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Monitoring and Control of Renewable Energy Sources using Synchronized Phasor Measurements
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Monitoring and Control of Renewable Energy Sources using Synchronized Phasor Measurements
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Luigi Vanfretti1, 2, Maxime Baudette1, Austin D. White2 1
Electric Power Systems Department, School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden; 2Oklahoma Gas & Electric Co. Oklahoma, USA
1. Introduction Recent environmental concerns with fossil fuel production have stemmed a global trend of increased share of renewable energy production. In several western countries, the full potential of hydro-power has already been exhausted, which has resulted in wind energy being the fastest growing energy technology in the last two decades [1]. Moreover the expansion of the share of electricity production from wind power and solar will continue, especially in Europe [2] and in the USA where 30 states have enforceable renewable portofolio standards or other mandated renewable capacity policies [3]. Unpredictable challenges for power system operation and control continue to emerge as renewable energy sources are brought into commercial operation. With the increased amount of intermittent power sources, one of the observed impacts is an adverse effect on generation power output within a balancing authority’s control area. Wind power integration has recently presented challenges in short term operation with regards to system dynamics. Particularly, in the case of Oklahoma Gas & Electric (OG&E), wind power curtailment has been enforced to maintain power supply quality and continuity when the interaction of wind farms with the power grid brings undesirable dynamics into play [4]. In this case, these undesirable dynamics came in the form of subsynchronous (i.e. fast) oscillations which were first detected due to the impact of the power quality supply at the consumer level. A very recent article in the New York Times [5] shows that these unpredictable challenges are likely to continue to emerge when new plants are brought into operation due to the lack of operation experience and adequate tools for monitoring and control. Photo-voltaic (PV) generation in the German Electric Grid [6] has increased grid stability issues (e.g. “The 50.2 Hz Risk”) which may endanger the operation of the entire interconnected grid. It becomes apparent that new means for monitoring and control technologies are needed to cope with the new challenges that the integration of renewable energies bring to the power grid. Real-time monitoring and control tools could help mitigate these undesired phenomena, by providing software applications to operate the Renewable Energy Integration. http://dx.doi.org/10.1016/B978-0-12-809592-8.00031-7 Copyright © 2017 Lawrence E. Jones. Published by Elsevier Inc. All rights reserved.
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system with more flexibility, better adapting to phenomena emerging from intermittent generation sources [7]. The effects of increasing amounts of wind power in a power system has been studied, and several problems regarding transient stability have been investigated [8]. However, it is only very recently that some Transmission System Operators (TSOs) have noticed the presence of forced sub-synchronous resonance in the grid due to large scale wind power plants. These oscillations have been observed thanks to the recent adoption of Phasor Measurement Unit (PMU) technology and typically appear in the 3e15 Hz frequency range [4,9e13]. The oscillations can occur during high wind penetration periods, but are typically aggravated by a change in system impedance due to a system fault or transmission line switching. The impact can be substantial and even observable at the consumer level as voltage flicker. The reasons for these forced oscillations are turbineeconverter interactions [14] and they differ from the more commonly observed inter-area oscillations below 1 Hz. As the consequences can be observed at all voltage levels and because these wind farm oscillations have not yet been specifically characterized, it could be supposed that such oscillations could damage equipment on the transmission system or at the customers end. It could also be supposed that the phenomenon could be amplified by the increasing use of wind power. Thus a better ability to monitor the behaviors of wind farms connected to the bulk grid appears necessary. PMU technology allows for means of detection of resonance conditions and it has been found that curtailing the wind power plant output disrupts the oscillatory condition enough to reduce the impact to customers. Such an example is shown in Figure 1, where a forced oscillation is brought under control by curtailing the wind power plant output by 50%. This chapter gives an overview of how synchrophasor technology can be applied for developing real-time PMU applications, which help in monitoring and control of unwanted dynamics that are a product of renewable energy sources interacting with the power grid. The remainder of this chapter is organized as follows. Section 2 gives an overview of PMU-based monitoring and control systems and describes different environments for developing PMU applications. Section 3 illustrates the development of real-time monitoring tools for the detection of sub-synchronous wind farm oscillations, as an example of how synchrophasor technology can be effectively used for dynamic monitoring of renewable energy sources. Section 4 presents testing and validation experiments performed using historical data and laboratory experiments. Finally, Section 5 gives an outline for the development of new PMU applications that can aid in the monitoring and control of renewable energy sources and their interaction with the grid.
2. Real-time monitoring using synchrophasors 2.1 Wide area monitoring systems The ability to monitor fast dynamic behaviors such as the sub-synchronous oscillation phenomena is tied to the performance of the measurement equipment used. The sampling rate has a strong impact on the highest frequency observable from the measurements. Traditional equipment and monitoring tools have an asynchronous sampling frequency of typically one sample every few seconds. While this was sufficient to monitor very slow steady state phenomena, it fails to capture faster dynamic behaviors, see Figure 1.
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FIGURE 1 Phasor measurement unit (PMU) and traditional equipment measurements Supervisory Control and Data Acquisition (SCADA) of a wind power plant during a fast dynamic event (a) Curtailment of the power output (PMU) (b) Comparison between Phasor measurement unit and SCADA. (For color version of this figure, the reader is referred to the online version of this book.)
PMUs are devices able to measure the voltage and current phasors of the three phase network with a reporting frequency of typically 30e60 samples per seconds. The frequency of the system is internally computed at a higher sampling rate and reported also at 30e60 samples per second.1 This allows the study of dynamic phenomena occurring up to a frequency of half the reporting rate. The reference for the angle of the phasors is derived from a GPS clock and all the measurements are coupled to a time stamp allowing for the alignment and synchronization of PMUs spanning an entire interconnection. 1
Instantaneous active and reactive power can be computed from the voltage and current phasors.
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Phasor Data Concentrators (PDCs) have the role of collecting and forwarding the data of several PMUs. Since all the measurements have a time stamp acquired from the GPS reference, the PDC can align the measurements in time. PDCs can be configured for several usages, the main being the ability to output a stream of time-aligned measurements from several PMUs. It can also be configured to store and archive data or perform calculations on the measurements. The usage of PMUs to collect data on wide-area power systems is rather recent. It has enabled system operators to monitor their transmission system with more information as well as to conduct advanced analyses [7], particularly for inter-area modes. This technology offers a great potential for building monitoring systems [15].
2.2 Fast prototyping environment for PMU software applications The traditional implementation of Wide Area Monitoring Systems (WAMS) relies on a monolithic software architecture. That forces the implementation of applications in a closed software solution (often proprietary) or directly into the PDC. In contrast, the infrastructure deployed at SmarTS Lab uses a modular approach based on application connecting to the PDC to receive measurements. A deeper comparison of both approaches can be found in [16]. This section described the approaches adopted at SmarTS Lab and OG&E for developing synchrophasor applications.
