Research Journal of Recent Sciences ________________________________________________ ISSN 2277-2502Vol. 2(7), 8-13, July (2013) Res.J.Recent Sci. International Science Congress Association 8 Intelligent Passive Anti-Islanding Protection for Doubly Fed Induction Generators Madani S.S., Abbaspour A., Rezaie Benam M. and Ranjbar A.M. Sharif University of Technology, Tehran, IRAN Available online at: www.isca.in Received 13th February 2013, revised 25th March 2013, accepted 16th April 2013 AbstractThe integration of wind generation units into power system introduces several issues including islanding operation. Therefore, the system should be protected from islanding phenomenon by a fast and reliable islanding detection method. In this paper an intelligent anti-islanding protection approach is proposed to detect islanding states for Doubly Fed Induction Generator (DFIG) units. Different features based on rate of change of voltage, frequency, active power and reactive power at DG bus are employed to construct feature vectors. Because of intermittency of wind power, different generating states for DFIG unit are assumed. Probable events are simulated under system operating states to construct classification data set. Decision tree algorithm due to its high classification speed, implication simplicity and high accuracy, is used to classify instances. The proposed method is tested on typical distribution system including DFIG and different loads. The studies showed that this method succeeds in DFIG anti-islanding protection with high accuracy and negligible false trips. Because of high detection speed of decision tree algorithm, the proposed method is capable to protect the system from asynchronous reconnection of auto-reclosers. Keywords: Anti-islanding, distributed generation, intelligent learning algorithms, wind generation. IntroductionGrowth of environmental concerns, limitation in fossil fuel resources and need for sustainable energy resources has led to fast development of renewable energy systems. Among different renewable energy sources, wind energy is expanding rapidly due to low operation cost, zero CO emission and high efficiency and Doubly Fed Induction Generators (DFIGs) has become one the most favorable alternatives in wind power market. Nowadays, wind generation units from several kilowatts to several megawatts of rated power have been connected to distribution system in the context of distributed generation. This kind of generation has many advantages such as increasing reliability and decreasing the transmission and distributed loss. Hence distributed generation has recently gained a lot of momentum in the power industry; however, there are some new challenges related to connecting distributed generation units to the grid. One of the most important challenges is to detect unplanned islanding of distributed generation systems. Islanding is defined in IEEE standard as "a condition in which a portion of an area electric power system (EPS) is energized solely by one or more local EPSs through the associated points of common coupling (PCCs) while that portion of the area EPS is electrically separated from the rest of the area EPS". Islanding condition is an undesirable situation because it will create a shock hazard for utility personnel. Islanding operation also may cause damage to the network equipments and consumers in the case of out-of-phase reconnection of the grid by the auto-reclosers due to phase difference between the grid and island voltage. Other islanding condition drawbacks include lack of grounding, change in fault power, uncoordinated protection, voltage and frequency control problem and power quality degradation5-6. Due to these issues, IEEE Standard recommended disconnection of the DGs in the island4,7. Islanding detection techniques were categorized into local and communication-based or remote techniques which are depicted in figure 1. Figure-1 Islanding detection methods Communication-based techniques use a continuous signal that is sent from utility to the downstream DGs. These techniques are expensive compared to the local islanding detection methods at the present time. Local techniques can be grouped into active and passive islanding detection methods. Active methods are based on deliberately injecting some sort of disturbances into the grid and monitoring its response. When the utility is disconnected and an island is formed, injected disturbances cause abnormal condition that is detectible as islanding condition10. Generally power quality degradation is the major concerns about active islanding detection methods . Systems Islanding Detection Local Techniques Remote Techniques Active Passive Hybrid Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 2(7), 8-13, July (2013) Res. J. Recent Sci. International