Performance Assessment of SCADA Based Wind

International Journal of Electrical Power System and Technology Vol. 1: Issue 2

www.journalspub.com

Performance Assessment of SCADA Based Wind Turbine: Condition Monitoring Approaches Saptarshi Roy Department of Electrical Engineering, NIT Warangal, Telengana, India E-mail: [email protected]

INTRODUCTION Condition monitoring is nothing but health assessment of equipment. Like human being, the equipments also need a doctor for periodic check of health in order to keep their health in good condition. Diagnostic is a term with regard to identify disease of human being. The same term is used in industry with respect to find any disorder in any equipment. Overhauling and retrofitting is another two terms associated with condition based monitoring. Overhauling means renovation. Any equipment like transformer, motor, generator, turbine, etc. are checked with respect to their present performance and possibility of good or bad performance in future, and the weak or prone to damage parts are repaired or replaced. This process is called overhauling.[1–3] Retrofitting is nothing but one is to one replacement of the spares of the equipment with duplicate one instead of original e.g. Previously bulk oil circuit breakers are mostly used in industry, but now a day’s BOCB are obsolete and they are retrofitted by Vacuum circuit breakers. SCADA means supervisory control and data acquisition. In today’s context SCADA based protections are most commonly used in power systems. As today’s power network is larger, complex and wide spread, SCADA and WAMS based power system protection are automatic choice. SCADA data are readily available and contains valuable information about the performance and load history of wind turbines, and can be

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

effectively used as a tool for condition monitoring. Condition observing methodologies have demonstrated their potential in wind industry by giving ceaseless checking of the wind turbines, and recognizing shortcoming marks on account of event of issues. Force bends developed from wind pace and dynamic force yield estimations give a set up technique for breaking down wind turbine execution. operational information from wind turbines are utilized to evaluate the likelihood conveyance capacities speaking to the force bend of existing turbines so that deviations from expected conduct can be recognized. Continuous observing of wind turbine wellbeing utilizing mechanized disappointment recognition calculations can enhance turbine detecting so as to unwaver quality and decrease upkeep costs disappointments before they achieve a calamitous stage and by disposing of pointless booked support. A SCADA data based condition monitoring system uses data which is collected at the wind turbine controller. It is a costeffective way to monitor wind turbines for early warning of failures and performance issues. The power curve is an important parameter for the assessment of the performance of a wind turbine; it relates the power output to the wind speed. Traditionally, power curves provide an expected relationship under standard operating conditions, e.g. with turbulence maintained within specified limits and air-density corrections applied.[1]

Page 1

Performance Assessment of SCADA

Saptarshi Roy

WIND TURBINE SYSTEM A wind turbine system consist of three main components namely 1. A Turbine 2. A Nacelle 3. A Tower

wind’s linear energy into rotational one. 2) Vertical Axis – The gear box is installed at the top of the covers, which requires additional stabilizing structure of the system.

Turbine The turbine is also called low speed rotor, which is usually having 2–6 blades. The most common number of blades used in turbine is 3.The blades are aero dynamic and they are made of composite material like carbon or plexigas. There are two types of wind turbines available. 1) Horizontal Axis – Most common type, having horizontal axis positioned shaft which helps, the conversion of the

Nacelle: Consist of gear box, generator and control system and Xaw mechanism. The Nacelle is connected to the tower through Xaw mechanism. Tower: Tower is the support or structure on which main foundation stands sill. Its main purpose is to support nacelle and resist vibration due to wind speed variation.

NACELLE COVER Control Electronics GEAR BOX

GENERATOR

ROTOR

XAW MECHANISM HUB

TOWER

FOUNDATION

Fig. 1. Major Components of a Wind Turbine. SOME COMMON TERMINOLOGIES USED IN CONTEXT TO WIND TURBINE ARE Stall Control: The blade position is fixed but stall of the wind appears along the blade at higher wind speed.

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

Active Stall Control or aerodynamic control: The blade angle is adjusted to create stall on blades. Pitch Control: The blades are turned out of the wind turbine at higher wind position.

Page 2

International Journal of Electrical Power System and Technology Vol. 1: Issue 2

www.journalspub.com

Lift and Drag Force: These are the two forces regarding blade aerodynamics introduce radial and axial force in the blade.

pitch angles, the effective blade surface facing the wind direction can be controlled. Pitch control also facilitates starting and emergency stopping of the turbine.

