Condition monitoring of a wind turbine generator using a standalone wind turbine emulator

Abstract

The intend of this paper is to give a description of the realization of a low-cost wind turbine emulator(WTE) with open source technology from graze required for the condition monitoring to diagnose rotor and stator faults in a wind turbine generator (WTG). The WTE comprises of a 2.5 kW DC motor coupled with a 1 kW squirrel-cage induction machine. This paper provides a detailed overview of the hardware and software used along with the WTE control strategies such as MPPT and pitch control. The emulator reproduces dynamic characteristics both under step variations and arbitrary variation in the wind speed of a typical wind turbine (WT) of a wind energy conversion system (WECS). The usefulness of the setup has been benchmarked with previously verified WT test rigs made at the University of Manchester and Durham University in UK. Considering the fact that the rotor blades and electric subassemblies direct drive WTs are most susceptible to damage in practice, generator winding faults and rotor unbalance have been introduced and investigated using the terminal voltage and generated current. This wind turbine emulator (WTE) can be reconfigured or analyzed for condition monitoring without the need for real WTs.

Keywords

condition monitoring (CM) / wind turbine emulator (WTE) / wind turbine generator (WTG) / maximum power point tracking (MPPT) / tip speed ratio (TSR) / rotor faults / stator faults

Introduction

The modern technological progress of the wind turbine (WT) systems center on operation and maintenance cost reduction [ 1] and operational reliability. One of the major reasons for the WT downtime is the wind turbine generator (WTG) [ 2, 3]. Figure 1 illustrates the failure rate in the German and Danish wind power plants [ 3]. The restore of wind turbine drive train can be very expensive if the generator faults lie unnoticed [ 3, 4].

Of all the possible faults occurred in wind turbine generator (WTG), the faults related to stator account for a significant percentage [ 3, 5, 6]. The inter-turn short circuit belongs to this category of stator faults. It can cause catastrophic damage to the machine in a very short time, making any fault compensation impossible thereafter [ 5, 6]. Therefore, the condition monitoring (CM) system should be able to detect the fault quickly.

The rotor eccentricity caused by asymmetry brings carry some secondary failures that result in serious system breakdown [ 7– 9]. The rotor of the generator has high thermal, electrical and mechanical, stresses, and is, therefore, prone to faults rising over long periods. To detect such faults, cost-effective, simple, consistent and universally applicable WT CMS is still required today [ 10]. The rationale of this research is to meet such a demand. Generator electrical signals are used for the CM as they are much cheaper than other CM methods such as vibration [ 10].

The assessment of the fault diagnosis process on the real system can be hazardous as this may cause damage to the wind turbine [ 11]. Therefore, satisfactory models of the test rig are required for CM evaluation [ 10, 11]. A wind turbine emulator (WTE) is an imperative hardware to imitate wind energy conversion systems. WTE generates the same amount of energy and produces the same torque as produced by an actual WT for a given wind velocity [ 10– 12]. A WTE comprises of two mechanically coupled electrical machines (see Fig. 2a), with motor M1 imitating the wind torque, and M2 stand-in as a generator. Generally a DC machine [ 11– 14] or an induction machine [ 15, 16] is used for implementing the motor M1. The DC machine is cheaper and requires less maintenance [ 11]. The small size, low cost, and low maintenance requirements make induction generators based wind turbine systems widely used and studied [ 17].

The objective of this paper is to present a depiction of the realization of a low-cost WTE with open source knowledge from graze required for the condition monitoring to diagnose rotor and stator faults in WTG. The paper presents the information on the hardware and software used along with the WTE control strategies such as MPPT and pitch control. Considering the rotor blades and electric subassemblies direct drive, WTs are most susceptible to damage in practice, generator winding faults and rotor unbalance are introduced and investigated using the terminal voltage and generated current.

Mechanical model of the wind turbine

The aerodynamic output power (P_o) of the turbine is given as [ 12, 18]

(1) P_o={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\rho C_{p}A{v}^{\mathrm{3}},

where A (WT swept area) = pR², r is the air density, v is the wind speed, and C_p is the power coefficient.

