Conventional generation is and will be further replaced by wind energy. Only wind parks in the 100-200 MW have similar capacity as conventional generation and as only large turbines have a MW size (>8 MW is reached), the number of data is enormous if one wants to follow individual turbines to derive failure data.
A limited number wind park production data can be found on the ENTSO-E Transparency platform, for instance planned maintenance for the wind park or some larger grid connection problems. Yet, either no information on outages or a very limited number of outages was found (f.i. full production loss of a wind park). It is evident that to note outages of individual turbines is a lot of work which is also confirmed by the discussion on NERC wind generation, already taking place some years. While notifying wind turbine outages appear to be mandatory in the US, production companies found this difficult. Commercial firms aid meanwhile in having these data generated.
Discussion in the KISSY Working Group also has been going on for some years to introduce wind. Evidently such outages have to be generated by turbine and wind park SCADA systems, wind turbines do not have KKS-codes (a new coding system RDS-PP was developed) and lack of wind and too much wind (cut-in and cut-out of a turbine) should be taken into account. Interaction between turbines, for instance due to wake, is an issue that should be thought about.
The outage data are aggregated data for 2 wind parks (Baltic 1 consisting of 21 turbines in total 48 MW, Baltic 2 consisting of 80 turbines in total 288 MW). As according to literature one may expect 1 – 2 failures per turbine per year, unavailability records showing 2 – 5 MW losses should be abundant. They are not. This is consistent with only reporting large (order of magnitude 100 MW) outage data.
Advanced modelling (as wind data must be local) is probably used in the ENTSOE Transparency Platform wind data. Figure 5.1 shows the amount of variation in wind production (forecasted) for December 2018. It would be interesting to see if typical patterns would be present (storm season, low production, variation over the day with land warming up, etc.). Evidently, in order to keep the grid frequency constant, such variations must be balanced by conventional generation. Evidently, this will cause more wear and tear on such power plants and reserve (for the in Germany so called Dunkelflaute, meaning a period without renewable generation from sun and wind) must be kept ready and paid for. The reserve plants market is still being discussed by major players.
Figure 5.1 Example of Intraday wind production forecast.
The author has helped to carry out a study for the production situation on a Caribbean Island, having diesels, electricity production from a refinery as well as small size wind turbine parks. It was found that hourly wind production records were held manually and publicly assessable wind information from an airfield nearby was available. While not part of the original study, results were derived such as shown in figure 5.2 below. The results are interesting as the island demand is 100 – 120 MW with during the months analyzed 0 – 40 % wind production. Some wind production data are shown in figure 5.3. The wind data of the wind parks were correlated to wind measured about 1 km further away at the airport. Evidently, wind production by a single array of turbines is dependent on wind direction. Part of the spread may be explained by the difference between measurement point of wind and having wind production by the hour while wind speed is averaged over the hour. At least 15 Minute wind speeds, as local as possible, should help to reduce the spread further.
The problem for the utility company is NOT a period with much wind, as diesel generation is relatively easy to stop, decrease and increase (by varying the amount of diesels in operation). The problem is to predict wind production to such an extent that the utility is able to plan and carry out maintenance during day hours at acceptable costs and to have sufficient reserve power for months without wind. Similarly, electricity production on this island is dependent on industry (a refinery) and therefore on economic conditions.
The situation, probably less extreme, might be similar for the Netherlands in the future with a large amount of renewables in the grid and short term decision making on generation and reserves by politics. Please note the discussion in the Netherlands on coal production from relatively young large coal fired power plants, to be closed. Yet, technically this is an interesting situation as by using the characteristics of the wind turbines and local wind conditions (both on direction and wind speed) one should be able to predict wind generation and, given a sufficient window for decision making, solve the above questions for reserve power and plant maintenance.
Similar types of analysis are also possible for North Sea windfarms. One should use: □ The production curve per turbine type as given by the manufacturer
□ Transparency data for total park production
□ A correction for wind at hub height versus metrological (10 m) reference height □ Actual wind conditions including gustiness, if unavailable one can use for
example the KNMI North Sea wind data or computer generated GRIB files □ As turbines influence another being in each other’s wake, some analysis of
array effects and/or wind direction
As an example see figure 5.4, which is comparable to 5.3 except for the amount of power. Also for North Sea windfarms, the amount of data necessary for analysis is substantial but doable.
To conclude:
Renewables such as wind power require high quality big data for analysis purposes. Yet the physics of wind turbines are known, prediction of wind based on GRIB files is possible and it should be feasible to have a few days ahead power predictions in order to match generation resources. To derive failure characteristics of single wind turbines in large wind parks is a major amount of effort shown as per [7].
Acknowledgements
Discussions and help was much appreciated from Mr. Stefan Prost at VGB, Dr. Ralf Uttich at RWE and the Helpdesk for the ENTSO-E Transparency data. Statements in the paper not necessarily coincide with their opinion. The same is valid for any of the companies involved or mentioned in this paper.
References
[1] Aydt, J., Lehougre, J., Uttich, R., Prost, S., VGB-database supports performance analysis VGB PowerTech 9/2017.
[2] Aydt, J., Meier, H., Bauer, F., Prost, S., Lehougre, J. Technical Benchmarking – A Tool for Better Performance, VGB PowerTech 9/2017.
[3] VGB-Richtlinie Verfügbarkeit von Wärmekraftwerken, VGB-RV 808, 6. Ausgabe 1999, as well as VGB-Powertech Terms of Utility Industry, Booklet 3, Fundamentals and systematics of availability determination for Thermal Power Plants, 7th Edition 2008
[4] Wels, H., Forecasting the reliability of components in thermal power plants using the VGB database KISSY VGB PowerTech 10/2015.
[5] Pearl, J. , Mackenzie, D. The Book of Why, The New Science of Cause and Effect, Penguin, ISBN 978-0-141-98241-0, 2019.
[6] Slobbe, L., Working with Administrative Health Data, finding solid ground in the data morass, ISBN/EAN 978-94-028-1452-1, 2018.