improved efficiency, responsiveness, and robustness. This underscores the importance of co-optimizing design and control parameters, especially for complex, uncertain systems. The proposed methods provide an effective tool for next-generation automated design of smart, adaptable systems.

Strain- and stress-based parametric optimization of fiber-reinforced elastomers under finite deformations

In this work, a parametric optimization framework is developed to deal with fiber-reinforced elastomeric matrices. The fiber orientations are in a continuous set to obtain the set of angles that minimizes a functional given by the designer. Although this topic has been extensively studied in recent years, the vast majority of works use either gradient-based or global-based methods to define the composite fiber orientation, with the first being very prone to reach poor local minima and the latter increasing the computational burden exponentially with the number of design variables. Besides that, considering finite strain hyperelasticity, the number of works is substantially reduced. The main goal of this research is to study the efficiency of a local-based derivative-free algorithm in the optimization of problems considering finite deformation and material nonlinearity assumptions in fiber composite design. In order to show the proposed methodology efficiency, two optimization problems are studied: strain energy density minimization and mechanical stress minimization, with the latter further divided into a global and a cluster approach. Therefore, the main novelty of this work is to deal with fiber-reinforced elastomers optimization by means of a direct search algorithm in a constrained nonlinear optimization. The methodology can easily be implemented in existing finite element codes and the results show the pros and cons of both types of optimization.

A parameter-optimization framework for neural decoding systems.

Real-time neuron detection and neural activity extraction are critical components of real-time neural decoding. They are modeled effectively in dataflow graphs. However, these graphs and the components within them in general have many parameters, including hyper-parameters associated with machine learning sub-systems. The dataflow graph parameters induce a complex design space, where alternative configurations (design points) provide different trade-offs involving key operational metrics including accuracy and time-efficiency. In this paper, we propose a novel optimization framework that automatically configures the parameters in different neural decoders. The proposed optimization framework is evaluated in depth through two case studies. Significant performance improvement in terms of accuracy and efficiency is observed in both case studies compared to the manual parameter optimization that was associated with the published results of those case studies. Additionally, we investigate the application of efficient multi-threading strategies to speed-up the running time of our parameter optimization framework. Our proposed optimization framework enables efficient and effective estimation of parameters, which leads to more powerful neural decoding capabilities and allows researchers to experiment more easily with alternative decoding models.

A process optimization framework for laser direct energy deposition: Densification, microstructure, and mechanical properties of an Fe[sbnd]Cr alloy

Laser Direct Energy Deposition (DED) is a metal additive manufacturing technique with the ability to fabricate large and complex parts through deposition of metal powders. However, achieving high-density parts and targeted build heights using DED can be challenging due to the large number of highly sensitive process variables. This work proposes a robust fabrication parameter optimization framework to generate process maps for primary parameters in DED, including laser power, scan speed, mass flow rate, hatch spacing, and layer height. Simple single-track experiments were utilized to map out the parameter space, and a combination of geometric criteria for hatch spacing and layer height were proposed to determine parameter sets that achieve both targeted build heights and mitigate porosity formation. Using this framework, specimens with >99 % density and consistent mechanical properties were successfully fabricated over a wide range of process parameters for an Fe-9wt.%Cr (Fe9Cr) alloy, a surrogate for radiation damage-resistant reduced activation ferritic/martensitic (RAFM) steels. Processing these materials using DED is of particular interest in the development of plasma facing components for nuclear fusion applications. The microstructure and mechanical properties of as-printed Fe9Cr were characterized using optical and electron microscopy, X-ray diffraction, and uniaxial tensile tests. As-printed Fe9Cr displayed ~25 % elongation and ultimate tensile strengths of up to 475 MPa which is comparable to similar wrought alloys. The proposed framework will allow for accelerated DED parameter optimization for novel alloy systems, as well as open the possibility for local microstructure control while simultaneously mitigating defect formation.

Adaptive selection strategy of shape parameters for LRBF for solving partial differential equations

Radial basis function (RBF) is a basis function suitable for scattered data interpolation and high dimensional function interpolation, wherein the independent shape parameters have a direct impact on the accuracy of calculation results. Research in this field is mostly concerned with the shape parameter selection strategy based on the premise of global distribution or block regional distribution. In this paper, a shape parameter selection strategy is proposed, which is used for the local RBF collocation method (LRBF) for solving partial differential equations. It overcomes many limitations of the traditional methods applied to LRBF. In this strategy, a set of twin matrices similar to the interpolation matrix are constructed to evaluate the error of the model. In addition, the penalty term contained in the twin matrix is used to relax the influence of the far end region on the target node. Since the objective problem is nonlinear, a particle swarm optimization algorithm (PSO) is employed to minimize the training objective and adjust the shape parameters of the basis function at each iteration. Extensive numerical results showed the effectiveness of the error estimation strategy, by providing a good shape parameter and better solution accuracy. At the end of the paper, the generality of the shape parameter optimization framework based on this strategy is discussed through three examples.

