Changes In Operating Conditions Research Articles

An Adaptive Inertia and Damping Control Strategy Based on Enhanced Virtual Synchronous Generator Model

In modern converter-dominated power systems, total inertia is very variable and depends on the share of power generated by renewable-based converter-interfaced generation (CIG) at each specific moment. As a result, the limits required by the grid codes on the rate of change of frequency and its nadir or zenith during disturbances become challenging to achieve with conventional control approaches. Therefore, the transition to a novel control strategy of CIG with a grid-forming power converter is relevant. For this purpose, a control algorithm based on a virtual synchronous generator (VSG) is used, which simulates the properties and capabilities of a conventional synchronous generation. However, due to continuously changing operating conditions in converter-dominated power systems, the virtual inertia formed by VSG must be adaptive. At the same time, the efficiency of adaptive algorithms strongly depends on the used VSG structure. In this connection, this paper proposes an enhanced VSG structure for which the transfer function of the active power control loop was formed. With the help of it, the advantages over the conventional VSG structure were proven, which are necessary for the effective adaptive control of the VSG parameters. Then, the analysis of the impact of the VSG parameters on the dynamic response using the transient characteristics in the time domain was performed. Based on the results obtained, adaptive algorithms for independent control of the virtual inertia and the parameters of the VSG damper winding were developed. The performed mathematical modeling confirmed the reliable and effective operation of the developed adaptive control algorithms and the enhanced VSG structure. The theoretical and experimental results obtained in this paper indicate the need for simultaneous development and improvement of both adaptive control algorithms and VSG structures used for this purpose.

Resonance diagnostics of production space of generative systems of artificial intelligence

The development of artificial intelligence generative systems (AIGS) in the modern world requires addressing issues related to the quality, stability, and efficiency of the generated content. In this context, resonance diagnostics become of paramount importance. The purpose of this study is to explore the possibilities of applying resonance diagnostics for detecting, analyzing, and resolving problems in artificial intelligence generative systems. To achieve the set goal, the following tasks were identified: analysis of the theoretical foundations of resonance diagnostics; investigation of the potential of using resonance signals to adjust AIGS learning parameters; studying the impact of resonance diagnostics on the stability and adaptation of AIGS to changing operating conditions. The study conducted an analysis of resonance diagnostics in the context of AIGS and revealed its powerful influence on addressing issues related to system quality and productivity. The research demonstrated that resonance diagnostics can be used to achieve realism, diversity, and quality of generated content. Additionally, it was determined that it can contribute to enhancing the stability and adaptation of systems to varying operational conditions

Load Carrying Capacity Enhancing Design and Lubrication Investigation of the Magnetic-Water Double Suspension Elastic Support Thrust Bearing

Aiming at the problem that the traditional water-lubricated bearing cannot carry the heavy load and adapt to the constantly changing operating conditions for the high-power Rim Driven Thruster (RDT), the principle structure of the Magnetic Water-double-suspension Elastic-support Thrust Bearing (MWETB) is designed and the optimal structure parameters of the bearing are selected using simulation. To demonstrate the reliability of the MWETB under the RDTs’ actual working conditions, performance tests, which include the magnetic flux density, magnetic force, and lubrication performance, are carried out. The simulation and experimental results indicate that the optimal offset ratios are in two intervals, and the magnetic alignment and sheath materials have a great effect on the load reduction. The load-carrying force has obvious zoning characteristics with the change in bearing clearance. Besides, compared with the water-lubricated thrust bearings, the MWETB has advantages in terms of minimum film thickness and friction coefficient.

Adaptive machine learning based control of multi-temperature, multi-module latent thermal storage ensembles

Phase change material thermal energy storage systems can be used to alleviate disparities in renewable energy supply and demand. Performance of such systems can be enhanced using a cascade of multiple phase change materials but efficiency benefits currently rely on precise optimization of design parameters. Therefore, there is motivation to develop storage technologies which provide greater flexibility for applications with dynamic operating conditions. In this study, a novel system is proposed called a multi-temperature, multi-module ensemble which consists of multiple phase change materials combined in series and parallel configurations. The system is optimized and controlled by an artificial neural network trained using a multi-objective optimization scheme. The performance for the novel system is compared to two baseline systems: a single phase change material system and a cascaded system. Across a wide range of fixed operating conditions, the results show that the ensemble reduces the rate of exergy destruction by 60% compared to the single module system and achieves a 10 °C improvement in performance for matching target outlet temperature compared to the cascaded system. Over a dynamically changing load profile, the results show that the multi-temperature, multi-module ensemble with artificial neural network controller is the preferable design as it achieves the excellent exergy efficiency of a cascaded system while maintaining the flexibility needed to respond to dynamically changing operating conditions. Furthermore, this novel approach widens the potential applications of latent thermal energy storage by providing a means to standardize manufacturing to drive down system cost and allowing the system to adapt to unforeseen design parameters.

