Drivetrain Test Research Articles

Deviation of drive torque and rotational speed of a wind turbine drivetrain between test bench and wind turbine simulations

Wind turbines are a major contributor to a clean and independent energy mix in the future and therefore need to have as little downtime as possible. Downtime can be minimised by increasing the robustness of the wind turbine. The drivetrain is one of the major contributors of downtime in wind turbines. One way of increasing the robustness of wind turbine drivetrains is extensive drivetrain testing and loop-back optimization of the design and control. On the drivetrain test bench torque and other wind loads are applied. To ensure that the drivetrain reaction to the loads is the same as of the erected wind turbine the accuracy of the application of the loads needs to be sufficient. Only if the reaction loads of the drivetrain are similar to the rotor loads of the erected wind turbine a robustness increase can be achieved via the loop-back optimization after the tests. This paper therefore quantifies the deviations occurring in torque and rotational speed of the hub between the erected wind turbine and the test bench for a specific wind turbine. To do so the results of a test bench simulation are compared to results of an aerodynamic simulation of the wind turbine. For the investigated wind turbine drivetrain, the deviation in drive torque increases slightly with higher windspeeds. The torque values are maximum 2 % higher at the wind turbine compared to the rated torque. The rotational speed values are represented very well regarding the time series as well as the occurring frequencies. The speed values deviate maximum 0.5 % compared to the rated speed. The deviations are a result of differences in an underestimation of the drivetrain efficiency in the aerodynamic simulation and the inertia of the drivetrain on the test bench and wind turbine. Since the deviations are small the abstraction losses at the test bench regarding torque and speed are acceptable.

Rigid-flexible coupled modeling of compound multistage gear system considering flexibility of shaft and gear elastic deformation

Compound multistage gearing systems are widely used in rotating machinery to provide high speed-reduction ratios and heavy load capacity. Modeling and characterizing the dynamic behavior of compound multistage gear transmission systems provides valuable insights for the design and fault diagnosis of transmission systems. However, modeling compound multistage gear transmission systems still has some challenges due to 1) the complex interactions among multistage gear systems and 2) the flexibility, e.g., gear elastic deformation related to gear time-varying mesh stiffness (TVMS), which is affected by load and has not yet been addressed in analytical method, and shaft flexibility. In this paper, a multistage rigid-flexible model of compound multistage gear transmission systems is proposed to comprehensively understand their dynamic behaviors. The proposed method concerns the flexibility of gears and shafts and further constructs a dynamic analysis model. By this, the load-dependent TVMS considering gear tooth elastic deformation is calculated and compared with the existing method. After that, the ordinary and partial differential equations of gears and shafts are fused to characterize the coupling behaviors among multistage subsystems. Finally, the proposed model is solved using the variables separation method and modal superposition method. The accuracy of the constructed model is verified by experimental response collected from a wind turbine drive train test rig. All the characteristic frequencies and the coupled dynamic response of the whole system are obtained and analyzed. The modal energy distribution of the proposed model is depicted to illustrate the flexibility and load influence on the system operational modal, which provides a theoretical foundation for the design and fault diagnosis of a compound multistage gear system.

Multi-Degree-of-Freedom Load Reproduction by Electrohydraulic Digital-Servo Loading for Wind Turbine Drivetrain

Many drivetrain testing facilities have been built to reproduce multi-degree-of-freedom loads, thus simulating real wind conditions for evaluations of the reliability and durability of turbine subsystems. In this paper, the electrohydraulic schemes for the non-torque loading of a wind turbine’s drivetrain test benches are first analyzed. To deal with the control inaccuracy caused by the drastically increasing loading force, along with the rapid development of large-scale wind turbines, a multi-cylinder electrohydraulic digital-servo loading (MEDSL) technology is proposed. A novel electrohydraulic digital-servo cylinders group is designed. The proposed MEDSL can provide continuous and accurate load recurrence under wider wind conditions by varying the operational area of the cylinders group. Moreover, a sliding mode controller (SMC) is designed to realize the large dynamic loading of the MEDSL system. By comparing the SMC to a traditional PID controller in a servo-valve controlled cylinder, both simulation and experiment results proved the advantage of the proposed SMC. Accordingly, extensive experiments with a 4-cylinder case were carried out on a real full-loading bench using the SMC-based MEDSL device. The excellent tracking performance under complicated signals that represent the real wind loads demonstrated the feasibility and effectiveness of the proposed MEDSL technology and the SMC method.

