PERFORMANCE EVALUATION OF PS-600 kw WIND TURBINE

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1 Proceedings of the IASTED International Conference Power and Energy Systems (AsiaPES 2013) April 10-12, 2013 Phuket, Thailand PERFORMANCE EVALUATION OF PS-600 kw WIND TURBINE Jyoti D. Nimbal SGI, Electrical Engg., Atigre Sivakumar S. Subramanyam Deputy General Manager Suresh H. Jangamshetti EEED Basaveshwar Engg, college Kolhapur, Maharashtra, India RRB Energy Ltd., Chennai, India Bagalkot, Karnataka, India ABSTRACT The evaluation of PS-600 kw wind turbine is of prime importance as the wind turbine Annual Energy Production (AEP) undertaken for study does not meet the desired value. The two key elements of wind turbine technology are the system availability and turbine performance. This paper presents modeling of Weibull distribution parameters to assess the wind potential and to carry out performance analysis of the wind turbine at Mangalapuram, TN, India over the period from April 2009 to March 2010 at 50 m height. The various liabilities of wind turbine are studied for the efficient energy production. Thus the capacity factor observed for PS-600 kw wind turbine was % with AEP being kwh which resulted in 18 % power loss. Therefore the turbine under study is noted to have lesser capacity factor than the desired value which results in lesser AEP. The theoretical results obtained are verified by using Windographer which is wind data analysis software. Further extrapolating to higher hub height and escalating the rotor swept area would yield in the increased AEP. KEY WORDS Wind Turbine, Annual Energy Production, Weibull distribution parameters, Performance evaluation. NOMENCLATURE : Mean wind velocity. Vi : Actual wind speed. N : Number of different values of wind speed Fi : Numbers of observations. Σ : Standard deviation. A : Area through which the wind passes. f(v) : Weibull probability density function. C : Weibull distribution function Scale parameter K : Weibull distribution function Shape parameter Г : Gamma Function. : Coefficient of performance : Transmission efficiency : Generator efficiency : Cut in wind velocity : Rated wind speed : Furling wind speed 1. Introduction The wind resource assessment and the ensuing estimation of the yearly energy yield are of vital significance since they determine the project yield. The energy accessible from wind is proportional to the third power of the wind speed. It is necessary to use at least one full year of wind data to study wind speed variation during the seasons. Top of hilly areas or open areas of flat land are preferably the potential wind farm sites. Having selected the site, the subsequent pace is to assess the local long-term wind climate by orientation to existing data or by long term monitoring [1]. Anemometers fitted on tall masts are used for the wind measurements. Height of the mast may be the hub height of the turbine to avoid further correction in wind speed due to surface shear. Wind speed distribution can characteristically be described in terms of Weibull distribution. A convenient way to approximate a continuous wind speed distribution from the discrete pragmatic values is to find a best fit Weibull distribution [2]. The performance monitoring allows identify every possible origin for inadequate energy production, insufficient WT performance, failing wind resource assessment or seasonal variations of the wind potential. The Annual Energy Production (AEP) calculation of a wind turbine is of essential consequence in the evaluation of any project. The verification of energy yield of the entire wind farm can be determined by the difference between real energy production and theoretically attainable production over a certain time period [5]. The theoretically attainable energy production has to be evaluated by taking into consideration the technical availability and the predicted wind farm losses of the wind farm. The chief intention of the present study is to model the wind speed variation using the Weibull distribution parameters (shape parameter k and scale parameter c) and to do performance analysis for wind energy for the site located at Mangalapuram, Tamil Nadu. In this study, wind data and actual generation data for a period of one year DOI: /P