A sequential cross-product knowledge accumulation, extraction and transfer framework for machine learning-based production process modelling

ABSTRACT Machine learning is a promising method to model production processes and predict product quality. It is challenging to accurately model complex systems due to data scarcity, as mass customisation leads to various high-variety low-volume products. This study conceptualised knowledge accumulation, extraction, and transfer (KAET) to exploit the knowledge embedded in similar entities to address data scarcity. A sequential cross-product KAET (SeqTrans) is proposed to conduct KAET, integrating data preparation and preprocessing, feature selection (FS), feature learning (FL), and transfer learning (TL). The FS and FL modules conduct knowledge extraction and help address various practical challenges such as changing operating conditions and unbalanced datasets. In this paper, sequential TL is introduced to production modelling to conduct knowledge transfer among multiple entities. The first case study of auxetic material performance prediction demonstrates the effectiveness of sequential TL. Compared with conventional TL, sequential TL can achieve the same test mean square errors with 300 fewer training examples when facing data scarcity. In the second case study, balancing anomaly detection models were constructed for two gas turbines in the same series using real-world production data. With SeqTrans, the F1-score of the anomaly detection model of the data-poor engine was improved from 0.769 to 0.909.

Improvement in Identification Accuracy of a Failure Diagnostic System for a Reusable Rocket Engine

As a technology for safe and efficient operation of reusable rockets, we are developing failure diagnosis technology for reusable rocket engines. In order to follow the changes in rocket engine operating conditions, a failure diagnostic method which monitors an error vector: the difference between the predicted and measured values of the sensors was developed. The method contains anomaly detection by Mahalanobis distance and failure identification by support vector machines (SVMs). In this report, combinations of monitoring sensors of SVMs for each failure mode were optimized by using design of experiments. By using optimal sensor combinations, the F-score of SVMs were improved in all failure modes. From the results of the orthogonal table experiments, it was supposed that sensors which show the difference in failure modes are important to distinguish failure modes. In addition, a failure classifier combined with the optimized SVMs for each failure mode was developed and demonstrated. The performance of the combined failure classifier with the optimal sensor combination was mostly greater than that with all sensors. However, degradation of the classification performance was also obtained. It is necessary to consider how integrate the results of SVMs which are optimized individually.

A Novel Unsupervised Clustering and Domain Adaptation Framework for Rotating Machinery Fault Diagnosis

Deep learning-based fault diagnosis methods require a large number of labeled datasets. However, considering the changing operating conditions, it is impractical to obtain labeled datasets for all cases. Therefore, this study proposes a new unsupervised clustering and domain adaptation framework to circumvent data deficiency and domain issues. The proposed framework comprises two steps: unsupervised clustering and domain adaptation. In the unsupervised clustering, an expectation-maximization adversarial autoencoder, which combines an expectation-maximization algorithm with an adversarial autoencoder, is used for feature extraction and subspace mapping. Subsequently, the mapped features are clustered using a Gaussian mixture model. In the domain adaptation, a domain synchronization that is based on the symmetric Kullback-Leibler divergence metric is used to infer the relationship between the source and target domain clusters. The experiments on two rolling-element-bearing datasets validate the effectiveness of our method. Specifically, our method performs domain adaptation without retraining, which is promising for real industrial applications.

