Refurbishment of Binga Power Plant in Philippines

J.T.Billdal P.T.K.Østby H.Lysaker Ø.Gjerde Rainpower Norge AS Rainpower Norge AS Rainpower Norge AS Rainpower Norge AS

PO Box 144 PO Box 144 S.P.Andersensvei 7 PO Box 144

2027 Kjeller 2027 Kjeller 7031 Trondheim 2027 Kjeller

Norway Norway Norway Norway

1 Introduction

The Philippines face large growth, also enforcing increased power need. The country is dependent on import of

fossil fuel. To utilize the natural resources in an effective way is vital for the developing the economy. The Binga

power plant is situated in the Benguet province in the Philippines. The power plant was put into operation in 1960

with 4 turbines. SN Aboitiz Power.Benguet, Inc, part of the SNAP group, which is a joint venture owned by the

Norwegian based company SN Power and the domestic company AboitizPower, bought the power plant from the

government in 2007 during the privatisation program, and initiated a rehabilitation program to increase plant

capacity.

By constructing a new headrace, increasing the intake, modernization of all electric systems and upgrade the

turbines, the plant can now generate 140 MW with the four Francis turbine. The rehabilitation works stated in May

2010 and was completed in 2013. The units were refurbished one at a time, while the rest were in operation.

The project was completed on time, with no incidents and under budget for SN|Aboitiz. This was the result of a

good effort by the project administration, Norconsult (the Consultant) as well as the suppliers: Turbine: Rainpower,

Generator: Koncar, MIV:Hubei Hongcheng and EBOP:ABB amongst others. The power plant is now a stable

supplier in the grid, serving valuable capacity to a relatively weak grid.

Table 1: Main data of turbine

2 Turbine operation and performance The refurbished turbine was originally specified to operate continuously from 16.5 to 33.1MW but after the units

were commissioned and found to operate well at all conditions the continuous range was increased from 0MW to

36MW.

Four topics were of specific importance when evaluating the possible increment of the continuous operating range:

Mechanical integrity, pressure pulsations/stability, cavitation and waterway dynamics.

Speed 327 [rpm]

Head 144.3 [m]

Flow 22 [m3/s]

Power 36 [MW]

Outlet diameter 1.8 [m]

Runner Blades 17 [-]

Guide Vanes 24 [-]

Speed Number 0.41 [-]

Fig 1 Turbine Cross Section

2.1 Mechanical Integrity

To ensure safe operation of the turbine it is important to have knowledge of the stresses in the runner. State of the art

CFD/FEA calculations can, to some extent, predict the dynamic and static stresses in the runner. New methods have

shown promising results predicting the stresses at half to full load. Some (1) have also investigated the possibility to

calculate the stresses at speed no load operation. Currently the methods are not good enough to safely predict

stresses in the runner.

It was therefore decided to measure the stresses in the runner since the units will operate far outside of normal

operating ranges for Francis turbines.

The stress measurement was conducted by Norconsult using a 12 channel data logger. 10 strain gauges were

mounted on the trailing edge of two blades and two pressure transducers were mounted in the runner hub between

the runner blades.

Fig 2 Data logger and sensors Fig 3 Strain gauge positions

The strain gauges were mounted close to the trailing edge near the hub and band on both pressure and suction side of

the blade. This is where the FEA analysis predicted that the highest stress amplitudes could be found.

Fig 4 Predicted stress amplitudes

The basis for investigation of the runner integrity was measured and numerical stress levels. Numerical analysis

gave the distribution of the stresses on the runner blades including maximum stress. Calculated stress at the same

location as the measured stress were found and compared with measured stress giving a stress ratio between

measured and calculated stress. This ratio was used as a multiplication factor to the calculated stress, and the result

was the maximum stress level that occurs in the runner. This maximum stress was evaluated against fatigue.

Method used for these calculations was SN curves combined with Palmgren-Miners rule for damage accumulation.

SN curves, also called Wöhler curves, are used to predict number of cycles to failure. These curves show dynamic

stress (Δσ) at the ordinate and number of cycles before failure (n) on the abscissa.

Measurements were made for different load cases, i.e. a number of cycles with constant amplitude loading for each

load case were summarized up by means of Palmgren-Miners rule to calculate the total damage.