2.2.1 Statnett’s synchrophasor SDK To implement PMU applications using a modular paradigm, a Software Development Kit (SDK) developed by Statnett SF (the Norwegian Transmission System Operator) [17] can be utilized. The aim of the SDK is to facilitate research, fast prototyping and testing of real-time synchrophasor applications. The chosen development environment is LabView, in which creating graphical user interfaces is simple. However, there is no standard programing interface in LabView to communicate with PMUs or PDC servers using the IEEE C37.118.2 protocol [18] and bring PMU measurements in the programing environment. Statnett’s Synchrophasor SDK [17] provides a real-time data mediator, enabling this feature for LabView, as well as a library of functions that allow fast prototyping.
2.2.2 SmarTS Lab environment The traditional set-up for WAMS is replicated at SmarTS Lab. The adopted approach makes use of Hardware-In-the-Loop (HIL) simulation, replacing the power system by models executed in real-time on a simulator equipped with reconfigurable analog inputs/outputs [19]. The rest of the WAMS architecture is replicated, with PMUs and a PDC server, building a prototyping platform depicted in Figure 2. The lab is also equipped with a PDC server dedicated to the gathering of PMU measurements from Nordic universities, as a part of the STRONg2rid project [20]. This platform, depicted in Figure 2, allows for the testing of applications using real-time data from the Nordic grid.
2.2.3 Oklahoma Gas and Electric Oklahoma Gas and Electric Company (OG&E) began using synchrophasor technology in 2008 with a single hardware PDC and eight multifunction PMUs (line protection relays.) The live synchrophasor data was streamed through the PDC to a PC software client for visualization. It was quickly realized
2. Real-time monitoring using synchrophasors
(a) OPAL -RT
Testing workstation
Voltage
Current
cRIO 9074 PMU GPS antenna
SEL-5073 PDC Ethernet
(b) Testing workstation
Lund
Tampere
NTNU
PMU PMU IEEE C37.118.2
Luleå PMU
WAN Internet
Chalmers PMU
SEL-5073 PDC at KTH
KTH PMU
FIGURE 2 Diagram of the set-up at SmarTS Lab for wide area monitoring systems development and testing (a) Hardware-in-the-loop simulation (b) Nordic phasor measurement units (PMUs). (For color version of this figure, the reader is referred to the online version of this book.)
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that a need existed to further examine data from interesting events which had occurred in the past. Unfortunately, the software purchased at the time did not allow for viewing of historical data. So the utility developed a system to archive the data to a database along with a custom software application named PhasorView that could display the synchrophasor data, both live and archived. With the initial fleet of only eight PMU’s on the 345 and 500 kV Extra High Voltage (EHV) system, OG&E made observations at a rate of 30 samples per second and established a baseline for what would be considered normal operating conditions. They also joined the North American Synchrophasor Initiative (NASPI) and began streaming data to the host site at the Tennessee Valley Authority (TVA). This allowed for the utility to contribute their portion of the grid to the system wide view of the U.S. Eastern Interconnection. For the first time, the utility was able to observe how events on the OG&E system affected the interconnection and vice versa. The utility has expanded the PMU coverage of the transmission system by simply adding communications to existing substations with PMU capable devices already installed. High bandwidth communications were added to all EHV and other critical substations as part of a security initiative in 2005. PMUs have gradually been networked over the years, which now cover about 40% of the transmission system. This includes 100% of the EHV system, 100% of wind farms, 90% of the fossil generation fleet, and 34% of the HV system. In total there are now over 200 transmission lines, autotransformers and generators monitored by PMUs. OG&E has been using synchrophasors primarily for situational awareness, disturbance analysis, and wind power plant integration/monitoring. The utility has a tool for the Supervisory Control and Data Acquisition / Energy Management System (SCADA/EMS) software to bring synchrophasor data into the state estimator, which is currently being tested. The technology is used to assess the stability of the system and proactively find equipment problems. It is also used to monitor how the system responds to faults and assess the voltage recovery from these disturbances. Synchrophasors have also proved very useful for integrating renewable energy into the grid and monitor power quality. Application development at OG&E has been an iterative approach spanning several years. The company has maximized the use of open source software (OSS) tools to achieve desired goals. With the release of the Grid Protection Alliance’s OpenPDC in October 2009, OG&E began the process to restructure the synchrophasor system to take advantage of the flexibility offered by OpenPDC. One of the major concerns regarding the initial configuration was the ability of the hardware PDC to handle hundreds of PMU devices available on the OG&E system. The single hardware data concentrator could handle approximately 40 PMUs. In order to connect more devices, it was necessary to scale out the system architecture. This means that all data could not be centrally concentrated. OpenPDC allowed for the flexibility to achieve the goal of handling hundreds of PMUs. The company developed a custom VB.net Action Adapter inside OpenPDC to do all of the processing and inserting of data into the SQL database. The company’s first problem back in 2008 was visualization of the data, which led to the development of the PhasorView software. PhasorView is a application that plots data queried from the historian. Figure 3 shows a screenshot of the PhasorView application. The interactive Geographic Information System (GIS) interface on the left represents an accurate view of the transmission system and substations on which radar and lightning data can also be displayed. The plots on the right show the synchrophasor data for different power system quantities, from the selected PMUs. The bottom right polar plot shows the system voltage angle spread. Above the GIS interface is a legend for the selected PMUs. The software in live mode serves as a situational awareness tool, using GIS to display the lines and substations within the service territory along with plots of the synchrophasor data. Real time weather
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FIGURE 3 Screenshot of PhasorView at Oklahoma Gas & Electric. (For color version of this figure, the reader is referred to the online version of this book.)
radar and lightning strike data is displayed to anticipate where disturbances may occur on the system. With the company’s widespread PMU deployment, the technology is used as a system wide fault recorder. Synchrophasor data serves as the top level overview and then the substation level relay and Digital Fault Recorder (DFR) data can be used to further investigate the event. One of the challenges of having over 200 terminals is how to effectively give a good overview of the system. OG&E addressed this by assigning a priority to each terminal that dictates which data is displayed for a given map location and zoom level.
3. Detection tools for wind farm oscillation monitoring The effects of high frequency oscillations presented in the Introduction are undesirable [4], thus new efficient detection tools were developed. These monitoring algorithms allow fast oscillation detection from PMU measurements. The approach developed at SmarTS Lab is presented in Section 3.1 and the in-house tool developed at OG&E in Section 3.2.