Science Congress Association 9 with high penetration rate of distributed generation that use active islanding methods, may face serious power quality problems. Several active methods have been proposed in literature including active frequency drift10, slip-mode frequency shift11 Sandia frequency shift12, etc. Passive islanding detection methods make decision based on measurement of system parameters such as like voltage amplitude, frequency or phase. These methods basically rely on detection of abnormalities when the utility is disconnected and island is formed. Passive methods do not inject any disturbance into the distribution system so the power quality is not degraded. Several passive islanding detection techniques have been developed in recent years. Rate of change of frequency13, rate of change of voltage14, vector surge relays15, voltage unbalance variation and total harmonic distortion16, and rate of change of phase angle difference17 are examples of proposed passive methods . In the state with balance in power between load and, the islanding phenomenon will result in negligible deviation in electrical parameters and passive methods fail to detect the islanding and non-detection zone is one of the significant concerns about the passive islanding detection methods. In the recent decades, intelligent algorithms have been used in different fields18-20. Intelligent data mining approach have been employed for passive islanding detection21-23. In El-Arroudi et al21 decision tree algorithm is used to threshold setting of islanding relays. Four features were used to train the classifiers. A set of features including gradient of electric parameters and total harmonic distortion is used to classify the data set in El-Arroudi Kh et al22. In Najy W.K.A. et al23 a statistical signal processing algorithm is utilized to extract features from voltage and frequency waveform. Accuracy of this technique is acceptable but the delay of statistic processing section makes this technique slower than other islanding detection methods. In this paper an intelligent islanding detection technique for doubly fed induction generator is proposed. Decision tree algorithm is used to classify instances. The proposed islanding detection scheme can be successfully used in anti-islanding protection of DFIG units. Material and MethodsDFIG Model: Rapid development of wind energy usage has been closely related to advancement of wind turbine and control systems. Doubly Fed Induction Generators (DFIGs) have received much attention for wind energy conversion in the recent years. The term "doubly fed" refers to the fact that rotor and stator voltages are applied separately from rotor side converter and the grid, respectively. A common used model for DFIG is shown in figure 2. In this model, the stator is connected to the low voltage balanced three-phase grid. Back-to-back PWM converters consist of a converter connected to rotor called "rotor side converter" and a converter connected to the grid called "grid side converter". These two converters are controlled independently of each other. The rotor side converter provides proper rotor excitation and the grid side converter controls the power flow between the AC side and DC bus. This converter allows the DFIG to be operated in both sub-synchronous and over-synchronous speeds24. In sub-synchronous condition, power flows from the rotor to the grid, whereas in over-synchronous condition, power flows in opposite direction. In both conditions, power direction in stator side, is from stator to the grid. Figure-2 Doubly fed induction generator model The Concept of Intelligent Anti-Islanding Protection: Intelligent anti-islanding protection uses data that is provided by locally monitoring the connection bus of the DG. The monitoring can be done via sampling voltage and current signal at the DG bus. A feature calculation block transforms the sampling data into proper features and constructs an n-dimensional feature vector. These vectors are inputs for an intelligent classification algorithm which is able to create classifiers with islanding detection capability. The general passive anti-islanding protection procedure is depicted in figure 3. Figure-3 Intelligent Anti-Islanding Protection Diverse features can be extracted by monitoring the grid to build an n-dimensional feature vector. Feature vector and class for ithinstance are: DFIG bus DFIG Sampling Features Calculation Feature Vector ( FV ) Intelligent Classification Algorithm Islanding detection Classifiers Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 2(7), 8-13, July (2013) Res. J. Recent Sci. International Science Congress Association 10 12 [,,,] iiii n FVfff =  (1) {1,1} Î- (2) Where f is the th feature for the th instance and is class of thinstance (-1 for non-islanding and 1 for islanding). Islanding detection classifiers are trained by labeled feature vectors called “training data set”. The classifiers are tested on a set of non-labeled feature vectors called “test data set”. Therefore, the main objective of data intelligent islanding detection methods is to classify testing feature vectors as “islanding” or “non-islanding” classes. Figure 4 illustrates training and testing procedure for islanding detection. Figure-4 Training and testing the islanding detection classifiers Decision Tree Classification Algorithm: In this paper, decision tree approach25-26 is used for classification the input feature vectors as weak classifier. Decision tree algorithm is capable to break down complex decision making process into union of several simpler decisions thus the results are easier to interpret25. In the first step of decision tree, entire space is considered as a root node. An initial split is made using a predictor variable, segmenting the root node into two child nodes. These child nodes are chosen among all possible child nodes and contain the purest data. Splits can then be made from the child nodes. A leaf (terminal) node is one where no more splits are made27. Predictions are made based on the make-up of leaf nodes. To use a decision tree to make a prediction, the split decisions are followed until a leaf node is reached. Intelligent Anti-Islanding Protection Data Set: Intelligent anti-islanding protection requires a data set which is composed of feature vectors. Anti-islanding protection features are defined based on mean variation of an electrical parameter over a predefined time interval. Rate of change of electrical parameter ’ which is chosen as th feature, can be expressed as: ()()() ()(3) iiiptpttptft tt D+D- == DD In this paper a four-dimensional feature vector is applied in order to detect islanding condition for DFIG anti-islanding protection. The elements of the feature vector are: () ()(4) DFIGPtft t D = D () ()(5) DFIGQtft t D = D () ()(6) DFIGbusftft t D =D () ()(7) DFIGbusVtft t D Where DFIG and DFIG are DFIG active and reactive output power, respectively. DFIG bus, and DFIG bus are electrical frequency and voltage amplitude at the DFIG bus, respectively. The intermittency of wind power as well as load variation should be taken into account for data set construction in order to increase classification accuracy. Different system operating states are considered in order to train islanding detection classifier. Produced power of DFIG as well as loads are divided into discrete states. Combination of these states can be used to build operating states of the system. Diverse events may occur in the grid and each has a specific signature on feature vector. List of events can be extracted from different sources such as standards, testing practices, system topology studies, failure rate of transformers and lines, historical fault reports, etc. Each event should be simulated under abovementioned system operating states. Results and DiscussionFigure 5 shows single-line diagram of a 13.8 KV distribution system used to demonstrate the proposed islanding detection algorithm. The studied system consists of a DFIG unit, connected to bus 4 and three loads connected to bus 3. Rated values for distribution system of figure 5 are given in appendix. The simulation studies are carried out using PSCAD/EMTDC software. System Operating States: To cover probable operating states, different wind generation and system loading states are assumed. Three states for wind generation and three loading states are assumed in order to construct the classification data set. Combination of generation and loading states, results in 9 (=3*3) operating states. System Events: Different events are defined to be simulated under aforementioned states and are listed in table 1. Event number 1, 2, 3, 4 and 5 simulate non-islanding condition. Short circuit (event number 5) is cleared after 5 ms without any circuit Training Testing Feature Class Vector FV 1 FV -1   FV 1 Intelligent Classification Algorithm Trained Classifiers Training Data Set Feature Class Vector FVn+1 ? FVn+2 ?   