Investigation on performance evaluation of electric vehicle batteries under different drive cycles

The drive cycles influence the design, sizing, rating, and selection of electric vehicle components. So, it is required to test the Electric Vehicle (EV) on different standard drive cycles in the simulation environment before testing in real-time to evaluate the life cycle and determine the efficient and economical design of the EV. Different standard driving cycles are available to evaluate the performance of the EVs, but these drive cycle patterns vary from country to country due to differences in road conditions, traffic density, and driver disciplines. The range and performance of the EV battery mainly depend on its cell chemistry. In this research paper, the performance of different EV batteries on various standard international and Indian drive cycles has been evaluated. To determine the performance of the EV battery, an EV drive train test system has been developed, Nissan leaf 2018 EV model data has been taken in this paper. FOC control strategy for PMSM traction motor has been developed. To reduce the harmonics and improve quality of the switching losses, Space Vector Pulse Width Modulation (SVPWM) algorithm has been adopted for PWM Inverter. Battery energy consumption, and cost per charge based on the distance travelled by the vehicle are calculated for comparing different EV batteries to evaluate their performance.

From Strain to Loads: Development of a Measurement Solution for Wind Turbine Transmission Input Loads during Drivetrain Testing.

As wind energy is paving the way for the energy transition from fossil to renewable energy sources, the ongoing trend of increasing the rated power of wind turbines aims to reduce the overall cost of wind energy. The resulting increase in drivetrain loads motivates the need for wind turbine (WT) drivetrain testing in the development phase of critical components such as the WT main gearbox (GB). While several WT system test benches allow for the application of emulated rotor loads in six degrees of freedom (6-DOF), the drivetrain input loads can significantly differ from the GB 6-DOF input loads due to the design of the drivetrain under test. However, currently available load measurement solutions are not capable of sensing GB input loads in 6-DOF. Thus, this work aims to develop a methodology for converging signals from a purposely designed sensor setup and turbine specific design parameters to compute the GB 6-DOF input loads during WT testing. Strain gauges (SG) and accelerometers have been installed on the low-speed shaft (LSS) of a WT drivetrain under test at the 4MW WT system test bench at the Center for Wind Power Drives. Using the data of the aforementioned sensors, a methodology for computing the GB input loads is developed. The methodology is validated through comparison to the applied loads data provided by the aforementioned test bench. The results demonstrate the high promise of the proposed method for estimating the GB input loads during WT drivetrain testing.

From Simulations to Accelerated Testing: Design of Experiments for Accelerated Load Testing of a Wind Turbine Drivetrain Based on Aeroelastic Multibody Simulation Data

The trend of increasing the power output and nominal load capacities of wind turbines (WT) over time has been driving the construction of testing facilities with increasing load capacities for testing WT drivetrain components prior to field deployment. Due to the high investment and operational costs of such facilities, a need exists to design accelerated tests that cover load situations corresponding to expected field conditions while maintaining high time-efficiency. This investigation addresses this need by presenting a methodology to achieve the following goals. Firstly, identifying ranges and combinations of WT 6-degree of freedom (6-DOF) rotor loads is to be expected in the field. This is achieved using aeroelastic multibody simulations (MBS) of an MBS WT model being subjected to simulated wind fields covering the design load cases outlined in the IEC 61400-1 standard and by analyzing the simulated time-series data to design accelerated tests that efficiently and realistically cover the design space of the variables, e.g., 6-DOF rotor loads, to be applied during WT drivetrain testing. The designed tests are to take place on a purpose-built test rig that allows for the application and control of the 6-DOF drivetrain input loads and rotational speed. Using the proposed method, accelerated tests were designed that efficiently cover load combinations within the realistic regions of the design space. A comparison with a full factorial design of experiments shows a significant (95+ %) reduction in total test time as well as the ability of the proposed method to help to avoid unsustainable and unrealistic load conditions within the design space that could result in costly, unintended drivetrain failures during testing.