3.1 Monitoring tool at SmarTS Lab Traditional monitoring tools for inter-area oscillations estimate the frequency and the damping for each oscillatory mode with two separate algorithms. A similar strategy is adopted in this case, with one algorithm dedicated to the estimation of the amount of energy in the oscillations and the other
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dedicated to frequency estimation. The three main algorithms used by the PMU application are described next.
3.1.1 Fast oscillation detection and frequency estimation The proposed oscillation detection algorithm in this paper builds from work in [21]. As highlighted in [22], it is desirable for a fast oscillation detection tool to provide information about oscillatory behavior at different bands of the spectrum. The frequency spectrum of interest with potential oscillatory activity starts from 0.1 Hz and the maximum frequency according to the Shanon criterion [23] considering the sampling frequency of the PMUs (up to 50 Hz reporting rate in the lab). To cover such a broad frequency span, four instances of the algorithm can be executed in parallel. Four instances are therefore included, each of them being independently configured to monitor one particular frequency range. For example, the range 5e15 Hz can be used for detection of wind farm controller interactions. After pre-processing the real-time measurements, each frequency range can be separated by four different band-pass filters set to the boundary frequencies of each range. The output of the high-pass filter provides a measure of the “oscillatory activity” at the selected frequency range. The Root Mean Square (RMS) value of this output is used for energy computation, which implies that the following computations are performed sequentially: squaring, averaging and finally computing the square-root of the signal. A low-pass moving average filter is used to extract the main trend of the squared signal. This is necessary so that a persistent and stable signal is provided to the forthcoming trigger level comparison, which indicates if the computed energy exceeds a pre-set level. The tool also implements a frequency estimation algorithm for highlighting which frequency component is active within a given frequency band. It features two parallel methods for the estimation, the non-parametric Welch’s method [24] and a parametric Auto-Regressive Method [23]. For further details on their application to power systems refer to [25].
3.1.2 Real-time tool The Monitoring Tool has been developed to detect oscillations occurring in the power system from real-time PMU measurements. The reporting rate allows to identify different categories of phenomena, which are listed in the previous Section. The Monitoring Tool therefore presents an interface with four instances, of the algorithms running in parallel, grouped under the name Module. As mentioned earlier, the Monitoring Tool has been developed with the objective of providing an intuitive visual tool. The resulting Graphical User Interface (GUI) is depicted in Figure 4. The graph on the top-right of the interface is a representation of the buffered input signals received by the tool. the signal displayed can be chosen by the user among the available input signals. This graph is used for tuning the outlier removal algorithm. The top left part of the GUI serves to configure the tool and is divided into tabs to cover the configuration of each Modules (highlighted on Figure 4.) In a Module, the graph on the right presents the power spectrum density, which is the output of the frequency estimation algorithm. The graphical display on the left presents the results of the energy detection algorithm with LED indicators and a graph. The graph provides a history to easily corroborate the energy computed with the input signal displayed.
3.1.3 Offline replay tool The original idea was to build a tool consuming real-time data from PMU/PDC streams. At this time no recorded measurements from TSOs were available, but later during the project some recorded measurements were received from OG&E, leading to the development of the Replay Tool, in order to exploit them.
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Module’s config. RT-data acquisition config.
Real-Time data display
Module 1
Module 2
Module 3
Module 4
FIGURE 4 Screen shot of the interface of the Monitoring Tool. (For color version of this figure, the reader is referred to the online version of this book.)
In the Monitoring Tool presented previously, several configuration options have been highlighted. While some options do not require any knowledge of the power system studied and can therefore be tuned directly, others have to take in consideration the properties of the power system, especially for the frequency estimation algorithm parameters. The tool thus needs calibration and archived data was used for this purpose. The Replay Tool has been developed using the same code as the real-time tool. However, additional software has been developed so that it can use archived data instead of using live PMU streams. The interface is almost identical to the Monitoring Tool. The processing algorithms are identical, however this tool has some specific features. For example it allows to scroll along the replayed data, which can be a useful feature to get a quick overview of the content of the selected file.
3.2 Oklahoma Gas & Electric OG&E, along with many other utilities in the U.S. Great Plains region, has a large wind generation resource potential. Many large scale wind farm facilities varying in size from 100 to 300 MW have been brought online, with many more under development. Currently the Southwest Power Pool’s generation interconnection queue is approaching 30 GW of wind resources, making it one of the nation’s most prominent sources of renewable energy. Determining how these vast resources will be integrated into the regional power grid proves to be a challenge and synchrophasor technology is able to provide the tools necessary to do so in a reliable manner. Each new wind farm facility brought online in OG&E’s service territory is accompanied by PMU measurements at the point of interconnection. In December 2010, the utility began observing sub-synchronous oscillations on the transmission system in a concentrated portion of the grid in northwestern Oklahoma as shown in Figure 1. These oscillations were found during periods of high wind generation, above 80% of the nameplate capacity. The voltage oscillations observed were as high as 5% fluctuation at an oscillatory frequency of
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around 14 Hz as shown in Figure 1. This level of voltage fluctuation exceeded the IEEE 141-1993 standards for objectionable flicker, and it was confirmed that the impact was observable to the area’s distribution customers. The problem has been localized to specific wind farms and the utility is undergoing efforts with the turbine manufactures to resolve the problem. This phenomenon could not be observed with traditional SCADA monitoring and without synchrophasor technology, the problem would have taken much longer to identify and resolve. The benefit of having PMU measurements at the point of wind farm interconnection is to ensure that customers receive clean power while maintaining the level of system stability necessary for reliable power system operation. After observing these instances of voltage oscillations, the company implemented a Fast Fourier Transform (FFT) based detection program to detect the oscillations and send email notifications when it requires corrective action. Figure 5 shows the application running on the server in real-time to detect these oscillations.
4. Testing and validation 4.1 SmarTS Lab For testing the monitoring tool, Hardware-In-the-Loop (HIL) simulation was used with the development of a power system model capable of recreating the event described in Section 1 and additional perturbations [26]. The power system model is equipped with two variable loads introducing random variation and sinusoidal variation that will excite low frequency dynamics in the power system. The
FIGURE 5 Fast Fourier transform real-time application developed by Oklahoma Gas & Electric. (For color version of this figure, the reader is referred to the online version of this book.)
