IN
DEGREE PROJECT ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS
STOCKHOLM SWEDEN 2021,
Evaluation of Energy Losses in a Wind Farm
GABRIEL CLAESSON
Abstract
In recent years, the power production from wind power and other renewable energy sources has increased significantly. Arguably, this is due to the greenhouse effect and the economic benefit of generating power from the wind. Wind power has the potential to replace other non-renewable power sources. Thus, it is of interest to maximize the efficiency of the wind farms, to produce the most power to the grid. This implies that the power losses within the wind farms needs to be minimized. Evidently, there are expected and unavoidable power losses within the wind farm. However, when there are unexpected power losses or the power losses exceed what is expected, the question arises as to what the reason for this is and if it is possible to avoid or decrease these power losses.
In this thesis, a case study is conducted in collaboration with Skellefte˚a Kraft. An existing wind farm is studied, as the case company noted inconsistencies in power losses. One section has a larger share of power losses than the other section in the wind farm. Thus, it is of interest to the case company to find out why this is, to find the source to these power losses, as unnecessary power losses mean a loss of profit as well as loss of useful power produced from renewable energy sources. This suggests that if the sources of the power losses are identified and the power losses are deemed unnecessary for the operation of the wind farm, the case company can take actions accordingly. Hence, this thesis aims to identify the sources of the power losses and to create basis to whether these power losses are justified and necessary for the operation of the existing wind farm or not.
To study the power losses in the existing wind farm, a model is devel- oped utilizing load flow analysis. The load flow analysis is based on real hour power production data of the year of 2019. Thus, several load flow calculations are carried out to modify the system parameters to optimize the accuracy, and to verify the model. The model is then used to estimate and evaluate the power losses within the wind farm, and for identification of the sources of the power losses.
The results of the case study prove that a rather accurate model was successfully developed. The model indicates that, for the year of 2019, the difference in power losses between the two sections of the wind farm was primarily due to the de-icing systems. The de-icing system of one section constituted for a significantly larger share of the power losses in that section than what the de-icing system of the other section constituted for the power losses in the other section. This suggests that the de-icing system needs to be evaluated further. Due to the design of the wind farm, there is an additional transformer in one of the sections. Through utilizing the model, the power losses of the additional transformer were estimated.
For the year of 2019, the model indicated that the power losses of the additional transformer contributed rather insignificantly to the difference in power losses between the two sections.
Sammanfattning
Under de senaste ˚aren har kraftproduktionen fr˚an vindkraft och andra f¨ornybara energik¨allor ¨okat avsev¨art. Det beror bland annat p˚a v¨axthus- effekten och de ekonomiska f¨ordelarna med att generera el fr˚an vind- kraft. Vindkraften har potential att ers¨atta andra icke f¨ornybara ener- gik¨allor. D¨arf¨or ¨ar det av intresse att maximera vindkraftparkernas effek- tivitet och maximera den genererade effekten ut p˚a eln¨atet. Detta inneb¨ar att effektf¨orlusterna inom vindkraftsparkerna b¨or minimeras. Det finns f¨orv¨antade och oundvikliga kraftf¨orluster inom vindkraftsparken. N¨ar det uppst˚ar ov¨antade effektf¨orluster eller effektf¨orlusterna ¨overstiger det som f¨orv¨antas, blir fr˚agan om vad orsaken ¨ar och om det ¨ar m¨ojligt att undvika alternativt minska p˚a dessa effektf¨orluster.
I detta arbete genomf¨ors en fallstudie i samarbete med Skellefte˚a Kraft. En befintlig vindkraftspark studeras, eftersom f¨oretaget har no- terat ov¨antade effektf¨orluster. En sektion har st¨orre andel effektf¨orluster j¨amf¨ort med den andra delen av vindkraftsparken. D¨arf¨or ¨ar det av in- tresse f¨or f¨oretaget att ta reda p˚a anledningen till detta, eftersom on¨odiga effektf¨orluster inneb¨ar ekonomiska och effektm¨assiga f¨orluster. Det bety- der att om k¨allorna till effektf¨orlusterna identifieras och ¨ar on¨odiga f¨or driften av vindkraftsparken kan f¨oretaget vidta ˚atg¨arder d¨arefter. Syftet med detta arbete ¨ar d¨arf¨or att identifiera k¨allorna av effektf¨orlusterna och identifiera om dessa effektf¨orluster ¨ar n¨odv¨andiga f¨or driften av vind- kraftsparken eller inte.
F¨or att studera effektf¨orlusterna i den befintliga vindkraftsparken ut- vecklas en modell med hj¨alp av lastfl¨odesanalys. Lastfl¨odesanalysen ¨ar baserad p˚a realtidsdata f¨or kraftproduktion under ˚ar 2019. S˚aledes utf¨ors flera lastfl¨odesber¨akningar f¨or att modifiera modellens systemparamet- rar, f¨or att ¨oka noggrannheten och f¨or att verifiera modellen. Modellen anv¨ands sedan f¨or att uppskatta och utv¨ardera effektf¨orlusterna inom vindkraftsparken och f¨or identifiering av k¨allorna till effektf¨orlusterna.
Resultaten fr˚an studien visar att en noggrann modell kunde utvecklas.
