Design of an Online Intelligent Alarming System for Cascading Failures of Group of Wind Farms Jianan Mu, Student Member, IEEE, Hongbin Sun*, Senior Member, IEEE, Qinglai Guo, Member, IEEE, Wenchuan Wu, Member, IEEE, Fengda Xu, Boming Zhang, Fellow, IEEE Department of Electrical Engineering, State Key Laboratory of Power Systems Tsinghua University Beijing 100084, China
[email protected],
[email protected],
[email protected] Abstract—In China, large-scale wind power is integrated to the power grid in a concentrating way by connecting a group of wind farms together. Each wind farm is consisted of hundreds of wind turbines and covers a large geographical area. However, such a system is vulnerable to occasional faults which can easily develop into cascading failures of adjacent wind farms, making the wind farms lose most of their power in a very short time. Cascading failures have occurred several times in reality. It is urgently necessary to construct an online assistant system to detect, analyze and explain the cascading events timely and effectively. Given that Phasor Measurement Units (PMUs) are widely installed in Chinese wind farms, the dynamic process can be recorded effectively, supplemented with Supervisory Control and Data Acquisition system (SCADA) signals. In this paper, a conceptual design of an online intelligent alarming system for cascading failures of wind farms based on PMUs and SCADA is introduced. The system is justified using real data collected in wind farms. Index Terms-- wind farm, intelligent alarming, cascading failures, PMU, SCADA.
I.
INTRODUCTION
Wind power has become the most important and practical renewable energy resource. By the end of October 2012, the total installation capacity of wind power in China reaches 52.22GW [1], [2]. Large-scale wind power is often distributed in sparsely populated districts and covers wide areas. It is first generated by wind turbines connected with low-voltage feeders within a wind farm, whose capacities are usually 1-3 MW. Then power from adjacent wind farms is gathered together through transmission lines to an extra high voltage substation, where the wind power is integrated to the main power grid. Such a system of wind farms is vulnerable to faults due to its wide-spreading area and close interconnections. Actually, a real system of a group of wind farms in northern China experienced several large-scale cascading failures every year. Security has become a major issue for large-scale integration of wind power in China.
This work was supported by National Key Basic Research Program of China (973 Program) (2013CB228203), National Science Foundation of China (51277105) and National Science Fund for Distinguished Young Scholars (51025725).
978-1-4799-1303-9/13/$31.00 ©2013 IEEE
Cascading failures of wind farms is usually induced by local faults in a single wind farm. A number of researches have been done to investigate the fault characteristics inside a single wind farm [3], [4] as well as its spreading to adjacent wind farms [5]. Reference [3], [4] analyze the tripping mechanism of wind turbines based on control of doubly-fed wind turbines, characteristics of protections, low-voltage-ridethrough ability and configuration of reactive power compensation devices. Reference [5] focuses on the influences of some uncontrollable factors such as length of transmission lines and power output of wind farms, which is hard to be applied to direct control. Alarm-processing has been a popular area in power systems aiming at effectively analyzing big amounts of alarms emerging during urgent system conditions. Many approaches have been proposed including expert systems [6], analytical methods [7], [8], Petri-net [9], cause-effect network [10] and so on. In recent years, attention has been paid on alarmprocessing in digital substations [11] and under two-level architecture [12]. The above methods mainly utilize SCADA signals to do alarm-processing, while on the other hand many researches take use of discrete series (waveforms). Reference [13], [14] use finite impulse response filter to detect disturbances in PMU data while [15] uses wavelets to detect inception and ending of disturbances based on frequency measurements. Reference [16] uses CUSUM algorithm combined with BG algorithm to detect and sift abrupt changes in waveforms. Although wind farms are widely constructed and operated at present, alarm-processing serving for wind farms is still a blank area. It can be argued clearly that such an application is of great value rather than needless by the severe security problems faced in wind farms right now. In this paper, a conceptual design of an online intelligent alarming system is proposed. The system mainly takes use of PMU data, assisted with SCADA signals. In section II, characteristics of cascading failures of a group of wind farms are analyzed based on real data. Accordingly, basic analytical procedures are proposed and the alarming system is designed in section III. In section IV, an example is demonstrated to
verify the feasibility of the proposed system based on real case. Finally, conclusions are given in section V. II.
CHARACTERISTICS OF CASCADING FAILURES
As shown in Fig. 1, a partial system under GY substation is considered, from which wind farms are integrated into the main power network. The system is consisted of 3 substations, 2 switch stations and 15 wind farms. PMUs are installed on most of the busbars, power transformers and transmission lines in the substations and wind farms. A switch station has no power transformers or PMU measurements.