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Table 1 Experimental Testing Protocol Start
Oscillation Injection
Perturbations
Major Fault
End
Random load variation
Set perturbation at 10.83 Hz
Generating minor faults
Three phase fault and line opening
End of the oscillation injection
model performs the simulation of wind farms interaction by injecting oscillations at 10.83 Hz, which was chosen as the equivalent of the 13 Hz oscillations in a 50 Hz system (the original case occurred in the USA, where the nominal frequency is 60 Hz). The validation of the algorithms is performed with a scenario including several perturbations according to the experiment protocol summarized in Table 1, enabling the testing of the tool’s performance in different situations. The experiment has been performed several times to ensure that the configuration of the different parameters of the processing algorithms was appropriate. From the experiment scenario presented in Table 1, only the oscillation injection is reported is this Chapter. The full testing experiment of the tool is reported in [27]. The simulation of the power system model is started with both wind farms receiving an average wind speed of 12 m/s with 10% turbulence. The loads are also configured to have a sinusoidal profile at different frequencies. The processing algorithms of the Monitoring Tool detect the slow dynamic activity resulting from both load variations and wind turbulence, see Module 1 on Figure 4, where the spectral estimator highlights both frequency components at 0.4 and 0.8 Hz. Forced oscillations were then injected at first with 0.05 p.u. amplitude at the point of common coupling of the first wind farm. They can be observed in the frequency graph of the tool, see Figure 6(a). It can be noticed that the frequency range containing 10.83 Hz is active with the flag Danger!!!, see Figure 6(b), while the other frequency ranges remain inactive or with a low activity. This shows the fast reaction of the tool, its selectivity and its ability to estimate in real-time the level of energy in these oscillations. The frequency estimation algorithm does not update as quickly as the energy detection algorithm, the frequency of the injected oscillations is thus not detected as quickly. However, it can be noticed that the parametric method starts to show distinctively a peak at the right frequency, see Figure 6(b). After the beginning of the injection with 0.05 p.u. amplitude the injection is increased up to 0.07 p.u. amplitude. The resulting oscillations have a larger amplitude, as shown on the real-time data display on Figure 4, and the energy detection algorithm identifies an increase in the energy level in the oscillations. The frequency estimation algorithm also detects very precisely the frequency at which the oscillations are occurring as shown in Module 3 on Figure 4. Additional information on functionalities and testing results are available in [27].
4.2 Validation of the OG&E FFT detection program The OG&E FFT detection program utilizes the open source library Exocortex2. To validate that the program correctly calculates the FFT, the output is compared to MathCad’s FFT algorithm which is 2
http://www.exocortex.org/dsp/.
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FIGURE 6 Partial screen shot of the monitoring tool during the oscillation injection at 10.83 Hz (a) Frequency of the system (b) Module (10e12 Hz). (For color version of this figure, the reader is referred to the online version of this book.)
taken as reference3. Figure 7(a) shows the FFT analysis from both tools on a parcel of PMU measurements when oscillatory activity was present on the grid. The same data set was queried from the database and placed into a .csv file. Using MathCad to read the .csv file, the built in FFT algorithm is then used to generate an identical plot as shown in Figure 6. Using these two different calculation algorithms, the company was able to validate the proper functionality of the FFT detection program.
5. Conclusions This chapter described how synchronized phasor data applications can be developed to help grid operators in monitoring and control of renewable energy sources when unpredictable dynamic interactions arise. These type of challenges were illustrated for the case of sub-synchronous wind farm oscillations which have been captured in OG&E’s grid. The two applications described in this chapter illustrate how PMU data can offer added value providing real-time data to advanced algorithms that go 3
www.ptc.com/product/mathcad.
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FIGURE 7 Screenshots for validation during an oscillatory event (a) Oklahoma Gas & Electric fast Fourier transform detection program (b) Offline validation with MathCad. (For color version of this figure, the reader is referred to the online version of this book.)
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beyond monitoring and allow fast-detection of dynamic interactions of renewable sources in the grid. The accuracy and value of the implemented applications was tested and validated through both realtime hardware-in-the-loop simulation and through replay of historical data. This chapter also highlights the flexibility of independent software development of two subsynchronous wind farm oscillation monitoring tools implemented under different paradigms and software environments. These different software environments illustrate how new synchrophasor applications can be conceived and implemented without relying on a monolithic and closed software development system. At this time the only control action available to operators is to enforce wind farm output curtailment. Another means for mitigation is network reinforcement, which is costly and will often take a relatively long time to complete. However, sub-synchronous oscillations arising from wind farm interactions can be mitigated through the use of controllable devices [10]. One important aspect to further investigate is how the use of synchrophasor data can help in providing rich measurements containing information on sub-synchronous oscillations into the controls of Static Var Compensators, and other controllable devices. Recent applications to damping of low-frequency oscillations suggest that PMU-based sub-synchronous oscillation compensation could be effectively applied [28]. The prediction of unwanted dynamics emerging from interactions of renewable energy sources with the grid will ultimately depend on the ability to properly represent these dynamics instudy models [29]. As grid conditions change continuously, the representation of wind farmclusters into computer simulation programs for prediction will have to be updated. This task canbe time consuming, and one important use of phasor data could be the accurate estimation ofaggregated wind farm models from near-real time measurements. The representation of theseaggregated models could be updated using phasor data and supplied to dynamic securityassessment tools which can help pin-point undesirable dynamics during stressed network operation. Indeed, the flexibility offered by non-conventional software development systems for PMU applications offer unlimited opportunities to conceive new software tools. A clear example is the development of mobile monitoring applications presented in [20]. This example brings real-time synchrophasors outside the control room and into the hands of analysts and persons with different roles in a utility. Liberating real-time data from the control room may offer the opportunity to ask questions and develop understanding which could be used for developing new software applications that could facilitate the integration of renewable energy into the grid.
Acknowledgments The economical support of the institutions and funding bodies listed below is sincerely acknowledged: L. Vanfretti was supported by Statnett SF, the Norwegian Transmission System Operator, the STandUP for Energy collaboration initiative and Nordic Energy Research through the STRONg2rid project. M. Baudette was supported by EIT KIC InnoEnergy through Action 2.6 of the Smart Power project and by Statnett SF, the Norwegian Transmission System Operator.
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[25] Sanchez-Gasca J. Identification of electromechanical modes in power systems. Special Publication, TP462. IEEE Power & Energy Society; July 2012. [26] Baudette M. Fast real-time detection of sub-synchronous oscillations in power systems using synchrophasors [Master’s thesis]. KTH, Electric Power Systems; 2013. [27] Vanfretti L, Baudette M, Al-Khatib I, Almas MS, Gjerde JO. Testing and validation of a fast real-time oscillation detection PMU-based application for wind-farm monitoring. In: 2013 first international Black Sea conference on communications and networking (BlackSeaCom), Batumi, Georgia; July 2013. [28] Uhlen K, Vanfretti L, De Oliveira MM, Leirbukt A, Aarstrand VH, Gjerde JO. Wide-area power oscillation damper implementation and testing in the Norwegian transmission network. In: IEEE power and energy society general meeting; 2012. p. 1e7. [29] Zavadil R, Miller N, Ellis A, Muljadi E, Pourbeik P, Saylors S, et al. Models for change. IEEE Power Energy Mag 2011;9(6):86e96.
