Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal Processing Techniques
Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal...
Merizalde, Yuri;Hernández-Callejo, Luis;Duque-Perez, Oscar;López-Meraz, Raúl Alberto
applied sciences Article Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal Processing Techniques 1 , 2 , 3 Yuri Merizalde * , Luis Hernández-Callejo * , Oscar Duque-Perez and Raúl Alberto López-Meraz PhD School of the University of Valladolid (UVA), Faculty of Chemical Engineering, University of Guayaquil, Clemente Ballen and Ismael Perez Pazmiño, Guayaquil 593, Ecuador Department of Agricultural Engineering and Forestry, University of Valladolid (UVA), Campus Universitario, Duques de Soria, 42004 Soria, Spain Department of Electrical Engineering, University of Valladolid (UVA), Escuela de Ingenierías Industriales, Paseo del Cauce 59, 47011 Valladolid, Spain; email@example.com Unidad de Ingeniería y Ciencias Químicas, Universidad Verzcruzana, Circuito Universitario Gonzalo Aguirre Beltrán, Zona Universitaria, Xalapa 91000, Mexico; firstname.lastname@example.org * Correspondence: email@example.com (Y.M.); firstname.lastname@example.org (L.H.-C.); Tel.: +34-975-129-418 (L.H.-C.) Received: 12 August 2020; Accepted: 19 October 2020; Published: 22 October 2020 Abstract: In the wind industry (WI), a robust and effective maintenance system is essential. To minimize the maintenance cost, a large number of methodologies and mathematical models for predictive maintenance have been developed. Fault detection and diagnosis are carried out by processing and analyzing various types of signals, with the vibration signal predominating. In addition, most of the published proposals for wind turbine (WT) fault detection and diagnosis have used simulations and test benches. Based on previous work, this research report focuses on fault diagnosis, in this case using the electrical signal from an operating WT electric generator and applying various signal analysis and processing techniques to compare the eectiveness of each. The WT used for this research is 20 years old and works with a squirrel-cage induction generator (SCIG) which, according to the wind farm control systems, was fault-free. As a result, it has been possible to verify the feasibility of using the current signal to detect and diagnose faults through spectral analysis (SA) using a fast Fourier transform (FFT), periodogram, spectrogram, and scalogram. Keywords: wind turbine; electric generator; spectral analysis; fault diagnosis 1. Introduction Regardless of the maintenance strategies and models applied in the wind industry (WI) to detect and diagnose faults, the use of signals, such as vibration, acoustic, temperature, magnetism, and electrical signals, is an indispensable requirement. Each of these types of signal have their advantages and disadvantages. However, because all moving equipment produces some type of vibration, in the WI, the use of vibration signals predominates [1–3]. Even though the current signal does not use intrusive methods, the equipment used is inexpensive, easy to install, and also, according to reference [4,5], both the vibration and current signal can be used to detect failures of the electric generator and loads coupled to its axis. However, according to published reports, in the WI, current signals have rarely been used, and the existing research is based predominantly on laboratory studies. The processing of the signals used for the detection and diagnosis of faults is carried out using a variety of models in the time, frequency, and time–frequency domains. All signal processing techniques Appl. Sci. 2020, 10, 7389; doi:10.3390/app10217389 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 7389 2 of 28 have their own advantages and disadvantages, and the same applies to the domain in which the analysis is carried out [1–4]. Furthermore, the lack of ideal conditions to apply a speciﬁc technique directly prompts us to develop mathematical models that allow the detection and diagnosis of a speciﬁc type of fault that occurs in a particular component and in certain speciﬁc conditions of operation [6–8]. The combination of all these factors has given rise to a huge number of proposed methods, some of which are analyzed in more detail in the following sections. Based on the foregoing, the purpose of this research is the detection and diagnosis of electrical generator faults by means of the current signal of real wind turbines (WTs) in operation and the application of various techniques for processing and analyzing existing signals to: Analyze the models