A survey of sequential adaptive sampling strategy for transmission power loss measurement

Laboratory experiments for characterizing the power loss behavior in transmissions causes time and energy costs when performing the traditional factorial design strategy. This is because the space filling strategy may locate redundant samples with low information regarding the measurement targets. This article proposes a sequential adaptive sampling methodology for performing efficient and informative power loss measurements. The presented sampling methodology associates surrogate modeling based on a Gaussian Process approach with Subset Simulation. Gaussian Process Regression creates a statistical model with the estimation targets and the expression of uncertainty. Subset Simulation is applied to efficiently identify the regions where an objective function reaches a predefined critical threshold. The proposed adaptive sampling method is implemented for the real-time measurement of a 7-speed DCT on a drivetrain test bench. Compared to the traditional factorial design, the iterative adaption of the proposed method ensures an informative and effective measurement and an automatic termination with reduced time and energy cost.

Developing a Modular Tool to Simulate Regeneration Power Potential Using Orographic Wind-hovering UAVs

Applications of Unmanned Aerial Vehicles (UAVs) are often limited by flight endurance. To address the limitation of endurance, we propose a regenerative soaring method in this paper. The atmospheric energy from updrafts generated by obstacles such as hills and ships can be harvested by UAVs using a regenerative electric drivetrain. With fixed-wing aircraft, the vehicle can hover with specific wind conditions, and the battery can be recharged in the air while wind hovering. In order to research the feasibility of this regenerative soaring method, we present a model to estimate hovering locations and the amount of extractable power using the proposed method. The resulting modular regeneration simulation tool can efficiently determine the possible hovering locations and provide an estimate of the power regeneration potential for each hovering location, given the UAVs aerodynamic characteristics and wind-field conditions. Furthermore, a working regenerative drivetrain test setup was constructed and characterized that showcased promising conversion efficiencies and can be incorporated into existing UAVs easily.

Evaluation of dynamic testing of full-scale wind turbine drivetrains with hardware-in-the-loop

Dynamometer testing of full-scale wind turbine drivetrains involves subjecting integrated components to extreme static loads, fatigue loads, or dynamic loads to replicate field conditions. This paper provides a detailed evaluation of the uncertainty and errors in applied loads and measured responses, and their magnitude relative to established repeatability bounds for a drivetrain subjected to static and dynamic loads on the 7.5 MW test bench at the Clemson University Wind Turbine Drivetrain Testing Facility. An ideal drivetrain simulation model is utilized to isolate the influence of load tracking errors on test article responses. It is shown that the test article response variations are mainly driven by run out and clearances inherent to the drivetrain. For the dynamic profiles, the load tracking errors are within acceptable limits. A frequency analysis is used to show that the test bench controller tracking performance is acceptable for profiles with frequency content up to 1.5 Hz.

Experimental verification of predicted capability of a wind turbine drivetrain test bench to replicate dynamic loads onto multi-megawatt nacelles

A total of 27 test profiles from the IEC 61400-1 design load cases were tested using a 7.5-MW wind turbine drivetrain test bench and two multi-megawatt wind turbine drivetrains. Each test profile consisted of simultaneous vertical, lateral, and longitudinal forces, yawing and nodding bending moment, and rotational speed. These test-bench inputs were compared with the forces, bending moments, and speed that were applied to the wind turbine drivetrains to quantify the test-bench tracking error. This tracking error was quantified for a range of ramp-rate limits of the yawing and nodding bending moments. The experimental results were compared with predictions from an evaluation method for the capability of wind turbine drivetrain test benches to replicate dynamic loads. The method’s predictive capability was found to be sufficient for the goal of early screening and its formulation is applicable to any wind turbine drivetrain test bench and drivetrain design.