Modellen indikerar att skillnaden i effektf¨orluster mellan de tv˚a sektioner- na i vindkraftparken f¨or ˚ar 2019 fr¨amst berodde fr¨amst p˚a avisningssyste- men. Avisningssystemet i den ena sektionen utgjorde en betydligt st¨orre andel av effektf¨orlusterna ¨an i den andra sektionen. Detta tyder p˚a att avisningssystemet b¨or utv¨arderas ytterligare. P˚a grund av vindkraftspar- kens utformning finns det en extra transformator i en av sektionerna.
F¨or ˚ar 2019 indikerade modellen att effektf¨orlusterna f¨or den extra trans- formatorn knappt bidrog till skillnaderna i effektf¨orluster mellan de tv˚a sektionerna.
Acknowledgements
I would like to thank my supervisor, Stefan Stankovic at the Royal Institute of Technology at the Division of Electric Power and Energy Systems, for guidance and valuable feedback throughout the course of this thesis. I am grateful for your time and support.
I would like to thank my supervisor, Anders Larsson at Skellefte˚a Kraft, for many interesting discussions, feedback, and encouragement. Thank you for making this thesis possible. I would also like to thank the people at Skellefte˚a Kraft for the kind welcome and their generosity.
Many thanks to my examiner Lennart S¨oder, professor at the Royal Institute of Technology at the Division of Electric Power and Energy Systems, for the helpful comments and suggestions.
Contents
1 Introduction 1
1.1 Background . . . . 1
1.2 Research problem . . . . 3
1.3 Contributions . . . . 5
1.4 Delimitations . . . . 5
1.5 Covid-19 disclaimer . . . . 6
2 Theoretical background 7 2.1 Grey box . . . . 7
2.2 Load flow analysis . . . . 7
2.3 Quasi-dynamic simulation . . . . 8
3 Methodology 9 3.1 Research process . . . . 9
3.2 Information search . . . . 9
3.3 Data collection and preparation . . . . 10
3.3.1 Data collection and description . . . . 10
3.3.2 Management of missing data . . . . 10
3.3.3 Data selection and filtration of bad data . . . . 11
3.3.4 Training, verification and validation data . . . . 12
3.4 Model building . . . . 13
3.4.1 Software and configurations . . . . 13
3.4.2 Model assumptions . . . . 14
3.4.3 Model training . . . . 14
3.4.4 Model verification . . . . 17
3.5 Analysis of the final model . . . . 18
4 Case Study 19 4.1 Skellefte˚a Kraft . . . . 19
4.2 Case study wind farm . . . . 19
4.2.1 Wind farm description . . . . 19
4.2.2 Area description . . . . 20
4.2.3 Grid requirements . . . . 21
4.2.4 Wind turbine types . . . . 21
4.2.5 Cable . . . . 21
4.2.6 Three-winding transformers . . . . 22
4.2.7 Two-winding transformers . . . . 22
4.3 Model of the existing wind farm . . . . 24
4.3.1 Case model description . . . . 24
4.3.2 Case specific model assumptions . . . . 25
4.3.3 Characteristics of the wind farm sections . . . . 26
4.3.4 Active power, reactive power, and voltage measurements . 27 4.3.5 De-icing data . . . . 30
4.3.6 Model setup . . . . 32
5 Results 35 5.1 Framework for evaluating results . . . . 35
5.4 Estimated power losses . . . . 38
5.4.1 The de-icing system . . . . 39
5.4.2 The additional transformer . . . . 41
6 Analysis and discussion 42 6.1 Scrutiny of results . . . . 42
6.2 Data quality . . . . 44
6.3 Source of error and model improvement . . . . 44
6.4 Sustainability . . . . 45
7 Conclusion 46 7.1 Main findings . . . . 46
7.2 Future work . . . . 47
1 Introduction
This chapter aims to give an introduction to the topic of wind farm power losses starting from a wider societal context. The research problem together with the following research questions are then presented along with academic contribu- tions and delimitations.
1.1 Background
Renewable energy sources for electricity production are growing. To limit the amount of pollution and CO2 released, countries and international collabora- tion have increased their focus in finding new efficient ways to produce fossil free electricity [1]. In 2015, 195 countries agreed on 17 United Nations’ sustain- able development goals for 2030 where number seven was ‘affordable and clean energy’ [2] [3]. To transition from oil and coal as the primary energy resources efficient alternatives are necessary. The change is powered by decreased costs of wind turbines and solar panels [4]. To optimize for onshore wind power pro- duction, several wind turbines are often organized together in a selected area called a wind farm.
Wind power and specifically wind farms have seen a rapid increase in the last decade. In Sweden, the installed capacity and realized electricity production from wind power have increased by almost a factor of ten in the last ten years.
The share of wind power in Sweden’s total electricity production has subse- quently increased to over 10% [5]. To contribute to a continued development towards affordable and clean energy continuous investments are required to ex- pand the current fleet to wind turbines and wind farms. However, increasing installed capacity of wind power is not a definitive answer, focus must also be directed towards maintaining and improving current wind farms efficiency.
Wind farms are clearly a case for economies of scale [6]. With respect to wind farms some additional considerations need to be taken regarding power losses.
The potential energy losses reduce the wind farm’s overall efficiency and in turn lead to less electricity produced. Power losses are not only bad in pure economical terms, but it also decreases the share of renewable energy produced to the electricity grid. Subsequently, the sources for power losses are important to identify and mitigate.
