1
Detection of Minor Winding Deformation Fault in High Frequency Range for Power Transformer W. H. Tang, Member, IEEE, A. Shintemirov, Q. H. Wu, Senior Member, IEEE
Abstract—This paper presents a simplified distributed parameter model for minor winding deformation fault analysis of power transformers on the basis of frequency response analysis (FRA). The FRA data of an experimental transformer is employed as a reference trace, which are compared with the simulations of the simplified distributed parameter model concerning minor winding deformation faults. In order to perform quantitative analysis when a deformation fault occurs, three statistical indicators are used to analyze the FRA simulation data. It is suggested in the results that minor winding deformation faults can be detected at the frequency range above 1 MHz. Index Terms—Transformer winding, distortion fault, frequency response analysis.
I. I NTRODUCTION The FRA test, firstly proposed in [1], is a very sensitive technique for detecting winding movement faults caused by loss of clamping pressure or by short circuit forces. Variations in frequency responses may reveal a physical change inside a transformer, e.g. winding movement caused by loosened clamping structures and winding deformation due to shorted turns. By comparing a frequency response measured during maintenance with a fingerprint measurement obtained at an earlier stage, FRA is widely employed by utility companies as a comparative method in the low frequency range of several tens of KHz to 1 MHz. Differences may reveal internal damages of a transformer, hence inspections can be scheduled for repairing. However such a comparative method cannot quantify the change caused by the fault and locate it. In industrial practice, FRA is one of the most suitable winding diagnostic tools that can give an indication of displacement and deformation faults. It can be applied as a non-intrusive technique to avoid interruptive and expensive operations of opening a transformer tank and oil degasification and dehydration, which can minimise the impact on system operation and loss of supply to customers and consequently save millions of pounds in timely maintenance. A wide range of research activities have been undertaken to utilise and interpret FRA data for winding fault diagnosis, mainly including development of accurate winding models and elaboration of FRA measuring systems. In [2] analytical expressions were used to the estimate parameters of a lumpedparameter model based on the geometry of a transformer. However, such a lump-parameter model is limited in accuracy W.H. Tang, A. Shintemirov and Q.H. Wu are with the Department of Electrical Engineering and Electronics, the University of Liverpool, Liverpool L69 3GJ, U.K. Corresponding author is W.H. Tang, Tel: +44 151 7944615, Fax: +44 151 7944540, E-mail:
[email protected].
978-1-4244-6551-4/10/$26.00 ©2010 IEEE
at the high frequencies from 1 MHz to 10 MHz. Rudenberg [3] elaborated and extended the travelling wave theory for lossless transformer winding analysis. In [4], each turn of a winding is represented as a single transmission line, which makes multiconductor transmission line (MTL) models complex to operate in case of analysis of a winding with a large number of turns. A recent study in [5] demonstrated the potential for FRA result interpretation in an extend range of frequency up to 10 MHz, which involved simulations with a lump-parameter model and comparisons with field experiments. It was shown in [5] that FRA measurements at higher frequencies above 1 MHz are more sensitive to minor winding faults such as axial displacements and radial deformations of several discs in a winding. Therefore, it is necessary to investigate the sensitivity of a winding model and its applicability for detection of minor winding movement. It is summarized that it is of practical importance to conduct the research into the understanding of transformer behaviours during non-50 Hz conditions by employing FRA. Furthermore, it is necessary to develop an accurate winding model, which can accurately describe the signal propagation along a winding disc in both the low and high frequency ranges. There is a potential to improve FRA interpretation in the high frequency range, which is more sensitive to small changes of winding movements, and little field experience is available about the effectiveness to use high frequency FRA between 1 MHz to 10 MHz to detect winding movements for large transformers. This paper presents a simplified distributed parameter model of transformer winding for FRA. The model is used to simulate minor winding distortion faults for a single phase transformer. Two types of faults are simulated, i.e. minor axial displacements of discs and minor radial deformations of discs. Quantitative results using statistical analysis indicators are presented with discussions on the effect of minor winding faults on frequency responses. II. S IMPLIFIED DISTRIBUTED PARAMETER MODEL OF TRANSFORMER WINDING
Consider a continuous disc winding, since this type represents an essential part of high voltage (HV) transformer windings. Each disc of the winding consists of a number of turns wounded in the radial direction. In practice, continuous disc windings are wound to provide the same direction of a flowing current along all discs of a winding. In some transformers, the reinforcement of insulation at the ends of a winding results to a reduced number of turns for several discs closest to the winding ends. Thus, the conductor length
2 uinp Z inp
l
Zinp
C1dx
C1dx
G1dx
C1dx
G1dx
x
i1(t,0) u1(t,0)
g1 /dx
g1/dx
r1dx
L1dx
L1dx
r1dx
Cd12dx
K2/dx
K2/dx L2dx
r2 dx
Cd23dx
Gd23dx
r2 dx
Gd12dx
C2dx
L2dx
u2(t,x)
i2(t,x)
G2dx
Cd12dx
g2/dx
K2/dx
L2dx
Cd23dx
Gd12dx
g2/dx
g2/dx
r2 dx
C1dx
L1dx G1dx
Gd12dx
M12 dx
C2dx
r1dx
u1(t,x)
i1(t,x) Cd12dx
K1/dx
K1/dx
K1/dx
G1dx
dx g1 /dx
G2dx Gd23dx
Cd23dx
Gd23dx
M1n dx gn/dx Kn/dx
Cndx rn dx
rn dx in(t,x)
Gndx
Fig. 1.