Acceptance criteria for the tracking error of wind turbine drivetrain test bench when replicating dynamic loads

A 7.5-MW wind turbine drivetrain test bench has been used to apply dynamic loads from IEC 61400-1 design load cases to a multi-MW wind turbine drivetrain. A total of 15 test profiles were tested with each test profile demanding the simultaneous application of vertical, lateral, and longitudinal forces, yawing and nodding bending moments, and rotational speed. These inputs to the test bench were compared with the forces, bending moments, and speed that were applied to the wind turbine drivetrain to quantify the tracking error of the test bench and cross-coupling between forces and bending moments. The effect of the tracking error and cross-coupling on the dynamic response of the test article was quantified using multibody simulation. The tracking error was found not to significantly change the dynamic response of the drivetrain. The experimental and simulation results are used to recommend acceptance criteria for the tracking error when replicating dynamic loads.

Component matching chirplet transform via frequency-dependent chirp rate for wind turbine planetary gearbox fault diagnostics under variable speed condition

A component matching chirplet transform (CMCT) via frequency-dependent chirp rate with high time-frequency (TF) resolution is proposed for wind turbine planetary gearbox (WTPG) fault diagnostics under variable speed condition. The challenge of identifying fault-related frequencies with small intervals, due to the time-varying rotational speed and complex structure of WTPG, can be well addressed. Unlike conventional chirplet transform, CMCT can generate a family of frequency-dependent chirplets, which can match all frequency components with a sufficiently high TF resolution. Furthermore, this proposed method does not require any prior knowledge. The effectiveness of CMCT is validated by a numerical simulation and an experimental data collected from a wind turbine drivetrain test rig.

An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines

Fault diagnosis techniques are vital to the condition-based maintenance strategy of wind turbines, which enables the reliable and economical operation and maintenance for wind farms. Due to the complex kinematic mechanism and modulation characteristic, planet bearing is the most challenging component for fault diagnosis in wind turbine drivetrains. To address this challenge for planet bearing fault diagnosis, we propose an enhanced sparse representation-based intelligent recognition (ESRIR) method, which involves two stages of structured dictionary designs and intelligent fault recognition. In the first stage, the structured dictionary designs are achieved with the overlapping segmentation strategy, which exploits the strong periodic self-similarity and shift-invariance property in planet-bearing vibration signals to enhance the representation and discrimination power of ESRIR. In the second stage, the intelligent fault recognition of planet bearings is implemented with the sparsity-based diagnosis strategy utilizing the minimum sparse reconstruction error-based discrimination criterion. Finally, the applicability of ESRIR for planet bearing fault diagnosis has been validated with the wind turbine planetary drivetrain test rig, demonstrating that ESRIR yields the superior recognition accuracy of 100% and 99.9% for diagnosing three and four planet-bearing health states, respectively. Comparative studies show that ESRIR outperforms the deep convolution neural network and four classical sparse representation-based classification methods on the recognition performances and computation costs.

On Down-Scaled Modelling of Wind Turbine Drivetrains

Gearbox testing is an important and complex task that will become even more challenging as the wind industry moves towards ever-growing turbines. The burden of this task can be decreased by using reduced-scale models with similar characteristics as its industrial-scale equivalent. This work presents a step-by-step procedure to down-scale a gearbox to different fractions of its rated power while preserving its core properties: structural safety and frequency distribution. The parameters to be scaled are sub-divided according to their relation to the system’s integrity and dynamic behavior. After performing an overall scaling, it is possible to fine-tune the scaling factors, according to the user precision requirements. Simulations show that it is possible to down-scale a gearbox to 0.01 % of its rated power while having less than 10 % relative deviation on its pitting safety factor. These preliminary results show that wind turbine drivetrain testing can become more affordable by using down-scaled models in a structured manner.

