Varying Operating Conditions Research Articles

Cross-Domain Class Incremental Broad Network for Continuous Diagnosis of Rotating Machinery Faults Under Variable Operating Conditions

Cross-Domain Class Incremental Broad Network for Continuous Diagnosis of Rotating Machinery Faults Under Variable Operating Conditions

Performance evaluation of electrocoagulation process for removal of heavy metals from wastewater using aluminum electrodes under variable operating conditions

Heavy metals are commonly found in agricultural and industrial wastewater. Because of their high toxicity and negative effects on human health, their removal from wastewater is of great importance. Electrocoagulation is a promising technology for the removal of heavy metals. The removal of heavy metals, namely, lead, cadmium, zinc, chromium, nickel, manganese, and copper as individual elements and in mixtures of synthetic and real wastewater by electrocoagulation, was studied using aluminum electrodes as anodes and stainless steel electrodes as cathodes in monopolar connections for different reaction times and current densities. The results show that the removal percentages of individual elements were 100% for lead after 20 min at 5 mA/cm2, 98% for cadmium after 40 min at 15 mA/cm2, 98% for zinc after 40 min at 5 mA/cm2, 100% for nickel after 40 min at 5 mA/cm2, 98% for chromium after 60 min at 5 mA/cm2, 98% for manganese after 60 min at 15 mA/cm2, and 100% for copper at 40 min at 5 mA/cm2. The results of wastewater mixtures showed similar removal patterns but lower removal values than those for individual elements. Pseudo-second-order kinetic model best describes the kinetics of the removal of the heavy metals investigated.

An inertial control method for large-scale wind farm based on hierarchical distributed hybrid deep-reinforcement learning

Wind farm (WF) is gradually required to provide inertial support during frequency events nowadays. Existing WF inertial control methods have exhibited limitations in parameter estimation and optimal control parameter tuning under variable operating conditions. Moreover, traditional model-based methods suffer from computational efficiency problems due to their reliance on repeated iterations or derivative computations in optimization process. Thus, this paper proposes an inertial control method for large-scale WF based on hierarchical distributed hybrid deep-reinforcement learning (HDH-DRL). Firstly, the control objectives for the inertial control are defined, and are decomposed on the basis of wind turbines (WTs) division. Next, this paper presents a HDH-DRL based inertial control framework consisting of two levels. The upper-level control is achieved by the hybrid multi-agent DRL (HMA-DRL) algorithm with hybrid action exploration mechanism and multi-agent coordination. The consensus based lower-level control aims to achieve the consensus convergence of the control through the locally distributed interaction among WTs. Finally, the inertial control processes of the model are exhibited; the influences of DRL algorithms on the control are discussed; the computational speed and solution accuracy of the proposed method are compared with the model-based methods; the multi-scenario applicability and performance in local communication failures of the model are analyzed. The results demonstrate that the proposed method significantly improves the active power response and frequency stability, achieving rapid consensus convergence with a power deviation below 0.3%, surpassing traditional control methods by at least 0.2%; also, it demonstrates superior performance across various scenarios, including transient and steady-state conditions, with frequency enhancements up to 0.30% and exceptional stability under different wind conditions and communication disruptions.

Integrated application of synergetic approach for enhancing intelligent steam generator control systems

This article focuses on the integrated application of the synergetic approach to enhance the quality of intelligent steam generator control systems.By combining various techniques such as model-based control, adaptive control, and artificial intelligence, an efficient and flexible control system can be developed. Model-based control utilizes mathematical models of steam generators to formulate control algorithms and predict system behavior. Adaptive control enables the system to adapt to changing conditions by adjusting control parameters based on real-time measurements. Artificial intelligence techniques, including neural networks and genetic algorithms, facilitate learning, optimization, and data-driven decision-making processes. The objectives of this research are to investigate the benefits of the synergetic approach in steam generator control, including improved steam generation efficiency, optimized energy consumption, enhanced system stability and reliability, and adaptability to varying operating conditions and disturbances. The findings and conclusions of this study are expected to provide valuable insights for engineers, researchers, and professionals involved in the design and implementation of intelligent steam generator control systems. By integrating the synergetic approach, substantial enhancements in control quality can be achieved, leading to optimal operation and maximum efficiency of power plants.