Tip-Speed Ratio: It depends on the geometry of the wind turbine as well as rotational speed v, length of the blade R and Velocity of wind.

Pitch Angle: angle between blade surface and the plane of the wind rotor. Power Curve: Power curve shows the operating characteristics of a wind turbine. Below a typical shape of power characteristics is discussed:

Pitch: It is the ability to change the wind facing angle of the blades, in order to maintain a constant wind turbine speed, if the wind speed changes, with different

Cut in speed

Normal Wind Speed Cut out Speed

Power (pu)

Speed (m/sec)

Fig 2. Power Curve Characteristic of a Wind Turbine. Cut in Speed: Minimum wind speed needed to stop the turbine which generally depends on the turbine design, usually 3meters/sec for small turbine and 5–6 m/s for bigger turbine. Cut out Speed: The cut out wind speed is the point where the turbine should stop rotating due to potential damage that can be done if the wind speed can be increased more than that.

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

Power Coefficient (CP): CP = Pextracted/Ptotal CP = depends on the specific design of the wind turbine, specially particular aerodynamic structure of the blades. Each wind turbine has its own CP. It depends mainly on the tip-speed ratio and pitch angle.

Page 3

Performance Assessment of SCADA

Saptarshi Roy

0.7 59.33%

0.6

Cp

5

20

30

35

Wind Speed (m/sec)

Fig. 3. Power Coefficient Versus Wind Speed Characteristic of a Wind Turbine. WIND TURBINE POWER Bety’s Law: Bety’s law determines the maximum theoretical extracted power. The wind turbine extracts power energy from Kinetic energy of wind. It should be noted that the wind speed, after passing through the turbine is much lower than the speed when it reaches to the turbine. This means there are two wind speeds available. a) Before wind approaches to the turbine (Vb) b) After behind the turbine (Va) Kinetic Energy (K.E) of Wind Turbine: It can be expressed as U = 0.5 m vw2 = 0.5 (𝜌Ax) vw2 = 0.5 (𝜌𝜋R2) x vw2 m = mass of the packet of air flowing at speed Vw in x direction 𝜌 = air density in Kg/m3 A = Cross sectional area of rotor in squaremeter. x = Thickness of the parcel or packet or air

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

in meter. R = blade length in meter. The power in the beam PW is the time derivative of K.E. PW = dU/dt = 0.5 (𝜌𝜋R2) (dx/dt) Vw2 = 0.5 (𝜌𝜋R2) Vw3 So, PW α Vw3 The decreased wind speed after the wind turbine provides information about possible extracted energy from the wind. Pextracted = 0.5 (𝜌𝜋R2) (dx/dt)(Vb2Va2) = 0.5 (𝜌𝜋R2) ((Va + Vb)/2)(Vb2Va2) Ptotal = 0.5 (𝜌𝜋R2) Vb3 Pextracted/Ptotal = 0.5(Va+Vb) (Vb2 Va2)/vb3 = 0.5(1+ Va/Vb) (1Va2/vb2) The maximum power extracting, the ratio of the wind speed, after and before can be calculated:

Page 4

International Journal of Electrical Power System and Technology Vol. 1: Issue 2

www.journalspub.com

d(Pextracted/Ptotal)/d(Va/Vb) = 0 hence, we get Va/Vb=1/3 Therefore we get Pextracted / Ptotal(at Va/Vb=1/3) = 0.593 = 59.3% In other words, it is not possible to extract the all 100% of wind energy, due to aero dynamical losses. SCADA BASED CONDITION MONITORING APPROACHES OF WIND TURBINE The force bend demonstrates the operational regions of a wind turbine. Turbines don't work at low wind speeds; if the wind falls beneath a cut-in velocity for a predetermined span of time, the turbine will be switched off. Around the cut-in pace, it is regularly essential for the turbine to draw power from the force or electrical lattice to fire up or keep up pivot amid shortage of force needed to turn, which can bring about negative force generation. At higher wind rates, force increments give or take as the 3D square of the wind speed until appraised force is come to, which happens at the evaluated wind speed.[1] Comparison between power curves and operational data is difficult for a number of reasons. OEM curves are created under standard conditions, and are recorded using a specific methodology that is not possible to reproduce within an operating wind farm. OEM Curves are totally ideal conditions, which may vary operation to operation and time to time. Operational power curves created from SCADA data use wind speeds measured by the nacelle mounted anemometer. Results from these will differ from the standard power curve due to local turbulence, averaging period, and turbine condition. Wind speeds measured at the nacelle are significantly different from the upstream speeds from which the OEM power curves are constructed.