Kn/dx
Kn/dx
Lndx
Cndx
gn/dx
gn/dx
Gndx
Lndx
rn dx
Lndx
un(t,x) Cndx
in(t,l) un(t,l) Gndx
Cndx
Gndx
of the extreme discs can differ from those of the middle discs. However, in this study for the sake of simplicity, an equal conductor length is adopted for all winding discs. In this paper, a simplified distributed parameter model of transformer winding developed by the authors are employed to study the high frequency behaviours when a deformation fault occurs [6]. In order to obtain a general equivalent circuit of a winding, each disc is considered as an equivalent distributed parameter circuit with turns being stretched out in the radial direction of the winding, similar to an equivalent circuit of transformer winding proposed with respect to the axial direction [6]. Considering interdisc connections and taking into account the above simplifications, the equivalent distributed parameter circuit of a disc-type transformer winding at FRA testing can be depicted as in Fig. 1. In the figure the following notations for the winding parameters per unit conductor length are used:
ri Li Mi(i+1) Gi gi Gdi(i+1)
As is clear from the equivalent circuit, the discs numeration is marked from the top down and the direction of the space coordinate x is denoted from the left terminal of each disc towards the right end. Each disc has its own parameters signified by a corresponding disc number and the discs are connected to each other by a curve line. Thus, notations u1 (t, 0), i1 (t, 0) and un (t, l), in (t, l) denote the voltages and currents at an input terminal and an output end of the winding, i.e. at x = 0 for the first disc and x = l for the nth disc, and t denotes the time coordinate respectively. Note that the term “ground” in the definitions of the above capacitance C and conductance G means that the parameters can be considered as being between the winding and the core or the tank, or as interwinding in case of low voltage (LV) winding presence.
Zout
A Simplified distributed parameter model of transformer winding
Ki Ci Cdi(i+1)
Zinp , Zout impedances of measurement cables; l, a conductor length of a disc and a turn; n total number of discs in winding.
average interturn capacitance of the ith disc; average ground capacitance of the ith disc; average interdisc capacitance between the ith and (i + 1)th discs; average resistance of the ith disc; average inductance of the ith disc; average mutual inductance between the ith and (i + 1)th discs; average ground conductance of the ith disc; average interturn conductance of the ith disc; average interdisc conductance between the ith and (i + 1)th discs;
III. T RANSFER F UNCTIONS OF T RANSFORMER W INDING FOR FRA In order to develop a mathematical description of a disctype transformer winding, considering the equivalent circuit in Fig. 1, the equations have been derived to describe signal propagation along each disc of the winding including interdisc capacitances and conductances, which are subsequently combined into a matrix form similar to that of a MTL model [6]. The analysis of signal propagation is based upon the Telegraphers equations for lossy transmission lines In practice, frequency responses of a transformer winding are obtained in a form of transfer functions of selected input or output voltages or currents with respect to the injected source voltage signal. For the simplified winding model, the circuit equations can be expressed in the following form: ⎡ ⎤ ⎡ ⎤ ⎡I (s, 0)⎤ U1 (s, 0) 1 ⎢ 0 ⎥ ⎢ U2 (s, 0) ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎥⎢ ⎢ ⎥ ⎢ .. .. ⎥ , (1) ⎥·⎢ Ω(l) = ⎢ ⎢ ⎥ . ⎥⎢ . ⎥ ⎥ ⎢ ⎥ ⎢ ⎦⎣ ⎣Un (s, 0)⎦ ⎣ 0 ⎦ Un (s, l) In (s, l) where U (s, x) and I(s, x) are the Laplace transforms of the voltage u(t, x) and current i(t, x) correspondingly and Ω(l) is a matrix of (n + 1) × (n + 1) order [7]. To derive an expression for a winding transfer function, the following terminal condition is applied: Un (s, l) = Zout In (s, l).