2 from April 2009 to March 2010 at height of 50m have been collected and analyzed. Further the obtained results are verified with the Windographer software which is a powerful wind data analysis program is being used to read data files from met towers and perform calculations for various aspects so as to hit upon the wind turbine energy production. 2. Methodology The wind site selected for study is located at Mangalapuram, Tamil Nadu, India. The site is equipped with 50 m galvanized guyed tubular wind mast, with gin pole monitoring. By using NRG Symphonie Data logger the wind data is measured at 50m and 40m heights. The Data logger records wind speed in meters per second (m/s) at an averaging interval of ten minutes. Cubic mean cube root of wind speeds and Weibull statistical model parameterize the incessantly changing wind speed distributions [2]. The Actual generation data of the PS- 600 kw WT for the whole year from April 2009 to March 2010 recorded is taken up to do the further analysis to find out the various parameters which decide the WT performance. The methodology for the above mentioned study is discussed in below sections: 2.1. Weibull distribution The Weibull probability density function is a particular case of Pearson type III or generalized Gamma distribution with two parameters which makes it extra resourceful to fit the observed data convincingly well [3]. This method is well thought-out as a standard approach which is far and wide established for evaluating local wind load probabilities. Owing to its flexibility and ease the Weibull distribution is repeatedly used in the field of life data analysis. The Weibull wind velocity probability density function can be represented as: Where; f(v) is the probability of observing wind velocity vi, c is the Weibull scale parameter and k is the dimensionless Weibull shape parameter Wind speed statistics The long-term records of wind speed have to be statistically analyzed to study wind power in a meticulous site. Weibull distribution has been used to evaluate the potential of wind power. Thus it is essential to know the probability density distribution of the wind speed which is merely the distribution of the proportion of time spent by the wind within narrow bands of wind speed [4]. The cubic mean cube root (CMC) of wind speed V m for each month is calculated as : (1) (2) Where, V m is mean wind velocity in m/s, actual wind speed in m/s, N is number of different values of wind speeds observed, is the numbers of observations for a specific wind speed and n is 3 for cubic mean cube root. The standard deviation σ for each month is calculated using: The shaping factor and scale factor of Weibull distribution are given as: Where σ is the standard deviation, V m is the average wind speed; Г is the gamma function which is given as: An additional significant measure for evaluating a turbine's performance at a given site is plant load capacity factor [5]. The capacity factors are computed by using: Where, k and c are the shape and scale parameters; the cut in wind speed; is the rated wind speed and is the furling wind speed Performance evaluation of wind turbine Data collection and theory for analysis The wind energy generation data of the PS-600 kw wind turbine was collected from the Mangalapuram site, with the help of RRB Energy Ltd., Chennai, for the performance analysis of the system, in The collected data of the system were analyzed for the various parameters and for evaluating the performance of the system. These parameters are as follows: Capacity factor/ plant load factor The capacity factor (C.F) is a measure of the annual energy production. The AEP describes the energy production performance of a wind turbine. It is defined as (3) (4) (5) (6) (7) is 8

3 the ratio of the (actual or estimated) energy produced to the energy production that would yield from operation at full rated power for an energy hour of the year [5]. CF = Energy production (Power rating x8760 x 100) Wind system reliability The value for a wind farm for a given period can be built up from the everyday values of accessibility for each wind turbine. Availability is defined as the ratio of hours that the wind system was able to generate power to the number of hours in the time period [5]. Machine availability (%) = Hours of wind turbine capable of operation Grid available hours in period Grid availability (%) = Grid available in period Hours in period System Availability (%) = Machine availability x Grid availability Modern wind farms habitually attain availability values of 98 per cent or greater as compared with 60 per cent or less in the early eighties Power coefficient The coefficient of power of a wind turbine signifies the conversion efficiency of the wind energy of the wind into mechanical energy, which in turn is used to drive the generators. Thus it differs from the overall system efficiency seeing that it doesn t include the losses in transmission (mechanical) and in electrical power generation [6]. In horizontal axis machines the theoretical limit is known as Betz limit, which is around 0.593(16/27 or 59.3%). For high-quality turbines it is in the range of 35-45%. The wind turbine is characterized by non-dimensional curves of the power coefficient, as a function of both tip speed ratio λ, and the blade pitch angle. The tip speed ratio for wind turbines is the ratio between the rotational speed of the tip of a blade and the actual velocity of the wind.it is basically non dimensional in nature and the high efficiency 3-blade-turbines have tip speed ratios of 6 7 [6]. It can be expressed as: Where R is the WT rotor radius, ω is the mechanical angular velocity of the WT rotor and is the wind velocity. For the wind turbine used in the study, the following form approximates Cp as a function of λ and β: (8) (9) Therefore the performance of wind power project has to be studied for wind turbine suitability. This analysis emphasizes on the system energy generation, system reliability and the efficiency of the system regarding wind energy conversion. 3. Results and Discussion The wind turbine specifications at the study site are: Make and Model Vestas- V47 Cut-in Speed, V C 4 m/s Rated speed, V R 15 m/s Cut-out speed, V F 25 m/s Rated Power, P R 600 kw Table 1: Weibull parameters for the year at Mangalapuram, wind site at a height of 50m CMC wind speed Std deviation Weibull parameters Capacity factors Month V m Shape Scale σ (m/s) (m/s) k c Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Annual Table 2: Windographer results of Weibull parameters for the year at Mangalapuram, wind site at a height of 50m Month Mean Std. Dev. Weibull k Weibull c (m/s) (m/s) (m/s) (m/s) Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar All data