•
Powers of all the wind farms declined slowly in the disturbance. However the slopes and decreases are different.
•
Voltages reset to normal levels after about 13s. This was due to the actions of reactive power compensation devices.
Figure 2. Power of wind farms during cascading failures
Drop
Drop
Figure 1. Topology of a group of wind farms connected to GY substation
A cascading failure incident happened in December 21st, 2011. The failures caused the wind farms to lose 85% of their power in about 5seconds. Post-disturbance analysis revealed that a line-to-ground fault first occurred in WDS wind farm and induced cascading failures of adjacent wind farms. Although current (I) and reactive power (Q) were also recorded, it was found that they were somewhat irregular and not able to reflect the process properly. On the other hand, active power (P) and voltage (U) were good reflections of major events and trends during the cascading failures. Active powers of all the wind farms and voltages of all the wind farms and substations are depicted in Fig. 2 – Fig. 4. Not all the curves are labeled on the right due to limited space. It is observed that: •
Voltages of all the substations and wind farms had a steep drop on the instant of disturbance, regardless of the location of the disturbance. The voltage drops indicated two distinct faults during the process.
•
Voltages of all the substations and wind farms swelled slowly (compared to the steep drop) after the initial disturbance.
Figure 3. Voltage of wind farms during cascading failures in a 5s scale
Amplify
Figure 4. Voltage of wind farms during cascading failures in a 15s scale
Usually, a cascading failure process can be elaborated as follows: a disturbance (e.g. a line-to-ground fault) first occurs, which causes a sudden change in the voltage. This sudden
change in the voltage is a global effect and occurs in different wind farms simultaneously. As wind turbines in China right now have very weak low-voltage-ride-through or highvoltage-ride-through ability, some wind turbines are tripped by protections immediately causing power loss. Given that reactive power compensation devices such as capacitor banks and SVC/SVGs are still keeping the original states as before, reactive power appears excessive, making the voltage swelled. When the voltage reaches the upper limits of wind turbines’ settings, more wind turbines are tripped and in turn deteriorate the conditions. Voltage swells and power losses happen in turn to develop a cascading failure including many wind farms. If a second disturbance occurred, the above process repeat to make the conditions worse. At last, reactive power compensation devices are adjusted automatically or manually which makes the voltage dropped to normal levels immediately.
state. During the disturbance state, the alarming system runs in a loop and conducts event identification, power loss counting, recording and outputting in turn. When ending conditions are satisfied, the alarming system jumps out of the loop and do some summary work, for instance generating the analysis report and figuring out the causal relations between events. It is noticed that input data are involve in every single step, providing materials for analysis. PMU data are compulsory while SCADA signals are good complementation.
of ry ve er o c w Re Po
Co n Di firm stu at rb ion an o ce f
Other examples revealed that cascading failures in wind farms may also happen in non-fault conditions. For instance, heavy load may lead to decreasing voltage, which triggers the low voltage protection for wind turbines. III.
Figure 5. State transition diagram of the concerned wind farms system
DESIGN OF THE ALARMING SYSTEM
According to the analysis on the cascading process in the previous section, basic approaches on how to conduct online alarm processing can be derived. It includes: •
detecting the inception of disturbance based on big sudden changes of voltages; subsequent power losses and voltage swells are needed to justify the detection;
•
recording power losses of different wind farms in real time; number of tripped wind turbines in each wind farm is estimated according to the operating condition before disturbance;
•
sudden restoration of voltages can be regarded as a sign of adjustment of reactive power compensation devices and ending of the disturbances;
•
events such as sudden rises or drops of voltages and powers or slower swells and decreases of voltages and powers are recorded, which will subsequently be organized in a chronological list or a causal tree respectively;
•
SCADA signals including opening/closure of breakers, operations of protections and on/off of reactive power compensation devices are good complementation to the PMU data.
Conceptually, the operation of a wind farm group system can be divided into three states: normal, disturbance and recovery, similar to the classical four-state division of power system. The transitions between different states are illustrated in Fig. 5. The flow chart of the alarming system is thus proposed based on this three-state understanding of the wind farm group system in Fig. 6. According to Fig. 6, once the system is started, it assumes that the system is in its “normal” state. A detection block is responsible for detection of new disturbances. If the detection is further confirmed by other evidences, it is judged that a disturbance occurred and the system turns into “disturbance”
Figure 6. Flow chart of the proposed system
Each single block can be elaborated as follows: 1.
