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An Online Intelligent Alarm-Processing System Based on Abductive Reasoning Network Jianan Mu, Wenchuan Wu, Member, IEEE, Hongbin Sun*, Member, IEEE, Qinglai Guo, Member, IEEE, Yang Zhang, Boming Zhang, Fellow, IEEE
Abstract—Large amounts of alarms coming into control center when faults occur have harassed operators for a long time, especially when there are false alarms, missing alarms or multiple faults. An online intelligent alarm-processing system based on abductive reasoning network (ARN) is developed to deal with the alarms automatically. Models and real-time data of the power system are collected from both supervisory control and data acquisition system / energy management system (SCADA/EMS) and fault information system, including primary and secondary models. First, when alarms arrive, rules reflecting cause-effect relationship between faults and alarms and between alarms themselves are established automatically, and the whole abductive reasoning network is constructed. Then, all the coming alarms are located on the network and traced back to the roots. Third, possible diagnoses are sifted and picked out according to their false alarm rate and missing alarm rate. At last, the diagnosis results are presented to the operators in several friendly manners. The system has been applied to the district control center of Jiaxing, China and is functioning well. Index Terms—Abductive reasoning network (ARN), alarm processing, fault diagnosis, SCADA/EMS, fault information system.
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I. INTRODUCTION
perators have long been harassed by the huge amounts of alarms coming up to the control center during a fault, which hinder them from identifying and handling the fault properly and instantly [1]. Though 20 years has passed, the problem still exists for the present power systems. For example, there were approximately 240 sequence of events (SOE) alarms appeared in 20 minutes during a simple singlephase-to-ground fault, among which the first 50 alarms appeared in a few seconds, according to our investigation in a control center covering 17 500kV transmission substations in North China in 2010. An effective automated online alarm processor is in urgent need which enables the operators to identify the root cause and developing process of the faults as soon as they occur in order to help make better decision and reduce time of restoration [2]. Such an alarm processor should This work was supported in part by National Science Fund for Distinguished Young Scholars of China (51025725), National Science Foundation of China (50807025, 50877043), National High Technology Research Program (2011AA05A101, 2011AA05A118) and Tsinghua University Initiative Scientific Research Program. Hongbin Sun* is the corresponding author(e-mail:
[email protected]). Jianan Mu, Wenchuan Wu, Qinglai Guo, Yang Zhang and Boming Zhang are all with the Department of Electrical Engineering, State Key Laboratory of Power Systems, Tsinghua University, Beijing 100084, China.
978-1-4673-2729-9/12/$31.00 ©2012 IEEE
meet the following requirements: z Accurate and robust estimation on location and nature of faults, better with root cause estimation; z Ability to deal with the uncertainty caused by malfunction of protective relays and circuit breakers; z Deal with the complex protection schemes in modern large-scale power system; z Adaptive to configuration and topological changes of power network ; z Ability to deal with continuous flow of alarms; z An understandable way to display the result. Much work has been done in dealing with this critical issue, among which the most widely used and successful method is expert or knowledge-based system, due to its flexibility, understandability and the symbolic nature of reasoning with alarms [3]. An expert system written in Prolog based on both knowledge about protection schemes and information on operated relays and tripped circuit breakers was proposed in [4]. Another expert system processing the time-stamped alarm lists in Portuguese Control Centers was developed in [5]. A logic-based expert system introduced in [6] converted the expertise into Boolean functions which are logically complete and sound. However, many of the expert systems suffer from the slowness in reasoning and difficulty in maintenance with the knowledge base when applied to alarm processing of large-scale power network. Furthermore, expert systems lack the ability to adjust to the configuration and topology changes of the power system, especially when complicated faults occur. Many other fault diagnosis methods have also been applied to power system fault diagnosis including artificial neural networks (ANNs) [7,8], cause-effect networks [9], Bayesian networks [10,11], fussy Petri nets [12-14], 0-1 optimization [15,16], information theory [17], etc. All of these methods can be categorized into two approaches. The first approach infers fault sections from generated alarms step by step according to established rules or inference structure, which is similar to the way that human diagnose the faults. This is referred to as inference-based approach. The other approach models fault diagnosis as an integer optimization problem, in which an optimal solution is searched representing the best fault hypothesis to explain the alarms. This is referred to as optimization-based approach. Inference-based approaches are more direct and robust while optimization-based approaches are more accurate and able to search in the entire solution space of faults. In this paper, an online intelligent alarm-processing system
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based on abductive reasoning network (ARN) is proposed and applied to a real district power system. The main contributions of this paper are: z Integrate the model and data in both supervisory control and data acquisition system / energy management system (SCADA/EMS) and fault information system; z Build and maintain an object-oriented secondary model structure which contains detailed protection information required by the alarm processing; z Propose a new efficient online alarm-processing method which is adaptable to the varying protection scheme and primary topology, and can handle and identify false alarms, missing alarms and multiple faults; z Present the diagnosis result in friendly ways; z Develop an online software system and apply it to the district control center of Jiaxing, China.