Design, Production, and Verification of a Switched-Reluctance Wheel Hub Drive Train for Battery Electric Vehicles

This contribution deals with the topic of the consistent further development of a wheel hub motor for battery electric vehicles (BEV) based on the principle of an outer rotor switched reluctance machine (SRM). The research work presented in this paper was founded by the ERDF.NRW program, Investment for Growth and Employment and the European Regional Development Fund. The R&D project was named Switched-Reluctance fo(u)r wheel (SR4Wheel). Based on the experience made by first prototype Evolution 0 (EVO 0), developed in the Laboratory for Automation Engineering, Power Electronics and Electrical Drives of the Cologne University of Applied Sciences (CUAS), the test results of EVO 1, as well as the redesign, EVO 2 is presented in this paper. The prototype EVO 0, a first proof of concept leads to several optimizations and lessons learned for the predecessor model EVO 1. The overall target of developing such a gearless outer rotor wheel hub motor is the full integration of the complete machine including its power electronics into the given space between the original friction brake and the rim. Furthermore, due to the additional integration of the power electronics, great opportunities in terms of new vehicle design as well as retrofitting capabilities of already existing vehicle platforms can be achieved. Thereby, further drive train assembly space like the engine compartment is no longer necessary. The SRM does not require magnets for torque production which leads to independence from the changeable commodity prices on the rare earth element markets. This paper presents the developing process, testing, and verification of the innovative drive train concept starting with the final CAD of EVO 1. During the testing and verification process a machine characteristic mapping is performed on a drive train test bench and subsequently the results of a finite element analysis (FEA) are plausibility checked by the test bench results. The process continues with energy conversion test scenarios of the project demonstrator vehicle on a roller test bench focused on noise vibration harshness (NVH) behavior and efficiency. As a conclusion, the gained knowledge by evaluating two EVO 1 prototypes on the rear axle of the test vehicle, and the design for the front axle drive train EVO 2 will be presented. As a major task on the front axle, the limited space due to the large disc brake can be identified and solved.

Fault Diagnosis of Wind Turbine Gearboxes Based on DFIG Stator Current Envelope Analysis

The drivetrains of many utility-scale wind turbines have a gearbox connected with a doubly fed induction generator (DFIG). Since gearbox failure is a major contributor to the high maintenance cost of wind turbines faced by the wind industry, it is important to perform fault diagnosis for gearboxes. Among different gearbox fault diagnosis methods, those using current signals collected from generator terminals have shown their merits in terms of complexity, cost, and reliability. In this paper, a new method that uses a DFIG stator current signal for the fault diagnosis of wind turbine drivetrain gearbox in nonstationary conditions is proposed. In the proposed method, the dc offset and high frequency noise of the current signal are first eliminated. Then, the envelope of the current signal is obtained by using the Hilbert transform. The current envelope signal only contains nonstationary frequencies that are proportional to the DFIG shaft rotating frequency. Next, a synchronous resampling algorithm is designed to convert the current envelope signal with a constant time interval to a resampled current envelope signal with a constant phase angle interval. Finally, the power spectral density analysis is used to obtain the frequency spectrum of the resampled current envelope signal from which the constant characteristic frequencies can be easily identified for the purpose of fault diagnosis. Laboratory test data collected from a 1/3 hp DFIG wind turbine drivetrain test rig and field data obtained from three 1.6-MW DFIG wind turbines are used to show the superiority of the proposed method.