An important result from the measurements was to find at which normal load operation the highest stress occurred.

The highest measured stress in the runner, peak-to-peak (multiplied with the notch concentration factor of 1.5), is

shown in below figure where it can be seen that maximum stress occurred at a load around 20 MW.

Fig 5 Peak stress levels corrected with FEA

This gives an indication of which normal load range will have the highest stress and hence most rapidly will

consume life time.

Another important finding was that highest stress occurred during load reject. A log of the stress during a start/stop

sequence was found as well.

The below table shows the influence on life time related to different load cases per 1000 hour/event operation.

Fig 6 Partial damage for different operations

The method used to calculate life time is a so called local stress method where the life time analysis is based on

highest stress in the component that will be checked towards actual SN curve. This method is commonly used,

however, it doesn’t take into account global effects like different scatter of defects in different part of the

construction, neither is size effects.

Hence chapter 4 will discuss some alternative methods we are currently investigating for later use in runner life time

predictions.

2.2 Cavitation

Operating the turbine at low- to no-load conditions can cause cavitation damage in the hub wall due to inter blade

vortices (2). High load operation will typically cause cavitation near the trailing edge of the blade close to the ring.

To ensure good cavitation performance the runner was designed according to Rainpowers STORMTM

design

philosophy with heavy CFD optimization. This design philosophy has in recent years proven excellent performance

even at low loads.

In December 2012, after 6000h of operation with more than 1500h of operation between 0 MW and 5 MW, Unit 4

was inspected for cavitation damages. The inspection concluded that no cavitation pitting nor cavitation shadows

could be observed on either the runner or the runner cone.

Fig 7 Runner as seen during cavitation inspection after 6000h

2.3 Pressure pulsations

The turbines operate very smoothly, and have no big pulsation issues. However, during commissioning it was

noticed that the units had a small area of operation, near 20MW, with elevated pressure pulsations. These pulsations

were noticeable in the spiral casing, on the unit vibration meters and the draft tube. Pressure sensors used during the

stress measurements also found the same pulsations. While the pulsations generally are dominated by the guide vane

passing frequency above 10MW, near 20MW the pulsations are dominated by a frequency below the rotational

frequency, 2-4Hz, and most are likely caused by a Reinganz vortex.

Fig 8 Pressure measurements from the runner channel

Guide Vane Passing Frequency

Reinganz vortex

The pulsations do not cause any operational problems and are within guarantee levels, but large enough to make the

19MW-20MW range the area of operation where the runner experiences the heaviest loads during steady state

operation.

Fig 9 Draft tube pressure pulsations

2.4 Waterway dynamics

As the output power was increased, the discharge increased equally. Such a discharge increase can increase both

pressure and speed during load rejections. A thorough evaluation was conducted with internally developed software

to ensure that neither transient speed nor pressure would rise above guarantee values.

Fig 10 Transient Behavoiur of the turbine Fig 11 Binga Waterway model

2.5 Efficiency

The efficiency was measured using the thermodynamic method according to IEC 60041and the result was good. The

measured weighted efficiency was about 0.5% higher than the guaranteed value. Especially is the efficiency at high

and partial load higher than the guarantee values.

Fig 12 Efficiency measurement

0

1

2

3

4

5

0 5 10 15 20 25 30 35 40

Dra

ft t

ub

e P

ress

ure

p

uls

atio

ns

[mW

c 9

5%

Co

nf]

Turbine power [MW]

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Effi

cien

cy [

%]

Discharge [m³/s]

Average points at guarantee discharge - temperature, head andefficiency corrected to HP=144.3 m and TP=20 deg CGuarantees - 144.3 m

3 Startup schemes

During the stress measurements, Norconsult tested two different startup schemes, normal and soft start. During

normal start the ramp rate is set to 3%/s while the start opening is set to 18%. For the soft start these values were

reduced to 0.3%/s and 11%. This change increases the time needed to accelerate the unit from standstill. Normally

it takes about 37s while soft start takes 95s.