(2)
With the above terminal condition, the transfer function H(s) of a transformer winding for FRA tests is calculated as the ratio of Un (s, l) and U1 (s, 0) [6]: H(s) =
Ω(n+1,1) Zout Un (s, l) = . U1 (s, 0) Ω(1,1) Zout − Ω(n+1,n+1) + Ω(1,n+1) Ω(n+1,1) (3)
3
IV. Q UANTITATIVE A NALYSIS U SING S TATISTICAL I NDICATORS In practice, an analysis of frequency response measurements is conducted by experts using a visual comparison of the responses, plotted on the same graph, and, therefore, requires expert’s knowledge for result interpretations [8]. To overcome this problem, many efforts have been directed to establish a reliable quantitative analysis procedure using the statisticallybased performance indicators such as a correlation coefficient ρ(X, Y ), a spectrum deviation σ(X, Y ) and an absolute sum of logarithmic error (ASLE), etc. [8]–[12]. These three indicators are mostly used for FRA to carry out “time-based” (reference) comparative quantitative analysis of deviations between two frequency responses, which are defined as follows: S
ρ(X, Y ) =
i=1 S
i=1
σ(X, Y ) =
S 1 S i=1
ASLE(X, Y ) =
x i yi
x2i ·
S
i=1
;
(4)
yi2
xi − (xi + yi )/2 (xi + yi )/2
Fig. 2. 2
+
yi − (xi + yi )/2 (xi + yi )/2
2
S 1 |20 log10 yi − 20 log10 xi |, S i=1
;
(5) (6)
where vectors X = {x1 , x2 , . . . , xS } and Y = {y1 , y2 , . . . , yS } denote data vectors of two frequency responses and S is the number of frequency points in the observed frequency interval. If the shapes of X and Y are similar to each other, the correlation coefficient ρ(X, Y ) approaches 1 whereas and the spectrum deviation σ(X, Y ) approaches 0. ASLE is introduced for FRA, since the winding transfer function responses are mostly defined as the ratio of the output signal to the input signal using a logarithmic vertical axis. Thus, ASLE quantifies graphically presented frequency responses in dB units [11]. The above indicators can be used to analyse FRA measurements individually in different frequency ranges to evaluate the effect of various winding conditions on the responses at different frequencies. V. R ESULTS AND DISCUSSIONS OF MINOR WINDING FAULTS ON FREQUENCY RESPONSES
In practice, each phase of a power transformer includes LV and HV windings, which are connected to each other via interwinding capacitances and mutual inductances as shown in Fig. 1. This interwinding coupling as well as a terminal connection mode of one winding, i.e. open or short circuited, grounded, etc., affects measured frequency responses of the other winding. A single-phase experiment transformer is used in this paper to investigate the effects of minor winding faults on frequency responses. A single-phase experiment transformer without a core, consisted of a 30-double disc HV winding and a 23-turn helical LV winding, is used to measure HV winding input admittance frequency responses using standard FRA equipment [13]. The transformer’s geometrical dimensions are provided in [13] and
Geometrical dimensions of experimental transformer (mm)
presented in Fig. 2. The transfer function derived in equation (3) is employed to simulate frequency responses of the above transformer. Using the derived transfer functions, numerical simulations of frequency responses of winding input impedance and resonances under very fast transient overvoltages is undertaken. The simulations are compared with the experimental and simulation results obtained using lumped parameter and MTL models [6]. As mentioned in the introduction, it is shown that FRA measurements at higher frequencies above 1 MHz are more sensitive to minor winding faults such as axial displacements and radial deformations of several discs in a winding [5], [14]. Therefore, in order to investigate the sensitivity of the developed model and its applicability for detection of minor winding movements, the following cases are studied [5]: • minor axial displacements of discs 10 and 11, 30 and 31, 50 and 51, representing top, middle and bottom parts of the HV winding of the experimental transformer in Fig. 1 respectively, simulated by changing interdisc capacitances Cd of the corresponding discs to 30% from their original values; • minor radial deformations of discs 10 and 11, 30 and 31, 50 and 51, representing top, middle and bottom parts of the HV winding of the experimental transformer in Fig. 1, simulated by changing ground capacitances C of the corresponding discs to 30% from their original values. Considering the experimental transformer for the model simulations with FRA, its frequency response simulations are presented in the form of the winding transfer function using equation (3). Preliminary simulations revealed insignificant deviations between the responses, corresponded to various minor winding movements, and the reference, which are difficult to analyze using only a graphical comparison. Therefore, the three statistically-based performance indicators, i.e. correlation coefficient ρ(X, Y ) (equation (4)), spectrum deviation σ(X, Y ) (equation (5)) and ASLE (equation (6)), introduced