This thesis is done in collaboration with Skellefte˚a Kraft. The company provided the case study that is presented later in the report. The core reason for the case study is that Skellefte˚a Kraft have found inconsistencies in power losses in one of their wind farms. Thus, there are unexplained and unidentified power losses within the wind farm that is of interest to examine. This thesis intends to address the issue of unidentified power losses. Furthermore, it seeks to give basis to explain the power losses in the case study.
Presumably, there are zero systems that are ideal. This applies to power systems too. The power system is not a hundred percent efficient, likewise, the wind power system is not either. The power in the wind cannot be utilized to a full extent. It is not possible to completely extract the energy contained in the wind.
One can also expect other power losses in a wind power system.
The power losses within a wind farm can be categorized as internal power con- sumption and internal power losses. The internal power consumption can be defined as the power consumed related to the operation of the wind farm. This includes the wind turbines, the cooling systems, de-icing system, the control sys- tem of the individual wind turbines and the wind farm, and other local power consumption such as maintenance buildings. In colder climates, the de-icing system is expected to constitute a significant part of the internal power con- sumption. It could constitute up to as much as about 20 percent of the power losses [7]. The internal power losses can be defined as the power losses related to the components within the wind farm. This includes the transmission losses within the cables, the transformer power losses, and other power losses such as the ones at the point of common coupling (PCC) within the switching station.
If there are unknown or unexplained power losses in the wind farm i.e. the actual power losses in the wind farm is higher than the theoretical, the question arises to where these power losses occur and if they are justified.
An individual wind turbine can both produce and consume active and reactive power which depends on how the wind farm is controlled and the specifications of the wind turbine. The active power is the real power dissipated in the elec- trical circuit. The reactive power is the futile power which flows between the power source and the load [8]. Through the control of the power factor of the wind turbine, the active and the reactive power is controlled. The power factor indicates the phase shift between the voltage and the current. In wind turbines the power factor set point is most often set equal to one, or at intervals close to one [9]. This is to minimize the losses and the heating in the converter.
Through the regulation of the consumption and the production of the reactive power the wind turbines make it possible to manage the voltages throughout the wind farm at desired levels. It depends on the control system of the wind farm but often the voltage at the PCC is managed to stay at a specific level [10].
The wind farm may also be used to provide stability for the rest of the grid, meaning that the wind farm can provide the grid with reactive power compen- sation. Hence, usually there is a set requirement of an interval for the reactive power at the PCC.
It is useful to model the power flows in the wind farm to identify the source of the power losses and to find the internal power consumption that is unjustified. This gives information as to where improvements are desired. Load flow analysis is a numerical analysis tool of electric power flow in electric power systems, which can be used to analyse steady state operations, to calculate voltages, current flows, and power losses. In general load flow analysis is useful for investigations of power systems with regards to fault analysis, system stability as well as for future expansion plans of the power system or alternative designs of power systems [11].
Wind power production fluctuates as the wind changes [12]. Therefore, load flow analysis can be utilized to interpret and evaluate the wind farm power system over a period with several different scenarios of power production. Which could be useful to explain power losses in the wind farm. Quasi-dynamic simulations carry out multiple load flow calculations for longer periods of time, which is helpful to identify what happens in the wind farm [13]. This thesis aims to investigate the factors which affect power losses within a wind farm by creating and evaluating a load flow model.
1.2 Research problem
The purpose of a wind farm is to extract energy from the wind and to generate power to be utilized. The amount of available power, generated by a wind farm, depends on numerous factors, for instance the energy in the wind, the design of the wind farm, the design of the control system, the components, the location, the internal power losses, and the internal power consumption in the wind farm.
There are expected power losses and expected internal power consumption in a wind farm. The main problem is if there are unnecessary power losses in wind farms. These power losses have economic and environmental consequences.
There are some challenges with power loss identification and estimation in wind farms. One aspect is that it is expensive to thoroughly measure specific power losses and to gather extensive data within the wind farm. Specific measurements are needed if the power losses are to be calculated. Thus, the estimated power losses depend on the quality of the measurement data. The exact power losses could be difficult to calculate due to redundancy of data, inaccuracy of data, and/or the lack of data. Estimating power losses utilizing models would provide information to where the power losses occur. This would give basis to what area would need improvement to decrease the power losses in the power system, in this case wind farms.
In this thesis, a case study is conducted regarding an existing wind farm. The case company, Skellefte˚a Kraft, noticed irregularities in power losses in one of their wind farms. The power losses were higher in one section of the wind farm compared to the other section. This is curious and is of interest to investigate the reasons for this. Thus, the thesis seeks to identify the source of the unexplained power losses. There are several approaches to the problem that can be taken. It is possible to set up measurement meters around the wind farm to gain power consumption data of components. Subsequently, compare the measurements acquired to the theoretical power consumption to see if there are excessive power consumption or power losses. This would probably not be the most economical approach, as measurement instruments are expensive. It also depends on the size of the wind farm regarding how many measurement instruments are needed and how many components within the wind farm are to be examined. Another approach is to create a model of the wind farm to identify the source of the power losses.
To create a representative model of the existing wind farm, there are certain
the ‘Grey box’ contains the physical information and the structure of the wind farm together. The parameter values of the components of the wind farm are unknown. What goes on within the ‘Grey box’ is unknown [14]. However, load flow analysis can be utilized to model and solve the ‘Grey box’ and create an accurate representation of the wind farm. In other terms, the load flow analysis provides a solution to the state of the power system with respect to the active and the reactive power generation and demand and the voltage. The model could give an indication of supposedly unnecessary power consumption or power losses in the wind farm, by comparison of the model output result and the actual output of the wind farm.