The goal of the soft start is to reduce the pressure loads on the runner. This is achieved as a slower acceleration

requires less torque and thus less force on the blades. Fig 13 shows the rainflow counting of the stresses measured

near the crown of the runner. It clearly shows how the Normal start has a much higher concentration of large stress

ranges. Using SN-curves as described earlier to calculate the life time cost of a start stop shows that the Normal start

is roughly 3 times as damaging as a soft start.

Fig 13 Rainflow count of stresses from two different startup schemes

4 Alternative life time prediction method discussion There exist several methods to predict a runner’s lifetime (or residual lifetime), given operational pattern and stress

levels. Different methods can be advantageous for different situations. For all methods it is crucial to have as good

as possible fatigue data’s for the actual runner material.

Material Properties &

Crack Growth

Deterministic Probabilistic

Implicit Fatigue Crack Growth

SN Curve (a>𝑎𝑐𝑟𝑖𝑡) “Crack initiation”

Local Stress Weakest Link

Explicit Fatigue Crack Growth 𝑑𝑎

𝑑𝑛= 𝐶(ΔK)𝑚

Single Defect Random Defect

Here the Weakest Link (3) method will be briefly presented as an alternative method to the Local Stress method.

The Weakest Link method is based on SN curves just as the Local Stress method. The method assumes that the

probability that a component survives is the product of the probability for all small elements of which the

component is built from to survive. The probability that an element survives is a function of stress amplitude,

characteristic fatigue strength, material scatter of defects and the components size.

A runner has often internally different type of goods, for example casting, forging and welding. This method can

allow for different material quality in different zones of the runner. Probable lifetime can hence be calculated from

the weakest element of the runner which may not necessarily be at the point where the runner has highest stress.

In the table listing different methods for fatigue calculations it is for some methods stated “Implicit Fatigue Crack

Growth SN Curve (a>𝑎𝑐𝑟𝑖𝑡) Crack initiation”, this statement is based on the relation found between SN curves and

1

10

100

1000

10000

100000

0 50 100 150 200

Nu

mb

er

of

cycl

es

[-]

Stress range [MPa]

Normal Start

Soft Start

fracture mechanics by Kitagawa and Takahasi (4) and typically 𝑎𝑐𝑟𝑖𝑡 will be around 1mm for runner material. Hence

for cracks > 𝑎𝑐𝑟𝑖𝑡 fracture mechanics on the form 𝛥𝜎0 =𝛥𝐾𝑡ℎ

𝐹√𝜋𝑎 can be used. This is built into the weakest link theory

with a critical defect density function, z1(𝜎𝑎,R,n) defined as the expected number of defects per unit volume of the

material that yields a fatigue strength (𝜎𝐴 ≤ 𝜎𝑎) less than the (equivalent) stress amplitude at a stress ratio R and

fatigue life n.

For a small element the probability of survival (Ps) can be expressed as Ps(ΔV) = 1 - z1(𝜎𝑎,R,n)ΔV, which means if

a big defect (here expressed through the 𝜎𝑎) in the z1 function gives a value of 1 the actual element will not survive

and hence the whole component may break down – like a weakest link in a chain.

The probability of survival for one element can be expressed as: 𝑃𝑠,𝑒𝑙𝑒𝑚 = 𝑒𝑥𝑝 {−(𝜎͞ 𝑎

𝜎͞𝐴0∗ )

𝛽𝜎͞}

where:

𝜎 𝑎 is the Weibull amplitude given as: 𝜎 𝑎=(1

𝑉0∫ 𝜎𝑎

𝛽𝜎͞ 𝑑𝑉)1/𝛽𝜎͞

𝑉

𝜎𝐴0∗ is the characteristic fatigue limit of the reference fatigue test specimen.

𝛽𝜎 is the Weibull stress exponent.

V is the component volume

V0 is the reference volume

This gives a distribution of scatter data’s in an actual range of 𝛽𝜎 = 10 – 40, where 10 has much scatter data (typical

poor weld or casting) and 40 with little scatter data (typical forging or plate).

The sketch below shows typical different quality zones internal in the runner with casting, plate weld and different

weld quality. All is normally in 13Cr 4Ni material.