described as rule. Basically there are two kinds of rules, including rules between fault and protection and rules between protection and breaker.
II. ARCHITECTURE The architecture of the proposed system is shown in Fig. 1. On one hand, the alarm-processing system retrieves primary model of the power system from SCADA/EMS in the form of common information model (CIM) files based on IEC 61970. On the other hand, the system retrieves secondary model from fault information system through the protocol of IEC 60870-5-103. Both of the models update on a daily basis. The secondary model directly from fault information system is too preliminary and rough to meet the requirements of alarm processing. Therefore, intelligent parse analysis is implemented for the supplement of detailed information of protections and correlation with the primary devices. In addition to models, the alarm-processing system also receives real-time alarms from SCADA/EMS and fault information system. When alarms representing operations of protection or trips of circuit breaker arrive, they are firstly integrated in the real-time database. Then real-time rules are derived from the secondary model database. Both of the alarms and rules are put into the fault diagnosis process based on ARN. The results of the diagnosis are presented in three forms: z Alarm list indicating the root cause of each alarm; z Diagnosis report containing the number and time range of the alarms, all possible diagnoses and the false alarms and missing alarms under each diagnosis; z Alarm event tree which directly presents the causeeffect relations of faults and alarms in a graphic way. III. ONLINE ALARM PROCESSING BASED ON ARN A. Automatic Generation of Rules When faults occurred, protections detect them and emit signals to trip breakers. Therefore the “fault Æ protection Æ breaker” relationship is the basic cause-effect relationship during faults. In the proposed system, such relationship is
Fig. 1. Architecture of the proposed system.
We noticed that certain kinds of protection operate upon certain types of faults of a primary device, instead of all kinds of faults of that device. For example, zero sequence protections only operate upon ground faults. Therefore, it is important to distinguish between different kinds of faults of the same device. Given the different operating mechanism of main protection and backup protection, the automatic generation of rules will be discussed in the following three parts. 1) Rules Between Fault and Main Protection: Main protections are designed to clear faults at soon as possible. Therefore, the rules between fault and main protection are unconditional. All the main protections should be traversed to establish rules between fault and main protection. 2) Rules Between Fault and Backup Protection: Backup protections are designed as a backup method to clear faults subsequently when clearing is not accomplished at once, no matter due to the failure of main protections or breakers. Therefore, the rules between fault and backup protection should be established on the condition that the fault has not been cleared by the time that the backup protection’s preseted time delay runs out. Because it is difficult to judge the precise time when fault occurs, an indirect way is adopted to test the condition: the route between the backup protection and the primary device protected is recorded, if a) No breakers on the route tripped: then the rule between fault and backup protection should be established; b) Certain breakers on the route tripped: if the protection which tripped these breakers is of shorter time delay than the considered backup protection’s, then the rule should be established, otherwise the rule should not.
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Another characteristic of backup protections is that they cover more than one primary device. Therefore, topology analysis is needed based on real-time primary topology and zone of the backup protection to establish all the rules between fault and backup protection 3) Rules Between Protection and Breaker: Generally, protections trip the breakers where they are installed. For example, a line protection trips the breaker at the installed end of that line. Therefore, topology analysis is carried out to establish rules between protection and breaker. But there are exceptions, such as certain transformer protections trip the bus connection breaker first. In such situation, certain trip schemes should be added when establishing the rules according to the specifications of the district. B. Some Basic Concepts For convenience of understanding, some basic concepts used in the proposed methodology are defined or illustrated. 1) Abductive Reasoning Network (ARN):The causeeffective relationship between faults and alarms can be easily expressed as directed graph, as shown in Fig. 2. Such directed graph is called abductive reasoning network (ARN). Nodes in ARN represent faults or alarms and links between them represent rules, from cause to effect. In Fig. 2, there are one fault node and four alarm nodes. The solid nodes means that the alarms really occurred while hollow ones means they didn’t. A link is dashed when it links to hollow nodes.