This means that if the load flow analysis is to be used to investigate the existing wind farm, there must be a lot of data available. It depends on the set up of the model, as the input and the output data of the model would be different.
In any case, the data needed are measurements of the active and reactive power generation and demands as well as voltage data of the wind turbines and the connection to the grid. The load flow analysis provides a single snapshot solution of the state of the power system. Thus, multiple load flow analysis must be done to evaluate the unknown parameters of the power system, the wind farm.
This is because otherwise the estimated unknown parameters would not be correctly estimated and correspond to reality. Several parameters cannot be estimated from a single state, a single solution, as the estimation would only correspond to that specific state. There is also the possibility that there are multiple estimations of the parameters that satisfies the solution. Thus, the model would probably not represent other states of the wind farm accurately.
Additionally, the power losses and the power consumption of the wind farm would not be represented accurately, and it would not be possible to properly provide a basis to where the unnecessary power losses occur. Therefore, several data measurements must be available, arguably, the more data available the better representation of the wind farm can be modelled.
To make an accurate model of the wind farm, the output of the load flow cal- culations is compared to the actual measured values of decided quantities. The comparison between the result of the model and the actual measured values of the wind farm gives an error of the model. This error can be estimated utiliz- ing a couple of tools. For instance, the mean square error (MSE) is calculated as the average squared difference between the estimated values and the actual values, which provides an accuracy estimate of the model. This means that the smaller the MSE value becomes, the more accurate to reality the model be- comes [15]. Hence, to make the model more accurate to reality, the parameters of the model are to be configured to minimize the error, the MSE. When the error estimate is small enough, the model of the wind farm depicts reality close enough. Thereafter, the model can be utilized to look for improvement options, or areas of improvement. In other terms, to see where the power consumption and the power losses occur, thus, appropriate actions can be implemented.
The research problem described above lead into the following research questions for the thesis:
• What factors affect power losses in wind farms?
• How can power losses in wind farms be identified by utilizing a power flow model?
If an accurate model of the wind farm is created, the source of the power losses and power consumption could possibly be identified. Thereafter, these power losses or power consumptions could be evaluated to whether they are necessary to the operation of the wind farm or not. The expectation is that there are some power losses in the existing wind farm that are unnecessary high in one of the sections compared to the other. The internal power consumption of the wind farm in both sections should be very similar to one another. If it proves that the internal power consumption of for instance the wind turbines in the two sections differ from one another, further studies should be conducted. This is for justification and verification purposes, and if this is true appropriate actions should be taken.
1.3 Contributions
The thesis contributes primarily with additional information and insights to the case company, about the power losses within the existing wind farm studied.
The thesis investigates the production data of the wind farm for the year of 2019, and the power losses in the wind farm. Through examination of data and a developed model, the sources of power losses were evaluated and estimated.
Hence, the thesis provides more information to the company of the source of power losses and further areas to improve upon within the wind farm, to decrease the power losses. Secondly, the thesis contributes with further research to power loss identification in wind farms, utilizing load flow analysis. It provides further knowledge of how the load flow analysis can be incorporated in power loss estimation utilizing extensive data. It also provides further knowledge of the sources of power losses in wind farms in subarctic climates.
1.4 Delimitations
The load flow model of the existing wind farm presented puts emphasis on the identification of power losses during operation utilizing annual hourly data.
The data from the existing wind farm was acquired for the years of 2017, 2018 and 2019. However, the data from 2017 and 2018 was considered insufficient and invalid to use due to inconsistency. Thus, the model development and the evaluation of the model is limited to the data of the year of 2019. The developed load flow model is designed to use as a basis for identification of the power losses of the existing wind farm. It is meant to be used as a tool for identification and
1.5 Covid-19 disclaimer
This thesis was conducted during the spring of 2020, during the Covid-19 pan- demic. The pandemic affected the thesis in various ways. A planned field trip was cancelled abruptly.
2 Theoretical background
In the following chapter the relevant theoretical background is presented. The theoretical background includes the information to the relevant concepts in the thesis together with theory of the load flow model used in the case study.
2.1 Grey box
The concept of the ‘Grey box’ is used in this thesis. The concept is useful as it provides information related to how to interpret the problem of power losses in the existing wind farm and how to approach it. The information regarding the existing wind farm is presented later in the thesis. The concept is utilized as the physical structure of the existing wind farm is known but there are uncertainties of the parameter values of components. In short, there is data of the power production from the wind turbines and the power output to the grid, but there is no data of what occurs in between the two. Hence, the system in between can be viewed as the ‘Grey box’, which is to be evaluated [16].
The concept of the ‘Grey box’ is a combination of the ‘Black box’ and the ‘White box’. The ‘Grey box’ is meant to be used as a tool to determine errors in systems because of the operation and/or the structure of the system. The ‘Black box’ is the representation of a system or a device or an object, where one does not know what occurs within the ‘box’ or how the ‘box’ acts and works. The structure of the ‘box’ is unknown. The ‘Black box’ is observed indirectly, one observes the input and the output of the ‘box’, whereas one cannot observe how the input is converted to the output [17]. The ’White box’ is the representation of a system, currently most often used in software development. It is purely theoretical. Where one knows what occurs within the ‘box’ and how the ‘box’
act and work. The structure of the ‘White box’ is known. The ‘White box’ is observed directly, with the knowledge of what the software code does [18].