Study on one-dimensional performance prediction of multi-stage axial turbine based on the blade height

Abstract As turbine operating conditions change, the blade height and tip clearance undergo continuous alterations due to the combined effects of thermal stress, aerodynamic forces and centrifugal forces, subsequently influencing the turbine performance. To take this effect into account in turbine performance prediction, this study considers the influence of fluid-heat-structure coupling on blade height and tip clearance and establishes a one-dimensional comprehensive prediction method for multi-stage axial turbine performance considering blade height. When compared with experimental results from a four-stage axial turbine, by considering the fluid-thermal-solid coupling effects, the average relative error in total pressure ratio prediction is reduced from 3.76 % to 1.99 % and the average relative error in total temperature ratio prediction is reduced from 2.03 % to 1.26 %. Compared with the traditional flow prediction method, the prediction results of turbine characteristics considering blade height and tip clearance changes in this paper are closer to the experimental results.

The Interdependence of Environmental Activities and Investment Attractiveness: Finances of Russian Metallurgy

All countries now share a long-term vision of the importance of implementing technology development and transfer to improve climate resilience and reduce greenhouse gas emissions. Metallurgical enterprises play a significant role in achieving this goal, since they produce a large amount of carbon dioxide emissions into the atmosphere. In connection with the changing operating conditions and changing markets of presence, the issues of ensuring their investment attractiveness are acquiring obvious importance in the framework of the finances of Russian metallurgical companies. The object of the study is an assessment of the investment attractiveness of Russian metallurgical companies. The subject of the study is the relationship between the investment attractiveness of metallurgical companies and the results of their environmental protection activities. The purpose of this study is to identify the interdependence of environmental metrics and the investment attractiveness of steel companies. The methodological basis is a regression analysis of the impact of environmental metrics on the investment attractiveness of metallurgical companies. The authors chose the following indicators of environmental performance: CO2 emissions, energy consumption, water recycling, waste. To assess the investment attractiveness of metallurgical companies, the following indicators were used: revenue, EBITDA, investment in R&D. The authors concluded that the environmental activities of companies have a significant impact on their investment attractiveness. The scientific novelty of the study lies in identifying the interdependence of environmental protection activities and the investment attractiveness of Russian metallurgy companies. The results of the study can be used by both Russian steel companies and institutional investors as part of the development of an investment strategy.

Scenarios for Hydrogen Production from a Full-Scale Reversible Solid Oxide System with Electrolyte-Supported Stacks

Reversible solid oxide cell technology is of increasing interest and is emerging as a promising grid-flexible support system [1] with storage [2]. Among the types of cell architecture available, electrolyte-supported cells have proven to be better in terms of long-term stability and degradation of the cell, albeit sacrificing on performance as compared to anode-supported cells [3]. This makes electrolyte-supported cells better suited architecture to be used in reversible systems where higher stability is required due to the changing operating conditions in each mode. In the present work, validated cell and stack models of Nexceris electrolyte-supported cells are used to model a full-scale reversible solid oxide system designed for hydrogen production.EIS data from symmetric button cells and polarization curves from 5cm×5cm full cells are used to validate the stack model. A net 100 kWe system in fuel cell mode is modelled with electrolyte-supported stacks from Nexceris operating at a nominal temperature of 800 oC. The system is run at thermoneutral conditions in electrolysis mode and at 0.5 A/cm2 in fuel cell mode to balance between efficiency and hydrogen production. The levelized cost of hydrogen production is calculated using a price-taker model with the system pumping the produced hydrogen into a natural gas/hydrogen pipeline. The effect of various parameters, such as the capacity factor of the system, the operating pressure, grid-electricity pricing, etc. - are investigated on the levelized cost of hydrogen.[1] Guenther Glenk and Stefan Reichelstein, “Reversible Power-to-Gas systems for energy conversion and storage”, Nature Communications 13, 2010 (2022).[2] Evan Reznicek and Robert J. Braun, “Techno-economic and off-design analysis of stand-alone, distributed-scale reversible solid oxide cell energy storage systems”, Energy Conversion and Management 175, 2018.[3] Marco A. Buccheri, Anand Singh, and Josephine M. Hill, “Anode- versus electrolyte-supported Ni-YSZ/YSZ/Pt SOFCs: Effect of cell design on OCV, performance and carbon formation for the direct utilization of dry methane”, Journal of Power Sources 196, 2011. Figure 1

Common misinterpretations of thermal signatures on polycrystalline PV modules under different operational conditions