The term power coefficient (C_p) is the fraction of the power extracted by the turbine, which depends on the shaft speed, wind speed, and the mechanical parameters such as the pitch angle (b) and shape of the blades. The power coefficient is a function of both pitch angle (b) and tip speed ratio (l).

(2) {{\displaystyle C}}_p=f(\beta ,\lambda ),

For fixed pitch WT (b = 0°), the power coefficient is only dependent on tip speed ratio (l), which is given by

(3) \lambda ={\displaystyle \frac{\omega R}{v}},

where w is the rotor angular velocity (rad/s), and R is the radius of the aerodynamic disk of a wind turbine. For power coefficient, the numerical approximation is given by

(4) C_p(\lambda ,\beta )=C_{\mathrm{1}}({\displaystyle \frac{C_{\mathrm{2}}}{{\lambda }_i}}-C_{\mathrm{3}}\beta -C_{\mathrm{4}})e^{-C_{\mathrm{5}}/{\lambda }_i}+C_{\mathrm{6}}\lambda ,

(5) {\displaystyle \frac{\mathrm{1}}{{\lambda }_i}}={\displaystyle \frac{\mathrm{1}}{\lambda +\mathrm{0.08}\beta }}-{\displaystyle \frac{\mathrm{0.035}}{{\beta }^{\mathrm{3}}+\mathrm{1}}}.

The mechanical model of a wind turbine is as shown in Fig. 2(b), and the mechanical equation of the generator side is [ 18]

(6) {\displaystyle \frac{T_t}{G_b}}-{T^{\prime }}_g=({\displaystyle \frac{J_t}{G_b^{\mathrm{2}}}}+{J^{\prime }}_g){\displaystyle \frac{d{w^{\prime }}_g}{dt}},

where {T^{\prime }}_g is the mechanical torque given by the electrical generator, {w^{\prime }}_g is the revolving speed of the shaft, T_t is the wind turbine shaft mechanical torque, and J_t is the blade interia. The objective of the WTE is to replicate the turbine torque environment of the real wind turbine [ 12, 18]. To simplify this, a motor M₁, acting as a torque source, is coupled to the electrical generator M₂, as shown in Fig. 2(a).

(7) T_m-T_g=(J_m+J_g){\displaystyle \frac{d{w}_g}{dt}},

Using the speed scaling and power scaling factor n, the torque applied by the motor is

(8) T_m={\displaystyle \frac{T_t}{nG}}-J{\displaystyle \frac{d{w}_g}{dt}},

where J is the equivalent inertia of the WTE.

Wind turbine emulator

Using the fundamentals discussed in Section 2, the test-rig was designed to emulate a small, horizontal axis wind turbine (HAWT) with the parameters listed in Table 1.

The test rig was realized by replacing the wind characteristics, gearbox and turbine rotor with a wind turbine model, a DC motor, and an IG, as illustrated in Fig. 3.

Separately excited DC motor

To implement the DC motor control strategy, the study of its dynamic characteristics is essential. Figure 4 depicts an equivalent circuit model of the separately excited DC motor. The dynamic equation of the DC motor is given by

(9) T_{\mathrm{e}}-T_{\mathrm{L}}=J{\displaystyle \frac{d\omega }{dt}},

(10) T_{\mathrm{e}}=C_{\mathrm{T}}\phi i_{\mathrm{a}}{T}_{\mathrm{e}}=C_{\mathrm{T}}\phi i_{\mathrm{a}},

(11) V_{\mathrm{t}}=r_{\mathrm{a}}{i}_{\mathrm{a}}+L_{\mathrm{a}}{\displaystyle \frac{d{i}_{\mathrm{a}}}{dt}}+C_{\mathrm{T}}\phi \omega ,

where T_L is the load torque, T_e is the electromagnetic torque, L_a is the armature inductance, and C_T is the torque constant. Using Eqs. (10)-(12), the dynamic characteristic of the motor is given by