Detection: detection refers to sensitive and reliable detection of disturbances based on PMU voltage data. The method described in [16] can be used to execute the task. It assumes that the values in a time series are independent random variables whose probability density function only has one varying scale parameter, usually mean value. The sum of logarithm of likelihood ratio is calculated for two adjacent time series with the same length, which is compared to a pre-configured setting to identify abrupt changes. The logarithm of likelihood ratio is defined as:
s ( y ) = ln
Pθ1 ( y )
(1)
Pθ0 ( y )
The CUSUM is calculated for the most resent time series
S=
i
∑
s ( yn )
(2)
n =i − N +1
in which N is the length of selected time series.
θ0
θ1
and
is calculated using the two adjacent time series.
Moreover, changes of voltage differences are often used as an effective way to detect faults [17]:
ΔU = ui − ui −1 − ui −1 − ui − 2
(3)
2.
Confirm: confirmation has to be made in case of false detection of disturbance. Given that real disturbances are accompanied with sudden power losses, PMU power data is a good selection to confirm the disturbance. If SCADA signals are available, signals such as “general accident” or “protection operation” are also good confirmations.
3.
Event identification: event identification is a core function of the alarming system and is extensible according to different requirements. Basically, events can be classified as “instant events” and “period events”. Instant events only have a time stamp while period events have a time interval instead. The inception time counts while a chronological ordering is needed. Basic events should include impulses, sudden drop/rise, slower swell/decrease and fluctuations of voltage and power. All types of events can be observed in the presented data. In fact, indirect events can be correlated to the direct events listed above using causal links based on domain knowledge.
4.
Count power loss: power losses for all the wind farms are counted in real time. Once the power stops to decline, number of wind turbines tripped can be estimated according to the operating conditions before disturbance.
5.
Record and output: intermediate results should be output for records and demonstration even during disturbances.
6.
End: the condition indicating ending of the disturbance may not be very evident. Different criteria can be adopted according to expertise. Possible options include recovery of voltage, recovery of power, or manual reset.
7.
Figure 7. Data structure of the proposed system
IV.
EXAMPLES
An example is illustrated based on data regarding the cascading failures on December 21st, 2011 in GY area. A lineto-ground fault first occurred at the 35kV busbar in WDS wind farm, as shown in Fig. 8. The inception of the disturbance was captured using (3) at 12:40: 13.03 indicated by the voltage of WDS, referring to the initial fault, as shown in Fig.9. So did the second fault at 16.57s and recovery of voltage at 25.72s. Slower swelling of power of wind farms can be identified using CUSUM algorithm with fairly large parameter N, e.g. N=100. After exceeding the upper threshold, the swelling is not deemed over until keeping below the lower threshold continuously, as shown in Fig. 10. Part of the events extracted is listed in table I in chronological order.
Figure 8. Single-line diagram of WDS wind farm
Generate results: results refer to those analysis results that has to be performed after the whole disturbance process is over. These may include a final report responsible for analyzing the initial causes, recording the disturbance process and counting the losses, an accumulation graph of power of wind farms with marks on major events on it, and a causal tree of the events.
The data structure of the system is illustrated in Fig. 7. It is used to store incoming data, intermediate data and final results. Figure 9. Sudden change detection for WDS’s voltage difference
V.
Figure 10. Changing detection for power in BF using CUSUM
TABLE I. Time 12:40:13.03 12:40:13.06 12:40:13.7012:40:14.40
St
CONCLUSIONS
This paper analyzes cascading failure process in a group of wind farms based on real data and proposes an online intelligent alarming system aiming at helping the system operators to detect, analyze and explain the cascading failures effectively, timely and vividly. Furthermore, post-disturbance analysis is often required in actual cases and brings repeating boring work of collecting and analyzing the recorded data. The proposed system will undoubtedly become a great help to operating personnel. Future work includes design and test of reliable detection and event extraction algorithms, combination of PMU and SCADA data, refinement of the analysis into individual wind farms, and implementation of a prototype system.
PART OF EVENT LIST
WDS QLS
Content Negative impulse of U Negative impulse of U
JX
Slow decrease of P by 56.36 MW
12:40:16.57
JX
Negative impulse of U
12:40:25.77
WDS
Sudden drop of U to normal level
Remarks inception inception caused by high voltage secondary disturbance end
Power loss counting after the end of disturbance is shown in Table II. As a vivid demonstration of the process, accumulation graph of power of the wind farms is used both for real-time display and summary, as shown in Fig. 11. TABLE II.
