Sub Synchronous Oscillations under High Penetration of Renewables—A Review of Existing Monitoring and Damping Methods, Challenges, and Research Prospects
Abstract
:1. Introduction
2. Sub Synchronous Oscillation Phenomenon
2.1. Sub Synchronous Resonance (SSR)
2.1.1. Self Excitation
2.1.2. Torque Amplification (TA)
2.2. Device Dependent Sub Synchronous Oscillation (DDSSO)
2.3. Sub Synchronous Control Interaction (SSCI)
2.4. Sub Synchronous Torsional Interaction (SSTI)
3. SSO in Renewable Energy Systems
3.1. SSO in Wind Power Plants
3.2. SSO in Solar PV
3.3. SSO in Hydro Power Plant
3.4. SSO in Other Renewable Energy Sources
4. Existing SSO Monitoring Techniques
4.1. SSO Parameter EstimationBased Techniques
4.2. Techniques Based on Sub Synchronous State Estimation (SSE)
4.3. Artificial Intelligence (AI)Based Techniques
5. Challenges Associated with Existing SSO Monitoring Techniques
 Most of the SSO monitoring methods discussed in Section 4 above use measurements obtained by PMUs. Phasor measurement units provide information regarding current and voltage phasors, frequency, and rate of change of frequency using phasor estimation algorithms. Furthermore, PMUs scattered at different locations can transmit data to the phasor data concentrator (PDC) in a span of approximately 1 µs, enabling efficient and reliable realtime monitoring of power system dynamics [65,66]. However, the following key issues associated with synchro phasor technology poses limitations on SSO detection using PMU measurements:
 ◦
 Available PMU technology is designed only to extract the fundamental component of the signals. The increased use of renewable energy sources and addition of power electronic devices to the grid have introduced a large number of inter harmonics that are noninteger multiples of the fundamental signal, thus changing the measurement of the fundamental component.
 ◦
 Furthermore, one sub synchronous oscillation frequency component could be caused by two harmonics, resulting in aliasing [67]. Therefore, appropriate techniques must be employed to address the above issues and enhance the performance of PMUs before using PMU measurements for SSO detection.
 The maximum available reporting rates of PMUs installed at present are limited to 100 Hz/120 Hz. If the SSO frequency is above half of the reporting frequency, the corresponding frequency cannot be restored according to the Nyquist Shannon sampling theorem [68]; hence, the SSO cannot be monitored accurately. In case the reporting rate of the installed PMUs is 50 Hz/60 Hz, the frequencies above 25 Hz and 30 Hz, respectively, cannot be observed.
 Although PMU measurements are commonly used for the available SSO monitoring techniques, certain algorithms, such as the sub/super synchronous algorithm proposed in [57], require the use of instantaneous signals for SSO identification as PMU data are insufficient. In such cases, data from fault recorders must be used. However, acquiring data from fault recorders will be difficult and time consuming as they are placed in local stations individually. Furthermore, direct comparisons cannot be made among the nontime synchronized data obtained from fault recorders at different locations [54].
 The computational burden of noise filtering processes of standard SSO identification algorithms can be stated as another challenge associated with existing techniques [52]. Hence, advancements must be made to eliminate the noise component from signals used for SSO identification.
6. Existing SSO Mitigation Methods
6.1. FACTS Devices
6.2. Converter Control of Distributed Energy Resources (DER)
6.2.1. Wind FarmBased Converter Controls
6.2.2. Solar PVBased Converter Controls
6.2.3. Battery Energy Storage SystemBased Converter Controls
6.3. Other SSO Damping Methods
 A.
 Special purpose shunt voltage source converter (VSC)
 B.
 Proper control of series capacitor
 C.
 Sub synchronous frequency relay
 D.
 Power system design improvements
7. Challenges of the Existing Mitigation Methods
 Although ample research has been done on the use of FACTS devices for the mitigation of SSR and are proven to be effective, it is not economical to use FACTS devices just for the purpose of SSR damping due to the high cost involved in installation and maintenance. Therefore, research is required to find more costeffective solutions for the mitigation of SSO.
 The augmentation of converter controls is a cheaper alternative for SSO damping. However, most of the proposed SSO damping control strategies are based on the modification of GSC and RSC of type 3 (DFIGbased) WTGs. Limited research has been conducted so far based on the modification of type 4 WTGs and PV plants for SSO damping. Additionally, some of the proposed methods fail to damp SSO under practical scenarios. For instance, the damping controller in [17] cannot damp out the SSO successfully when the time delay associated with the WAMS signal used as the input signal to the damping controller is taken into consideration.
 Experimental validation of the proposed SSO damping methods has been conducted based on the model aggregation technique in simulation studies. However, a practical system could be comprised of different types of WTGs and controllers which cannot be considered during the simulations. Hence, the performance of the damping controllers may not be as satisfactory under actual conditions.
 Although special purpose shunt VSC is an economically feasible solution for the mitigation of SSO, the challenge in tracking the fundamental and sub/super synchronous components which keep varying during the oscillation incident pose limitations on the use of special purpose shunt VSC for SSO damping [89].
8. Conclusions and Future Work
 As PMUs are the widely used technology at present for dynamic monitoring of the power grid, measures should be taken to improve the accuracy of sub synchronous components reported by them. If not, the algorithms developed to identify SSO components will be meaningless. Hence, further research must be conducted to develop PMUs that can report sub synchronous frequencies accurately.
 Studies on the impact of noise on SSO identification and techniques to eliminate noise from the measurement signals is another area of research requiring attention.
 Use of Artificial Intelligence for SSO identification is an emerging area of research. As the SSO does not occur all the time, running algorithms to calculate SSO parameters of all PMU data will be unnecessary. Research is required to develop both deep learning (DL) and machine learning (ML) techniques for SSO identification.
 Further studies are required to develop cost effective SSO damping techniques based on the modifications of PV plant converter controls.
 A method to trace fundamental and sub/super synchronous components simultaneously is required such that effective SSO damping could be achieved.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Device Type  SSRIGE  SSRTI  SSTI  SSCI 