4 The results of wind statistics at 50m height are tabulated in Table-1. The average wind velocity for the year from April 2009 to March 2010 at Mangalapuram wind site is found to be 7.39 m/s and the corresponding standard deviation is 4.22 m/s. The Weibull shape parameter K obtained is 1.78 and scale parameter C is Capacity factor of 28% is found for the selected wind site. It is observed from Table-1 that the highest wind speed occurs in July while the lowest wind speed occurs in February. The average annual wind speed is found to be 7.39 m/s. The Weibull probability density function, estimated is more accurately representing the wind speed variation; it will be used to calculate the average electrical output power. Thus the operation of wind turbine is limited by the cut-in speed and the cut-off speed. The Windographer results are tabulated as shown in Table-2.The average wind velocity for the year from April 2009 to March 2010 at Mangalapuram wind site is found to be 6.57 m/s and the corresponding standard deviation is 3.69 m/s. The Weibull shape parameter K obtained is 1.80 and scale parameter C is The obtained values by theoretical calculations and Windographer results differ slightly because of the method adopted for calculating the mean wind speed differs. The former is calculated by using the Cubic Mean Cube root while the latter uses the root mean square for the computation of the mean wind speed. Therefore it can be seen that the values obtained by CMC are accurate. 3.1 Performance of PS-600 kw wind turbine The energy production mainly depends on the system reliability i.e. Machine availability, grid availability and the wind characteristic of the selected site. The power generation report containing energy generation, operating hours, grid OK hours etc., were collected from RRB Energy Ltd., Chennai. The energy generated, system machine reliability (machine availability, grid availability) and capacity factor or plant load factor for the wind turbine during one year of operation from April 2009 to March 2010 are studied. The performances of PS-600 kw wind turbine at 50m hub height are tabulated in Table-3. The capacity factor of the machine was found to be 24% over a period of one year of operation as shown in Figure-3 whereas the AEP was kwh. Energy production in the month of July is highest i.e., kwh and was observed lowest in the month of February i.e., 1587 kwh as shown in Figure-1. The results are verified by using the Windographer wind data analysis software as shown in Table-4, wherein the capacity factor of the machine was found to be 28.4 %. The capacity factor of the Windographer differs slightly from the theoretical values due to consideration of losses in the latter analysis. Further the AEP result obtained was kwh. In the month of July the production was the highest i.e kwh and the lowest was kwh in the month of February. 3.2 Availability for wind power generation Operational hours and grid available hours of the wind turbine were considered to calculate the machine availability and grid availability as shown in Figure-2.The energy generation with respect to generation hour and operation hour of the wind turbine system are given in Table 2. From the total 24 hours of the day, break down and grid unavailability are subtracted, and then operating hours of the system are known. Grid availability describes regarding availability of the grid OK hours as a fraction of the total period. The product of operational availability and grid availability gives the system availability. The annual average machine availability and grid availability was found as % and % respectively. Therefore, the annual average system availability comes to %. The machine availability and grid availability are shown in Figure 2, for one year of operation of the wind turbine. Hence comparing both availabilities, it was observed that machine availability is found to be more than grid availability. Fig.2: Machine availability, grid availability at Mangalapuram site Fig.1: Energy generation of the wind turbine at Mangalapuram site 10