4
−20
−30
Magnitude, dB
−40
−50
−60 6
normal winding top discs shift middle discs shift bottom discs shift
−70
−80 4 10
5
6
10
10
6
Frequency, Hz
Fig. 3. “Time-based” (reference) comparison of the HV winding responses of the experimental transformer (axial displacements of top, middle and bottom discs): log frequency scale TABLE I R ELATIVE C HANGES OF S TATISTICAL I NDICATORS IN C ASE OF M INOR A XIAL D ISPLACEMENT IN D IFFERENT L OCATIONS OF THE HV W INDING OF THE E XPERIMENTAL T RANSFORMER Fault Discs in Winding
Frequency Range
Statistical Indicators ρ(X,Y) σ(X,Y) ASLE
Discs 10, 11 (top part)
0.1 −1 MHz 1 − 2 MHz 2 − 3 MHz 3 − 5 MHz
0.9980 0.9963 0.9990 0.9990
0.0279 0.0599 0.0266 0.0266
0.3428 0.7367 0.3267 0.3271
Discs 30, 31 (middle part)
0.1 −1 MHz 1 − 2 MHz 2 − 3 MHz 3 − 5 MHz
0.9993 0.9991 0.9998 0.9998
0.0188 0.0339 0.0127 0.0113
0.2313 0.4162 0.1556 0.1390
Discs 50, 51 (bottom part)
0.1 −1 MHz 1 − 2 MHz 2 − 3 MHz 3 − 5 MHz
0.9994 0.9997 0.9999 0.9999
0.0158 0.0175 0.0065 0.0067
0.1938 0.2150 0.0799 0.0826
in section IV, are employed to quantitatively analyze FRA simulation results. 1) Minor Axial Displacements: Figures 3 and 4 show the transfer function responses by simulating minor axial shifts of the discs, representing top, middle and bottom parts of the winding in the frequency range of 10 kHz – 5 MHz in the log and linear frequency scales respectively. It can be noticed from both the figures that there are no apparent changes between the responses at frequencies below 1 MHz. On the other hand, more significant changes occur at higher frequencies in the 1− 3 MHz diapason mainly due to magnitude deviations and slight shifts of resonant frequencies. In addition, there is a strong effect of the axial displacement locations on the frequency responses. In general, the simulated results are consistent with those reported in [5], [14] regarding both computer simulations and experimental tests.
Fig. 4. “Time-based” (reference) comparison of the HV winding responses of the experimental transformer (axial displacements of top, middle and bottom discs) linear frequency scale
For a more detailed study, the results of quantitative analysis using the statistical indicators in the 4 different frequency ranges, 10 kHz − 1 MHz, 1 − 2 MHz, 2 − 3 MHz and 3 − 5 MHz, are listed in Table I. According to the table, the changes in frequency responses, due to disc axial displacements, are more detectable in the 1 − 2 MHz range regardless of the fault locations. With respect to the fault locations, the axial displacements of the top winding discs appear to be most recognisable. 2) Minor Radial Deformations: To simulate minor radial deformations of winding discs, it is assumed that the ground capacitances C is changed as noted earlier. Figures 5 and 6 illustrate the transfer function responses simulating minor radial deformations of the discs, representing top, middle and bottom parts of the winding using the the log and linear frequency scales respectively. As seen from the figures, there are no apparent deviations between the reference and other traces. However, a more detailed observation reveals tiny deviations, mostly in magnitude, which may be evaluated using a quantitative analysis. The results of quantitative analysis for the above defined 3 frequency ranges are listed in Table II. It can be assumed that the results are, in general, inconclusive, although the responses slightly more deviate at frequencies below 1 MHz. Despite of appearing of the graphically distinguishable deviations between the responses in Figs. 4 and 6 due to the minor axial displacements and radial deformations respectively, the correlation coefficient ρ(X, Y ) values are almost equal to 1 as indicated in Tables I and II. This is explained by the fact the coefficient cannot clearly differentiate frequency responses, possessing similar shape and resonance points but different in magnitudes [11], and can be considered as inappropriate for the analysis of both the considered cases.
