Variable Shaft Speed Research Articles

Fuel-economy-optimal power regulation for a twin-shaft turboshaft engine power generation unit based on high-pressure shaft power injection and variable shaft speed

Due to the high power-to-weight ratio characteristic of turboshaft engines, the power generation unit based on it holds immense potential in the hybrid electric propulsion system (HEPS) of flying cars. To mitigate environmental pollution and reduce fuel consumption, while enhancing the fuel economy of turboshaft engine power generation unit (TEPGU) across the entire power output range, this paper investigates a dual-motor electronic regulation architecture for twin-shaft TEPGU. According to the different variable combinations of TEPGU architecture, two modes and their respective fuel-economy-optimal power regulations are proposed. Initially, by considering the efficiency distribution of motors, we construct an Extended co-working balance model based on the Newton-Raphson method. Subsequently, different operating modes are proposed and discussed, studying the characteristic laws of the power generation unit under each mode. For the variable speed mode and the power split mode, this article proposes a shaft-speed-feature-projective searching (SFPS) method and a multi-dimensions-feature-projective searching (MFPS) method based on characteristic dimensionality reduction projection. Finally, a comparative analysis of the fuel-saving effects of the SFPS and MFPS regulation methods is conducted, and the reasons for enhancing fuel efficiency are discussed. The simulation results show that the model of TEPGU is in good agreement with the experimental data, and the error of specific fuel consumption(SFC) in the full power range is less than 5.7 %. Besides, comparative results indicate that the strategies based on MFPS and SFPS can achieve a maximum instantaneous fuel consumption reduction of 14.38 % and 10.82 %, respectively. The MFPS strategy exhibits a fuel consumption saving of 12.69 % throughout the driving cycle, demonstrating superior performance in fuel economy.

Control Method of Load Sharing between AC Machine and Energy Storage Bank in the DC Grid

The article presents the issues related to load-power sharing in direct-current grid and a novel control method has advantages over known solutions. Unlike many similar-sounding papers, this article shows an attempt at creating fully controllable non-isolated system that allows for load-power sharing between a permanent magnet alternator equipped with machine-side converter (MSC) and a dual active bridge (DAB) tied to batteries or supercapacitor. The current-based load-power sharing is an essential feature of parallel-connected direct-current generators, and all types of voltage sources, in this way are contributing power to the system. To keep the optimal efficiency of the alternator, the rotational speed changes rely on proper mapping of the driving combustion engine. System components include a self-excited synchronous generator (SESG), operating at variable shaft speed, as well as batteries and supercapacitors that provide electricity for sudden electrical-load changes on the distribution grid. The core of the presented system is in a power-distribution method that consists of a programmed-controller structure allowing precise current distribution. A novelty of the proposed method is the use of a cascaded system of current and DC voltage regulators that allows for precise power-distribution control. In contrast with previously presented solutions, the proposed system allows for fast and accurate control of currents, loading parallel-connected DC voltage sources for wide-range generator speed changes. In the presented solution, both converters have been equipped with Schottky diodes, preventing the flow of equalizing currents between closed transistors in the parallel mode of operation. An experimental test-stand of the described system is presented with its theoretical basis and experimental results.

Explainable AI for Bearing Fault Prognosis Using Deep Learning Techniques.

Predicting bearing failures is a vital component of machine health monitoring since bearings are essential parts of rotary machines, particularly large motor machines. In addition, determining the degree of bearing degeneration will aid firms in scheduling maintenance. Maintenance engineers may be gradually supplanted by an automated detection technique in identifying motor issues as improvements in the extraction of useful information from vibration signals are made. State-of-the-art deep learning approaches, in particular, have made a considerable contribution to automatic defect identification. Under variable shaft speed, this research presents a novel approach for identifying bearing defects and their amount of degradation. In the proposed approach, vibration signals are represented by spectrograms, and deep learning methods are applied via pre-processing with the short-time Fourier transform (STFT). A convolutional neural network (CNN), VGG16, is then used to extract features and classify health status. After this, RUL prediction is carried out with the use of regression. Explainable AI using LIME was used to identify the part of the image used by the CNN algorithm to give the output. Our proposed method was able to achieve very high accuracy and robustness for bearing faults, according to numerous experiments.

Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-Based Deep Neural Network.

Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis models that have been developed for classifying various fault types in gearboxes. However, the classification results of the conventional fault classification models degrade when they are applied to gearbox systems with multi-level tooth cut gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in discriminating the gear fault types. Due to the improved computational capabilities of modern systems, the application of deep neural networks (DNNs) is getting popular in a variety of research fields, such as image and natural language processing. DNNs are capable of improving the classification results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this research, an adaptive noise control (ANC) and a stacked sparse autoencoder–based deep neural network (SSA-DNN) are used to construct a sensitive fault diagnosis model that can diagnose a gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicatedness. An ANC is applied to gear vibration characteristics to remove a significant level of noise along the frequency spectrum of vibration signals to fix the most fault-informative components of each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault-informative components to separate the fault types considered in this study. Furthermore, the implementation of the SSA-DNN is substituted for feature extraction, feature selection, and the classification processes in traditional fault diagnosis schemes by high-performance unity. The experimental results show that the proposed model outperforms conventional methodologies with higher classification accuracy.