5 Month Table 3: AEP, Machine availability and Grid availability with capacity factor of the wind turbine at Mangalapuram Energy Capacity Machine Grid System generated factor Operating Line availability availability availability (kwh) (%) hours hours (%) (%) (%) Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Total Average Table 4: Windographer data analysis software results of the PS-600 kw wind turbine performance Month Valid Data Points Hub Height Wind Speed Time at Zero Output Time at Rated Output Mean Net Power Output Mean Net Energy Output Capacity Factor (m/s) (%) (%) (kw) (kwh/yr) (%) Jan 4, , Feb 4, , Mar 4, , Apr 4, , May 4, ,42, Jun 4, ,56, Jul 4, ,68, Aug 4, ,11, Sep 4, ,18, Oct 4, , Nov 4, , Dec 4, , Overall 52, ,95,

3.3 Capacity factor / plant load factor of the wind turbine The capacity factor of the site was calculated using the total energy generated to the expected energy generated at rated power capacity of

6 3.3 Capacity factor / plant load factor of the wind turbine The capacity factor of the site was calculated using the total energy generated to the expected energy generated at rated power capacity of the wind turbine (refer Table-3). The annual average capacity factor of the wind turbine was found as 24.26%. In the month of July-09 the capacity factor was found maximum i.e % and lowest was 0.4% in the month of February-10 as shown in Figure Power coefficient of the wind turbine Power coefficient for a turbine rotor is the most critical performance parameter. This coefficient determines the optimum performance of a turbine under all operating and design parameters. The maximum Power coefficient was observed 44 % in the month of March 2010 considering zero degree blade pitch angle. Figure-5 graphically shows the various power coefficients of PS-600 kw wind turbine for different blade pitch angles. Fig.3: Capacity factors of PS-600 kw wind turbine 3.4 Unavailability of the wind turbine Very high wind speeds that are greater than cut out speed and faults found in the operation of the wind turbine such as power down, grid down, manual stop and main control supply are the reasons for break down/ shut down of the wind turbine which affect the energy production of the machine. The percentage of unavailabilility of the wind turbine for each month for one year of operation during the period from April- 09 to March-10 is presented as pie chart shown in Figure-4. Fig.4: Details of the wind turbine unavailability for the one year of operation Fig.5: Power coefficient of wind turbine for different blade pitch angle 4. Conclusion To understand the wind energy assessment and the performance of wind turbine during one year of operation this study was undertaken. Relevant data for the analysis was collected from RRB Energy Ltd., Chennai. Complete one year data was used for the study to know the wind energy potential and to evaluate the performance of the wind turbine. The capacity factor at the Mangalapuram wind site was found to be 28% with an annual average wind velocity of 7.39 m/s. For one year operation of wind turbine the wind energy generation was found to be less than the estimated generation from the analysis. The capacity factor of the PS-600 kw wind turbine was found to be 24.26% which is lesser than the estimated range and hence results in lesser AEP than desired. Energy generation of the system was also affected due to availability of the operation which is mainly machine availability, grid availability and the system availability. In performance of the system, it was observed that as the wind speed increases, power coefficient of the turbine increases and later on decreases. Further it was proposed that the tower height should be increased due to the fact that extrapolating to higher hub heights would yield in the increased AEP. 12

7 References [1]. Jangamshetti, S.H.; Ran, V.G.; Optimum siting of wind turbine generators, Energy Conversion, IEEE Transaction on, Volume 16, Issue 1, March 2001 Page(s): [2]. Jangamshetti, S.H., RAU V.G.: Site matching of wind turbine generators: a case study, IEEE Trans. Energy Convers., 1999,14, (4), pp [3]. Sunita Tambakad, S.S. Doddamani and Jangamshetti, S.H., Identification of Optimum Turbine Parameters for Low Wind Speed Regimes, Proceedings of the IASTED, Beijing, China, 2009, pp [4]. Nimbal, J.; Naik, R.L.; Jangamshetti, S.H.; "Wind data analysis: A case study Power, Signals, Controls and Computation (EPSCICON), 2012 International Conference, 3-6 Jan [5]. Khambalkar, V.P.; Karale, D.S.; Gadge, S; Performance evaluation of a 2MW wind power project Journal of energy in Southern Africa, Vol 17 No 4, November [6]. Abdin, E.S and Xu. W, Control Dedign and Dynamic Performance Analysis of Wind Turbine- Induction Generator Unit, POWERCON 98, 1998 International Conference on Components, Circuits, Devices & Systems; Power Energy & Industry Applications, pp vol.2. 13

Performance evaluation of a 2 MW wind power project

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