A novel approach to evaluate dynamic performance for photovoltaic system using software platform

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.

Offshore field experimentation for novel hybrid condition monitoring approaches

This study details the development of a fully automated pipeline for the condition monitoring of wind turbine drive trains. Vibration data is collected using hardware designed and manufactured in-house and used directly to monitor the condition of the drive trains. The complex nature of wind turbine vibration signals, due to the large number of components and highly variable operating conditions, makes drive train condition monitoring a challenging task. This paper details the full data measurement and analysis flow from sensor to insights and proposes a hybrid automated pipeline with signal processing and data-driven techniques to address the complexity of dealing with wind turbine vibration data. The vibration signals are directly employed to estimate the wind turbine’s instantaneous angular speed to compensate for any rotation speed fluctuations. Pre-processing is performed on the speed-independent signals to evaluate condition indicators in both the time and spectral domain for the vibration signals and their envelopes. Machine learning is then employed to distinguish the healthy state of the machine from a faulty one using the computed condition indicators. Besides the scalar indicators, also two-dimensional vibration decompositions such as the cyclic spectral correlation maps are used as inputs to the machine learning pipeline. This comprehensive and automated approach ensures both an early and reliable fault detection. Experimental results demonstrate that the fully automated hybrid pipeline can effectively be used for fleet-based health tracking of offshore wind turbine drivetrains.

Comparative Study of MPPT Techniques in Solar PV Systems: A Comprehensive Review

Abstract: Maximum power point tracking(MPPT) is a very important technique which is employed in photovoltaic systems to extract maximum available power from the Photovoltaic panels under varying operating conditions. This paper presents a comparative study of different MPPT techniques, including traditional methods like Perturb and Observe (P&O), Incremental Conductance (Inc Con), Constant voltage method, as well as intelligent methods like Fibonacci algorithm-based search MPPT and Particle Swarm Optimization (PSO) Technique. The advantages and disadvantages of each technique are discussed in detail, along with their suitability for different applications. The paper concludes by highlighting the limitations of existing techniques and proposing a problem statement for developing a novel MPPT technique that addresses these limitations. Traditional MPPT techniques like Perturb and Observe , Incremental Conductance, and constant voltage method are straightforward to deploy and computationally economical. However, they can encounter issues such as being trapped in local maxima and exhibiting slower tracking rates during swift changes in irradiance. Intelligent MPPT techniques, such as Fibonacci algorithm-based search MPPT, and Particle Swarm Optimization (PSO) Technique are more adaptable to complex operating conditions and have faster tracking speeds, but they are also more computationally complex and expensive to implement. The limitations of existing MPPT techniques include sensitivity to partial shading conditions, limited efficiency under dynamic irradiance conditions, and the requirement for accurate system parameters. To address these limitations, a novel MPPT technique should be developed that is efficient under partial shading and dynamic irradiance conditions, robust to inaccurate system parameters, and has low computational complexity and implementation cost.

Novel reference condition independent method for estimating performance for PV modules based on double-diode model

The traditional methods for performance estimation mainly include two steps: determine the model parameters under reference condition and calculate the model parameters at varying operating conditions through transformation equations. These methods have the disadvantages of being influenced by the selection of reference condition. A novel reference condition independent method is proposed based on double-diode model (DDM) for parameter calculation and performance estimation at varying operating conditions for photovoltaic (PV) modules. In the proposed method, a new set of transformation equations is reconstructed according to the dependence of physical parameters in DDM on irradiance and temperature. All coefficients in the proposed transformation equations are independent of the information under reference condition and applicable to all varying operating conditions, which eliminates the influence of reference condition. The coefficients are optimized by fitting measured I–V data at varying operating conditions using guaranteed convergence particle swarm optimization technology, and used for model parameters calculation at varying operating conditions. Then the output property of PV module under any operating condition can be determined independently, first against the reference condition and then against the model parameters under reference condition. The accuracy and applicability of proposed method is validated by massive experimental data for six different types of PV modules under wide range of operating conditions. Moreover, three traditional methods are selected to compare with the proposed method. The proposed method shows better accuracy in I–V, P–V curve and maximum power point estimation under different irradiance and temperature conditions, which can be further used to predict the output power of PV system.