The ‘Grey box’ concept incorporates parts of both the ‘Black box’ and the
‘White box’. The ‘Grey box’ combines the two ‘boxes’ in the sense that the structure of the model is known together with internal data as well as math- ematical models used. However, certain parameter values of the model are unknown [19].
2.2 Load flow analysis
The load flow analysis is a tool for examination of power systems. It computes the steady state characteristics of a power system with defined power genera- tion and determined transmission network structure. It solves the system of nonlinear complex power balance equations with iterative numerical methods.
It provides a solution to represent the state of the power system in a particular moment. It is utilized to examine the current state of power systems, addition- ally it is used to provide basis for future expansion plans of power systems, and
in this thesis is the Newton-Raphson method.
The load flow analysis can be utilized to evaluate a power system in terms of power losses. If there are several data points available, of power generation and demand and voltages, several load flow analyses can be carried out. Thus, it is possible to utilize the load flow analysis as a tool to estimate the parameters of the power system for several scenarios, and to determine if the parameters of the model are well corresponding to reality. It would thereby be possible to find out if the components operate as expected regarding the power losses providing reliable information of the sources and the magnitudes of the power losses around the wind farm. This means that identification of excessive power losses or excessive power consumption can be done. Therefore, with several data points, the power losses of the power system can be investigated utilizing load flow analysis.
2.3 Quasi-dynamic simulation
The quasi-dynamic simulation carries out multiple load flow calculations over a certain time period. As the tool utilized time-based parameters, one evalu- ates the system in several scenarios under normal operation conditions. One compares the output of the quasi-dynamic simulation for the model with the actual output of the electric power system. Thus, one can adjust parameters to increase the accuracy of the model. Arguably, the more data points one has access to, the more accurate one can evaluate and model the system [20].
3 Methodology
In the following chapter the methodology will be presented.
3.1 Research process
The thesis investigates the factors that cause the power losses in an existing wind farm. As explained in chapter 1.1 the power losses in the wind farm can be put into two categories: internal power consumption and internal power losses. The internal power consumption refers to the power consumed related to the operation of the wind farm. The different operations include the power used for the wind turbines to function, the control system, the de-icing system for the wind turbine blades in cold climates, running of local facilities, and the cooling system for the converters. The internal power losses refer to the power losses related to the components within the wind farm. This includes the transmission losses of the cables, losses in transformers and the power losses within the substations.
The research problem in question has to do when there are unidentified power losses in the wind farm. If the sources of the power losses are unknown, there could be excessive power losses within the wind farm. Some power losses could be considered inevitable, however, it is of great interest to try and minimize these losses. To minimize the power losses, the first step is to identify the source. As stated, some power losses might be inevitable and justified for the wind farm to stay operational. The study examines the option of how load flow analysis can be utilized to identify unknown power losses in an existing wind farm. The research process is displayed in figure 1. The research process with reference to figure 1 is explained in detail in the following subsections.
Figure 1: Research process
3.2 Information search
The first block in the research process was the information search. The in- formation search consisted of retrieving and reviewing the available knowledge and literature within the field of power losses in wind farms. A literature study was conducted. Additional sources of information came from the case company.
The sources from the case company included: test reports from the wind farms, different databases of operation data covering the last five years, mapping of wind farm structure and various components.
After the initial research which gave a basic understanding of the underlying systems, discussions were held with the supervisor at the case company. The discussions resulted in a research problem which was specific to the case study.
To broaden the scientific approach to the research problem, discussions were held with the supervisor at the Royal Institute of Technology which led to the formulation of the research questions and research problem presented in the thesis.
Along with the rest of the thesis work, additional sources of information were used to complement and deepen the analysis of the paper. This was done in parallel to the following three blocks shown in figure 1. Notably, additional data and test reports from the case company were studied to fully understand the underlying system of the wind farm.
The result of the information search was not only the formulation of the research questions and research problem, but also the approach to try to answer the resulting research questions and problem formulated. The information search suggested that in order to identify and explain the factors behind power losses in wind farms a model for simulation and evaluation was needed. The model chosen was a load flow model which together with historical data from a wind farm could be simulated to identify potential power losses.
3.3 Data collection and preparation
The second block in the research process was the data collection and preparation.
The data collection and preparation consisted of several steps described below.
3.3.1 Data collection and description
The data used in the case study was collected from the case company’s internal databases. The data consisted of generation data of the wind turbines, the voltage data of the wind turbine buses, the load data of the different loads throughout the wind farm and the output power data from the wind farm to the grid at the PCC. There are also measurements of the hourly power consumption of the loads in the wind farm. The available data was hourly values from 2017 to 2020. The data was downloaded from the databases to Python and the model with Excel as an intermediary. In total roughly 26 000 data points were collected.
3.3.2 Management of missing data
For different time periods between 2017 and 2020 there were missing data points for several of the wind turbines. In these cases, it is important to deal with the corresponding missing data points before feeding them into the model. There are several different ways to deal with missing data and the appropriate method depends on the characteristics of the data as well as the quantity available. In cases where just a few data points from individual wind turbines are missing, the chosen approach was to fill in the values with those of other nearby, correlated
wind turbines. As such, the information was not lost even though the particular missing data point became an estimation rather than the measured occurrence.