A prediction method for ship added resistance based on symbiosis of data-driven and physics-based models

The prediction of ship added resistance in waves is an important part of ship design and operation. A prediction method that takes into account accuracy, efficiency, robustness and generalization ability is required. In this study, a prediction method for ship added resistance in head waves based on symbiosis of data-driven and physics-based models is proposed. The physics-based model is constructed based on the 2D strip method to provide physics-based information and constraints. The data-driven module is constructed based on the fully connected neural network structure and the radial basis function, providing a data-driven model parameter optimization framework. Results indicate that the Data-driven and Physics-based Symbiotic Model (DPSM) has an obvious adaptive correction effect on the results of its embedded physics-based model, and has a stronger generalization ability than the fully data-driven model. Prediction results of the DPSM are in good agreement with experimental results, whether for the training ships or the unfamiliar testing ships. Finally, a posteriori model parameter analysis shows the reason why the DPSM has advantages of accuracy and generalization ability.

Global modelling of soil carbonyl sulfide exchanges

Abstract. Carbonyl sulfide (COS) is an atmospheric trace gas of interest for C cycle research because COS uptake by continental vegetation is strongly related to terrestrial gross primary productivity (GPP), the largest and most uncertain flux in atmospheric CO2 budgets. However, to use atmospheric COS as an additional tracer of GPP, an accurate quantification of COS exchange by soils is also needed. At present, the atmospheric COS budget is unbalanced globally, with total COS flux estimates from oxic and anoxic soils that vary between −409 and −89 GgS yr−1. This uncertainty hampers the use of atmospheric COS concentrations to constrain GPP estimates through atmospheric transport inversions. In this study we implemented a mechanistic soil COS model in the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model to simulate COS fluxes in oxic and anoxic soils. Evaluation of the model against flux measurements at seven sites yields a mean root mean square deviation of 1.6 pmol m−2 s−1, instead of 2 pmol m−2 s−1 when using a previous empirical approach that links soil COS uptake to soil heterotrophic respiration. However, soil COS model evaluation is still limited by the scarcity of observation sites and long-term measurement periods, with all sites located in a latitudinal band between 39 and 62∘ N and no observations during wintertime in this study. The new model predicts that, globally and over the 2009–2016 period, oxic soils act as a net uptake of −126 GgS yr−1 and anoxic soils are a source of +96 GgS yr−1, leading to a global net soil sink of only −30 GgS yr−1, i.e. much smaller than previous estimates. The small magnitude of the soil fluxes suggests that the error in the COS budget is dominated by the much larger fluxes from plants, oceans, and industrial activities. The predicted spatial distribution of soil COS fluxes, with large emissions from oxic (up to 68.2 pmol COS m−2 s−1) and anoxic (up to 36.8 pmol COS m−2 s−1) soils in the tropics, especially in India and in the Sahel region, marginally improves the latitudinal gradient of atmospheric COS concentrations, after transport by the LMDZ (Laboratoire de Météorologie Dynamique) atmospheric transport model. The impact of different soil COS flux representations on the latitudinal gradient of the atmospheric COS concentrations is strongest in the Northern Hemisphere. We also implemented spatiotemporal variations in near-ground atmospheric COS concentrations in the modelling of biospheric COS fluxes, which helped reduce the imbalance of the atmospheric COS budget by lowering soil COS uptake by 10 % and plant COS uptake by 8 % globally (with a revised mean vegetation budget of −576 GgS yr−1 over 2009–2016). Sensitivity analyses highlighted the different parameters to which each soil COS flux model is the most responsive, selected in a parameter optimization framework. Having both vegetation and soil COS fluxes modelled within ORCHIDEE opens the way for using observed ecosystem COS fluxes and larger-scale atmospheric COS mixing ratios to improve the simulated GPP, through data assimilation techniques.

Phenomenology and capabilities of mutually guided laser and neutral particle beams for deep space propulsion

Here we present a detailed analysis of a new concept for beam propulsion that extends the propagation range through space by circumventing fundamental limitations on the beam divergence. Using a combination of spatially overlapped continuous laser and neutral particle beams, opto-mechanical forces and light refraction are simultaneously exploited to produce self-guided modes. The aim of the present work is to elucidate the relevant physical phenomena, their relation to the conditions for self-guiding, and practical considerations for the design parameters of such a propulsion system. Methods for computing the lowest order axisymmetric self-guided modes are presented along with the resulting spatial mode structure. Key non-dimensional parameters are derived, including the use of linear stability analysis to determine features of the dynamic interaction. Based on these findings, a beam parameter optimization framework is proposed and applied to a 100-year interstellar flyby mission to Proxima b. With 200 GW of combined laser and particle beam power, we find that a 0.3 kg spacecraft could be accelerated to 0.044c over a distance of 4.37×106 km (0.029 AU) using a small 1 m2 beam cross-section. Compared to an equivalent particle beam with no self-guiding, this represents an over 50× increase in payload. Similar payload mass would be possible by laser propulsion with an approximately 104 m2 area laser array. These results suggest that self-guided beam propulsion possesses unique attributes that could enable the acceleration of larger spacecraft to relativistic velocities and enhance scientific capabilities for future interstellar and interstellar-precursor missions.