Micro-macro analysis of Hyperelastic auxetic lattice structures under finite-strain regime

This paper addresses the intricate analysis of lattice structures, pivotal components in engineering applications, confronted by challenges arising from their diverse unit cells (UCs) and complex behavior across micromechanical and macro-scale dimensions. The present study deals with a comprehensive analysis of lattice structure with honeycomb and re-entrant auxetic unit cells under large deformation via both a robust finite element analysis (FEA) and experimental tests. The proposed FEA is developed based on the hyperelastic Mooney–Rivlin strain energy function and the novel exact motion field that can fully describe the projection of cross-section. The results demonstrate that mechanical parameters such as Young's modulus, Poisson's ratio, and shear modulus have significant nonlinear behaviors with respect to UC geometrical parameters that are crucial for optimization across varied operational conditions. Additionally, the lattices made of TPU material and fabricated by Fused Deposition Modeling are tested under three-point bending and compression considering contact interaction. The results reveal highly nonlinear responses due to instabilities in some links and material nonlinearity. Furthermore, the behavior of lattice structures is exceedingly dependent on the orientations and types of UCs. It also can be found that both of the proposed FEA and constitutive model are in good agreement with experimental data.

Experimental and numerical investigations on film cooling characteristics and hole shape improvement of turbine vane at one engine inoperative condition

This study investigates the film cooling characteristics of four cooling schemes of a gas turbine vane at various mass flux ratios (8.0%, 8.5% and 9.0%) using the Pressure Sensitive Paint (PSP) technology. The improved cooling scheme involves the alteration from cylindrical holes to laid-back fan-shaped holes and laid-back holes on the pressure surface and the leading edge. Considering the complex fluctuations of the real engine operating environment, the one engine inoperative (OEI) situation is numerically simulated to explore the turbine vane's cooling performance at varying operating conditions. Experimental and numerical results indicate significantly enhanced film cooling effectiveness of vane with shaped holes (improved cooling schemes) compared to the cylindrical hole (baseline), particularly at higher mass flux ratio (MFR = 9.0%). At three OEI conditions, the film coverage of improved cooling schemes is significant compared to the baseline on the pressure side. When the OEI condition is increased from 100% to 120%, the area-averaged cooling effectiveness of the shaped hole vane decreases by 4.3%, due to the interaction between the freestream and lift-off cooling jet.

Deep adaptive sparse residual networks: A lifelong learning framework for rotating machinery fault diagnosis with domain increments

Rotating machinery operates continuously for long periods of time under varying conditions in actual industrial environments. The number of fault samples increases with equipment operating time, whereas differences in data distribution are inevitable because of varying operating conditions. As a result, fault diagnosis of rotating machinery poses challenges related to the fault domain increments. In addition, the issue of catastrophic forgetting, where previously learned knowledge significantly affects the model's performance, remains a critical concern. Deep adaptive sparse residual network (DASRN), a new lifelong learning-based method, is proposed to overcome these challenges. The DASRN model is constructed based on an improved deep residual network. The model uses a task-aware dynamic masking strategy to adjust the retention and utilization of blank weights in the network, thereby balancing the model's memory and learning capabilities, mitigating catastrophic forgetting. Furthermore, throughout the entire training process of the model, a dynamic data removal strategy is applied to eliminate easily distinguishable samples. This approach adaptively allocates higher weights to more challenging samples, enhancing the model's learning efficiency while consolidating the sparsity within the model. Finally, the effectiveness of the proposed DASRN is validated on bearing datasets from the Chinese CRH380A high-speed train gearbox and integrated wheelset transmission test rig. Results demonstrate that DASRN provides an effective solution for catastrophic forgetting and domain incremental scenarios in rotating machinery fault diagnosis tasks.