Monitoring and Control of Renewable Energy Sources using Synchronized Phasor Measurements
31
Luigi Vanfretti1, 2, Maxime Baudette1, Austin D. White2 1
Electric Power Systems Department, School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden; 2Oklahoma Gas & Electric Co. Oklahoma, USA
1. Introduction Recent environmental concerns with fossil fuel production have stemmed a global trend of increased share of renewable energy production. In several western countries, the full potential of hydro-power has already been exhausted, which has resulted in wind energy being the fastest growing energy technology in the last two decades [1]. Moreover the expansion of the share of electricity production from wind power and solar will continue, especially in Europe [2] and in the USA where 30 states have enforceable renewable portofolio standards or other mandated renewable capacity policies [3]. Unpredictable challenges for power system operation and control continue to emerge as renewable energy sources are brought into commercial operation. With the increased amount of intermittent power sources, one of the observed impacts is an adverse effect on generation power output within a balancing authority’s control area. Wind power integration has recently presented challenges in short term operation with regards to system dynamics. Particularly, in the case of Oklahoma Gas & Electric (OG&E), wind power curtailment has been enforced to maintain power supply quality and continuity when the interaction of wind farms with the power grid brings undesirable dynamics into play [4]. In this case, these undesirable dynamics came in the form of subsynchronous (i.e. fast) oscillations which were first detected due to the impact of the power quality supply at the consumer level. A very recent article in the New York Times [5] shows that these unpredictable challenges are likely to continue to emerge when new plants are brought into operation due to the lack of operation experience and adequate tools for monitoring and control. Photo-voltaic (PV) generation in the German Electric Grid [6] has increased grid stability issues (e.g. “The 50.2 Hz Risk”) which may endanger the operation of the entire interconnected grid. It becomes apparent that new means for monitoring and control technologies are needed to cope with the new challenges that the integration of renewable energies bring to the power grid. Real-time monitoring and control tools could help mitigate these undesired phenomena, by providing software applications to operate the Renewable Energy Integration. http://dx.doi.org/10.1016/B978-0-12-809592-8.00031-7 Copyright © 2017 Lawrence E. Jones. Published by Elsevier Inc. All rights reserved.
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system with more flexibility, better adapting to phenomena emerging from intermittent generation sources [7]. The effects of increasing amounts of wind power in a power system has been studied, and several problems regarding transient stability have been investigated [8]. However, it is only very recently that some Transmission System Operators (TSOs) have noticed the presence of forced sub-synchronous resonance in the grid due to large scale wind power plants. These oscillations have been observed thanks to the recent adoption of Phasor Measurement Unit (PMU) technology and typically appear in the 3e15 Hz frequency range [4,9e13]. The oscillations can occur during high wind penetration periods, but are typically aggravated by a change in system impedance due to a system fault or transmission line switching. The impact can be substantial and even observable at the consumer level as voltage flicker. The reasons for these forced oscillations are turbineeconverter interactions [14] and they differ from the more commonly observed inter-area oscillations below 1 Hz. As the consequences can be observed at all voltage levels and because these wind farm oscillations have not yet been specifically characterized, it could be supposed that such oscillations could damage equipment on the transmission system or at the customers end. It could also be supposed that the phenomenon could be amplified by the increasing use of wind power. Thus a better ability to monitor the behaviors of wind farms connected to the bulk grid appears necessary. PMU technology allows for means of detection of resonance conditions and it has been found that curtailing the wind power plant output disrupts the oscillatory condition enough to reduce the impact to customers. Such an example is shown in Figure 1, where a forced oscillation is brought under control by curtailing the wind power plant output by 50%. This chapter gives an overview of how synchrophasor technology can be applied for developing real-time PMU applications, which help in monitoring and control of unwanted dynamics that are a product of renewable energy sources interacting with the power grid. The remainder of this chapter is organized as follows. Section 2 gives an overview of PMU-based monitoring and control systems and describes different environments for developing PMU applications. Section 3 illustrates the development of real-time monitoring tools for the detection of sub-synchronous wind farm oscillations, as an example of how synchrophasor technology can be effectively used for dynamic monitoring of renewable energy sources. Section 4 presents testing and validation experiments performed using historical data and laboratory experiments. Finally, Section 5 gives an outline for the development of new PMU applications that can aid in the monitoring and control of renewable energy sources and their interaction with the grid.
2. Real-time monitoring using synchrophasors 2.1 Wide area monitoring systems The ability to monitor fast dynamic behaviors such as the sub-synchronous oscillation phenomena is tied to the performance of the measurement equipment used. The sampling rate has a strong impact on the highest frequency observable from the measurements. Traditional equipment and monitoring tools have an asynchronous sampling frequency of typically one sample every few seconds. While this was sufficient to monitor very slow steady state phenomena, it fails to capture faster dynamic behaviors, see Figure 1.
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FIGURE 1 Phasor measurement unit (PMU) and traditional equipment measurements Supervisory Control and Data Acquisition (SCADA) of a wind power plant during a fast dynamic event (a) Curtailment of the power output (PMU) (b) Comparison between Phasor measurement unit and SCADA. (For color version of this figure, the reader is referred to the online version of this book.)
PMUs are devices able to measure the voltage and current phasors of the three phase network with a reporting frequency of typically 30e60 samples per seconds. The frequency of the system is internally computed at a higher sampling rate and reported also at 30e60 samples per second.1 This allows the study of dynamic phenomena occurring up to a frequency of half the reporting rate. The reference for the angle of the phasors is derived from a GPS clock and all the measurements are coupled to a time stamp allowing for the alignment and synchronization of PMUs spanning an entire interconnection. 1
Instantaneous active and reactive power can be computed from the voltage and current phasors.
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Phasor Data Concentrators (PDCs) have the role of collecting and forwarding the data of several PMUs. Since all the measurements have a time stamp acquired from the GPS reference, the PDC can align the measurements in time. PDCs can be configured for several usages, the main being the ability to output a stream of time-aligned measurements from several PMUs. It can also be configured to store and archive data or perform calculations on the measurements. The usage of PMUs to collect data on wide-area power systems is rather recent. It has enabled system operators to monitor their transmission system with more information as well as to conduct advanced analyses [7], particularly for inter-area modes. This technology offers a great potential for building monitoring systems [15].