The other nearby wind turbine which was used to fill in the missing data was chosen based on geographical proximity.
For missing power consumption data of individual wind turbines, the same ge- ographical proximity logic was used to fill in missing values. For other missing values of different larger systems, the previous value has been used where ap- plicable. For the instances where there were missing voltage data, the data point was removed as the voltage data was considered the validation data for the model to be evaluated against.
3.3.3 Data selection and filtration of bad data
After collecting the data and managing missing values of the data points there is another step which needs to occur. The data needs qualitative work to evaluate if it is useful for the model. By looking at the amount of missing data a decision was taken to only use the hourly measurements from 2019 and remove the data from 2017 and 2018. The data of 2019 was comparatively more consistent.
There was considerably more data missing from 2017 and 2018 than from 2019.
The resulting amount of data points (8760) was considered enough to perform the load flow calculation on even after further filtration.
Another qualitative step included filtering out outliers as well as other data points which logically did not add up in the different measurements. In this case, a certain criterion was created to evaluate whether a data point was to be included or not in the training and evaluation of the model. A data point which did not fulfil the criterion was removed.
For a viable hour of the year, the individual data point should support the following criterion:
• Each data point should contain sufficient information for the training of the model as well as for the validation of the model. This means that there must be enough data to run the load flow calculation and enough data to validate the result of the load flow calculation. The validation data is used to compare the result of the load flow calculation with. Thus, the data needed for each data point is the generation data of the wind turbines, both active and reactive power as well as data of the voltage at the wind turbines. Furthermore, the data of the active power output at the PCC is needed and the data of the power consumption of the loads in the wind farm is needed. It depends on the model setup, what data is needed to run and what data is needed to validate the load flow calculations, this is further addressed in subsection 3.3.4: Training, verification and validation data.
• The measured active power produced from all the wind turbines combined should be close to the measured output active power to the grid, at the PCC. It should be within a certain interval depending on the data and the size of the wind farm. This is due to large variations of produced active power during the year and to neglect outliers and data points where the data is insufficient. The interval should ensure to not eliminate data points excessively. The interval of acceptance chosen for the measured active power produced and the measured active power output to the grid was +/- 8% of the installed power of the wind farm;
• The loads within the wind farm that consume a combined amount of power less than 0.1% of the power produced by the wind farm are to be neglected. This criterion is not to be utilized if there is data of numerous loads in the wind farm, suppose the loads power consumption would add up to more than 0.1%;
After applying the criterion on the 2019 data a total of 7420 data points re- mained. This was considered enough to proceed with the model building and load flow analysis.
3.3.4 Training, verification and validation data
The available data has to be separated into two parts, two sets of data. The two datasets are referred to as the input dataset and the validation dataset.
The input dataset consists of the data that is used as input to the model, to run the load flow calculations. The validation dataset consists of the data that is used for comparison with the output of the model to evaluate how good the estimated model is, to compare with the result of the load flow calculations.
The data in the two datasets is dependent on the setup of the model.
To run the load flow calculations, as mentioned before, the types of buses in the power system, the wind farm model, need to be identified. This defines what type of input data is used to run the simulations and what type of data is used as validation data. For the PQ-buses the input data needed is the active and reactive power data for both load and generation. For the PU-buses the input data needed is the active power data of the generation and load and the voltage data. For the slack bus, the input data needed is the reference point for the voltage together with the active and reactive power load data. As such, the input dataset needs to include the measured data of these quantities for the respective bus type.
The output of the load flow calculations depends on the types of the different buses. The output of the PQ-buses is the voltage. The output of the PU-buses is the reactive power. The output of the slack bus is the active and reactive power generation data. The output quantities are compared with the validation dataset. The validation dataset needs to include the measured data of the quantities for the respective bus type. The comparison is used to calculate the error estimates, which is used as the estimation criteria described in subsection 3.4.3: Model training.
Table 1: Datasets
Dataset Input data
quantity
Validation data quantity Training dataset
(75% of available data points) Pg, Qg, Pld, Qld Ppcc Ug
Verification dataset
(25% of available data points) Pg, Qg, Pld, Qld Ppcc, Ug Table 1 shows the setup of the datasets used in this thesis. As shown the input data in this thesis is the generation data. This is the active power production (Pg) and the reactive power production (Qg) of each wind turbine in the wind farm. The input data also includes the load data, the active and reactive power (Pld), (Qld). The output of the load flow model will be the voltage data at the wind turbines (Ug) and the active power data (Ppcc) at the PCC. As such, the validation data in this thesis will be the voltage data at the wind turbine buses and the active power data at the PCC. The validation data is to be compared to the output of the load flow model. This is explained further in the section 3.4: Model building.
3.4 Model building
The model building chapter presents the method used when configuring the model and using the data prepared to perform load flow calculation. The de- scriptions of the methods used for estimation of the model parameters are pre- sented below. This includes the building process, the assumptions, the training process, and the verification process of the model.
3.4.1 Software and configurations
The program DigSilent Power Factory was used for model building. The soft- ware is used by the case company and was provided for this thesis. The program was utilized to create a single line diagram of the wind farm with the main com- ponents. It was also used to solve the load flow calculations within the wind farm during operation.