Tracing the carbon capture energy distribution in a natural gas combined cycle power plant under variable operating conditions

A thermodynamic analysis method with a new criterion was introduced to reveal and dynamic track the internal energy consumption distribution of natural gas combined cycle power plants with CO2 capture. The new method decomposes the capture energy consumption into separation energy consumption and system integration energy consumption caused by gas turbines, steam turbines, heat recovery steam generators and steam extraction. The results indicate that load variation has little effect on the energy penalty of the separation process, which accounts for over 50 % of the total capture energy consumption and is stable at around 0.8 MJe/kg CO2 for MEA-based cases, while the integration performance becomes better with load decreasing, since the superior matching between extraction steam and reboiler parameters. Besides, exhaust gas recirculation technology reduces energy consumption caused by steam extraction by around 67 %∼85 % and brings an additional penalty of 0.1–6 MW on the gas turbine under 40–100 % load conditions. Therefore, exploring new absorbents and optimizing the matching between extraction steam and reboiler parameters are the critical points for capture system without exhaust gas recirculation. In contrast, for capture system with exhaust gas recirculation, energy consumption caused by the deterioration performance of gas turbines is crucial under high operation load, while absorbent innovation is dominant at low load. With the aid of the thermodynamic analysis method introduced in this paper, a better understanding of CO2 capture systems under variable load conditions can be obtained, which may be helpful for reducing energy consumption in CO2 capture technologies.

Improving precision and robustness in level control of coupled tank systems: A tree seed optimization and [formula omitted]-analysis approach

Background:Efficient control of liquid levels in interconnected tank systems is a fundamental challenge in various industries, including chemical processes, wastewater treatment, and manufacturing. The traditional approach to achieving precise control has relied on Proportional–Integral–Derivative (PID) controllers, whose performance largely depends on tuned parameters. However, tuning PID controllers for complex and nonlinear systems like coupled tanks remains a challenging task. In recent years, nature-inspired optimization algorithms have gained prominence in control system design. The tree seed optimization (TSO), inspired by the dispersion of tree seeds in search of optimal growth conditions, has shown promise in solving complex optimization problems. This study seeks to explore the application of TSO in tuning PID controllers for coupled tank systems. Methodology:This research employs the TSO to optimize PID controller parameters for enhanced liquid-level control in coupled tank systems. The optimization process involves integrating performance metrics and closed-loop gain constraints to achieve optimal control of coupled tank systems. The imposed constraints aim to ensure robustness and system stability in the face of uncertainties and disturbances. Besides, μ analysis quantifies the robustness of the system by assessing its ability to tolerate uncertainties. Findings:Simulation studies conducted in this research verify the efficacy of the proposed TSO-based approach for tuning PID controllers in coupled tank systems. Compared with various methods, the TSO consistently yields better control performance, reduces settling time, and minimizes overshoot. The robustness of the optimized controllers is also evaluated, showing the ability to handle varying operating conditions effectively.

Multi-modal data cross-domain fusion network for gearbox fault diagnosis under variable operating conditions

Gearbox fault diagnosis is a critical aspect of machinery maintenance and reliability, as it ensures the safe and efficient operation of various industrial systems. The cross-domain fault diagnosis method based on transfer learning has been extensively researched to enhance the engineering applications of data-driven methods. Currently, the state-of-the-art gearbox cross-domain fault diagnosis primarily relies on single-modal data, which may not capture the full information needed for robust fault diagnosis under varying conditions. To address this issue, we propose a novel multi-modal data cross-domain fusion network that utilizes vibration signals and thermal images to capture comprehensive information about the gearbox’s health conditions. First, one-dimensional and two-dimensional convolutional neural networks are constructed for feature extraction and fusion of multi-modal data. Then, the Maximum Mean Discrepancy loss is introduced to achieve cross-domain feature alignments within the modal. Finally, the cross-modal consistency learning strategy is constructed to enhance the cross-domain diagnosis performance of the model. To validate the effectiveness of the proposed method, we conducted experiments on a real-world gearbox test rig. Experimental results demonstrate that the proposed method is superior to single-modal methods and existing fusion methods in terms of diagnosis performance, proving that the proposed method offers a promising solution for gearbox fault diagnosis in industrial settings.