2.2 Fast prototyping environment for PMU software applications The traditional implementation of Wide Area Monitoring Systems (WAMS) relies on a monolithic software architecture. That forces the implementation of applications in a closed software solution (often proprietary) or directly into the PDC. In contrast, the infrastructure deployed at SmarTS Lab uses a modular approach based on application connecting to the PDC to receive measurements. A deeper comparison of both approaches can be found in [16]. This section described the approaches adopted at SmarTS Lab and OG&E for developing synchrophasor applications.
2.2.1 Statnett’s synchrophasor SDK To implement PMU applications using a modular paradigm, a Software Development Kit (SDK) developed by Statnett SF (the Norwegian Transmission System Operator) [17] can be utilized. The aim of the SDK is to facilitate research, fast prototyping and testing of real-time synchrophasor applications. The chosen development environment is LabView, in which creating graphical user interfaces is simple. However, there is no standard programing interface in LabView to communicate with PMUs or PDC servers using the IEEE C37.118.2 protocol [18] and bring PMU measurements in the programing environment. Statnett’s Synchrophasor SDK [17] provides a real-time data mediator, enabling this feature for LabView, as well as a library of functions that allow fast prototyping.
2.2.2 SmarTS Lab environment The traditional set-up for WAMS is replicated at SmarTS Lab. The adopted approach makes use of Hardware-In-the-Loop (HIL) simulation, replacing the power system by models executed in real-time on a simulator equipped with reconfigurable analog inputs/outputs [19]. The rest of the WAMS architecture is replicated, with PMUs and a PDC server, building a prototyping platform depicted in Figure 2. The lab is also equipped with a PDC server dedicated to the gathering of PMU measurements from Nordic universities, as a part of the STRONg2rid project [20]. This platform, depicted in Figure 2, allows for the testing of applications using real-time data from the Nordic grid.
2.2.3 Oklahoma Gas and Electric Oklahoma Gas and Electric Company (OG&E) began using synchrophasor technology in 2008 with a single hardware PDC and eight multifunction PMUs (line protection relays.) The live synchrophasor data was streamed through the PDC to a PC software client for visualization. It was quickly realized
2. Real-time monitoring using synchrophasors
(a) OPAL -RT
Testing workstation
Voltage
Current
cRIO 9074 PMU GPS antenna
SEL-5073 PDC Ethernet
(b) Testing workstation
Lund
Tampere
NTNU
PMU PMU IEEE C37.118.2
Luleå PMU
WAN Internet
Chalmers PMU
SEL-5073 PDC at KTH
KTH PMU
FIGURE 2 Diagram of the set-up at SmarTS Lab for wide area monitoring systems development and testing (a) Hardware-in-the-loop simulation (b) Nordic phasor measurement units (PMUs). (For color version of this figure, the reader is referred to the online version of this book.)
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that a need existed to further examine data from interesting events which had occurred in the past. Unfortunately, the software purchased at the time did not allow for viewing of historical data. So the utility developed a system to archive the data to a database along with a custom software application named PhasorView that could display the synchrophasor data, both live and archived. With the initial fleet of only eight PMU’s on the 345 and 500 kV Extra High Voltage (EHV) system, OG&E made observations at a rate of 30 samples per second and established a baseline for what would be considered normal operating conditions. They also joined the North American Synchrophasor Initiative (NASPI) and began streaming data to the host site at the Tennessee Valley Authority (TVA). This allowed for the utility to contribute their portion of the grid to the system wide view of the U.S. Eastern Interconnection. For the first time, the utility was able to observe how events on the OG&E system affected the interconnection and vice versa. The utility has expanded the PMU coverage of the transmission system by simply adding communications to existing substations with PMU capable devices already installed. High bandwidth communications were added to all EHV and other critical substations as part of a security initiative in 2005. PMUs have gradually been networked over the years, which now cover about 40% of the transmission system. This includes 100% of the EHV system, 100% of wind farms, 90% of the fossil generation fleet, and 34% of the HV system. In total there are now over 200 transmission lines, autotransformers and generators monitored by PMUs. OG&E has been using synchrophasors primarily for situational awareness, disturbance analysis, and wind power plant integration/monitoring. The utility has a tool for the Supervisory Control and Data Acquisition / Energy Management System (SCADA/EMS) software to bring synchrophasor data into the state estimator, which is currently being tested. The technology is used to assess the stability of the system and proactively find equipment problems. It is also used to monitor how the system responds to faults and assess the voltage recovery from these disturbances. Synchrophasors have also proved very useful for integrating renewable energy into the grid and monitor power quality. Application development at OG&E has been an iterative approach spanning several years. The company has maximized the use of open source software (OSS) tools to achieve desired goals. With the release of the Grid Protection Alliance’s OpenPDC in October 2009, OG&E began the process to restructure the synchrophasor system to take advantage of the flexibility offered by OpenPDC. One of the major concerns regarding the initial configuration was the ability of the hardware PDC to handle hundreds of PMU devices available on the OG&E system. The single hardware data concentrator could handle approximately 40 PMUs. In order to connect more devices, it was necessary to scale out the system architecture. This means that all data could not be centrally concentrated. OpenPDC allowed for the flexibility to achieve the goal of handling hundreds of PMUs. The company developed a custom VB.net Action Adapter inside OpenPDC to do all of the processing and inserting of data into the SQL database. The company’s first problem back in 2008 was visualization of the data, which led to the development of the PhasorView software. PhasorView is a application that plots data queried from the historian. Figure 3 shows a screenshot of the PhasorView application. The interactive Geographic Information System (GIS) interface on the left represents an accurate view of the transmission system and substations on which radar and lightning data can also be displayed. The plots on the right show the synchrophasor data for different power system quantities, from the selected PMUs. The bottom right polar plot shows the system voltage angle spread. Above the GIS interface is a legend for the selected PMUs. The software in live mode serves as a situational awareness tool, using GIS to display the lines and substations within the service territory along with plots of the synchrophasor data. Real time weather
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FIGURE 3 Screenshot of PhasorView at Oklahoma Gas & Electric. (For color version of this figure, the reader is referred to the online version of this book.)
radar and lightning strike data is displayed to anticipate where disturbances may occur on the system. With the company’s widespread PMU deployment, the technology is used as a system wide fault recorder. Synchrophasor data serves as the top level overview and then the substation level relay and Digital Fault Recorder (DFR) data can be used to further investigate the event. One of the challenges of having over 200 terminals is how to effectively give a good overview of the system. OG&E addressed this by assigning a priority to each terminal that dictates which data is displayed for a given map location and zoom level.