5
TABLE II R ELATIVE C HANGES OF S TATISTICAL I NDICATORS IN C ASE OF M INOR R ADIAL D EFORMATION IN D IFFERENT L OCATIONS OF THE HV W INDING OF THE E XPERIMENTAL T RANSFORMER Fault Discs in Winding
Frequency Range
Statistical Indicators ρ(X,Y) σ(X,Y) ASLE
Discs 10, 11 (top part)
0.1 −1 MHz 1 − 2 MHz 2 − 3 MHz 3 − 5 MHz
0.9999 0.9999 0.9999 0.9999
0.0095 0.0078 0.0091 0.0072
0.1161 0.0962 0.1122 0.0889
Discs 30, 31 (middle part)
0.1 −1 MHz 1 − 2 MHz 2 − 3 MHz 3 − 5 MHz
0.9999 1.0000 1.0000 1.0000
0.0080 0.0036 0.0014 0.0008
0.0977 0.0443 0.0176 0.0098
Discs 50, 51 (bottom part)
0.1 −1 MHz 1 − 2 MHz 2 − 3 MHz 3 − 5 MHz
0.9999 1.0000 1.0000 1.0000
0.0082 0.0020 0.0016 0.0009
0.1009 0.0251 0.0195 0.0110
6
6
−20
Fig. 6. “Time-based” (reference) comparison of the HV winding responses of the experimental transformer (radial deformations of top, middle and bottom discs): linear frequency scale
−30
Magnitude, dB
−40
−50
−60
normal winding top discs deformation middle discs deformation bottom discs deformation
−70
−80 4 10
5
10
6
10
Frequency, Hz
Fig. 5. “Time-based” (reference) comparison of the HV winding responses of the experimental transformer (radial deformations of top, middle and bottom discs): log frequency scale
VI. C ONCLUSIONS In conclusion, the simplified distributed parameter model has shown to be useful for minor winding deformation analysis. The study on minor winding movements shows that these winding conditions are generally detectable at frequencies above 1 MHz. However, a more reliable measurement method needs to be developed to eliminate the effect of measurement setup at higher frequencies. It is also deduced that the statistical indicators of the spectrum deviation and ASLE are suitable for the quantitative analysis compared with the correlation coefficient concerning FRA in the high frequency range. R EFERENCES [1] E.P. Dick and C.C Erven, Transformer diagnostic testing by frequency response analysis, IEEE Trans. on Power Apparatus and Systems, PAS97, Vol. 6, pp.2144-2150, November-December, 1978.