Thermal modelling of a small wind turbine gearbox for condition monitoring

This study details the development of a mathematical thermal model of a small wind turbine gearbox for use in condition monitoring. The model was optimised and partially validated using experimental data from a wind turbine drivetrain test rig. The model was then used to mimic bearing faults, by simulating additional heat losses at respective faulty components. The extent to which the thermal behaviour changed as a result of a fault was studied, with a view to use such an approach to detect and locate faults.

Fault Diagnosis of Wind Turbine Gearbox Based on Deep Bi-Directional Long Short-Term Memory Under Time-Varying Non-Stationary Operating Conditions

Fault diagnosis of wind turbine (WT) gearboxes can reduce unexpected downtime and maintenance costs. In this paper, a new fault diagnosis framework is proposed based on deep bi-directional Long Short-term Memory (DB-LSTM). Even though deep learning has been used in fault diagnosis of rotating machines, deep learning diagnosis models with the input of raw time-series or frequency data face computational challenges. Additionally, the deviation between datasets can be triggered easily by operating condition variation, which will highly reduce the performance of fault diagnosis models. However, in most studies, several constant operating conditions (e.g., selected some rotational speeds and loads) are used in the experiments, which may not reflect time-varying non-stationary operating conditions of WT gearbox and cannot be applicable in real-life applications. In this work, the experiments are designed that the real rotor speed of WT spindle input to the WT drivetrain test rig to simulate the actual time-varying non-stationary operating conditions. Ten common time-domain features are all fed into the DB-LSTM network to construct fault diagnosis model, which eliminates the need for selecting suitable features manually and improves training time. Vibration data collected by three accelerometers are used to validate the effectiveness and feasibility of the proposed method. The proposed method is also compared with four existing diagnosis models, and the results are discussed.

Rotor-Current-Based Fault Diagnosis for DFIG Wind Turbine Drivetrain Gearboxes Using Frequency Analysis and a Deep Classifier

Fault diagnosis of drivetrain gearboxes is a prominent challenge in wind turbine condition monitoring. Many machine learning algorithms have been applied to gearbox fault diagnosis. However, many of the current machine learning algorithms did not provide satisfactory fault diagnosis results due to their shallow architectures. Recently, a class of machine learning models with deep architectures called deep learning has received more attention, because it can learn high-level features of inputs. This paper proposes a new fault diagnosis method for the drivetrain gearboxes of the wind turbines equipped with doubly-fed induction generators (DFIGs) using DFIG rotor current signal analysis. In the proposed method, the instantaneous fundamental frequency of the rotor current signal is first estimated to obtain the instantaneous shaft rotating frequency. Then, the Hilbert transform is used to demodulate the rotor current signal to obtain its envelope, and the resultant envelope signal contains fault characteristic frequencies that are in proportion to the varying DFIG shaft rotating frequency. Next, an angular resampling algorithm is designed to resample the nonstationary envelope signal to be stationary based on the estimated instantaneous shaft rotating frequency. After that, the power spectral density analysis is performed on the resampled envelope signal for the gearbox fault detection. Finally, a classifier with a deep architecture that consists of a stacked autoencoder and a support vector machine is proposed for gearbox fault classification using extracted fault features. Experimental results obtained from a DFIG wind turbine drivetrain test rig are provided to verify the effectiveness of the proposed method.

Reduced vibration and Model validation of DFIG for wind turbine

The vibration system of wind turbine doubly-fed induction generator (DFIG) includes generator, elastic support and frame. The work in this paper was conceived to test whether the elastic support change could change the vibration severity. So establish the Simpack simulation model and modified theoretical model to research the vibration of 4 elastic support and 5 kinds rotor mass unbalanced condition. At the same time, the experiment was carried out on the drive train test rig, experimental results are basically consistent to simulation results, and it is proved that the research in the paper is correct. Meanwhile, it proved that modified theoretical analysis model can be use to vibration analysis in frequency domain and the Simpack simulation model can be use to vibration analysis in time domain(such as trend analysis) for the rotor unbalance.