FV ? Trained Classifiers Test Data Set Feature Class Vector FV+1 FV+2   FV Output 1 - 1 - 1 Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 2(7), 8-13, July (2013) Res. J. Recent Sci. International Science Congress Association 11 breaker operation, so this event should not be classified as an islanding event. Event number 6, 7, 8, 9 and 10 simulate islanding condition. Figure-5 Case study distribution system Table-1 Predefined system events Event # Event Type Element Islanding Status 1 Load Outage L1 -1 2 Load Outage L2 -1 3 Load Outage L3 -1 4 Line Outage Line 3 -1 5 Short Circuit - -1 6 Circuit Breaker Trip cb 1 1 7 Circuit Breaker Trip cb 2 1 8 Circuit Breaker Trip cb 3 1 9 Circuit Breaker Trip cb 4 1 10 Line Outage Line 1 1 Abovementioned events are simulated under each operating states. Combination of events and operating states result in 90 (=10×9) simulation cases. Classification Results: In this section classification results for test cases is proposed. Randomly 80% of total features (72 features) are chosen for training and 20% (18 features) are used to test the proposed classification algorithm. Final results of 18 randomly chosen features are listed in table 2. Table-2 Intelligent anti-islanding protection results Test Case # DFIG Generation State (H: High M: Medium L: Low) System Loading State (H: High M: Medium L: Low) Event # Islanding state Intelligent Anti-Islanding Protection Results 1 H M 1 -1 -1 2 H M 1 -1 -1 3 L L 2 -1 -1 4 M H 2 -1 -1 5 H L 2 -1 -1 6 L H 3 -1 1 7 H M 4 -1 -1 8 M L 4 -1 -1 9 M H 5 -1 1 10 H M 5 -1 -1 11 L H 7 1 1 12 M L 7 1 -1 13 L H 7 1 1 14 L H 7 1 1 15 L M 8 1 1 16 H L 9 1 1 17 M M 10 1 1 18 H H 10 1 1 Number of False Detection Cases 3 Number of Correct Detection Cases 15 Detection Accuracy 83.3 % Bus 4 (DFIG bus) T2 Line 4 Line 2 Bus 3 (PCC) Bus 1 Utility Line 3 DFIG T1 cb2 cb3 cb4 T3 L1 T4 T5 L2 L3 Bus 2 Line 1 cb1 Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 2(7), 8-13, July (2013) Res. J. Recent Sci. International Science Congress Association 12 The results show that the proposed algorithm can detect the islanding condition with high accuracy. The algorithm has 15 correct and 3 false classifications (test case number 6, 9 and 11). In test case number 6 and 9 false islanding trip is sent to protection system and in case number 11 islanding condition is not detected by protection system. In case number 3, disconnecting of L3 at its High state causes severe changes in electrical parameters which similar to islanding condition, consequently the algorithm has classified this case as islanding condition and false trip is sent to protection system. In case number 9, a short circuit has occurred. In this condition electrical parameters change sharply and the classification algorithm classifies this case as islanding condition. In case number 11, islanding condition due to negligible difference between generation and demand, is not detected. In this condition because of related balance in the island, disconnection has insignificant effect on the electrical parameters and detection algorithm fails to detect islanding condition. This case represents non-detection zone of proposed protection method. ConclusionIn this paper an intelligent passive anti-islanding protection method for DFIG units is proposed. The Classification approach is used in order to detect islanding condition. Decision tree algorithm is chosen as the main classifier. Intermittent behavior of wind generation is considered in training data set construction by assuming different generation states for DFIG unit. Rate of change of voltage, frequency, active power and reactive power are employed as feature vector elements. Selected features do not require any mathematical transformations so feature calculation requires less time than transformation-base algorithms. The proposed DFIG anti-islanding protection method was capable to detect islanding condition with high accuracy. The results showed that the proposed method has negligible false islanding trips. Consequently, application of this method will increase the system reliability and reduces the expected energy not supplied by DFIG due to its low rate of false trips. Appendix: This appendix contains data for case study depicted in Figure 5. The base voltage and power are chosen as 13.8 KV and 10 MVA, respectively. Frequency of system is 50 Hz. Nominal values of distribution system elements are as below: DFIG unit rated Power: 2.2 MW. T1: rated MVA=10 MVA, rated kV= 69/13.8 kV, Dyn, Z=0.00667+j0.0533 p.u., R=20; T2: rated MVA=3.0 MVA, rated kV= 13.8/0.48 kV, Dyn, Z=0.0821+j0.575 p.u.; T3: rated MVA=1.5 MVA, rated kV= 13.8/0.48 KV, Dyn, Z=0.0329+j0.023 p.u.; T4: rated MVA=1.0 MVA, rated kV= 13.8/2.4 kV, Dyn, Z=0.021+j0.1094 p.u.; T5: rated MVA=3.75 MVA, rated kV= 13.8/2.4 kV, Dyn, Z=0.0244+j0.148 p.u., RG=3.5 ; L1: rated power =1.25 MW, rated kV=0.48 kV; L2: rated power =1.0 MW, rated kV=2.40 kV; L3: rated power =3.2 MW, rated kV=2.40 kV; Line 1: rated kV=69.0 kV, Z=0.00151+ j0.00296 p.u.; Line 2: rated kV=13.8 kV, Z=0.03760+ j0.05127 p.u.; Line 3: rated kV=13.8 kV, =0.06141+ j0.03066 p.u.; Line 4: rated kV=13.8 kV, =0.06065+ j0.10150 p.u.; References1.Byeon G., Park I. K., and Jang G., Modeling and Control of a Doubly-Fed Induction Generator (DFIG) Wind Power Generation System for Real-time Simulations, Journal of Electrical Engineering & Technology, 5(1), 61-69 (2010)2.Pipattanasomporn