Operating Points Research Articles

Effect of Coupling Impedance on Stability Assessment of Grid-Forming Converters Under Various Grid Conditions

The phenomenon of frequency coupling is widely observed in grid-forming (GFM) converters due to the presence of asymmetrical controls and nonlinear blocks. However, the factors influencing frequency coupling have not been thoroughly explored. This paper introduces a systematic small-signal impedance model of GFM converters that intuitively reflects the factors affecting frequency coupling and provides a detailed analysis of how coupling impedance affects stability. It is demonstrated that the influencing factors of coupling impedance are an active power loop and reactive power loop. Specifically, the active power loop influences coupling impedance characteristics near the fundamental frequency, while the reactive power loop impacts the entire frequency range. This paper first reveals that the reactive power loop has a more pronounced effect on frequency coupling than the active power loop. Additionally, the variation in the steady-state operating points also affects the degree of frequency coupling of the GFM converters, primarily affecting the coupling impedance characteristics beyond the fundamental frequency, and the low operating points tend to affect system stability adversely. Finally, simulation results validate the accuracy of the mathematical model and theoretical analysis.

New Rotating Coanda-Type Intake for Sediment-Laden Flows

Tyrolean and Coanda types of water intake structures are the most widely preferred bottom intake structures used to divert water from channels and river systems for various purposes, such as energy production, irrigation, and domestic use. Estimation of withdrawal water and filtered sediment amounts are important to obtain maximum efficiency from an intake structure. The purpose of this study is to numerically analyze a newly patented rotating Coanda-type intake system to achieve the highest Water Capturing Efficiency and Sediment Release Efficiency. The newly patented system (patent no: TR2021011306B) presented here incorporates Coanda screens with variable void ratios, autonomously selected based on the sediment characteristics of the inflowing water. The process of selecting and positioning the appropriate screen is fully automated, requiring no human intervention. The system rotates under motor control to place the suitable screen at the operational point based on the specific requirements of the incoming sediment-laden flow.

Identification of sweetpotato virus disease-infected leaves from field images using deep learning

IntroductionSweetpotato virus disease (SPVD) is widespread and causes significant economic losses. Current diagnostic methods are either costly or labor-intensive, limiting both efficiency and scalability.MethodsThe segmentation algorithm proposed in this study can rapidly and accurately identify SPVD lesions from field-captured photos of sweetpotato leaves. Two custom datasets, DS-1 and DS-2, are utilized, containing meticulously annotated images of sweetpotato leaves affected by SPVD. DS-1 is used for training, validation, and testing the model, while DS-2 is exclusively employed to validate the model’s reliability. This study employs a deep learning-based semantic segmentation network, DeepLabV3+, integrated with an Attention Pyramid Fusion (APF) module. The APF module combines a channel attention mechanism with multi-scale feature fusion to enhance the model’s performance in disease pixel segmentation. Additionally, a novel data augmentation technique is utilized to improve recognition accuracy in the edge background areas of real large images, addressing issues of poor segmentation precision in these regions. Transfer learning is applied to enhance the model’s generalization capabilities.ResultsThe experimental results indicate that the model, with 62.57M parameters and 253.92 Giga Floating Point Operations Per Second (GFLOPs), achieves a mean Intersection over Union (mIoU) of 94.63% and a mean accuracy (mAcc) of 96.99% on the DS-1 test set, and an mIoU of 78.59% and an mAcc of 79.47% on the DS-2 dataset.DiscussionAblation studies confirm the effectiveness of the proposed data augmentation and APF methods, while comparative experiments demonstrate the model’s superiority across various metrics. The proposed method also exhibits excellent detection results in simulated scenarios. In summary, this study successfully deploys a deep learning framework to segment SPVD lesions from field images of sweetpotato foliage, which will contribute to the rapid and intelligent detection of sweetpotato diseases.

Optimal dispatch scheme considering system operational flexibility

To enhance the system's capacity to manage emergencies and foster the efficient utilization of newly generated energy, this paper introduces a redefined concept of system operation flexibility and proposes a two-stage optimized economic dispatch model. This model is tailored to ensure the system's operational flexibility. In this method, net load is pinpointed as the primary driver of flexibility demand, and a data-driven approach is employed to capture the uncertainties associated with net load across various risk levels. As flexibility supplier, conventional units work in tandem to optimize their operating points, fulfilling flexibility demand while adhering to the Do Not Exceed (DNE) baseline, thereby ensuring that system flexibility remains within safe limits. A case study on the modified SG-126 bus system demonstrates that the proposed methodology effectively highlights flexibility shortfalls and validates its efficiency in terms of cost and inter-regional coordination.

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.

Experimental characterization of a reversible heat pump – Organic Rankine cycle pilot plant as a thermally integrated Carnot battery

This paper reports a first detailed experimental characterization of a reversible heat pump – Organic Rankine Cycle pilot plant, presenting its potential as a thermally integrated Carnot battery for efficient energy storage. A total of 150 stationary operating points were investigated in both heat pump and ORC modes. The study analyzes and discusses the main trends and interactions on system and component level. As expected, heat pump and ORC operation show an opposing behavior. While heat pump operation yields a maximum COP of 9.5 at a 15 K temperature lift, the peak net ORC efficiency reaches 4.4 % with a 40 K temperature gradient. If additional storage losses are neglected, this leads to a power-to-power (round-trip) efficiency of up to 41.8 % for the Carnot battery. The absence of a liquid receiver in the ORC mode leads to notable subcooling and increased condensation pressures, impacting the expander’s power generation due to excess refrigerant accumulation. Moreover, the experimental results reveal that the reversible screw machine is the critical factor influencing efficiency. The screw machine’s maximum isentropic efficiencies are 78 % and 66 % in compressor and expander operation, respectively. The reliability of the results was verified by means of an energy balance and reference operating points, underlining high reproducibility. Additionally, potential system improvements were derived from the experimental results. Altogether, this paper successfully proofs the concept of reversible heat pump – ORC systems and provides experimental results for ongoing advancements in this field.

Regulation strategies and optimizations of the expander and pump in organic Rankine cycle under off-design conditions

For the present investigations of the off-design performance of organic Rankine cycle systems, which are mainly based on indirect parameters such as temperature and pressure, it is difficult to provide direct theoretical guidance for the actual operation of the system. Therefore, this paper introduces a novel, directly regulated coupling model based on a quasi-two-stage single screw expander and a multistage centrifugal pump. In addition, to enhance the matching between the expander and the pump during the regulation, the coupled model is optimized using the particle swarm optimization algorithm. The results reveal that the net efficiency initially increases and then decreases when exploring regulation strategies for the expander and the pump, suggesting the existence of optimal operating points. Specifically, after applying the particle swarm algorithm, the net efficiency reached 13.23 % at an expander speed of 5076 RPM and a pump frequency of 46.8 Hz, reflecting a better match between the pump and expander regulation with the heat source conditions. For instance, at a heat source temperature of 200 °C, the net efficiency increased by up to 1.45 % compared to the original results.

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.

In 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. Firstly, 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. The 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. The 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.