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

Comparisons can be made between operational power curves created using data from different months. An alternative approach is developed in reference.[4] regarding the condition based monitoring of wind turbine. The novelties are: The Condition monitoring is based on the review of the general performance of a wind turbine over a range of operational conditions rather than based on its instantaneous dynamic responses. So, the Condition monitoring result is potentially more reliable; A dedicated algorithm is developed for pre-processing Wind turbine SCADA data. It reduces the calculation errors caused by ‘outliers’ and thus enhances the reliability of Condition monitoring result; A specific Condition monitoring strategy is developed based on interpreting the SCADA data collected before wind speed reaches the rated wind speed of the turbine. So, the reliability of the Condition monitoring result is potentially guaranteed attributed to the absence of nonlinear control effects that could damper the fault features; and A Condition criterion is specially designed for quantitatively assessing the health condition of a Wind turbine under varying operational conditions.[4] In this method Wind Turbine captures energy from wind by blades and converts the energy to be mechanical energy and then transmits it through drive train, and finally converts the energy to be electrical power by generator. However, this energy flow might be disturbed, if any fault occurs in the wind turbine. The symptoms can be various e.g., the power vs wind speed curve of the turbine could deviate

Page 5

Performance Assessment of SCADA

Saptarshi Roy

from its normal position in the presence of a fault. However, Wind turbine SCADA data are not only influenced by the structural integrity of the turbine but also affected by many other factors (e.g. wind shear and turbulence). As a consequence, the dynamic responses of a Wind turbine, such as bearing vibration and temperature, often vary over wide ranges.[4] This significantly increases the difficulty of Condition based monitoring through this approach.

requiring scheduling and integration services in order to automate the process of collection from geographically distributed wind park servers consisting of varying technologies.[5] Data are collected from a variety of turbine types, with various age and specifications from various manufacturers. Therefore a common challenge is data normalization in order to achieve a uniform format. Information is at first put away in partitioned databases in a supposed "Organizing Area" and afterward through a procedure of Data Transformation, variables are mapped to a typical naming tradition and fundamental credibility checks are performed to pass the information quality door. The fault diagnosis part is done by modeling of expected systems.

The SCADA work log is explained with the figure as below (Figure 4). Initially a Failure Mode Assessment is performed in order to identify significant root cause analysis and their relationship with the available load data. The latter is gathered and stored in a central server, typically

Failure Mode Assesment

Data Collection and Storage

Fault Diagonistic

Damage Calculation and Failure Probability

Reporting , Actions Fig. 4. Overall Workflow, Diagnostics and Prognostics with SCADA Logs.

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

Page 6

International Journal of Electrical Power System and Technology Vol. 1: Issue 2

www.journalspub.com

An extensive variety of factual and sign preparing procedures are accessible for deficiency diagnostics and choice setting aside a few minutes arrangement information. These incorporate neural systems, hereditary calculations, Bayesian deduction, fluffy rationale and numerous others. A down to business methodology is the utilization of framework reaction demonstrating (additionally alluded to as procedure displaying), which depends on comprehension of the framework conduct and physical standards. This methodology has the point of interest that if the framework can for the most part be surely knew, the degree to which different parameters impact the framework can be watched and the outcomes can unmistakably be deciphered.[5]

exists but also the natures of the problem, possible root cause analysis and ideally the required response. Furthermore, in order to define the appropriate and available techniques for modeling of specific subsystems and failure modes, a link must be made between technology, failure modes and data. The root cause analysis can be done and the net figure (Figure 5) may look like a fish-bone structure. In Industry, the Quality Assurance department is calls it as fish bone structure. Root cause analysis, fish bone structures are important tool in Industry which usually stores in Company’s SAP data base and useful while assessing the quality status of a company. A six sigma quality company produces less than 3.4 defects per million of the samples they produce.[6–8]

An effective monitoring system is capable of identifying not only that a problem Failure Modes

