Operating Points Research Articles

Multiobjective Optimisation of Flotation Variables Using Controlled-NSGA-II and Paretosearch

Finding the optimum operating points for the maximisation of flotation recovery and concentrate grade can be a very difficult task, owing to the inverse relationship that exists between these two key performance indicators. For this reason, techniques that can accurately find the trade-off are critical for flotation process optimisation. This work extracted well-assessed Gaussian process predictive functions as objective functions for a comparative multiobjective optimisation study using the paretosearch algorithm (PA) and the controlled elitist non-dominated sorting genetic algorithm (controlled-NSGA-II). The main aim was the concomitant maximisation of the copper recovery and the concentrate grade. Comparison of the two applied techniques revealed that the PA discovered the best set of the pareto-optimal solution for both the recovery (93.4%) and concentrate-grade (17.4 wt.%) maximisation.

U-net with ResNet-34 backbone for dual-polarized C-band baltic sea-ice SAR segmentation

Abstract In this study, the U-net with ResNet-34, i.e. a residual neural network with 34 layers, backbone semantic segmentation network is applied to C-band sea-ice SAR imagery over the Baltic Sea. Sentinel-1 Extra Wide Swath mode HH/HV-polarized SAR data acquired during the winter season 2018–2019, and corresponding segments derived from the daily Baltic Sea ice charts were used for training the segmentation algorithm. C-band SAR image mosaics of the winter season 2020–2021 were then used to evaluate the segmentation. The major objective was to study the suitability of semantic segmentation of SAR imagery for automated SAR segmentation and also to find a potential replacement for the outdated iterated conditional modes (ICM) algorithm currently in operational use. The results compared to the daily Baltic Sea ice charts and the operational ICM segmentation and visual interpretation were encouraging from the operational point of view. Open water areas were located very well and the oversegmentation produced by ICM was significantly reduced. The correspondence between the ice chart polygons and the segmentation results was also reasonably good. Based on the results, the studied method is a potential candidate to replace the operational ICM SAR segmentation used in the Copernicus Marine Service automated sea-ice products at Finnish Meteorological Institute.

An ultrasensitive multimodal intracranial pressure biotelemetric system enabled by exceptional point and iontronics.

The accurate monitoring of vital physiological parameters, exemplified by heart rate, respiratory rate, and intracranial pressure (ICP), is of paramount importance, particularly for managing severe cranial injuries. Despite the rapid development of implantable ICP sensing systems over the past decades, they still suffer from, for example, wire connection, low sensitivity, poor resolution, and the inability to monitor multiple variables simultaneously. Here, we propose an ultrasensitive multimodal biotelemetric system that amalgamates an iontronic pressure transducer with exceptional point (EP) operation for the monitoring of ICP signals. The proposed system can exhibit extraordinary performance regarding the detection of minuscule ICP fluctuation, demonstrated by the sensitivity of 115.95 kHz/mmHg and the sensing resolution down to 0.003 mmHg. Our system excels not only in the accurate quantification of ICP levels but also in distinguishing respiration and cardiac activities from ICP signals, thereby achieving the multimodal monitoring of ICP, respiratory, and heart rates within a single system. Our work may provide a pragmatic avenue for the real-time wireless monitoring of ICP and thus hold great potential to be extended to the monitoring of other vital physiological indicators.

Nonlinear second order plus time delay model identification and nonlinear PID controller tuning based on extended linearization method

PID control systems based on the first order plus time delay model (FOPTD), which approximate the full system dynamics, are well-accepted for a wide range of linear processes. While such controllers can be applied to overdamped nonlinear processes, they often experience excessive overshoots and oscillations for general nonlinear processes. To overcome this limitation, we propose a novel method to design a nonlinear PID controller based on the second order plus time delay (SOPTD) model. The system nonlinearity requires parameter adjustments of the linearized model across operational ranges. Hence, in this work, it is handled by the extended linearization method (ELM), ensuring local stability under the assumptions of slow and small changes in operating points. Importantly, the model achieves global input-to-output stability even without the above constraints, provided there are no structural and parametric errors. The resulting nonlinear SOPTD model can describe changes in process gain and two time constants as the operation point varies. We demonstrate the applicability of our approach with a polymerization reactor simulation and liquid-level control experiments.