(12) {\displaystyle \frac{d}{dt}}\left[\begin{array}{l}{i}_{\mathrm{a}}\\ \omega \end{array}\right]=\left[\begin{array}{ll}-{\displaystyle \frac{r_{\mathrm{a}}}{L_{\mathrm{a}}}}& -{\displaystyle \frac{C_{\mathrm{v}}}{L_{\mathrm{s}}}}\\ {\displaystyle \frac{C_{\mathrm{T}}}{J}}& \mathrm{0}\end{array}\right]\left[\begin{array}{l}{i}_{\mathrm{a}}\\ \omega \end{array}\right]+\left[\begin{array}{ll}{\displaystyle \frac{\mathrm{1}}{L_{\mathrm{a}}}}& \mathrm{0}\\ \mathrm{0}& -{\displaystyle \frac{\mathrm{1}}{J}}\end{array}\right]\left[\begin{array}{l}{V}_{\mathrm{t}}\\ T_{\mathrm{L}}\end{array}\right].

The specifications of the DC motor is listed in Table 2.

Self- excited three phase induction generator

A suitable capacitor bank and load is required for supplying the machine VAR requirement in a stand-alone system. Assume that the load is an R (per phase value) series circuit connected to the stator winding. The currents and voltages equations, are given as [ 9, 10, 18]

(13) {\displaystyle \frac{d}{dt}}{V}_{ds}={\displaystyle \frac{\mathrm{1}}{C}}(i_{ds}- i_{dL})+{\omega }_s{V}_{qs},

(14) {\displaystyle \frac{d}{dt}}{i}_{dL}={\displaystyle \frac{\mathrm{1}}{L_{\mathrm{ch}}}}(V_{ds}-R_L i_{dL})+{\omega }_s{i}_{qL},

(15) {\displaystyle \frac{d}{dt}}{V}_{qs}={\displaystyle \frac{\mathrm{1}}{C}}(i_{qs}- i_{qL})+{\omega }_s{V}_{ds},

(16) {\displaystyle \frac{d}{dt}}{i}_{qL}={\displaystyle \frac{\mathrm{1}}{L_{\mathrm{ch}}}}(V_{qs}-R_L i_{qL})+{\omega }_s{i}_{d\mathrm{ch}},

where i_qL and i_dL are the q- and d-axis load currents.

The current drawn by each capacitor I_{\mathrm{cap}}={\displaystyle \frac{Q^{\mathrm{1}}}{E_{\mathrm{ll}}}}=\mathrm{0.85}\mathrm{A} . The capacitive reactance: X_c={\displaystyle \frac{E_{\mathrm{ll}}}{I_{\mathrm{cap}}}}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}\pi fC}}=\mathrm{498.38}\Omega . So, for excitation in full load condition, the capacitance (delta connected) required is: C={\displaystyle \frac{\mathrm{1}}{\mathrm{2}\pi f{X}_c}}=\mathrm{6.52}\mathrm{\mu F} . The parameters of the induction machine are presented in Table 3.

Interface circuit design and WECS control strategies

Interface circuit design

The controlled (thyristor) rectifier provided a low-impedance adjustable DC voltage for the motor armature, in that way providing speed control. The motor current and PWM signal generation circuit were designed. The current controller circuit was first simulated before real-time implementation. A CT based current sensor was used to sense the load current. A PI current controller was used to match the reference and actual current values. There were fewer variations in WT mechanical parameters in the real situation due to its inertia. Thus, over a small interval the reference current calculated could be assumed to be constant. The carrier signal was generated using a square wave generator and an RC integrator. The gating signals for the switches were derived by comparing a triangular wave with a control voltage level. This carrier signal and the signal of the PI controller were compared to generate PWM signals for the gate of thyristor based full wave semi converter through a pulse transformer. The mean value of the rectified voltage could be varied by altering the firing angle of the thyristors, thereby allowing the motor speed to be controlled. A schematic layout of the interface circuit developed in software is demonstrated in Fig. 5. The comparison of the actual current and reference current is displayed in Fig. 6. In real-time, the PI controller is implemented on LabVIEW.

WECS control strategies

Figure 7 shows the control schema that has been implemented which are WT modeling in LabVIEW, MPPT using a chopper controlled circuit, and pitch control scheme using a PI controller.