Synchronous generator  ✓  ✓  ✓  🗶 
WTG type 1  ✓  ✓  🗶  🗶 
WTG type 2  ✓  🗶  🗶  🗶 
WTG type 3  ✓  ✓  🗶  ✓ 
WTG type 4  🗶  🗶  🗶  ✓ 
HVDC  🗶  🗶  ✓  🗶 
SVC  🗶  🗶  ✓  ✓ 
Type of WTG  Risk/Possible Causes of SSO  Refs. 

Type 1 
 [26,27,28] 
Type 2 
 [26,29,30] 
Type 3(DFIG) 
 [14,23,25,26,31,32] 
Type 4(PMSG) 
 [26,33,34,35,36,37,38,39] 
Technique  Estimated Parameters  Features  Limitations  Refs. 

Fourpoint interpolated DFT for rectangular windowed phasor and DFT spectral analysis of Hann windowed phasor  Frequency, amplitude, and damping factor of SSO  Can be used even if the system is offnominal, dynamic, or noisy.  Application of the method is questionable when the offnominal condition is severe and the SSO frequency is high.  [53] 
DFT spectral analysis and correction of spectral analysis by construction of a waveform proportional to the spectrum of amplitudes of the measured synchro phasor  Frequency and amplitude of fundamental and SSO  Can accurately obtain fundamental and SSO parameters using synchro phasor measurements. 
 [54] 
DFT spectral analysis  SSO frequency  Easier to implement.  Does not consider the effect of power electronicbased devices on the level of sub synchronous variations.  [55] 
Hann windowed threepoint interpolated DFT  Frequency, amplitude, damping, and phase of SSO 
 If synchro phasor calculation methods other than DFT are used by PMU suppliers, the windowed synchro phasor model under SSO is different.  [56] 
Sub and super synchronous harmonic detection based on DFT  Frequency, amplitude, and phase of sub and super synchronous oscillations 
 Requires data of instantaneous signals, PMU data are insufficient.  [57] 
Multisynchro squeezing transform (MSST) and least squares estimation  Frequency, amplitude, damping, and phase of SSO 
 Possible data loss in PMU measurements must be addressed separately.  [58] 
Feature Extraction  Label Determination  Type of Classifier  Additional Features  Limitations  Refs. 

 Using FFT spectral analysis  MultiSVM classifier 
 Inability of existing PMUs to capture SSO accurately is not rectified before using PMU data to train the model.  [52] 
Amplitude percentage  Based on parameters K, M, and N determined according to the amplitude percentage distribution  SVM classifier  Adaptive duration threshold for alarming of SSO. 
 [62] 
Amplitude of fluctuation  Parameters K, M based on amplitude and saliency respectively  DT classifier  Has analyzed the adjustment of labeling criteria to suit the actual conditions. 
 [63] 
Device  Technique/Features  Advantages  Refs. 