Dynamic Formulation of the Double Multiple Stream Tube Model of Offshore Vertical Axis Wind Turbines

Abstract The paper proposes a novel algorithm to simulate Vertical Axis Wind Turbines in floating motion based on a low-fidelity approach. The conventional Double Multiple Stream Tube Model is formulated as a steady state model to deal with the inherent unsteady aerodynamics of VAWTs. The Double Multiple Stream Tube Model makes use of an average contribution of the blade passages over a revolution to solve the Blade Element Momentum equation. The model reduces the dependence of the solution (the induction field) to a space variable. Floating Vertical Axis Wind Turbines undergoes a variation of aerodynamic quantities according to wave periods, also different from the rotor period. This paper provides a new formulation of the Double Multiple Stream Tube Model to solve the turbine rotation in time and space, introducing the variation of the platform velocity and a revised approach to the downstream actuator disc. The most impactful parameter is found to be the wave frequency: wave periods at full-scale are lower than the ones of the turbine rotor, featuring optimal operating points at low tip speed ratios. The increase of the wave frequency highlights the differences of the average torque prediction for a single blade up to 10% between the unsteady and the standard formulation. In this context, the development of fast low-fidelity computational tools play a key role in the assessment of the preliminary designs of Floating Offshore Vertical Axis Wind Turbines and it will be used to optimise the aerodynamic design of the laboratory scaled model under surge motion.

Model-Adaptive Feedforward Structure for Enhanced Dynamic Response in SRM Current Controller Based on Operating Point Transformation

Model-Adaptive Feedforward Structure for Enhanced Dynamic Response in SRM Current Controller Based on Operating Point Transformation

Tensor power flow formulations for multidimensional analyses in distribution systems

In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of Power flows (PFs) in distribution systems. The presented algorithms are the base for a new TensorPowerFlow (TPF) tool and shine for their simplicity, benefiting from multicore Central processing unit (CPU) and Graphics processing unit (GPU) parallelization. We also focus on the mathematical convergence properties of the algorithm, showing that its unique solution is at the practical operational point. The proof is validated using numerical simulations showing the robustness of the FPI algorithm compared to the classical Newton–Raphson (NR) approach. In the case study, a benchmark with different PF solution methods is performed, showing that for applications requiring a yearly simulation at 1-minute resolution, the computation time is decreased by a factor of 164, compared to the NR in its sparse formulation. Finally, a set of applications is described, highlighting the potential of the proposed formulations over a wide range of analyses in distribution systems.

Fighting against forest fire: A lightweight real-time detection approach for forest fire based on synthetic images

Forest fires are known for their high level of randomness and unpredictability, which often lead to significant ecological damage and human life loss. Existing forest fire detection technologies are not capable of detecting small-scale flames or smoke in real time, thus failing to meet the demands of real-time detection of forest fires using Unmanned Aerial Vehicles (UAVs). To overcome these limitations, we propose an efficient and lightweight forest fire detection method that utilizes synthetic images and UAVs to achieve real-time and high-precision detection of forest fires against complex backgrounds. Firstly, we propose the Dilation Repconv Cross Stage Partial Network (DRCSPNet), which enhances the detection capabilities for multiscale flames and smoke using multi-branch parallel joint dilation convolution and batch normalization, while effectively extracting features from different stages of forest fires. Secondly, to mitigate challenges associated with extreme lighting in forest scenes and large contrast variation in fire images, we propose a Global Mixed-Attention (GMA) model across feature pyramids to enhance information lost in high-dimensional feature maps and increase the robustness of the model through a multiscale fusion strategy. Finally, we present the Lite-Path Aggregation Network (Lite-PAN) with varying scales to improve effective feature flow for multilevel forest fires, addressing challenges that arise from various climatic conditions. Furthermore, we employ Unreal Engine 5 to generate forest fire datasets in four scenarios to address the issue of relatively limited aerial forest fire datasets. According to the results of the experiment, our proposed method achieves 58.39% mAP(mean Average Precision) with 5.703 GFLOPs (Giga Floating Point Operations Per Second) while yielding a frame rate of 33.5 Frames Per Second (FPS) on NVIDIA Jetson NX. Extensive experiment results demonstrate our method has the advantage of being in real time, extremely accurate, and easily implementable compared to state-of-the-art techniques.