3. Detection tools for wind farm oscillation monitoring The effects of high frequency oscillations presented in the Introduction are undesirable [4], thus new efficient detection tools were developed. These monitoring algorithms allow fast oscillation detection from PMU measurements. The approach developed at SmarTS Lab is presented in Section 3.1 and the in-house tool developed at OG&E in Section 3.2.
3.1 Monitoring tool at SmarTS Lab Traditional monitoring tools for inter-area oscillations estimate the frequency and the damping for each oscillatory mode with two separate algorithms. A similar strategy is adopted in this case, with one algorithm dedicated to the estimation of the amount of energy in the oscillations and the other
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dedicated to frequency estimation. The three main algorithms used by the PMU application are described next.
3.1.1 Fast oscillation detection and frequency estimation The proposed oscillation detection algorithm in this paper builds from work in [21]. As highlighted in [22], it is desirable for a fast oscillation detection tool to provide information about oscillatory behavior at different bands of the spectrum. The frequency spectrum of interest with potential oscillatory activity starts from 0.1 Hz and the maximum frequency according to the Shanon criterion [23] considering the sampling frequency of the PMUs (up to 50 Hz reporting rate in the lab). To cover such a broad frequency span, four instances of the algorithm can be executed in parallel. Four instances are therefore included, each of them being independently configured to monitor one particular frequency range. For example, the range 5e15 Hz can be used for detection of wind farm controller interactions. After pre-processing the real-time measurements, each frequency range can be separated by four different band-pass filters set to the boundary frequencies of each range. The output of the high-pass filter provides a measure of the “oscillatory activity” at the selected frequency range. The Root Mean Square (RMS) value of this output is used for energy computation, which implies that the following computations are performed sequentially: squaring, averaging and finally computing the square-root of the signal. A low-pass moving average filter is used to extract the main trend of the squared signal. This is necessary so that a persistent and stable signal is provided to the forthcoming trigger level comparison, which indicates if the computed energy exceeds a pre-set level. The tool also implements a frequency estimation algorithm for highlighting which frequency component is active within a given frequency band. It features two parallel methods for the estimation, the non-parametric Welch’s method [24] and a parametric Auto-Regressive Method [23]. For further details on their application to power systems refer to [25].
3.1.2 Real-time tool The Monitoring Tool has been developed to detect oscillations occurring in the power system from real-time PMU measurements. The reporting rate allows to identify different categories of phenomena, which are listed in the previous Section. The Monitoring Tool therefore presents an interface with four instances, of the algorithms running in parallel, grouped under the name Module. As mentioned earlier, the Monitoring Tool has been developed with the objective of providing an intuitive visual tool. The resulting Graphical User Interface (GUI) is depicted in Figure 4. The graph on the top-right of the interface is a representation of the buffered input signals received by the tool. the signal displayed can be chosen by the user among the available input signals. This graph is used for tuning the outlier removal algorithm. The top left part of the GUI serves to configure the tool and is divided into tabs to cover the configuration of each Modules (highlighted on Figure 4.) In a Module, the graph on the right presents the power spectrum density, which is the output of the frequency estimation algorithm. The graphical display on the left presents the results of the energy detection algorithm with LED indicators and a graph. The graph provides a history to easily corroborate the energy computed with the input signal displayed.
3.1.3 Offline replay tool The original idea was to build a tool consuming real-time data from PMU/PDC streams. At this time no recorded measurements from TSOs were available, but later during the project some recorded measurements were received from OG&E, leading to the development of the Replay Tool, in order to exploit them.
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Module’s config. RT-data acquisition config.
Real-Time data display
Module 1
Module 2
Module 3
Module 4
FIGURE 4 Screen shot of the interface of the Monitoring Tool. (For color version of this figure, the reader is referred to the online version of this book.)
In the Monitoring Tool presented previously, several configuration options have been highlighted. While some options do not require any knowledge of the power system studied and can therefore be tuned directly, others have to take in consideration the properties of the power system, especially for the frequency estimation algorithm parameters. The tool thus needs calibration and archived data was used for this purpose. The Replay Tool has been developed using the same code as the real-time tool. However, additional software has been developed so that it can use archived data instead of using live PMU streams. The interface is almost identical to the Monitoring Tool. The processing algorithms are identical, however this tool has some specific features. For example it allows to scroll along the replayed data, which can be a useful feature to get a quick overview of the content of the selected file.
3.2 Oklahoma Gas & Electric OG&E, along with many other utilities in the U.S. Great Plains region, has a large wind generation resource potential. Many large scale wind farm facilities varying in size from 100 to 300 MW have been brought online, with many more under development. Currently the Southwest Power Pool’s generation interconnection queue is approaching 30 GW of wind resources, making it one of the nation’s most prominent sources of renewable energy. Determining how these vast resources will be integrated into the regional power grid proves to be a challenge and synchrophasor technology is able to provide the tools necessary to do so in a reliable manner. Each new wind farm facility brought online in OG&E’s service territory is accompanied by PMU measurements at the point of interconnection. In December 2010, the utility began observing sub-synchronous oscillations on the transmission system in a concentrated portion of the grid in northwestern Oklahoma as shown in Figure 1. These oscillations were found during periods of high wind generation, above 80% of the nameplate capacity. The voltage oscillations observed were as high as 5% fluctuation at an oscillatory frequency of
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around 14 Hz as shown in Figure 1. This level of voltage fluctuation exceeded the IEEE 141-1993 standards for objectionable flicker, and it was confirmed that the impact was observable to the area’s distribution customers. The problem has been localized to specific wind farms and the utility is undergoing efforts with the turbine manufactures to resolve the problem. This phenomenon could not be observed with traditional SCADA monitoring and without synchrophasor technology, the problem would have taken much longer to identify and resolve. The benefit of having PMU measurements at the point of wind farm interconnection is to ensure that customers receive clean power while maintaining the level of system stability necessary for reliable power system operation. After observing these instances of voltage oscillations, the company implemented a Fast Fourier Transform (FFT) based detection program to detect the oscillations and send email notifications when it requires corrective action. Figure 5 shows the application running on the server in real-time to detect these oscillations.
4. Testing and validation 4.1 SmarTS Lab For testing the monitoring tool, Hardware-In-the-Loop (HIL) simulation was used with the development of a power system model capable of recreating the event described in Section 1 and additional perturbations [26]. The power system model is equipped with two variable loads introducing random variation and sinusoidal variation that will excite low frequency dynamics in the power system. The
FIGURE 5 Fast Fourier transform real-time application developed by Oklahoma Gas & Electric. (For color version of this figure, the reader is referred to the online version of this book.)