Static var compensator (SVC)  SVC augmented with a damping controller installed at the induction generator terminal and uses generator speed deviation as the input signal.  Damping of selfexcited SSO and torsional oscillations can be significantly improved.  [69] 
SVC with a damping controller is installed at the point of common coupling (PCC) and line current is used as the feedback signal.  Communication delay associated with remote signals can be avoided as it uses a local feedback signal.  [70]  
SVCbased damping controller where the controller gain is adaptive to the level of wind generation.  Suitable for damping of SSO for a range of wind generation levels and effective as the wind generation levels varies under practical conditions.  [71]  
Adaptive NeuroFuzzy Interference Systems (ANFIS) controller is used as an auxiliary controller for SVC.  Can damp SSO under nonlinear system behavior of the power system.  [72,73]  
Thyristor controlled series capacitor (TCSC)  Constant current controller used for closed loop current control of a TCSC.  SSO due to IGE and torsional interaction can be damped effectively even during a severe fault.  [74] 
TCSC with auxiliary fuzzy logic damping controller (FLDC).  Pitch angle of the wind turbine can be controlled to an optimal level during times of high wind speed using the FLDC controls.  [75]  
Adaptive neurocontroller is designed to generate a control signal to adjust the firing angle of the TCSC such that the SSO can be damped out.  Since this is an AIbased technique, accurate system modeling is not required. Therefore, this method is well suited for applications involving nonlinear systems.  [76]  
Gate controlled series capacitor (GCSC)  SSR damping controller is designed for the GCSC using the root locus method. The input signal to the damping controller is the line current, which is a local signal.  Successful damping of SSR mode when line current is used as the input signal. However, it should be noted that the stability of the super synchronous mode is reduced.  [77] 
Residuebased analysis is conducted to select a suitable input signal such that the stability of both SSR and super synchronous modes can be increased.  A comparison among the three input signals rotor speed, line current, and voltage across series capacitor indicate that the optimum signal to be used as the input for the damping controller is the voltage across the series capacitor.  [78,79]  
Impedance controlled GCSC design for fixed speed wind turbine generator systems. It uses a proportionalintegral (PI) regulator to control the impedance of the GCSC, which in turn enables power flow control of the transmission line.  Successful in mitigating SSR due to IGE and TI in wind farms even at higher series compensation levels.  [80]  
Sub synchronous series compensator (SSSC)  SSSC augmented with an auxiliary damping controller where the damping controller is designed with two control loops based on rotor speed deviation and active power variation in a specific time interval.  SSSC with the damping controller shows a significant improvement in the transient stability margin compared to the same damping controller with a STATCOM.  [81] 
SSSCbased damping control strategy for a PMSGbased wind farm where the sub synchronous components during SSO will be extracted from the voltage at the point of common coupling (PCC) and incorporated into the original voltage control signal.  Can damp SSO in a PMSGbased wind farm successfully.  [82]  
Static compensator (STATCOM)  Firing angle of the inverter is controlled using a PI controller to modulate the exchange of reactive power between the STATCOM and the AC system, thereby stabilizing torsional SSR occurring in wind energy systems. PI parameters are tuned using the Nelder and Mead optimization method.  A simpler controller design, hence, requires less hardware for implementation. The Nelder and Mead method for parameter tuning ensures robustness in the selected PI parameters.  [83] 
Controls the angular difference between the STATCOM terminal voltage and the bus voltage at the PCC using output current of the reactive component of the STATCOM as the feedback signal.  Can suppress transient SSR occurring due to network faults. This method demonstrates a substantial reduction in shaft torque. Hence, the occurrence of cyclic fatigue can be avoided by using this STATCOM controller.  [84,85]  
STATCOM controller based on the control of both modulation index and phase angle.  Modulation index can control electrical modes effectively. It is also observed that the modulation index is more effective compared to the phase angle difference when the size of the wind farm is increased. This method can be used to mitigate SSR in both steady state and transient state.  [86]  
Unified power flow controller (UPFC)  Two sub synchronous damping control loops are added to the series and shunt branches of the UPFC.  Better damping of SSO can be achieved since damping controllers are embedded to both series and shunt branches of the UPFC.  [87] 
The ANFIS controller added to the shunt controller of the UPFC along with a fuzzy logic damping controller for optimal control of the pitch angle of the wind turbine during higher wind speed.  Mechanical and electrical power has been maintained in the same range by the optimal pitch angle control using the FLDC.  [88] 
Type of Modification  Technique  Advantages  Refs. 