A Novel Fast Contact Operating Mechanism of the Medium and Low Voltage Hybrid DC Current Limiting Circuit Breaker

In order to solve the problem of the slow initial speed caused by the large mass of the bistable permanent magnetic actuator (PMA) in the traditional bistable permanent magnetic–electromagnetic repulsion mechanism (PM-ERM), a novel fast contact operating mechanism is proposed by using the flexible spring system (SS) between the PMA and the ERM. The novel structure can separate the mass of the PMA and the ERM at the initial phase of the interrupting process, improve the initial speed of the contact and increase the initial opening distance of the contact. Firstly, the paper conducts an extensive investigation and analysis of the principle of the existing fast operating mechanism and points out the advantages and disadvantages of the existing mechanism. In order to meet the requirement of fast interrupting and improve the service life of the mechanism, a novel mechanism is proposed. And then, the working principle of the novel mechanism is introduced. The cooperative relationship between the ERM and the PMA and the working principle and performance parameter requirements of the ERM, SS and PMA are analyzed and designed. Finally, the feasibility of the novel mechanism is verified by the experiment. The results show that the opening distance of the novel operating mechanism can reach 2.25 mm in 1 ms. Compared with 1.24 mm of the traditional operating mechanism, it improves the initial opening distance of the contact by 81.5% and is conducive to the rapid interruption of the Hybrid DC current-limiting circuit breaker (HDCCLCB).

LCGSC-YOLO: a lightweight apple leaf diseases detection method based on LCNet and GSConv module under YOLO framework

IntroductionIn response to the current mainstream deep learning detection methods with a large number of learned parameters and the complexity of apple leaf disease scenarios, the paper proposes a lightweight method and names it LCGSC-YOLO. This method is based on the LCNet(A Lightweight CPU Convolutional Neural Network) and GSConv(Group Shuffle Convolution) module modified YOLO(You Only Look Once) framework.MethodsFirstly, the lightweight LCNet is utilized to reconstruct the backbone network, with the purpose of reducing the number of parameters and computations of the model. Secondly, the GSConv module and the VOVGSCSP (Slim-neck by GSConv) module are introduced in the neck network, which makes it possible to minimize the number of model parameters and computations while guaranteeing the fusion capability among the different feature layers. Finally, coordinate attention is embedded in the tail of the backbone and after each VOVGSCSP module to improve the problem of detection accuracy degradation issue caused by model lightweighting.ResultsThe experimental results show the LCGSC-YOLO can achieve an excellent detection performance with mean average precision of 95.5% and detection speed of 53 frames per second (FPS) on the mixed datasets of Plant Pathology 2021 (FGVC8) and AppleLeaf9.DiscussionThe number of parameters and Floating Point Operations (FLOPs) of the LCGSC-YOLO are much less thanother related comparative experimental algorithms.

Transient opto-thermal simulation analysis and experimental validation of LERP systems

Abstract Solid-state technology has revolutionized the lighting industry. However, efficiency-droop-limited LEDs introduce constraints to the luminances achieved, and as a result, laser diodes (LDs) are replacing them in the remote phosphor setup. This introduces a new family of lighting solutions, laser-excited remote phosphor (LERP) systems, which can outperform cutting-edge LEDs. LERP systems however have not yet reached their full potential as the high intensity laser beam induces high temperatures within the phosphor material whose emission characteristics heavily depend on temperature. For this reason, a simulation framework has been developed that combines optical and thermal analysis in order to study and optimize these systems and derive their temperature thresholds for sustainable long-term usage. The focus here is on transient analysis, where the interplay between optical and thermal effects can be accounted for and the time dynamics of the system can be investigated. This enables the study of operation points near or at the thermal quenching regime. Furthermore, advanced material models have been developed in order to incorporate the temperature-dependence. The experimental validation of the model has shown that experimental and simulated results are in good agreement.