Experimental study on the combustion and pressure drop characteristics of a pintle injector for LOX/kerosene rocket engine

Pintle injectors have attracted much attention in recent years for their excellent throttling and combustion performance. However, the combustion characteristics of cryogenic propellant on pintle injectors with varying operating conditions remain unclear. A LOX/kerosene throttleable rocket engine with a fuel-centered liquid-liquid pintle injector was designed and its pressure drop and combustion characteristics were tested. The pintle engine achieved stable combustion with the total flow rate and oxygen-fuel mixture ratio varying from 59 to 559 g/s and from 0.73 to 1.02. A calculation model for the gas ratio in the LOX channel of the pintle injector was established. The deviation of measurements from the calculated values did not exceed ±10%. The vaporization of LOX from the pintle injector in the hot test was calculated and the positive correlation between gas ratio and combustion efficiency was analyzed. For the phenomenon of low-frequency oscillation of pressure in the hot tests, the source and development process of the oscillation were studied by FFT. The primary frequency and amplitude of low-frequency oscillation had been discovered to exhibit a positive correlation with the gas ratio. Low pressure drop of injection was the main cause of low-frequency oscillation. The strong vaporization of LOX under low operating conditions made the combustion efficiency remain in a high range even if low-frequency oscillation was observed. This work can provide a reference for the design of pintle injectors for cryogenic liquid rocket engines that can be deeply throttled.

A novel method for reference parameters identification and electrical property estimation of PV modules under varying operating conditions

The parameters identification of the diode model under reference condition are crucial and have significant impact on output characteristics estimation of photovoltaic (PV) module under varying operating conditions. The traditional methods of extracting model parameters have always focused on solving the parameter optimization problem only under some certain condition, which neglects their effect of performance estimation under other operating condition. In this paper, a novel method is proposed to identify the reference physical parameters and enhance the accuracy of electrical property estimation for PV modules under varying operating conditions. New objective function with penalty function is constructed by considering the estimated performance not only under reference condition but also under multiple real operating conditions. The maximum permissible error (MPE) is proposed to constrain the accuracy for key electrical output indicators and added in penalty function to ensure the accuracy under reference condition. Taking into account the inequality constraints, the guaranteed convergence particle swarm optimization with penalty function (P-GCPSO) is introduced for parameter identification. A set of comparisons between the measured and calculated results of PV modules indicate that the proposed method substantially more accurate than other documented methods for both single and double diode model. For the six different materials tested, the root mean square error (RMSE) was compared. The RMSE of the single-diode model is reduced by at least 57.98% compared to the compared method, with a maximum reduction of 86.57%. Similarly, the RMSE of the double -diode model was reduced by at least 47.24% with a maximum reduction of 88.71% compared to the compared methods. Additionally, the tests with six different types of PV modules at varying environmental conditions for over one year prove that the proposed method is reliable and practical for real-world applications. The relative error of power generation reached a minimum of 2.20%. This proves the reliability and practicability of the proposed method in practical applications. The proposed method is envisaged to be valuable for both offline analysis and online monitoring applications where an accurate, fast and consistent performance estimation tool is required.

Pilot scale production of dietary fibers from Tunisian tomato and red pepper by-products

This research aims to valorize the by-products of tomato and red pepper processing underrated despite their richness in valuable compounds, essentially dietary fibers extracted from canning industry by-products using water as solvent. Therefore, varying operating conditions were tested on a laboratory scale, and the obtained fibers were characterized to choose the appropriate conditions for applying fiber extraction on a pilot scale. The selected parameters were applied (60 °C, 3 h) and the produced fibers were recovered. The Fourier Transform Infrared (FTIR) analysis indicated that the obtained by-products fibers were mainly cellulosic with similar structures, which proved that the scaling up did not affect the chemical structure of the fiber samples. These observations were confirmed by the X-ray diffraction (XRD) analysis and the Thermogravimetric analysis (TGA). The Scanning Electron Microscopy (SEM) analysis showed arranged fibrous forms for the pilot scale extracted fibers. The analysis of extraction solvents and the regenerated liquid corroborated the choice of operating conditions for the scaling up. The outcome of this study established that the extrapolation of the dietary fiber extraction from tomato pomace and red pepper pomace was carried out successfully on a pilot scale and might be feasible at an industrial scale. This green dietary fiber extraction encourages using this valuable extract in several fields, such as cosmetic and nutraceutical applications.