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Table 1 Experimental Testing Protocol Start
Oscillation Injection
Perturbations
Major Fault
End
Random load variation
Set perturbation at 10.83 Hz
Generating minor faults
Three phase fault and line opening
End of the oscillation injection
model performs the simulation of wind farms interaction by injecting oscillations at 10.83 Hz, which was chosen as the equivalent of the 13 Hz oscillations in a 50 Hz system (the original case occurred in the USA, where the nominal frequency is 60 Hz). The validation of the algorithms is performed with a scenario including several perturbations according to the experiment protocol summarized in Table 1, enabling the testing of the tool’s performance in different situations. The experiment has been performed several times to ensure that the configuration of the different parameters of the processing algorithms was appropriate. From the experiment scenario presented in Table 1, only the oscillation injection is reported is this Chapter. The full testing experiment of the tool is reported in [27]. The simulation of the power system model is started with both wind farms receiving an average wind speed of 12 m/s with 10% turbulence. The loads are also configured to have a sinusoidal profile at different frequencies. The processing algorithms of the Monitoring Tool detect the slow dynamic activity resulting from both load variations and wind turbulence, see Module 1 on Figure 4, where the spectral estimator highlights both frequency components at 0.4 and 0.8 Hz. Forced oscillations were then injected at first with 0.05 p.u. amplitude at the point of common coupling of the first wind farm. They can be observed in the frequency graph of the tool, see Figure 6(a). It can be noticed that the frequency range containing 10.83 Hz is active with the flag Danger!!!, see Figure 6(b), while the other frequency ranges remain inactive or with a low activity. This shows the fast reaction of the tool, its selectivity and its ability to estimate in real-time the level of energy in these oscillations. The frequency estimation algorithm does not update as quickly as the energy detection algorithm, the frequency of the injected oscillations is thus not detected as quickly. However, it can be noticed that the parametric method starts to show distinctively a peak at the right frequency, see Figure 6(b). After the beginning of the injection with 0.05 p.u. amplitude the injection is increased up to 0.07 p.u. amplitude. The resulting oscillations have a larger amplitude, as shown on the real-time data display on Figure 4, and the energy detection algorithm identifies an increase in the energy level in the oscillations. The frequency estimation algorithm also detects very precisely the frequency at which the oscillations are occurring as shown in Module 3 on Figure 4. Additional information on functionalities and testing results are available in [27].
4.2 Validation of the OG&E FFT detection program The OG&E FFT detection program utilizes the open source library Exocortex2. To validate that the program correctly calculates the FFT, the output is compared to MathCad’s FFT algorithm which is 2
http://www.exocortex.org/dsp/.
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FIGURE 6 Partial screen shot of the monitoring tool during the oscillation injection at 10.83 Hz (a) Frequency of the system (b) Module (10e12 Hz). (For color version of this figure, the reader is referred to the online version of this book.)
taken as reference3. Figure 7(a) shows the FFT analysis from both tools on a parcel of PMU measurements when oscillatory activity was present on the grid. The same data set was queried from the database and placed into a .csv file. Using MathCad to read the .csv file, the built in FFT algorithm is then used to generate an identical plot as shown in Figure 6. Using these two different calculation algorithms, the company was able to validate the proper functionality of the FFT detection program.
5. Conclusions This chapter described how synchronized phasor data applications can be developed to help grid operators in monitoring and control of renewable energy sources when unpredictable dynamic interactions arise. These type of challenges were illustrated for the case of sub-synchronous wind farm oscillations which have been captured in OG&E’s grid. The two applications described in this chapter illustrate how PMU data can offer added value providing real-time data to advanced algorithms that go 3
www.ptc.com/product/mathcad.
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FIGURE 7 Screenshots for validation during an oscillatory event (a) Oklahoma Gas & Electric fast Fourier transform detection program (b) Offline validation with MathCad. (For color version of this figure, the reader is referred to the online version of this book.)
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beyond monitoring and allow fast-detection of dynamic interactions of renewable sources in the grid. The accuracy and value of the implemented applications was tested and validated through both realtime hardware-in-the-loop simulation and through replay of historical data. This chapter also highlights the flexibility of independent software development of two subsynchronous wind farm oscillation monitoring tools implemented under different paradigms and software environments. These different software environments illustrate how new synchrophasor applications can be conceived and implemented without relying on a monolithic and closed software development system. At this time the only control action available to operators is to enforce wind farm output curtailment. Another means for mitigation is network reinforcement, which is costly and will often take a relatively long time to complete. However, sub-synchronous oscillations arising from wind farm interactions can be mitigated through the use of controllable devices [10]. One important aspect to further investigate is how the use of synchrophasor data can help in providing rich measurements containing information on sub-synchronous oscillations into the controls of Static Var Compensators, and other controllable devices. Recent applications to damping of low-frequency oscillations suggest that PMU-based sub-synchronous oscillation compensation could be effectively applied [28]. The prediction of unwanted dynamics emerging from interactions of renewable energy sources with the grid will ultimately depend on the ability to properly represent these dynamics instudy models [29]. As grid conditions change continuously, the representation of wind farmclusters into computer simulation programs for prediction will have to be updated. This task canbe time consuming, and one important use of phasor data could be the accurate estimation ofaggregated wind farm models from near-real time measurements. The representation of theseaggregated models could be updated using phasor data and supplied to dynamic securityassessment tools which can help pin-point undesirable dynamics during stressed network operation. Indeed, the flexibility offered by non-conventional software development systems for PMU applications offer unlimited opportunities to conceive new software tools. A clear example is the development of mobile monitoring applications presented in [20]. This example brings real-time synchrophasors outside the control room and into the hands of analysts and persons with different roles in a utility. Liberating real-time data from the control room may offer the opportunity to ask questions and develop understanding which could be used for developing new software applications that could facilitate the integration of renewable energy into the grid.
Acknowledgments The economical support of the institutions and funding bodies listed below is sincerely acknowledged: L. Vanfretti was supported by Statnett SF, the Norwegian Transmission System Operator, the STandUP for Energy collaboration initiative and Nordic Energy Research through the STRONg2rid project. M. Baudette was supported by EIT KIC InnoEnergy through Action 2.6 of the Smart Power project and by Statnett SF, the Norwegian Transmission System Operator.
References [1] Manwell JF, McGowan JG, Rogers AL. Wind energy explained: theory, design and application. John Wiley & Sons; 2010. [2] Europe Milborrow D. 2020 Wind energy targets, EU wind power now and the 2020 vision. Windpower Monthly Special Report. March 2011.
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