Data Streams

A

Active Power

Gear box Oil Temperature

B

Blade Pitch angle C

D

Rotor Speed

Fig. 5. Correlation between Failure Modes and Available Data Streams. In the above example, the parameter “Active Power” can be used as input for modeling of three of the identified failure models (A, B, C). Failure mode D is not

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

associated with any available data streams and may therefore require custom instrumentation for detection, if deemed a priority item.[5]

Page 7

Performance Assessment of SCADA

Some more known condition monitoring schemes are available e.g. – RIV testing, Partial Discharge testing for insulation of wind turbine generator, Degree of polymerization test (another insulation degradation test more useful in case of transformer condition monitoring) which are done on sophisticated and high profile R&D labs.[9–13] RIV Testing assesses radio voltage interference testing, determines the age and health condition of the equipment. Degree of polymerization assesses health of the equipment from the insulation degradation property as insulation is nothing but long chain of hydro carbons and its degree of polymerization deteriorates with respect to time that means the long chain of hydro carbons gradually broken into smaller pieces with the progress of time. Degree of polymerization test is more used in case of health assessment of an Industrial transformer. Other than these tests, some frequency response test is available to determine the structural displacement of the equipment. Displacement of components of the equipment may lead to damage of the equipment. All the prescribed tests should be done on the equipments depending on the availability and cost, expanse bearing capability of the customer. SOME FUTURE RESEARCH CHALLENGES WITH RESPECT TO CONDITION MONITORING OF WIND TURBINE ARE 1. Determine more cost effective monitoring strategy. 2. Development of reliable and accurate prognostic techniques. 3. Improvement of reliability, efficiency and flexibility of SCADA System. 4. Develop innovative, adapted, and efficient methods of harvesting and storing electric energy. 5. Smart and Wireless sensor development, etc.

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

Saptarshi Roy

REFERENCES 1. Gill S., Stephen B., Galloway S. Wind turbine condition assessment through power curve copula modelling. IEEE Trans Sustain Energy. 2012; 3(1). 2. Verma A.P. Performance monitoring of wind turbines – A data mining approach. PhD Thesis. University of Iowa, 2012. 3. Crabtree C.J. Survey of commercially available condition monitoring systems for wind turbines. Super Gen Wind. 2010. 4. Yang W., Court R., Jiang J. Wind approach of SCADA data analysis. Renew Energy. 2013; 53: 365–76p. 5. Christopher S., Gray, Langmayr F.et al. A Practical Approach to the Use of SCADA data for optimized wind turbine condition based maintenance. 6. Vachtsevanos G. et al. Intelligent Fault Diagnostics and Prognosis for Engineering Systems. John Wiley & Sons Inc. 2006. 7. Schlechtingen M., Santos I.F. Comparative Analysis of Neural Networks and Regression Based Condition Monitoring Approaches for Wind Turbine Fault Detection. Technical University of Denmark. 2010. 8. Besnard F., Bertling L. An approach for condition-based maintenance optimization applied to wind turbine blades. IEEE Trans Sustain Energy. 2010; 1: 77–83p. 9. Echavarria E., Hahn B., Bussel V. et al. Reliability of wind turbine technology through time. J Sol Energy Eng. 2008; 130. 10. Hahn B., Durstewitz M., Rohrig K. Reliability of wind turbines. Wind Energy. Berlin/Heidelberg, Germany: Springer; 2007: 329–32p. 11. Johnson K.E. Adaptive torque control of variable speed wind turbines. NREL/TP-500-36265. NREL, Golden, CO. 2004. 12. Kathryn E., Johnson, Paul A. et al. Development, implementation, and Page 8

International Journal of Electrical Power System and Technology Vol. 1: Issue 2

www.journalspub.com

testing of fault detection strategies on the National Wind Technology Center's controls advanced research turbines. Mechatronics in Press, Corrected Proof, Available online 14 January 2011. http://www.sciencedirect.com/sc ience/article/B6V43-51Y3WK81/2/ 9a 417deb38d5 f0c2e37f88654300e0dd. 13. Kim K., Ball C., Nwadiogbu E. Fault diagnosis in turbine engines using unsupervised neural network technique. SPIE Defense & Security Symposium. Orlando, FL; 2004.

JEPST (2015) 1-9 © JournalsPub 2015. All Rights Reserved

Page 9