Investigation on Effect of Nonlinear Parameters in torque accuracy of Permanent Magnet Synchronous Machines

EVs and PHEVs are mainly driven by inverter control topologies which will be driving permanent magnet synchronous machines. PMSM is widely used for high drive performance. However, due to parameter variations high torque accuracy is not achieved. The performance of the PMSM drives is widely affected which is caused mainly due to environmental factors and aging. This would include component tolerance, input signal tolerance, machine parameters, software algorithms, sensor accuracy and calibration. It is important to understand these factors and make the tuning throughout the lifecycle since it may show performance deviations over the time and may lead to field complaints. As a step-by-step procedure, the factors which affects the torque accuracy are identified as part of the literature review. This is given as input to a cause effect relationship model which could demonstrate the deviation points. Simulation is performed by varying the operating points at different regions in the e-drive system modeled in Matlab/Simulink. Simulation results are used with different operating points to find out the influential parameters contributing to the deviation. The outcomes derived from the simulations are analyzed and dominant factors are determined. It is observed that this behavior will vary according to the changes in speed and torque. Correlation coefficient is derived from the statistical analysis. The future scope will be applying the optimization techniques and verification of improvements.

Consistent Evaluation Methods for Microfluidic Mixers

The study presents a unifying methodology for characterizing micromixers, integrating both experimental and simulation techniques. Focusing on Dean mixer designs, it employs an optical evaluation for experiments and a modified Sobolev norm for simulations, yielding a unified dimensionless characteristic parameter for the whole mixer at a given Reynolds number. The results demonstrate consistent mixing performance trends across both methods for various operation points. This paper also proposes enhancements in the evaluation process to improve accuracy and reduce noise impact. This approach provides a valuable framework for optimizing micromixer designs, essential in advancing microfluidic technologies.

Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell

Fuel cell electric vehicles (FCEVs) are among the devices that have emerged in recent years. To provide electricity to the electric motors, they use a proton-exchange membrane fuel cell (PEMFC) as the primary energy source and a secondary source consisting of an energy storage system (battery or supercapacitors). The addition of these sources to the motors and accessories of a vehicle requires the association of static converters to condition the different power sources. In addition, a high-efficiency and enhanced-reliability power converter is essential to connect the PEMFC to the vehicle’s DC bus. This paper proposes a robust feedback controller for a four-phase interleaved boost converter used with PEMFC. The proposed controller has double loops based on a state-feedback controller, and an inner loop which translates the differential equation of the system into a state representation by linearization around its operation points. The reference current is generated by state feedback in the outer loop; the state variable is defined by using a change variable. The strong robustness and highly dynamic characteristics of the proposed controller are demonstrated through its performance in terms of output voltage, source current, and settling time. The findings indicate that the proposed controller achieves a response time of 20 ms, resulting in an over 50% improvement compared to the controllers referenced in the literature. Additionally, it reduces both current and voltage ripple, keeping them each below 10%. Further, the controller gains synthesis is validated using the linear quadratic regulator (LQR) technique as well as boundary conditions, and its robustness is verified, taking into account the uncertainty of various operating conditions and discrepancies in circuit components. A double-loop super-twisting sliding mode controller, a backstepping control algorithm, and a PI controller are selected for comparison and discussion. Subsequently, the effectiveness of the proposed controller is evaluated through simulation with the parameters of a 500 W fuel cell system.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

Evaluating Convolutional Neural Network Architecture for Historical Topographic Hardcopy Maps Analysis: A Study on Training and Validation Accuracy Variation

Convolutional Neural Networks (CNN) are widely used for image analysis tasks, including object detection, segmentation, and recognition. Given the advanced capability, this study evaluates the effectiveness and performance of CNN architecture for analysing Historical Topographic Hardcopy Maps (HTHM) by assessing variations in training and validation accuracy. The lack of research specifically dedicated to CNN’s application in analysing topographic hardcopy maps presents an opportunity to explore and address the unique challenges associated with this domain. While existing studies have predominantly focused on satellite imagery, this study aims to uncover valuable insights, patterns, and characteristics inherent to HTHM through customised CNN approaches. This study utilises a standard CNN architecture and tests the model’s performance with different epoch settings (20, 40, and 60) using varying dataset sizes (288, 636, 1144, and 1716 images). The results indicate that the optimal operation point for training and validation accuracy is achieved at epoch 40. Beyond epoch 40, the widening gap between training and validation accuracy suggests overfitting. Hence, adding more epochs does not significantly improve accuracy beyond the optimum phase. The experiment also shows that the CNN model obtains a training accuracy of 98%, validation accuracy of 67%, and F1-score overall performance of 77%. The analysis demonstrates that the CNN model performs reasonably well in classifying instances from the HTHM dataset. These findings contribute to a better understanding of the strengths and limitations of the model, providing valuable insights for future research and refinement of classification approaches in the context of topographic hardcopy map analysis.