Fig. 2. Example of abductive reasoning network.
2) Basic Sets: Define F as the set of all faults, A as the set of all alarms, H as the set of alarms that really happened. In Fig. 2, F = { f 1} , A = {a1, a 2, a 3, a 4} , H = {a 2, a 3, . a 4} . 3) Diagnosis: Define diagnosis Δ as a subset of F , i.e. Δ ⊆ F . In Fig. 2, { f 1} is a diagnosis. 4) Missing Alarms: Define missing alarms under diagnosis Δ as MA( Δ ) = Expect ( Δ ) − H ∩ Expect ( Δ ) (1) where Expect ( Δ ) means the set of alarms expected to happen under diagnosis Δ . In Fig. 2, MA({ f 1}) = {a1} . 5) False Alarms: Define false alarms under diagnosis Δ as FA( Δ ) = H − H ∩ Expect ( Δ ) (2) In Fig. 2, FA({ f 1}) = ∅ . 6) Missing Rate: Define missing rate of diagnosis Δ as MA( Δ ) (3) MissingRate( Δ ) = × 100% Expect ( Δ ) In Fig. 2, MissingRate({ f 1}) = 25% .
7) False Rate: Define false rate of diagnosis Δ as FA( Δ ) FalseRate( Δ ) = × 100% H
(4)
In Fig. 2, FalseRate({ f 1}) = 0 . 8) MaxFaults: Define MaxFaults as maximum fault multiplicity considered in the system, which denotes the system’s ability to handle multiple faults. Usually MaxFaults = 3 is enough for the diagnosis. 9) MrLimit and FrLimit: Define MrLimit ( ∈ [0,1) ) as the upper limit of missing rate and FrLimit ( ∈ [0,1) ) as the upper limit of false rate. C. Alarm Processing Method Based on ARN The proposed alarm processing method based on ARN is mainly composed of three steps. 1)Explain Alarms: When all the real-time rules are established as specified in section A, the whole ARN is subsequently constructed. Locate all the alarms that really happened in the ARN, then trace them backward along links that pointed to them, until a root node is reached, which means a possible fault that caused the alarms. Make all such faults to compose G . Obviously G must be a superset of the final diagnoses. 2)Sift Diagnoses: We suppose that the diagnosis should follow the Parsimony Principle, which means using less faults to explain the alarms if possible. Thus, if Δ is a possible diagnosist, then any superset of Δ should be dismissed. Define C as the set of possible diagnoses. In this step, traverse all the element Δ in the power set of G in the order from small | Δ | to large | Δ | , until | Δ |=
min{| G |, MaxFaults} , during the traversal: a) If Δ is superset of any element in C : skip; b) Otherwise calculate MissingRate( Δ ) , FalseRate ( Δ ) , MissingRate( Δ ) ≤ MrLimit and FalseRate if ( Δ ) ≤ FrLimit , add Δ to C ; 3)Process Results: a) If C = ∅ , then there is no faults and all the alarms are false alarms; otherwise go to b); b) If | C |= 1 , then there is only one possible diagnosis, output the result; otherwise go to c); c) If | C |> 1 , then pick out the diagnoses with minimum false rate. If there is only one such diagnosis, output the result. Otherwise, pick out the diagnoses with both minimum false rate and minimum missing rate. If there is only one such diagnosis, output the result. Otherwise, put all such diagnoses into L , go to d); d) More than one element in L is often caused by the rules’ inability to discriminate among similar types of faults of the same primary device. However it is difficult and unnecessary to discriminate among similar faults of a device in this situation. Therefore different faults of the same device are merged into a new one to represent the fault. For example,
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two-phase fault and three-phase fault can be merged into phase-to-phase fault. The advantages of the proposed alarm processing method based on ARN are listed below: z High performance under circumstances of false/ missing alarms and the ability to identify them; z Ability to handle multiple faults; z The result is easy to explain and demonstrate; z High efficiency. The method only concentrates on the rules and faults related to the limited alarms that really happened, so G in step 1) is quite small. Furthermore, the multiplicity of faults considered is limited to MaxFaults, which is usually less than 3. IV. APPLICATION EXAMPLE An example is given based on the real models and alarms in Jiaxing power system. Fig. 3 shows the structure of a partial network. TABLE I presents part of the protection schemes of the partial network, which is retrieved from the secondary database. Protection scheme on Wangdian Substation side is the same as Baitao’s. TABLE II shows the alarm reported during a line fault. The ARN method diagnoses the fault to be L4468 ground fault with 25% false alarm rate and 0 missing alarm rate. Diagnosis report and alarm event tree are presented to the operator while alarm list is updated with root causes. We notice that rule between f1 and p3 is invalid because c5 has been trip by p1 in advance.