POWER LOSSES
Wind farm
P before (MW)
P after (MW)
P loss (MW)
Wind farm
WDS QLS JX HJZ YY HD BT HJ
77.50 128.2 159.9 83.00 23.14 86.61 NaN 30.60
-0.50 -2.559 -1.779 -0.500 -6.23 -1.33 NaN -0.523
78.00 130.8 161.6 83.50 29.36 87.94 NaN 31.12
MC LY BF JLQ LHT JY ZB Total
[1] [2] [3] [4]
[5] [6] [7]
P before (MW)
P after (MW)
P loss (MW)
61.40 76.15 152.1 84.74 0 24.84 37.55
-0.89 -0.698 111.1 64.96 -0.296 0 -0.50 160.3
62.29 76.85 41.1 19.77 0.296 24.84 38.04 952.1
1112.3
REFERENCES
[8]
[9] [10]
[11]
[12] [13] [14] [15] [16] Figure 11. Accumulation graph of total power of the group of wind farms [17]
http://www.cec.org.cn/yaowenkuaidi/2012-03-15/81741.html. http://tj.cec.org.cn/fenxiyuce/yunxingfenxi/yuedufenxi/2012-1116/93803.html. J. B. Hu, D. Sun and Y. K. He, "Modeling and Control of DFIG Wind Energy Generation System Under Grid Voltage Dip," Automation of Electric Power Systems, vol. 8, pp. 21-26, 2006. (Chinese) G. G. Yan, M. C. Wang, G. Mu, et al., "Modeling of Grid-Connected Doubly-Fed Induction Generator for Reactive Power Static Regulation Capacity Study," Transactions of China Electrotechnical Society, vol. 7, pp. 98-104, 2008. (Chinese) X. Ye, et al., "Cascading tripping out of numerous wind turbines in China: Fault evolution analysis and simulation study," in Power and Energy Society General Meeting, 2012 IEEE, 2012, pp. 1-11. H. J. Lee, et al., "A fault diagnosis expert system for distribution substations," Power Delivery, IEEE Transactions on, vol. 15, pp. 92-97, 2000. W. X. Guo, et al., "An Analytic Model-Based Approach for Power System Alarm Processing Employing Temporal Constraint Network," Power Delivery, IEEE Transactions on, vol. 25, pp. 2435-2447, 2010. L. Tang, H. Sun, B. Zhang and F. Gao, "Online fault diagnosis for power system based on information theory," Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, vol. 23, pp. 5-11, 2003. X. Luo and M. Kezunovic, "Implementing fuzzy reasoning Petri-Nets for fault section estimation," Power Delivery, IEEE Transactions on, vol. 23, pp. 676-685, 2008. J. N. Mu, W. C. Wu, H. B. Sun, Q. L. Guo, Y. Zhang and B. M. Zhang, "An online intelligent alarm-processing system based on abductive reasoning network," in Power and Energy Society General Meeting, 2012 IEEE, 2012, pp. 1-5. L. H. Wei, W. X. Guo, F. S. Wen, G. Ledwich, Z. W. Liao and J. B. Xin, "An Online Intelligent Alarm-Processing System for Digital Substations," Power Delivery, IEEE Transactions on, vol. 26, pp. 16151624, 2011. Y. Wu, M. Kezunovic, and T. Kostic, "An Advanced Alarm Processor using Two-level Processing Structure," in Power Tech, 2007 IEEE Lausanne, 2007, pp. 125-130. PJ. F. Hauer and F. Vakili, "An oscillation detector used in the BPA power system disturbance monitor," Power Systems, IEEE Transactions on, vol. 5, pp. 74-79, 1990. R. P. Schulz and B. B. Laios, "Triggering tradeoffs for recording dynamics [in power systems]," Computer Applications in Power, IEEE, vol. 10, pp. 44-49, 1997. K. J. Mei, S. M. Rovnyak, and C.-M. Ong, "Dynamic event detection using wavelet analysis," in Power Engineering Society General Meeting, 2006. IEEE, 2006, p. 7 pp. T. F. Kang, W. C. Wu, H. B. Sun, B. M. Zhang, and S. Qian, "A hybrid fault event detection algorithm using fault recorder data," in Future Power and Energy Engineering (ICFPEE), 2010 International Conference on, 2010, pp. 38-41. "Handbook for CSC-103A(B) digital extra -high-voltage line protection device," Beijing Sifang Automation Corp., Beijing, CN, Jun. 2007.