Analytical representation and characteristics optimization for radial electromagnetic force of the switched reluctance motor under airgap eccentricity

Aiming at the influence of radial electromagnetic force on vibration and noise of the switched reluctance motor (SRM), the radial electromagnetic force under airgap eccentricity is represented based on the virtual displacement principle and Maxwell stress tensor. Multi-objective optimization of motor electromagnetic characteristics including magnetostatic torque, radial electromagnetic force fluctuation, and unbalanced radial force amplitude is completed. First, the analytical representation for the radial electromagnetic force is obtained by the virtual displacement principle, and the expression of the unbalanced radial force caused by airgap eccentricity is deduced. Then, the spatial distribution of the radial electromagnetic force is modeled by the Maxwell stress tensor, which makes the radial electromagnetic force characterization more comprehensive. In order to improve the electromagnetic characteristics of the SRM under airgap eccentricity, sensitivity analysis and multi-objective optimization are carried out to obtain the optimal solution set of the motor structure parameters. The simulation results show that the radial electromagnetic force fluctuation and the unbalanced radial force amplitude decreases by 64.09% and 23.05%, respectively, when the magnetostatic torque is sacrificed by 6.36%, which provides a framework for multi-objective optimization of motor structure parameters.

An energy performance contracting parameter optimization method based on the response surface method: A case study of a metro in China

As an effective mechanism for improving energy efficiency, energy performance contracting (EPC) is receiving increasing attention. However, due to many uncertain factors that affect contract parameters, a reasonable distribution of participants' interests is necessary. To reasonably determine EPC contract parameters, this paper proposes a framework based on Geometric Brownian motion (GBM) and the response surface method (RSM) to optimize EPC contract parameters. First, GBM is used to simulate the energy prices, energy savings, energy-saving revenue allocation, etc. Then, the RSM is proposed to optimize multiple contract parameters. Finally, the proposed method is applied to the Wuhan Metro Line 2 lighting system renovation project. The results demonstrate that (a) the contract parameter optimization framework based on the GBM-RSM provides an effective method for optimizing the combination of EPC contract parameters. (b) The GBM-based method of quantitative simulation effectively expresses the uncertain factors of energy-saving performance in EPC renovation and provides reasonable data support for optimizing EPC contract parameters. (c) The established response surface optimization model of EPC contract parameters is an efficiently fitted model. A comparison between the optimization results and actual application results shows the feasibility of response surface optimization of EPC contract parameters, which improves the benefits for the owner of energy-saving renovation and thus increases the owner's enthusiasm, while guaranteeing a reasonable return for energy-saving companies. Hence, this study provides an effective method for optimizing EPC contract parameters and offers a reference for similar problems.

Parametric Optimization and Decision Support Model Framework for Life Cycle Cost Analysis and Life Cycle Assessment of Flexible Industrial Building Structures Integrating Production Planning

Most industrial buildings have a very short lifespan due to frequently changing production processes. The load-bearing structure severely limits the flexibility of industrial buildings and is a major contributor to their costs, carbon footprint and waste. This paper presents a parametric optimization and decision support (POD) model framework that enables automated structural analysis and simultaneous calculation of life cycle cost (LCC), life cycle assessment (LCA), recycling potential and flexibility assessment. A method for integrating production planning into early structural design extends the framework to consider the impact of changing production processes on the footprint of building structures already at an early design stage. With the introduction of a novel grading system, design teams can quickly compare the performance of different building variants to improve decision making. The POD model framework is tested by means of a variant study on a pilot project from a food and hygiene production facility. The results demonstrate the effectiveness of the framework for identifying potential economic and environmental savings, specifying alternative building materials, and finding low-impact industrial structures and enclosure variants. When comparing the examined building variants, significant differences in the LCC (63%), global warming potential (62%) and flexibility (55%) of the structural designs were identified. In future research, a multi-objective optimization algorithm will be implemented to automate the design search and thus improve the decision-making process.

Optimal Adaptive Inertial Droop Control–Based Power System Frequency Regulation via Wind Farms

Wind farm–based frequency regulation of the power system has progressively attracted more attention owing to its higher power generation capacity. Among which the step start-up and adaptive inertial droop control of wind turbines with a maximum power point efficiently regulate the system frequency via fully utilizing the rotational kinetic energy. Besides, the coefficients of adaptive droop control and virtual inertial control will facilitate a significant influence on the frequency support performance and operation status of wind turbines for the power grid. To obtain optimal control parameters, a parameter optimization framework of a step start-up adaptive inertial and droop controller combined with the CGO algorithm is proposed, whose effectiveness is verified by a three-area four-terminal VSC-MT-HVDC–based WFs and AC system in MATLAB/Simulink. As a result, a set of parameters with a satisfactory and comprehensive dynamic control performance can be acquired by the proposed method under both 200 MW and 300 WM load increases compared with the trial-and-error approach.