To replicate the behavior of a real WT, a mathematical model was developed in LabVIEW. As exhibited in Fig. 7, the model developed was used to calculate the tip speed ratio (TSR) from the angular speed and wind velocity. The value of power coefficient C_p was obtained from pitch angle (b) and tip speed ratio (l) set as an input to the WT model. The reference torque for emulation was determined by dividing the WT power with its angular speed.

The maximum power can be obtained from the emulator, if the WT operates at the optimum TSR. However, the output mechanical power is a maximum at a particular value of angular speed for a constant wind speed. This is considered as the optimum angular speed for that wind speed. The value of optimum angular speed increases with the increase in wind velocity. Hence, the MPPT logic always tracks the l_opt irrespective of the wind speed. The MPPT control logic compares the actual TSR with the optimum TSR and generates the gate pulses for the chopper circuit using the PWM technique. As given in Eq. (1), the WT power output is proportional to the cube of wind velocity. Using the pitch control of WT, to control the speed and power at wind speeds above, the rated wind speed can be achieved. The pitch control mechanism controls the power output by reducing the power coefficient at higher wind speeds. Below the rated wind speed, the blade pitch is maintained at zero rad to obtain maximum power. While, the pitch controller increases the blade pitch as the wind speed passes over the rated value, the reduction in value of C_p by pitching compensates for the increase in WT power output under the influence of higher wind speeds.

The main tasks of the WTE control schema is the establishment of a bi-directional real-time communication with the machine; using a text file for getting the wind speed data; acquisition of generator stator currents and terminal voltage required for the condition monitoring; calculations of TSR, C_p(l,b) curve, the mechanical power and mechanical torque; computation of the generated electric power P_e and the pitch angle b; and implementation of MPPT and TSR algorithms.

Results

To validate the control strategies developed for condition monitoring, the wind turbine emulator setup was run for various experiments.

1)Response of WTE in dynamic conditions.

a)Step variations in wind speed;

b)Arbitrary variation in wind speed.

2)Response of SEIG at constant wind speed and full load.

3)Response of SEIG for short circuit fault using PSD.

4)Response of SEIG for rotor mass imbalance fault using PSD.

Response at full load conditions and constant wind speed

On the WT model, a wind profile varying from 6 m/s to 7 m/s was applied, as shown in Fig. 8. The reference power and current generated by the WT model also track the wind speed. From the WT model, the reference power obtained varies from 250 W to 360 W (Fig. 9), while the current calculated varies from 2.0 A to 2.4 A (Fig. 10).

Dynamic behavior of WTE: Arbitrary variation in wind speeds

In this experiment, an arbitrary variation in wind speed was applied in LabVIEW. The performance comparison for arbitrary variations in wind speed is shown in Fig. 11. The output power of WTE under all possible wind profiles is presented in Fig. 12. It is observed that the steady-state power of WTE is 420 W at a wind speed of 7 m/s. Again during gusty situations or step rise/fall or in gradual rise/fall in wind speed, the power delivered by the WTE is close to 420 W at a wind speed of 7 m/s (Fig. 13). Thus, it indicates that irrespective of how the wind attains its speed, the power output remains the same. From these experiments, it can be inferred that the response of the WTE is good under dynamic conditions and WTE imitates the actual behavior of the WT.

Benchmarking the response of SEIG with the test rigs made at the Universities of Manchester and Durham

The experimental results of the test rig made at NITK were verified with the research done at the University of Manchester and Durham University in UK. The results were benchmarked with the two different test rigs by analyzing the steady-state current spectra analysis for healthy machine [ 19– 21].

The comparison of different test rigs and data acquisition systems made at the University of Manchester, Durham University and NITK is summarized in Table 4. The rig at the University of Manchester operated as either a DFIG or a WRIG at user-defined fixed speeds. The rig at Durham University featured a WRIG with variable resistance in rotor circuits, driven at either a constant speed or under non-stationary and variable speed conditions. The low- cost test rig at NITK operated with an SCIG either at a constant speed or under non-stationary variable speed conditions. Harmonic contents of the generated current were obtained using the FFT function in LabVIEW. A number of frequencies of interest were identified using the analytical expressions given in Refs. [ 12, 13]. The comparison is tabulated in Table 5.