[2] E. Rahimpour, J. Christian, K. Feser and H. Mohseni, Transfer function method to diagnose axial displacement and radial deformation of transformer windings, IEEE Transactions on Power Delivery, Vol. 18, No. 2, pp.493-505, 2003. [3] R. Rudenberg, Electrical shock waves in power systems: traveling waves in lumped and distributed circuit elements, Cambridge, Massachusetts: Harvard University Press, 1968. [4] Y. Shibuya, S. Fujita and N. Hosokawa, Analysis of very fast transient overvoltage in transformer winding, IEE Proc.: Generation, Transmission and Distribution, Vol. 144, No. 5, pp.461-468, 1997. [5] M. Wang, A. Vandermaar, and K.D. Srivastava, Improved detection of power transformer winding movement by extending the FRA high frequency range, IEEE Trans. On Power Delivery, Vol. 20, No. 3, pp.1930-1938, 2005. [6] A. Shintemirov, W.H. Tang and Q.H. Wu, A novel hybrid winding model of disc-type power transformers for frequency response analysis, IEEE Transactions on Power Delivery, Vol. 24, No. 2, pp.730-739, April 2009. [7] J.L. Guardado and K.J. Cornick, A computer model for calculating steep-front surge distribution in mashine windings, IEEE Trans. Energy Convers., Vol. 4, No. 1, pp.95C101, March 1989. [8] S. A. Ryder. Diagnosing transformer faults using frequency responce analysis. IEEE Electrical Insulation Magazine, Vol. 19, No. 2, pp.1622, 2003. [9] G. M. Kennedy, A. J. McGrail, and J. A. Lapworth. Transformer sweep frequency response analysis (SFRA). Energize, (October):28-33, 2007. [10] S. A. Ryder. Methods for comparing frequency response analysis measurements”. In Conference Record of the 2002 IEEE International Symposium on Electrical Insulation, pp.187-190, Boston, USA, 2002. [11] D. K. Xu, C. Z. Fu, and Y. M. Li. Application of articial neural network to the detection of the transformer winding deformation. In Eleventh International Symposium on High Voltage Engineering, 1999, Vol. 5, pp.220-223, London, August 1999. [12] J. W. Kim, B. Park, S. C. Jeong, S. W. Kim, and P. Park. Fault diagnosis of a power transformer using an improved frequency response analysis. IEEE Transactions on Power Delivery, Vol. 20, No.1, pp.169-178, 2005. [13] N. Abeywickrama. Effect of dielectric and magnetic material characteristics on frequency response of power transformers. PhD thesis, Chalmers University of Technology, Sweden, 2007. [14] M. Wang, A. J. Vandermaar, and K. D. Srivastava. Transformer winding movement monitoring in service - key factors affecting fra measurements. IEEE Electrical Insulation Magazine, Vol. 20, No.5, pp.5-12, 2004.
6
W.H. Tang (M’05) received the B.Eng. and M.Eng. degrees in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 1996 and 2000, respectively, and the Ph.D. degree in electrical engineering from The University of Liverpool, Liverpool, U.K., in 2004. He was a Postdoctoral Research Assistant at The University of Liverpool from 2004 to 2006. Since 2006, he has held a Lectureship in Power Engineering in the Department of Electrical Engineering and Electronics, The University of Liverpool, U.K. His research interests are transformer modeling, power system operation, evolutionary computation, multiple criteria decision analysis and intelligent decision support systems.
A. Shintemirov was born in 1979. He studied in electrical engineering at Pavlodar State University named after S. Toraygirov, Kazakhstan, and received the M.Eng and Cand.Tech.Sci. (Ph.D.) degrees in 2001 and 2004, respectively. In 2009 he was awarded a Ph.D. degree in Electrical Engineering and Electronics from the University of Liverpool, UK. His research interests include power transformer winding modeling and condition assessment, power system modeling and evolutionary computation.
Q.H. Wu (M’91, SM’97) obtained an M.Sc.(Eng) degree in Electrical Engineering from Huazhong University of Science and Technology, Wuhan, China, in 1981. From 1981 to 1984, he was appointed Lecturer in Electrical Engineering in the University. He obtained a Ph.D. degree in Electrical Engineering from The Queen’s University of Belfast (QUB), Belfast, U.K. in 1987. He worked as a Research Fellow and subsequently a Senior Research Fellow in QUB from 1987 to 1991. He joined the Department of Mathematical Sciences, Loughborough University, Loughborough, U.K. in 1991, as a Lecturer, subsequently he was appointed Senior Lecturer 1995. In September, 1995, he joined The University of Liverpool, Liverpool, U.K. to take up his appointment to the Chair of Electrical Engineering in the Department of Electrical Engineering and Electronics. Since then, he has been the Head of Intelligence Engineering and Automation Research Group working in the areas of systems control, computational intelligence and electric power and energy. He has authored and coauthored more than 320 technical publications, including 135 journal papers, 20 book chapters and 2 research monographs entitled ‘IP Networkbased Multi-agent Systems for Industrial Automation - Information management, condition monitoring and control of power systems’ and ‘Protective Relaying of Power Systems Using Mathematical Morphology’ published by Springer. Professor Wu is a Chartered Engineer, Fellow of IET and Senior Member of IEEE. His research interests include nonlinear adaptive control, mathematical morphology, evolutionary computation, machine learning and power system control and operation.