Model-Based Predictive Rotor Current Control Strategy for Indirect Power Control of a DFIM Driven by an Indirect Matrix Converter

This paper presents a new control strategy using model-based predictive current control (MB-PCC) for a doubly fed induction machine (DFIM) driven by an indirect matrix converter (IMC). This strategy proposes the control of rotor currents, whose references are calculated from active and reactive stator power set points and the dynamic model of the DFIM. The control strategy works well in the four P-Q operating regions of the DFIM. The grid synchronization process is carried out by setting the P-Q power set points to zero. The results include the DFIM synchronization procedure as well as the active and reactive power control at variable shaft speed to validate the feasibility of the proposed strategy.

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram

Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful information from vibration signals, diagnosis of motor failures by maintenance engineers can be gradually replaced by an automatic detection process. Especially, state-of-the-art methods using deep learning have contributed significantly to automatic fault diagnosis. This paper proposes a novel method for diagnosing bearing faults and their degradation level under variable shaft speed. In the proposed method, vibration signals are represented by spectrograms to apply deep learning methods through preprocessing using Short-Time Fourier Transform (STFT). Then, feature extraction and health status classification are performed by a convolutional neural network (CNN), VGG16. According to our various experiments, our proposed method can achieve very high accuracy and robustness for bearing fault diagnosis even under noisy environments.

Process design, operation and economic evaluation of compressed air energy storage (CAES) for wind power through modelling and simulation

Compressed air energy storage (CAES) could play an important role in balancing electricity supply and demand when linked with fluctuating wind power. This study aims to investigate design and operation of a CAES system for wind power at design and off-design conditions through process simulation. Improved steady-state models for compressors, turbines and the CAES system for wind power were developed in Aspen Plus® and validated. A pseudo-dynamic model for cavern was developed in Excel. Compressor and turbine characteristic curves were used in model development for process analysis. In the off-design analysis, it was found that the CAES system for wind power at variable shaft speed mode utilise more excess wind energy (49.25 MWh), store more compressed air (51.55 × 103 kg), generate more electricity (76.00 MWh) and provide longer discharging time than that at constant shaft speed mode. Economic evaluation based on levelized cost of electricity (LCOE) was performed using Aspen Process Economic Analyser®, it was found that LCOE for the CAES system for wind power at variable shaft speed mode is lower than that at constant shaft speed mode. Research presented in this paper hopes to shed light on design and operation of the CAES system for wind power and cost reduction.

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder‐Based Deep Neural Network

Due to enhanced safety, cost‐effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing), but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder‐ (SSAE‐) based deep neural networks (DNNs), a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE‐DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.

MICROGRID BASED ON A GROUP OF AUTONOMOUS OPERATING SYNCHRONOUS GENERATORS

The current level of power electronics development allows creating and implementing new technologies for generation, transmission and distribution of electricity. Electric machines with variable shaft speed can be used as an energy source now. The functions of providing the parameters of the generated electric power are transferred to the converter devices. The combination of controlled energy sources and electrical receivers in microgrids allows reducing energy losses, increasing the reliability of electricity supply. The article is devoted to the constructions of a microgrid based on several autonomously operating synchronous generators. As an example, microgrid for power supply of compressor station based on own use generators of gas compressor units is considered.

An analytic and simulation study of a wound-rotor asynchronous machine at positive slides

The characteristics of a wound-rotor asynchronous machine (a double-fed machine) upon positive slides are studied in the present article. The double-fed machine (DFM) allows one to adjust the internal and external flows of the active and reactive power at any rotor speed. This feature of the DFM enables one to build various electromechanical systems, such as power-efficient electric drives for transport systems, power generators with variable shaft speed, power-efficient load stands for electric machines and combustion engines, and power-efficient stands for testing an asynchronous machine with the wound rotor without the additional load. The main modes of DFM operation, which are motor and generator modes of operation at a speed below a synchronous one, are studied. The analysis that has been carried out allows one to determine the main properties of the DFM: the direction of the rotor and stator active power flows proves that the positive power feedback appears at positive sliding; the motor mode of DFM operation occurs at the negative modulation phase, while the generator mode takes place at the positive one; the active power transformed in the machine is proportional to the modulation phase; and the sign of reactive power in the stator circuit depends on the relationship between the rotor voltage and the slide. A virtual model of the DFM is constructed in the SimPower System of Matlab-Simulink. It is used for studying the static and dynamic characteristics of the DFM. These characteristics confirm the theoretical results of the analysis.