Augmentation of the stator voltage control loop of the GSC  Damping controller using rotor angular speed as the input signal.  Can damp SSR successfully.  [91] 
Multichannel damping controller with modal speeds as inputs. Gain and phase compensation are performed on each input.  Ability to damp each oscillation mode.  [92]  
Fuzzy logic damping controller having rotor speed deviation as the input signal.  Can accommodate nonlinear system behavior under disturbance conditions. Hence, can be used over a range of operating conditions.  [93]  
Damping controller designed using observed state feedback control.  Any local signal can be used as the input signal. Therefore, communication delay associated with input signals obtained using WAMS could be overcome by considering a local signal.  [94]  
A nonlinear damping controller is designed using the feedback linearization technique and sliding mode control.  Can damp SSCI successfully and maintain the stability of the system under varying operating conditions.  [95]  
Addition of supplementary controllers on the qaxis inner controller of the GSC  Power system stabilizer designed using a probabilistic approach. DC link voltage deviation is used as the input signal.  Successful damping of SSCI over a range of operating conditions.  [96] 
Proportional feedback controller using electrical power as the feedback signal.  Simpler controller structure.  [97]  
Nonlinear controller based on the partial feedback linearization (PFL) technique.  Can successfully damp SSCI under varying atmospheric conditions as complete system dynamics are not required to be modeled when the PFL technique is used.  [98,99]  
Modifications of RSC controller  A novel two degree of freedom control mechanism is used for the design of the damping controller added to the RSC current control loop.  Can suppress SSR due to IGE successfully.  [100] 
Nonlinear damping controller based on PFL. It controls the current flow in the RSC to produce damping torque sufficient to damp critical torsional frequencies.  Can address the uncertainties associated with practical wind generation and able to sustain fault ride through operation of DFIG wind farm during severe contingencies.  [101]  
A nonlinear sliding mode control (SMC)based technique to eliminate the dynamics of the rotor circuit causing SSR. In this method, SMC is used to collapse the rotor dynamics by limiting the current flow to predefined values of reference currents.  A comparison of the method with the PFL technique in [101] shows that this method demonstrates consistent performance unlike the PFLbased controller of which the performance is affected at higher levels of series compensation.  [102]  
${H}_{\infty}$ damping controller using a multiinput multi output (MIMO) uncertain state space model.  Satisfactory SSCI damping during lower wind speeds, higher level of series compensation, and uncertain grid parameters.  [103]  
Installation of a sub synchronous suppression filter at the daxis inner current loop of the RSC.  Easier implementation and robustness to varying operating conditions.  [104]  
Installation of notch filters at daxis and qaxis of the RSC controller.  Implementation is easy and does not affect the DFIG dynamics.  [105] 
Technique  Advantages  Limitations  Refs. 

PVSTATCOM 

 [5] 
Adding an auxiliary damping controller to the main control loop of the PV plant.  Increased PV penetration can be used as an effective alternative to enhance power system stability and control.  The controller fails to damp SSR when the time delay associated with the WAMS signal is taken into consideration.  [17] 
GSC control of the PV farm is augmented with a damping controller which uses the voltage across the series capacitor as the input for the damping controller.  Voltage across the series capacitor is used as the input for the damping controller since rotor speed of the generator may not be effective when parallelly connected turbine generators are used.  Based on the assumption that there is no delay as the series capacitor voltage is monitored continuously.  [42] 
Device/Technique  Advantages  Limitations 

FACTS devices
 Can mitigate sub synchronous resonance effectively.  Installing FACTS devices just for SSR mitigation is not economical due to high cost involved. However, FACTS devices installed for reactive power compensation as per grid code requirements can be modified with additional controllers to mitigate SSR. 
Special purpose shunt VSC  Economically viable alternative.  Difficulty in tracking the frequency variations of fundamental and sub synchronous components simultaneously. 
Converter Controls 


Proper control of series capacitor  No extra cost as the method only involves bypassing the series capacitor upon detection of SSR.  Has a risk of triggering SSO during capacitor switching. 
Sub synchronous frequency relay  Can detect sub synchronous frequencies that coincide with other power system frequencies.  Cannot be used to mitigate SSCI. 
Power system design improvements, such as selection of proper series/shunt compensation levels    Shunt compensation leads to super synchronous resonance. 
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Perera, U.; Oo, A.M.T.; Zamora, R. Sub Synchronous Oscillations under High Penetration of Renewables—A Review of Existing Monitoring and Damping Methods, Challenges, and Research Prospects. Energies 2022, 15, 8477. https://doi.org/10.3390/en15228477
Perera U, Oo AMT, Zamora R. Sub Synchronous Oscillations under High Penetration of Renewables—A Review of Existing Monitoring and Damping Methods, Challenges, and Research Prospects. Energies. 2022; 15(22):8477. https://doi.org/10.3390/en15228477
Chicago/Turabian StylePerera, Uvini, Amanullah Maung Than Oo, and Ramon Zamora. 2022. "Sub Synchronous Oscillations under High Penetration of Renewables—A Review of Existing Monitoring and Damping Methods, Challenges, and Research Prospects" Energies 15, no. 22: 8477. https://doi.org/10.3390/en15228477