Hysteresis identification of joint with harmonic drive transmission based on Monte Carlo method

In this study, we analyze the hysteresis behavior of robotic joints equipped with harmonic drivers under various speeds and loads, focusing on a range of hysteresis models including the Bouc–Wen (BW), Fractional Order Bouc–Wen (FOBW), and their integration with the Krasnoselskii–Pokrovskii (KP) model. This integration, which forms the KP-FOBW model, is a novel aspect of our research, offering an enhanced understanding of non-linear and hysteresis behaviors in robotic joints. We employ the Monte Carlo method, particularly focusing on the Stochastic Gradient Hamiltonian Monte Carlo (SGHMC), for precise parameter identification. Our results demonstrate that the FOBW model, especially when combined with the KP model, provides a more accurate representation of the hysteresis curves under varying operating conditions. The KP-FOBW combination emerges as a powerful tool to predict the output torque in robotic joints. This study contributes to a deeper understanding of hysteresis in joints with harmonic drivers, showcasing its potential for complex dynamic modeling in robotics.

Performance analysis of machine learning based prediction models in assessing optimal operation of microgrid under uncertainty

Of late, the exponential rise in the global population is driving higher energy demand. However, the rapid depletion of conventional fossil fuels and growing environmental concerns have prompted the evolution of alternative energy sources. To this end, Microgrid (MG) with Renewable Energy Sources (RES) has emerged as popular means of small-scale localized power grid. However, planning of MG operation poses challenges due to the inherent variability and stochasticity in RES power output and energy demand. On account of this, the present study introduces a Stochastic Energy Management Strategy (SEMS) for a grid-connected MG incorporating Micro-Turbine, Fuel-Cell, RES, Battery Energy Storage, and electrical and heat energy demand. The stochasticity of RES is forecasted through a hybrid prediction model (sARIMA-GRU) and the uncertain demand is estimated via 'Monte Carlo Simulation.' The proposed problem is formulated as a dynamic non-linear stochastic optimization problem. It seeks to minimize the expected value of MG operational cost satisfying the practical constraints. Addressing this, a newly developed ‘Artificial Electric Field Algorithm (AEFA)' is utilized. Several case studies are performed to assess MG operation under varied operating conditions. Moreover, the present study analyses the impact of uncertainty on energy contribution from DER, grid dependency, and MG operation cost. Comparative analysis reveals that sARIMA-GRU outperforms other contemporary prediction models. It is noteworthy that the superior prediction accuracy of sARIMA-GRU leads to lower MG operation costs. Moreover, statistical analysis and convergence confirm the proficiency of applied AEFA over state-of-the-art Grey Wolf Optimization and Firefly Algorithm in solving the proposed problem.

Analog and Mixed-signal Integrated Circuits of 5Gand 6G Data Communication

The complex analog and mixed-signal transceivers where overall system performance is often limited by the weakest performing subsystem. While analog and mixed-signal integrated circuits have significantly advanced, the future of 5G and 6G transceiver design could be accelerated by including artificial intelligence. In this combination, analog integrated circuit design and operation would harness machine learning to identify, characterize, and act upon variations and anomalies in system performance. Focusing on 5G and 6G, this paper investigates solutions for a unified intelligent integrated transceiver: a conceptual combination of a traditional analog subsystem, a supporting digital subsystem that enables artificial intelligence, and dedicated feedback circuitry or sensors that monitor performance, efficiency, or reliability. Active and passive components and propagation channels are reviewed based on their merits of introducing intelligence. Holistically and for broader applicability, the paper conceptualizes and coins the notion of an ‘‘intelligent integrated system (IIS)’’, which brings forward a novel unified vision and approach toward context-aware subsystems that dynamically interact with ambient and varying operating conditions. To demonstrate viability, the paper concatenates a select set of measurement results.