used to detect the frequency components associated with faults. Obtain the spectrum of the current signal of an operating turbine. Study the eectiveness of signal processing techniques in detecting WT failures. Check the eectiveness of the WT control system to determine the status of the generator. Compare the results obtained with those of previously published studies. Due to the variety of stresses to which the rotary induction machine is subjected, there are a variety of failures that can occur in the stator, rotor, and bearings, as described in reference . The objective of this research is not to focus on a speciﬁc fault, but rather, applying the dierent signal processing techniques, try to detect and diagnose the faults that will be described in sections two and three. As one of the main objectives of this research is to use data from WTs in operation, and the only wind farm (WF) available to make the measurements was integrated by WTs that use SCIG, then the study will focus on this type of electric generator. The remainder of this original research is organized as follows. Section 2 analyzes the mathematical models used to determine the frequency components associated with faults in the SCIG using current signal analysis. In Section 3, the fundamentals and application of various signal analysis techniques are discussed, emphasizing the published techniques for fault detection in WTs using the SCIG current signal. Section 4 details the methodology and materials used for the experimental part of this research. Section 5 includes the results obtained by applying the techniques described in Section 3. Finally, the conclusions and recommendations are included in Section 6. 2. Modeling Electrical Generator Faults Using the Current Signal Due to its design, durability, and low cost, the use of the squirrel-cage induction machine predominates at the industrial, commercial, and domestic levels [10,11]. Although there are several types of generators, according to reference , in the WI, the doubly fed induction generator (DFIG), and the squirrel-cage induction generator (SCIG) predominate. The voltage signals, current, magnetic ﬁeld, magnetomotive force (MMF), torque, and power of an induction machine are characterized by its sinusoidal behavior . Since the speed of the rotor depends on the coecients of the associated dierential equations, which vary with time, the behavior of the materials used in the construction of the motor is not constant over time but depends on the position of the rotor. Under these conditions, it is hard to analyze signals in a spatial system, which is why in-plane analysis is preferred. For this purpose, using the Clarke and Park transforms, a change of variables is made. With the ﬁrst transformation, we go from a 3D system (abc) to a 2D plane (alpha-beta) that varies with the stator, while with the second one, we obtain a plane dq0 equivalent to a 2D plane that rotates at the same rotor speed but is oset by an angle . Since the three-phase induction machine generally does not use a neutral line, the main current does not have a homopolar component, and the three phases can be represented in the dq plane. In this plane, the stator remains ﬁxed (direct axis d) in relation to a rotor plane (quadrature axis q) that rotates at speed ! [13–15]. x Appl. Sci. 2020, 10, 7389 3 of 28 The transformation between the abc space system and the dq0 plane, when the latter is oriented at an angle with reference to the axis that remains ﬁxed, can be performed directly using Equations (1) and (2). When is zero, these Equations become (3) and (4), respectively [15–18]. 2 3 2 32 3 2 2 6 i 7 6 cos cos( ) cos( + ) 76 i 7 qs a 6 7 6 3 3 76 7 6 7 6 76 7 6 7 6 76 7 2 2 6 7 6 76 7 6 i 7 = 6 sin sin( ) sin( + ) 76 i 7 (1) 6 ds 7 6 76 b 7 3 3 6 7 6 76 7 4 5 4 54 5 i 0.5 0.5 0.5 i 0s c 2 3 2 32 3 6 i 7 6 cos sin 1 76 i 7 a qs 6 7 6 76 7 6 7 6 76 7 6 7 6 76 7 2 2 6 7 6 76 7 i = cos( ) sin( ) 1 i (2) 6 7 6 76 7 b ds 6 7 6 76 7 3 3 6 7 6 76 7 4 5 4 54 5 2 2 i cos( + ) sin( + ) 1 i c 0s 3 3 2 3 2 3 " # 6 i 7 1 1 a 6 7 6 1 76 7 i 2 qs 6 76 7 2 2 p p 6 76 7 = 6 76 i 7 (3) 4 3 3 56 7 6 7 i 3 ds 0 4 5 2 2 2 3 2 3 1 0 6 7" # 6 i 7 a 6 7 6 7 6 7 6 7 6 7 i 6 7 qs 1 3 6 7 6 7 6 7 i = (4) 6 7 b 6 7 6 7 2 2 6 p 7 6 7 6 7 i 4 5 ds 4 5 2 2 According to reference , the phase current of the DFIG can be expressed as a function of the ﬂow and torque vectors (the torque angle is 90 over the ﬂow), Equations (5) and (6), respectively, which can be represented in the dq plane, according to Equation (2). Since the variables of Equations (5) and (6) rotate at speed 2fs, they