Response of SEIG for short circuit fault using PSD

To prove the validity of WTE for condition monitoring, short circuit fault was introduced using the stator windings. Generated current and terminal voltage measurements were performed for a healthy stator winding and for the short circuit fault in the same phase. The sampling frequency used for the measurement is about 2 kHz. After reading the signal, it is decomposed by a power spectrum algorithm. Under healthy conditions as shown in Figs. 14 and 15, the shaft speed remains constant at 1480 r/min, the generated terminal voltage is 220 V, and the generated current is 0.3 A. The second set of experiments is performed at a constant wind speed and with short circuit fault. The generated terminal voltage and terminal current show a dip when a short circuit is applied as shown in Figs. 16 and 17 respectively. The inter-turn stator fault can produce specified frequency components in different signals as induced frequency component in axial leakage flux [ 5, 6],

(17) f_{\mathrm{short}}=f_s\left(k\pm n{\displaystyle \frac{(\mathrm{1}-s)}{p}}\right),k=\mathrm{1},\mathrm{2},\mathrm{3},\mathrm{...},(\mathrm{2}p-\mathrm{1}),

where p the number of pole-pairs, S is the per-unit slip, and f_s is the supply frequency.

The power spectrum of voltage in healthy conditions is given in Fig. 18. The odd harmonics are most evident at 50 Hz, 143 Hz and 239 Hz in full load conditions. In a faulty condition, with n = 3, k = 1, and slip= 0.27, side bands at 22.5 Hz, 77.5 Hz, 118.5 Hz and 177.5 Hz, around the first and third harmonics are quite evident as shown in Fig. 19.

Response of SEIG for rotor mass imbalance fault using PSD

In this set of experiments, fault conditions were emulated by introducing rotor eccentricity fault to the rig with load unbalances by mounting an unbalanced metal disk of weight to the shaft. Four different types of experiments were done by varying the load in healthy and faulty conditions. The classic behavior of induction machine with eccentricity fault is the existence of side bands f_e±f_r, whose amplitudes are monitored for fault diagnostics [ 10, 21, 22]. f_e(Hz) is the input frequency and f_r(Hz) is the rotational frequency. The same phenomenon also exists in WTG under steady-state conditions with a constant wind speed due to insignificant variations in the slip. In the case of healthy conditions, only the fundamental frequency f exists in stator current while, when a fault occurs, a number of harmonic frequency components also exist. Comparing the spectra of the modulating signals, it is apparent that the rotor asymmetry produces increments in magnitudes of the components f_e-f_r, and f_e + f_r, by approximately 30%, as shown in Fig. 20.

Conclusions

CM is an important task for WTG reliability improvement and availability. WTE is vital for evaluation of CM methods before implementing on the wind turbine for health monitoring and the diagnosis system. This paper contributes to the establishment of a low-cost scaled WTE, made of two mechanically coupled electrical machines with open source technology. The mathematical model build in LabVIEW along with various algorithms/circuits like MPPT, TSR makes it possible to emulate any real wind turbine in concurrence with real wind speed registers.

Various tests have been conducted on the test rig and the results are benchmarked with the proven test rigs made at Durham University and the University of Manchester in UK. For CM of WTG stator fault using the short circuit and rotor faults using mass imbalance, various tests have been performed. The response of terminal voltage and generated current has been analyzed both under healthy and faulty conditions. The experimental results confirm the feasibility and performance of the WTE.