Emissions and Flame Stability Assessment of Hydrogen Addition and Air Dilution in a Micro Gas Turbine Combustor Using 0D/1D Modeling by Chemical Reactor Network

Abstract Hydrogen emerges as a promising fuel for clean and sustain- able electricity production when utilized in micro gas turbines (mGTs). However, some challenges linked to hydrogen usage must be overcome as its high reactivity can lead to increased NOx emissions and flame instabilities. Literature suggests that combustion air humidification and/or exhaust gas recirculation (EGR) may be methods to reduce this reactivity. However, these measures are limited due to the small operating range of the mGT combustor. In this context, a 20 kWth mGT combustor is investigated un- der various inlet conditions to assess the effect of EGR towards increased flame stability and emission control when operating under hydrogen-enriched methane firing, using a Chemical Re- action Network (CRN) model. The CRN modeling is a fast and low computational complexity tool to model combustion perfor- mance and emissions by representing complex reactive flow fields through idealized reactor models, leading to reduced computa- tional costs. This study examines partial load conditions, fuel compositions, and the effect of combustion air dilution through EGR. The CRN model of the combustion process is designed based on combustion zones extracted from the main flow fields with similar thermo-chemical states from CFD, while emission predictions are experimentally validated for different operating points. Predicted NOx and CO emissions by the proposed CRN model show an acceptable agreement with the experimental data at high power loads. However, its accuracy diminishes for loads below 70% due to the variability in model tuning parameters across different loads, whereas the

Operation Analysis of Power Systems with HVDC Interconnections Using a Transient Stability Aware OPF Method

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided.

The design of a model predictive control strategy and performance analysis for pumped storage units and super capacitors in power systems

IntroductionSuper capacitors were regarded as one of the most effective frequency regulation technologies in power systems. The major issue of applying super capacitors for frequency regulation is the limited energy capacity and the high construction costs. The pumped storage units have been developed and applied in power systems for decades, whereas it was rarely operated to regulate the system frequency due to the operation limitations. The pumped storage units could be operated as a hydro generator under the generation mode and provide effective frequency regulation service with the conventional automatic generation control (AGC) system while its output power was constant under the pumping mode.MethodIn this paper, the pumped storage unit was controlled to participate in frequency regulation under the generation mode with super capacitors. Considering the AGC system could not consider the state of charging (SOC) of super capacitors for frequency regulation, an optimization model was proposed to determine the operation points for super capacitors and pumped storage units under a model predictive control (MPC) strategy to regulate the system frequency for power systems.Results and DiscussionBased on the various operation scenarios, the effectiveness and costs of pumped storage units and super capacitors to provide frequency regulation services were discussed. It was indicated that pumped storage units and super capacitors were effective technologies for power systems.

Optimization Strategies for Rydberg Atom-based AM Receiver Operational Parameters

Abstract The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches between the parameters of the received amplitude modulation (AM) signals and the system's linear workspace and demodulation operating points can cause severe distortion in the demodulated signals. To address this, the article proposes a method for determining the operational parameters based on the mean square error (MSE) and total harmonic distortion (THD) assessments, and presents strategies for optimizing the system’s operational parameters focusing on linear response characteristics (LRC) and linear dynamic range (LDR). Specifically, we employ a method that minimizes the MSE to define the system’s linear workspace, thereby ensuring the system has a good LRC while maximizing the LDR. To ensure the signal always operates within the linear workspace, an appropriate carrier amplitude is set as the demodulation operating point. By calculating the THD at different operating points, the LRC performance within different regions of the linear workspace is evaluated, and corresponding optimization strategies based on the range of signal strengths are proposed. Moreover, to more accurately restore the baseband signal, we establish a mapping relationship between the carrier Rabi frequency and the transmitted power of the probe light, and optimize the slope of the linear demodulation function to reduce the MSE to less than 0.8×10-4. Finally, based on these methods for determining operational parameters, we explore the effects of different laser Rabi frequencies on system performance, and provide optimization recommendations. This research provides robust support for the design of high-performance Rydberg atom-based AM receivers.