cannot be measured directly, so it is necessary to apply the inverse Park transform to obtain the phase currents according to Equations (7) to (9). As described previously , the dierent stresses that cause a torque on the rotor include coupled loads; unbalanced dynamic forces; torsional vibration; transient torques; magnetic forces caused by leakage ﬂux over the slots, making them vibrate at twice the frequency of the rotor; air gap eccentricity; centrifugal forces; thermal stresses caused by heat in the short-circuit ring and heat in the bars during starting (skin eect); residual forces due to casting; machining and welding. Under normal operating conditions, the spectrum of the signal has deﬁned components. However, the asymmetries of the generator and the loads coupled to it (gearbox, blades) transmit torsional vibrations that act on the rotor, causing variations in the speed, torque, air gap magnetic ﬂux and current bars. In this way, both mechanical and electrical faults manifest as lateral components of the fundamental wave. The number of harmonics and their amplitude depend on the magnitude of the fault [20,21]. i = i + A sin(2 f t + ' ) (5) sM sM sM v M i i i = i + A cos(2 f t + ' ) (6) sT sT sT v T 0 i i i (t) = i sin (2 f t + ' ) a 0 s 0 + fA cos[2( f f )t ' ] sMi s v M +A cos[2( f f )t ' ]g (7) sT s v T fA cos[2( f + f )t + ' ] sM s v M 2 i A cos[2( f + f )t + ' ]g sT s v T 2 2 i = i + i (8) sM0 sT0 sT0 ' = tg (9) sM0 As described in reference , in a fault-free machine, the rotor and stator currents should be balanced. However, due to small differences in the winding geometry and the nonlinearity of the Appl. Sci. 2020, 10, 7389 4 of 28 materials, an asymmetry arises that causes axial ﬂow dispersion. Under these conditions, the distribution of the harmonics in the air gap undergo alterations that can be easily detected, so that we can detect broken rotor bars, one-phase failure, dynamic eccentricity, a negative sequence phase, and short circuits in the rotor and stator windings. According to the same author, in a three-phase machine, with a full pole-pass and fed by a balanced frequency ! , the spatial distribution of the harmonics of the MMF about the stator and as a function of the air gap ﬂux is given by Equation (10). To apply these Equations to the rotor, is given by (11) or (12). Substituting these Equations in the general term of (10) and expanding it to obtain the ﬁrst terms, we obtain Equation (13), which provides the components of the frequency spectrum of the current induced in the rotor by the harmonics of the air gap. That is, the stator current spectrum (CS) includes the components of the supply current and those of the rotor. The presence of short circuits between turns produces an MMF with its own frequency spectrum that is superimposed on the main one to give rise to a new spectrum that is expressed by (14) and whose main term is (15). F = F cos (!t p ) + F cos (!t + 5p ) s 1 s 5 s (10) F cos (!t 7p ) + F cos (!t + 11p ) ::::::F cos (!t + np ) s s n s 7 11 = + = _r + ! t (11) r sr r ! = !(1 s)/p (12) F = F cos (s!t p ) + F cos ((6 5s)!t + 5p ) s 1 r 5 r (13) F cos ((7s 6)!t 7p ) + F cos ((12 11s)!t + 11p ) :::::: s s 7 11 " # X X (1 s) F = 0.5 F cos (k k ( )) k (14) s n 1 2 2 r " # (1 s) f = (k k ( )) k (15) 1 2 2 r As described in reference , the faulty and healthy squirrel-cage induction motor current is given by (16) and (17), respectively. According to reference , when there is a short circuit or static eccentricity in the stator, a negative sequence component appears, the amplitude of which depends on the percentage of shorted turns. As described in reference [24,25], the components due to stator failures ( f ) are given by Equation (18), while according to reference , in the case of a healthy motor, the main s f components are the ﬁrst and ﬁfth harmonics. In the case of an unbalanced voltage, regardless of slip, this fault shows itself mainly in the ﬁrst and third harmonics. According to reference , short circuits cause the components given by Equation (19). As described in reference , in a symmetrical stator, the CS contains the harmonics given by Equations (20) and (21), for which the harmonics determined by Equations (22) through (24) should be added, in case of asymmetry. As described in reference , another simple alternative for the early detection of stator faults depends on the magnitude of the negative sequence of the current, which allows us to obtain the negative impedance to be compared with the average winding impedance. i (t) = i (t) = i (t) 1 + k cos(! t) (16) a A a m f k I m L i (t) = I cos ! t '