References

[1] Kamel R M. Effect of wind generation system types on micro-grid (MG) fault performance during both standalone and grid connected modes. Energy Conversion and Management, 2014, 79: 232–245

[2] Tavner P J, Faulstich S, Hahn B, van Bussel G J W. Reliability & availability of wind turbine electrical & electronic components. European Power Electronics Journal, 2011, 20(4): 45–50

[3] Ribrant J, Bertling L. Survey of failures in wind power systems with focus on Swedish wind power plants during 1997–2005. IEEE Transactions on Energy Conversion, 2007, 22(1): 167–173

[4] Djurovic S, Crabtree C J, Tavner P J, Smith A C. Condition monitoring of wind turbine induction generators with rotor electrical asymmetry. IET Renewable Power Generation, 2012, 6(4): 207–216

[5] Gandhi A, Corrigan T, Parsa L. Recent advances in modeling and online detection of stator interturn faults in electrical motors. IEEE Transactions on Industrial Electronics, 2011, 58(5): 1564–1575

[6] Seshadrinath J, Singh B, Panigrahi B K. Single-turn fault detection in induction machine using complex-wavelet-based method. IEEE Transactions on Industry Applications, 2012, 48(6): 1846–1854

[7] Yang W, Tavner P J, Crabtree C J, Feng Y, Qiu Y. Wind turbine condition monitoring: technical and commercial challenges. Wind Energy (Chichester, England), 2014, 17(5): 673–693

[8] Djurovic S, Crabtree C J, Tavner P J, Smith A C. Condition monitoring of wind turbine induction generators with rotor electrical asymmetry. IET Renewable Power Generation, 2012, 6(4): 207–216

[9] Gong X, Qiao W. Imbalance fault detection of direct-drive wind turbines using generator current signals. IEEE Transactions on Energy Conversion, 2012, 27(2): 468–476

[10] Yang W. Condition monitoring the drive train of a direct drive permanent magnet wind turbine using generator electrical signals. Journal of Solar Energy Engineering, 2014, 136(2): 021008

[11] Attoui I, Omeiri A. Modeling, control and fault diagnosis of an isolated wind energy conversion system with a self-excited induction generator subject to electrical faults. Energy Conversion and Management, 2014, 82: 11–26

[12] Sahoo N C, Satpathy A S, Kishore N K, Venkatesh B.D. C. motor-based wind turbine emulator using LabVIEW for wind energy conversion system laboratory setup. International Journal of Electrical Engineering Education, 2013, 50(2): 111–126

[13] Arribas J R, Veganzones C, Blazquez F, Platero C, Ramirez D, Martinez S, Sanchez J A, Herrero Martinez N. Computer-based simulation and scaled laboratory bench system for the teaching and training of engineers on the control of doubly fed induction wind generators. IEEE Transactions on Power Systems, 2011, 26(3): 1534–1543

[14] Martinez F, Herrero C, de Pablo S. Open loop wind turbine emulator. Renewable Energy, 2014, 63: 212–221

[15] Mesemanolis A, Mademlis C, Kioskeridis I. Optimal efficiency control strategy in wind energy conversion system with induction generator. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2013, 1(4): 238–246

[16] Mesemanolis A, Mademlis C, Kioskeridis I. High-efficiency control for a wind energy conversion system with induction generator. IEEE Transactions on Energy Conversion, 2012, 27(4): 958–967

[17] Zou Y, Elbuluk M E, Sorez Y. Simulation comparisons and implementation of induction generator wind power systems. IEEE Transactions on Industry Applications, 2013, 49(3): 1119–1128

[18] Castell J, Espí J M, García-Gil R. Development details and performance assessment of a wind turbine emulator. Renewable Energy, 2016, 86: 848–857

[19] Yang W, Tavner P J, Crabtree C J, Feng Y, Qiu Y. Wind turbine condition monitoring: technical and commercial challenges. Wind Energy (Chichester, England), 2014, 17(5): 673–693

[20] Yang W, Tavner P J, Crabtree C J, Wilkinson M. Cost effective condition monitoring for wind turbines. IEEE Transactions on Industrial Electronics, 2010, 57(1): 263–271

[21] Djurovic S, Crabtree C J, Tavner P J, Smith A C. Condition monitoring of wind turbine induction generators with rotor electrical asymmetry. IET Renewable Power Generation, 2012, 6(4): 207–216

[22] Kral C, Habetler T, Harley R. Detection of mechanical imbalances of induction machines without spectral analysis of time-domain signals. IEEE Transactions on Industry Applications, 2004, 40(4): 1101–1106

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