The programming language Python was then utilized to communicate with DigSilent Power Factory to run the quasi-dynamic simulations and to extract the relevant results from the model and generate and calculate the error mea- surements of the model. Python version 3.8 was used.
3.4.2 Model assumptions
To enable load flow calculation there are some assumptions that are inherent to the model which exist to decrease the complexity of the problem. There is a significant number of variables that affect the power system, and all cannot be taken into account. There are variables that are more important and have a larger effect on the power system, the wind farm, than others. The assumptions presented below are all used in this thesis to decrease the complexity of the wind farm. Furthermore, the additional assumptions specific to the case study are presented in chapter 4.
The general assumptions of the analyses used in this thesis are:
• The system is assumed to have a constant frequency of 50 Hz;
• The system is assumed to have symmetrical operation. This implies that the system is balanced across all three phases and a one-phase equivalent model can be used;
• The wind turbines can both consume and produce reactive power. To what extent depends on the wind turbine;
• The initial values of the model parameters to be estimated are obtained from the literature. If there are test reports for the components, the measured samples are to be considered. For instance, the load losses of the transformer are not definite as they depend on the tap position. It is of value to set a standard tap position of the transformer and consider the load losses at that specific tap position. This is to consider the lack of data of the tap position. As it is unknown when and on what tap position is active, the standard is set to consider the average power losses for the year.
3.4.3 Model training
One of the purposes of the model is to create an accurate representation of the wind farm. The model training session is used to find out more information about the wind farm. The wind farm is initially represented with the ‘Grey box’ model because the physical information is known such as the layout to- gether with the components within the wind farm. There are, however, certain parameter values of the wind farm that are unknown. Going from a ‘Grey box’
that does not hold enough information of a model to an accurate representation of the wind farm can be found by estimating the ‘Grey box’ model parameter values. The error of the model can be measured as the difference between the output of the model and the real system measurements. It implies that the error of the model, the output results of the model compared to the actual measure- ments at the wind farm is to be minimized for the model to improve and more reliably depict the properties of the actual wind farm. The error is explained further down below.
One of the purposes of the training session of the model is to adjust the model parameters to minimize the overall error and thus create a more accurate repre- sentation of the wind farm. The model training session uses 75% of the available data points. The 25% remaining data points are used for model verification against other scenarios and to see how the model performs in other scenarios.
The training and verification datasets were divided in chronological order as the underlying data is time dependent.
Figure 2: Training Session of the Model
Figure 2 illustrates the training session of the model, which is to be explained further in depth. The training session is an iterative process to estimate the unknown parameters of the wind farm. As the figure shows, the input data together with the initial parameters are fed into the model, the ‘Grey box’.
The ‘Grey box’ contains the physical information of the wind farm: the type of components and the layout of the wind farm.
In the model training the wind turbine buses are considered PQ-buses which has implications on which input and output data that is to be considered. The input and output data of the PQ-buses is previously explained. The PCC is considered as the slack bus. As such, the input data is the voltage reference point.
The error of the model is estimated and evaluated through a couple of estima- tors; the mean square error (MSE), the root-mean square error (RMSE) and the weighted root-mean square error (wRMSE) [15] [21] [22]. The error estimates can be interpreted as measurements of accuracy and quality. The lower the val- ues of the error estimates, the better accuracy of the model. How the MSE, the RMSE and the wRMSE are calculated is explained in more detail below. The parameters of the model are then modified, and the simulations are run once again. A new error is then evaluated. This is done until the MSE, the RMSE and the weighted RMSE reach a plateau where they stop decreasing. As such, a more accurate representation of the wind farm is created.
To summarize the steps, a list is provided below:
1. The single-line diagram of the wind farm is created with the essential components, the transformers, generators and loads and the cables with the corresponding parameters;
2. The initial conditions of the model are chosen, the input data and the initial parameters of the components are chosen. The quasi-dynamic sim- ulations are run. In other words, a number of load flow calculations are carried out in sequence over the data points chosen as inputs to the model;
3. The results of all the load flow calculations are compared with the vali- dation data, which gives an error of the model. The error of the model is estimated through calculating three estimators: the MSE, the RMSE and the wRMSE;
4. The model parameters are updated, to decrease the value of the error estimates, to increase the accuracy;
5. Steps 2 to 4 are done repeatedly until the estimator values reach a plateau where they stop decreasing. As such, the error of the model is considered minimized.
The initial parameters of the components are assumed as the standard param- eters mentioned in the model assumptions. The initial parameters are the ones acquired from the literature and the test reports of the components. The pa- rameters values are then updated in the iteration process of the training session mentioned above.
The quasi-dynamic simulations carry out multiple load flow calculations. Uti- lizing several data points to carry out several load flow calculations means that the error estimates are calculated for several data points. Thereafter, the aver- age of the MSE values of the same quantity are calculated. To clarify, the MSE estimate of the voltage is calculated at the specific wind turbine and this is done for all wind turbines. Thus, providing an error estimate value of the voltage at all the wind turbines. The average of these MSE values becomes a measure for the accuracy of the model.
The MSE is calculated first, the RMSE and the weighted RMSE are calculated based on the MSE. Before calculating the error estimates it is important that the data compared (the resulting output and the validation data) are in the per unit system ([p.u.]). This is to make sure that all the parameters have the same impact on the MSE value. The voltages and the active power should influence the error estimation the same.