Thermal Infrared (TIR) images may be misinterpreted due to the dynamic nature of the operational current–voltage (I-V) and thermal characteristics of photovoltaic (PV) cells and modules. This study investigates and analyses the I-V and thermal characteristics of polycrystalline PV modules to understand the behaviour of abnormal thermal signatures (hot cells) under varying operational points on a module’s I-V curve. A change in operational conditions influences the operational voltage points such that the mismatched cells behave differently under changing load conditions, varying irradiance and partial shading. Mismatched cells can operate in reverse bias and cause abnormal thermal signatures when the module’s operational voltage is less than the maximum power voltage(VMP), 24 V. The mismatch becomes more significant when the operational voltage is 0.2 V, close to short circuit conditions. Cell partial shading of < 10% results in dynamics of the abnormal thermal signatures and can mislead decisions during TIR imaging inspections when bad cells do not show abnormal thermal signatures on TIR images. Carrying out TIR imaging on clean PV modules operating at < VMP will enhance mismatch and the bad cells can show as hot cells, unlike when operating at > VMP. Under low irradiance of < 600Wm−2, mismatched cells can easily emerge on TIR images, however, at irradiance levels > 600Wm−2, only cells having large isolated parts will cause significant mismatch and appear hot on TIR images. This study advances the understanding of the dynamics of operational I-V points and thermal signatures and can improve the operation and TIR imaging inspections of PV modules.

Real-time power optimization based on PSO feedforward and perturbation & observation of fuel cell system for high altitude

The control technology of the air supply system is key to the performance and reliability of the fuel cell system (FCS). However, due to the low oxygen pressure in high altitude environment, the power consumption of the air compressor is large, and the output power of FCS is seriously degraded. To effectively increase the output power of the FCS under changeable altitude conditions, this article introduces a real-time power optimization strategy based on the particle swarm optimization (PSO) feedforward and perturbation and observation (P&O) algorithm. Considering the change of total entropy and loss of centrifugal air compressor with altitude, the working characteristics of centrifugal compressors at different altitudes are studied. Aiming at the long optimization time of the traditional P&O method and the misjudgment problem when the external conditions change drastically, the PSO feedforward-based P&O method is designed to adjust the air compressor voltage. The results show that the proposed strategy has real-time power optimization capability and can adapt to altitude change. The average setting time is 1.8 s when the operating condition changes and the power decreases to 9.1% at 3000 m altitude under a higher load (300 A).

Multi-sensor signals multi-scale fusion method for fault detection of high-speed and high-power diesel engine under variable operating conditions

Detecting faults in high-speed and high-power diesel engines under complex variable operating conditions is highly challenging. Online vibration monitoring systems have been used in such diesel engines in key fields, in which vibration sensors are installed on each cylinder to enable comprehensive monitoring. In this paper, a fault detection method for diesel engines under variable operating conditions is proposed based on multi-sensor signal multi-scale fusion. Firstly, a preprocessing framework is established for the raw vibration signals collected from each cylinder to eliminate random interference and system noise. Then, the resulting signals are phase-aligned based on the engine firing sequence and analyzed using a signal correlation algorithm to produce a multi-sensor multi-scale similarity matrix (MSMSSM). Finally, a multi-branch residual convolutional neural network (MBRCNN) model is constructed with the MSMSSM as the input to detect abnormal health states of the diesel engine. Fault simulation experiments are conducted on a 12-cylinder V-type high-speed and high-power diesel engine test rig. The comparative test results indicate that the proposed MSMSSM-MBRCNN method shows both the highest accuracy of 95.28% and the lowest standard deviation of 3.57% compared to other typical methods. The multi-sensor signals multi-scale fusion method proposed in this paper fully utilizes the key information that remains basically consistent in the synchronous acquisition signals of multiple sensors under different operating conditions. This can effectively reduce the interference of operating condition changes and improve the accuracy and robustness of fault detection.

Statistical process control versus deep learning for power plant condition monitoring

This study compares four models for industrial condition monitoring including a principal components analysis (PCA) approach and three deep learning models, one of which is a new, lightweight version of another. We also propose a simple attention mechanism for enchancing deep learning models with better predictions and feature importance. Two datasets are used, one simulated from the Tennessee Eastman Process, the other from two feedwater pumps at a Danish combined heat and power plant. Our final results show evidence in favour of the PCA-based approach as it has detection ability comparable to the deep learning approaches as well as faster training time, fewer hyperparameters, as well as robustness to changing operating conditions. We conclude the paper by putting into perspective the importance of building up complexity incrementally with a recommendation to start modelling with simpler and well-tested models before the adoption of more advanced, less transparent models.