Sketch of a Francis runner with traditional manufacturing like Binga

The weakest link method is capable to deal with this, however the practical application may give some challenges

and further work should be made for the implementation with different zones. This applies both on the stress

calculation side and on the lifetime calculation, i.e on how to divide the zones and set boundary conditions.

Meanwhile a calculation of the runner with one quality of scatter defects should be fairly straight forward.

Summary of the Weakest-link life prediction method:

The method starts with smooth material SN curves and continues to check further implicitly through

fracture mechanics for defect a>𝑎𝑐𝑟𝑖𝑡 The method is global, that is all elements which makes up the component will be calculated to see if each

single element survives.

It can deal with different material qualities within the component, that is for a runner in principle treat

weld, casting, plate and forgings separate.

Depending of material quality predict the probability of survival

It takes into account the scaling effect. I.e. size of the runner versus the reference test specimen.

Rainpower is researching and developing these methods, in cooperation with Norwegian University of Science and

Technology and The Research Council of Norway, to better predict the life time of new and old Francis turbines.

5 Conclusion The refurbished turbines at Binga power plant have after commissioning had its continuous operating range of 50%-

100% extended to 0%-108% without any modifications. It has, through careful analysis and measurements been

verified that no harmful effects, neither high stresses nor cavitation, will occur in the turbine with the increased

operating range. All technical guarantee values have been fulfilled and the turbines now operate very well.

It is shown in this paper that the method of starting the turbines plays an important role in the life time of the runner,

but the most damaging event is a load rejection which equals more than 100 soft starts in terms of relative life time

cost.

The current deterministic methods for evaluating life time of Francis Runners, such as SN-Curves and Miner’s Rule

may not give a very qualified estimate. New probabilistic methods such as weakest link and random defect may with

more development become a better tool to give reasonable life time assessments for Francis runners.

Works Cited

1. Challenges in Dynamic Pressure and Stress Predictions at No-Load Operation in Hydraulic. Nenneman, B, et

al., et al. 2014. 27th IAHR Symposium on Hydraulic Machinery and Systems.

2. Introduction To Cavitation In Hydraulic Machines. AVELLAN, François . Timisoara : International Conference

on Hydraulic Machinery And Hydrodynamics, 2004.

3. Wormsen, A, et al., et al. Non-local stress approach for fatigue assessment based on wakest-link theory and

statistics of extremes.

4. Applicability of fracure mechanics to very small cracks or the cracks in the early stage. Kitagawa, H and

Takahashi, S. Boston : Proceedings of the Second International Coference on the Mechanical Behavoir of

Materials.

The Authors

J.T. Billdal received his PhD in Mechanical Engineering at the Norwegian Institute of Technology in 1988. He has worked as

hydraulic design engineer in Kvaerner and GE Energy. His key qualification is hydraulic design of Francis and Reversible Pump

Turbines. From 1996 to 2008 he was Professor (part-time) in hydraulic machinery at the Norwegian Institute of Technology,

Trondheim. Currently he holds the position of Department Manager, Turbine Design, Rainpower Technology.

H. Lysaker graduated as Master of Science in Mechanical Engineering from the Norwegian Institute of Technology (NTH),

Trondheim in 1994. He has worked as a research engineer in SINTEF/ Kværner/ GE/ Rainpower with focus on model testing

from 1995. In this time he has been involved in a number of model tests, and do also perform hydromechanical and efficiency

measurements on prototype turbines. His current position is as department manager of the Turbinlaboratoriet, Rainpower

hydraulic laboratory in Trondheim, Norway.

Ø. Gjerde received his B.Sc. in Mechanical Engineering at Oslo Technical-maritime school in 1981. He has worked as

mechanical design engineer in Kvaerner, GE Energy and Rainpower. His key qualification is mechanical design of Francis and

Reversible Pump Turbines. Currently he holds the position of Design Engineer, Turbine Design, Rainpower Technology.

P.T.K. Østby received his M.Sc in Mechanical Engineering from the Norwegian University of Science and Technology

(NTNU). Trondheim in 2007. He has worked as a hydraulic design engineer in Rainpower with focus on Francis turbines and

Reversible Pump Turbines. He currently studies for a PhD on dynamic stresses in Francis Turbines while holding the position of

turbine designer at Rainpower.