Equation (1) shows how the MSE is computed. Where Yi is the result value of the output of the model, ˆYi is the actual measured value, the validation data, and n is the number of data points.
M SE = 1 n
n
X
i=1
(Yi− ˆYi)2 (1)
The MSE is calculated for the different quantities. The MSE values for the same quantities are combined as an average. The average MSE for the voltage, the active power and the reactive power are all calculated separately. To calculate the average MSE value of the specific quantity, equation (2) is used.
M SEq= 1 n
n
X
i=1
M SE (2)
The M SEq is to denote the quantity, voltage, active power or reactive power.
Here the n denotes the number of MSE values of the same quantity. For instance, how many wind turbine buses where the MSE value is based on the voltage.
The root-mean square error and the weighted root mean square error are com- puted from the MSE values. The RMSE is computed using equation (3), as- suming that all weight coefficients are equal to one. The weighted RMSE is computed using equations (3) and (4), where the weight coefficients are decided.
These two estimates are calculated the same way as the MSE. The purpose of the weight is to control the importance of each quantity. Thus, the weight im- pacts how the accuracy of the model is estimated. This also means that the error estimates depend on the setup of the model, that some quantities have a larger impact on the accuracy than other quantities.
Equation 3 and 4 displays how the RMSE and the weighted RMSE are com- puted. Where wq is the weight of the quantity displayed in equation 4.
RM SE = v u u t
n
X
q=1
wqM SEq2 (3)
n
X
q=1
wq = 1 (4)
3.4.4 Model verification
The model verification process occurs after the training session has been per- formed. In the training session of the model, the parameter values were fine tuned to better represent the real system. The parameters investigated were modified to increase the accuracy of the model and decrease the values of the error estimates. This was done utilizing the load flow calculations of one dataset, as mentioned previously, which consisted of 75% of the data points available. In the verification of the model session, the performance of the model is evaluated using the dataset which consists of the 25% data points remaining.
The dataset consisting of the 25% of the data points available is used to see how well the model parameters were estimated in the training session of the model.
This is to see how the model performs with an independent dataset and to
would respond to other data points, for example when faced with new future scenarios.
To verify the model against the independent dataset, the power losses of the model is expected to deviate within a boundary to the actual power losses of the wind farm. As such, the load flow calculations are performed for the independent dataset, and the simulated active power output to the grid at the PCC is compared to the actual measured active power output to the grid at the PCC. The limits to how much the simulated can deviate from the measured active power at the PCC is not fixed for every model, as it depends on the size of the power system as well as the quality of data.
In this thesis, the model is considered verified when the deviation is in between +/- 0.04 in per unit at most for the simulated and the actual measured active power output at the PCC, where the base power is the installed power of the wind farm. Additionally, the simulated total amount of energy output to the grid must be within the interval of +/-1% of the total measured energy MWh output to the grid at the PCC.
The subsequent error estimate from the model verification further indicates whether the model is a good representation of the wind farm. The model ver- ification also evaluates the results of the model parameter estimates from the training session. Therefore, it is important that the limitations of the compo- nent parameters are taken into account in the training session.
3.5 Analysis of the final model
The fourth block in the research process is the analysis of the final model, as the research process figure 1 displays. The model developed is analysed and the output results are interpreted. The power losses of the developed model are estimated using the results from the load flow calculations. For the analysis of the power losses in the wind farm the load flow calculations are performed once more but on the entire dataset (that is 100% of the filtered 2019 data). The calculations result in an estimation of power losses in different components in the system. Here it is important that the model is trained so that when the final calculations are performed, it is based on the best available guess of the system parameter. The power losses estimated by the model are compared and analysed to identify the source/sources of the unexpected and excessive power losses. If there are any unexpected or excessive power losses, it is important to consider different possibilities to further investigate and potentially mitigate the power losses.
4 Case Study
In this chapter, the information regarding the case study is presented. First, the wind farm is described and then the model of the wind farm is presented.
4.1 Skellefte˚a Kraft
Skellefte˚a Kraft is a power company located in the northern part of Sweden, in the province V¨asterbotten. The company is one of the five largest electric power producers in Sweden, and the power produced is from purely renewable energy resources. The bulk of the power is produced from hydro and wind power. The company manages the transmission network of more than a 10000 km in the area. This includes maintenance, monitoring and development of the transmission network. Currently, the company manages five wind farms of a total of 130 wind turbines, and the largest wind farm consists of 99 wind turbines.
As previously mentioned, Skellefte˚a Kraft noticed that within the wind farm of the 99 wind turbines, there are inconsistencies in power losses. There is a larger share of power losses in one section of the wind farm than in the other. It is of interest to Skellefte˚a Kraft to find the reason for this, and if it is possible to decrease the power losses in the wind farm. The thesis addresses the issue of power losses in the existing wind farm and seeks to give basis to the differences in power losses between the two sections.
4.2 Case study wind farm
In this subsection a more thorough description of the studied wind farm is presented, together with component parameters and an illustrative model figure of the wind farm.
4.2.1 Wind farm description
The existing wind farm consists of 99 wind turbines with the installed capacity of approximately 247.5 MW, and an annual production of almost 700 GWh.
The wind farm is connected to the national transmission network, the Swedish TSO company, at 400 kV. There are certain requirements for the connection point of the wind farm which will be presented later in the report.
Table 2: Wind turbine distribution
Section I II
Cluster I II III IV
Number of wind turbines 30 30 21 18