Nonlinear coordination strategy between renewable energy sources and fuel cells for frequency regulation of hybrid power systems

This study proposes an advanced control strategy for the coordination of an energy storage system (ESS) based on fuel cells (FCs) and renewable energy sources (RESs) to enhance frequency dynamic performance in hybrid power systems (HPSs). The proposed coordination control strategy is based on the nonlinear proportional-integral (NPI) controller, which increases the system's flexibility in dealing with disturbances and changing operating conditions. In addition, it improves the system's dynamic response and attempts to address system weakness caused by highly penetrating RESs. The proposed NPI controller is optimally designed using a new optimization algorithm, called dandelion optimizer (DO), whose proficiency and effectiveness are verified by comparing its performance with other well-known optimization algorithms used in the literature; particle swarm optimization (PSO), grey wolf optimization (GWO), and ant lion optimization (ALO) algorithms considering various standard objective functions. Furthermore, the proposed NPI controller performs better than other control strategies used in the literature under load/RESs fluctuations. The effectiveness of the proposed nonlinear coordination control strategy is examined and investigated through a self-contained HPS that includes a diesel generator, RESs (i.e., photovoltaic and wind power plants), battery ESS, flywheel ESS, aqua electrolyzer for hydrogen production, FCs, electric vehicles, and customer loads. The simulation results carried out by the MATLAB software demonstrate the superior performance of the proposed DO-optimized NPI controller for HPS frequency regulation, even when the power system's parameters have substantial variations. Moreover, the results revealed that the proposed strategy significantly reduces the frequency deviation by approximately 95% compared to the conventional coordination strategy based on the fixed contribution of RESs and by 90% compared to the adaptive coordination control based on the PI controller.

THE EFFECT OF ORGANIZATIONAL RESOURCES, WORK INVOLVEMENT ON EMPLOYEE PERFORMANCE AND CUSTOMER LOYALTY WITH THE ROLE OF SERVICE CLIMATE

Quality human resources will basically be able to help the organization achieve its goals. Employees create a key element for organizations to achieve sustainable competitive advantage in today's dynamic and changing operating conditions. Every company wants its employees to have high loyalty and dedication to performance and the company, high loyalty can determine the company's progress and decline in the future. In addition to the need to increase employee loyalty, companies also need to create a conducive climate in the management of an organization or company. This study took a sample of 100 samples and used the Structural Equation Modeling (PLS –SEM) Data Analysis Method. SEM is one of the areas of statistical study that can be used to address research problems, where independent variables and response variables are immeasurable variables. he result of the PLS is that only the P Value is below 0.200, meaning that all variables influence each other strongly.

A Parameter Sensitivity Analysis of Hydropower Units under Full Operating Conditions Considering Turbine Nonlinearity

A parameter sensitivity analysis is an important part of the stability study of hydro turbine regulation systems, which helps operators to deepen their understanding of the characteristics and connections among the various parts of these systems. Considering that large hydropower stations undertake an essential regulation task in the power grid, the safety and stability of their operation cannot be ignored. To this end, taking a unit in a giant hydropower station in China as an example, a hydraulic–mechanical–electrical coupling model of the hydraulic turbine regulation system is established. A comprehensive parameter sensitivity indicator and parameter sensitivity analysis framework are proposed. On this basis, the sensitivity of the main system variables to parameter changes under full operating conditions is investigated by considering two different control modes of the unit (i.e., corresponding to different grid types). The results show that the sensitivity of the system state to the mechanical parameters of the generator is the highest in the power control mode, while the sensitivity to the electrical parameters of the generator and excitation system is higher in the frequency control mode. The sensitivity of the system with these key parameters also shows different patterns of change with a change in the unit operating conditions. The relevant findings can provide some theoretical guidance for the operation of hydropower stations and help to reduce the risk of system instability.