A Comparison Study Between Acoustic Sensors for Bearing Fault Detection Under Different Speed and Load Using a Variety of Signal Processing Techniques

The use of ultrasonic sensor technology to detect incipient and evolving defects in rotating components such as bearings and gears is more desirable due to their high resolution. In a previous study, the sensitivity of a variety of sensors including an air-coupled ultrasound transducer to bearing faults was analyzed and thoroughly discussed. This article investigates the effectiveness of two ultrasonic sensors, namely, air-coupled and piezoelectric ultrasound transducers for rolling element bearings damage diagnostics. The former is a noncontact sensor and the latter is a contact sensor. An accelerometer was also used as the baseline sensor for comparison purposes. A series of tests was carried out on a laboratory test rig running with defective and undamaged healthy bearings under variable shaft speeds and several radial loads. The data were analyzed using selected signal processing techniques covering time, frequency, and advanced joint time–frequency domains. The results showed that certain acoustic features were responsive to the variation of operational condition and the damage; the detection capability of the sensors varied depending on the defect size, its location, as well as the applied signal analysis technique.

Gearbox Deterioration Detection under Steady State, Variable Load, and Variable Speed Conditions

Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smirnov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.

Dynamic analysis and grinding tracks in the magnetic fluid grinding system

The differential equations of ball and float motions in the magnetic fluid grinding system are formulated. In this dynamic model, depending on the magnitudes of the contact loads, the ball will either contact or separate from the shaft and/or the float during the grinding process. Using the steady-state solutions as the initial conditions, the differential equations of motion are solved. Numerical results show that in contact cases, the effects of variable shaft speed and variable contact load on the ball-spin angle and the resultant area covered by the grinding tracks are small. However, when the ball separates from the shaft, the ball-spin angle varies with time because the ball has lost the driving force from the shaft, and thus the area covered by the grinding tracks on the ball surface increases. Furthermore, the combined effect of variable shaft speed and variable contact load on the appearance of grinding tracks is different from that of variable contact load alone, and this difference provides the ergodic possibility to grind a ball with high roundness using the magnetic fluid grinding method.

Autonomous renewable energy conversion system

This paper briefly reviews the need for renewable power generation and describes a medium-power Autonomous Renewable Energy Conversion System (ARECS), integrating conversion of wind and solar energy sources. The objectives of the paper are to extract maximum power from the proposed wind energy conversion scheme and to transfer this power and the power derived by the photovoltaic system in a high efficiency way to a local isolated load. The wind energy conversion operates at variable shaft speed yielding an improved annual energy production over constant speed systems. An induction generator (IG) has been used because of its reduced cost, robustness, absence of separate DC source for excitation, easier dismounting and maintenance. The maximum energy transfer of the wind energy is assured by a simple and reliable control strategy adjusting the stator frequency of the IG so that the power drawn is equal to the peak power production of the wind turbine at any wind speed. The presented control strategy also provides an optimal efficiency operation of the IG by applying a quadratic dependence between the IG terminal voltage and frequency V∼ f 2. For improving the total system efficiency, high efficiency converters have been designed and implemented. The modular principle of the proposed DC/DC conversion provides the possibility for modifying the system structure depending on different conditions. The configuration of the presented ARECS and the implementation of the proposed control algorithm for optimal power transfer are fully discussed. The stability and dynamic performance as well as the different operation modes of the proposed control and the operation of the converters are illustrated and verified on an experimental prototype.

Various induction generator schemes for wind-electricity generation

In this paper, various generator types and control systems employed in wind energy conversion systems have been reviewed with particular emphasis on systems including induction generators. Advantages and disadvantages of these systems and relative generator sizes are also discussed. Variable shaft speed schemes employing induction generators have been studied more closely and the important aspects of the findings are emphasized. Throughout the paper, the importance of maximizing the conversion efficiency is stressed. The parameters controlling the energy conversion are identified. Simple methods have been developed to determine the optimum parameters for a constant speed synchronous generator wind energy conversion system and a variable speed squirrel-cage induction generator wind energy conversion system. This last configuration is shown to have advantages from the viewpoint of energy production as well as some other aspects including simplicity and robustness.

差動式軸発電電動機の負荷特性

This differential driven shaft generator/motor system has been introduced first in 1987, practical system (200V, 200kW) is introduced to FUKAE-MARUIII in 1988. Since the fundamental structure of the machine is the same as conventional synchronous machine, the output frequency of the machine is corrected to constant by compensating motion of the movable stator.Test results of starting characteristics of large capacity induction motors (total 30% rated power of the ACG), and voltage regulating characteristics with variable shaft speed or variable load are good agreement to designed procedure.The machine can be driven as the electronic propulsion motor. Induction starting, synchronization and load characteristics of the machine are also discussed. Since emergency starting of propulsion motor can be established within up to one minute, it presents powerfull function to reliability of the propulsion system.

Ball and Taper Roller Bearings

This analytical treatment gives general considerations concerning “ true rolling ” of a rolling body in a circular path : firstly, for constant speed of its centre of gravity, and, secondly, for acceleration of its centre of gravity. From these are derived the relations of the dimensions, as function of the conditions, and the corresponding constructions for both taper roller bearings and ball bearings, which are essential to produce “ true rolling” at constant and variable shaft speeds.