M., Willingham M., and Rahma S., Implications of on-site distributed Generation for commercial/industrial facilities, IEEE Transactions on Power Systems, 20(4), 206–212 (2005) 3.Pujhari T., Islanding detection in distributed generation, master of technology thesis, Department of Electrical Engineering, National Institute of Technology, ruerkela, (2009) 4.Standard for Interconnecting Distributed Resources with Electric Power Systems, IEEE, Std. 1547, (2003)5.Aljankawey, A. S., Morsi W. G., Chang L., and Diduch C. P., Passive method-base islanding detection of renewable-based distributed generation: The issue, in Proc, IEEE Electrical power and Energy Conf., 1-8 (2010) 6.Mahat P., Chen Z., and Bak-Jensen B., Review of islanding operation of distribution system with distributed generation, in Proc. Power and Energy Society General Meeting, 1-8 (2010) 7.IEEE Standard Conformance Test Procedure for Equipment Interconnecting Distributed Resources with Electric Power Systems, IEEE Std. 1547.1, (2005) 8.Xu W., Zhang G., Li Ch., Wang W., Wang G., and Kliber J., A power line signaling technique for anti islanding protection of distributed generators- Part I: Scheme and analysis, IEEE Transactions on Power Delivery, 22(3), (2007) 9.Kunte R. S. and Gao W., Comparison and review of islanding detection for distributed energy resources, in Proc. North American Power Symposium, 1-8 (2008) 10.Lopez L. A. C. and Sun H., Performance assessment of active frequency drifting islanding detection methods, IEEE Transactions on, Energy Conversion, 21(1), 171-180 (2006) 11.Liu F., Kang Y., Zhang Y., Duan S. and Lin X., Improve SMS islanding detection method for grid-connected converters, IET Renew, Power Gener., 4(1), 36-42 (2010) 12.Zeineldin H. H. and Kennedy S., Sandia frequency-shift parameter selection to eliminate non-detection zones, IEEE Transactions on Power Delivery, 24(1), 486-487 (2009) 13.Freitas W., Xu W., Affonso C.M., and Huang Z., Comparative analysis between ROCOF and vector Surge Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 2(7), 8-13, July (2013) Res. J. Recent Sci. International Science Congress Association 13 relays for distributed generation applications, IEEE Transactions on Power Delivery, 20(2), 1315-1324 (2005) 14.Mahat P., Chen Z., and Jensen B. B., A hybrid islanding detect technique using rate of voltage change and real power shift, IEEE Transactions on, Power Delivery, 24(2), 764-771 (2009) 15.Freitas W., Huang Z., and Xu W., A practical method for assessing the effectiveness of vector surge relays for distributed generation applications, IEEE Transactions on, Power Delivery, 20(1), 57-63 (2005) 16.Jang S. I. and Kim K. H., An islanding detection method for distributed generation using voltage unbalance and total harmonic distortion of current, IEEE Transactions on Power Delivery, 19(2), 745-752 (2004)17.Samui A. and Samantaray S. R., Assessment of ROCPAD relays for islanding detection in distributed generation, IEEE Transactions on Smart Grid, 2(2), 391-398 (2011) 18.Pallavi P. and Vikal I., Obtaining a high accurate fault classification of power transformer based on dissolved gas analysis using ANFIS, Research Journal of Recent Sciences, 1(2), 97-99 (2012)19.Shamshirband S., and Za'fari A., Evaluation of the performance of intelligent spray networks based on fuzzy logic, Research Journal of Recent Sciences, 1(8), 77-81 (2012)20.Neeraj S. and Swati L. S., Overview of non-redundant association rule mining, Research Journal of Recent Sciences, 1(2), 108-112 (2012)21.El-Arroudi Kh. and Joós G., Kamwa I., Data mining approach to threshold settings of islanding relays in distributed generation, IEEE Transactions on Power Systems, 22(3), (2007) 22.El-Arroudi Kh., Joós G., Kamwa I., and McGillis D. T., Intelligence base approach to islanding detection in distributed generation, IEEE Transactions on Power Delivery, 22(2), (2007) 23.Najy W.K.A., Zeineldin H.H., Alaboudy A.H.K., and Woon W.L., A Bayesian passive islanding detection method for inverter-base distributed generation using ESPRIT, IEEE Transactions on Power Delivery, 26(4), 2687-2696 (2011) 24.Babu B.Ch. and Mohanty K.B., Doubly-Fed Induction Generator for Variable Speed Wind Energy Conversion Systems- Modeling & Simulation, International Journal of Computer and Electrical Engineering, 2(1), 141-147 (2010) 25.Safavian S. R. and Landgrebe D., A Survey of decision tree classifier methodology, IEEE Trans. Syst, MAN, Cybern,21(3), 660-674 (1991) 26.Lu M., Chen C.L.P., Hou J., and Wang Z., Multi-stage decision tree based on inter-class and inner-class margin of SVM, in Proc. IEEE Systems, Man, and Cybernetics Conf.,1875-1880 (2009) 27.Rokach L. and Maimoon O., Top-down induction of decision tree classifier- A survey, IEEE Trans. Syst., Man, Cybern, 35(4), 476-487 (2005)