An explainable unsupervised learning framework for scalable machine fault detection in Industry 4.0

Despite the diverse number of machine learning algorithms reported in the literature for machine fault detection, their implementation is mainly confined to laboratory-scale demonstrations. The complexity and black-box nature of machine learning models, the processing cost involved in appropriate feature extraction, limited access to labeled data, and varying operating conditions are some of the key reasons that curtail their implementation in practical applications. Furthermore, most such models serve as decision support tools, aiding domain experts in root cause analysis, and are not truly autonomous by themselves. To address these challenges, we present a lightweight autoencoder-based unsupervised learning framework to accurately identify machine faults against the changing operating conditions in a real-world scenario. The fault detection strategy is further strengthened by a model agnostic Shapley Additive exPlanations (SHAP)-based method (kernel SHAP) for identifying the most prominent features contributing to fault detection inference, the findings of which are then explored for identifying trends and correlations among prominent features and various types of faults. The framework is validated using two widely used and publicly available datasets for machine condition monitoring, as well as a large industrial dataset comprising 18 machines installed at three factories in India, monitored for several months.

SYSTEM DEVELOPMENT FOR ENHANCING BOILER PERFORMANCE: FROM PID CONTROL TO ADAPTIVE AND MODEL PREDICTIVE CONTROL FOR MORE EFFECTIVE OPTIMIZATION OF TEMPERATURE, PRESSURE, AND LEVEL CONTROL IN BOILER SYSTEM

When discussing control systems for boilers, the PID (Proportional-Integral-Derivative) control algorithm is one of the most well-known and widely used. The PID control algorithm has been proven effective in controlling temperature and pressure within boiler systems. However, as technology advances, there have been advancements in more sophisticated and efficient control systems. One of these advancements is the utilization of adaptive control and model predictive control. Adaptive control can adapt to changes in operational conditions and provide better performance compared to PID control. Meanwhile, model predictive control utilizes a mathematical model of the system to predict future behavior and make control decisions based on these predictions. Nevertheless, the PID control algorithm remains crucial and can deliver satisfactory performance in temperature and pressure control within boilers. The key to effective use of PID control lies in the selection of appropriate parameters and proper tuning. By employing the correct tuning methods, PID control can achieve optimal performance and high efficiency in boiler systems. Overall, the use of the PID control algorithm remains a good choice for temperature and pressure control in boiler systems, despite advancements in more advanced and efficient control systems. Model predictive control, on the other hand, utilizes a mathematical model of the boiler system to predict its future behavior. By solving an optimization problem over a predictive horizon, it determines the optimal control actions to be applied. This proactive approach allows model predictive control to account for constraints, nonlinearity, and system dynamics, leading to improved control performance and energy efficiency. In conclusion, while PID control remains a viable option for temperature and pressure control in boiler systems, the advancements in adaptive control and model predictive control offer more sophisticated and efficient alternatives. The choice of control strategy depends on the specific requirements, complexity, and desired performance of the boiler system. Combining different control techniques or employing advanced control algorithms can further enhance the overall control performance and optimize the operation of the boiler system.

On-demand tuning of mechanical stiffness and stability of Kresling origami harnessing its nonrigid folding characteristics

Being adaptive with respect to changing operating conditions and the environment is an imperative demand for advanced engineering structures. In this research, two Kresling origami units are stacked together to form an online and on-demand tunable dual-Kresling origami structure (D-KOS), allowing easy and significant change in the multistability profile and apparent stiffness without redesign. Specifically, by harnessing its nonrigid folding nature together with geometric characteristics, the D-KOS can be programmed to follow distinct paths of its strain energy contour as varying (rotational angle of the top plate of the D-KOS), which can be utilized to explicitly derive different mechanical properties. The D-KOS with identical Kresling origami units can exhibit various symmetric bistable behaviors with different energy barriers via adjusting . Such a structure becomes a monostable system as the tuning variable reaches a critical value. This change in the energy landscape and stability profile gives rise to a change in stiffness at its stable equilibria, which, as a result, creates a wide range of stiffness value one can achieve, including quasi-zero stiffness. Such tuning of does not require redesign of the structure and thus can achieve online and on-demand tailoring. To verify the concept, proof-of-concept prototypes are developed and utilized to validate the D-KOS’s mechanical tunability experimentally. Moreover, the different design parameters of the D-KOS can change its tunability. For example, the D-KOS with nonidentical Kresling origami units can possess even richer stability properties (being asymmetric bistable, symmetric bistable, and monostable) with various energy landscapes. In addition, one can also tailor the range of the adjustable stiffness by changing the design parameters, such as the ratio of the angle between the polygon side and the valley crease of the triangular facets, and the height of the Kresling origami unit.