No
I4
P6
backup, distance zero sequence
B3
I5
P7
main, differential
B4
I6
P8
main, differential
C3, C4, C6 C4, C5
TABLE II REPORTED ALARMS LISTS Station Content Root Cause
1
Time (ms) 0
Baitao
P1 operated
L4468 ground fault
2
11
Baitao
P7 operated
false alarm
3
15
P2 operated
L4468 ground fault
4
16
P10 operated
L4468 ground fault
5
29
Baitao Wangdia n Wangdia n
P9 operated
L4468 ground fault
6
55
C5 tripped
L4468 ground fault
C7 tripped
L4468 ground fault
P3 operated
false alarm
7
65
8
103
Baitao Wangdia n Baitao
Fig. 4. Diagnosis report
Fig. 3. Part of the power system in Jiaxing, China
Subst ation
Bai tao
TABLE I PART OF PROTECTION SCHEME OF THE SYSTEM Dev IED Prote Type Tripped ice ction Breaker P1 main, differential I1 main, differential, L44 P2 C5 zero sequence 68 backup, distance I2 P3 zero sequence L44 67
I3
P4
main, differential
P5
main, differential, zero sequence
C6
Fig. 5. Alarm event tree of the diagnosis result
V. CONCLUSIONS Practical alarm processing in power systems requires the combination of primary and secondary information. An online alarm-processing system is developed for district control
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center, utilizing models and real-time data from both SCADA/EMS and fault information system. Furthermore, an effective alarm-processing method based on abductive reasoning net is proposed and software system is developed accordingly. The system has already been applied to actual power system in Jiaxing, China. Tests show that the method is correct and the system is functioning well. VI. REFERENCES [1] [2] [3] [4]
[5] [6] [7]
[8] [9]
[10] [11]
[12] [13] [14] [15]
[16]
[17]
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VII. BIOGRAPHIES Jianan Mu received his Bachelor degree from the Department of Electrical Engineering, Tsinghua University in 2010. Now he is pursuing the Ph.D degree in Tsinghua University. His research interests include intelligent alarm processing and fault diagnosis in power systems. Wenchuan Wu (M’2006) was born in Jinhua, Zhejiang in China on Nov. 26, 1973. He graduated from the Department of Electrical Engineering, Tsinghua University, Beijing, China, in 1997 with MSc. He received his PhD degree from Tsinghua University in 2003 where he is now an associate professor. His special fields of interest include the EMS/DMS advanced applications, especially the online security and risk assessment. Hongbin Sun (M’2000) received his double B.S.degrees from Tsinghua University in 1992, the Ph.D from Dept. of E.E., Tsinghua University in 1997. He is now a full professor in Dept. of E.E., Tsinghua Univ, and assistant director of State Key Laboratory of Power Systems in China. From 2007.9 to 2008.9, he was a visiting professor with School of EECS at the Washington State University in Pullman. He is members of IEEE PES CAMS Cascading Failure Task Force and CIGRE C2.13 Task Force on Voltage/Var support in System Operations. His research interests include energy management system, voltage optimization and control, applications of information theory and intelligent technology in power systems. He has implemented system-wide automatic voltage control systems in more than 20 electrical power control centers in China. He won the first rank prize of Beijing science and technology progress in 2004, the first rank award of Chinese national high education achievements in 2005 and the second rank prize of Chinese national technology innovation in 2008 respectively. E-mail:
[email protected] Qinglai Guo (M’2009) was born in Jilin City, Jilin Province in China on Mar. 6, 1979. He graduated from the Department of Electrical Engineering, Tsinghua University, Beijing, China, in 2000 with B.S. degree. He received his PhD degree from Tsinghua University in 2005 where he is now an associate professor. His special fields of interest include the EMS advanced applications, especially the automatic voltage control and V2G. Yang Zhang received his Bachelor degree from the Department of Electrical Engineering, Tsinghua University in 2011. Now he is pursuing the Master degree in Tsinghua University. His research interests include power system modeling, especially for secondary model. Boming Zhang (F’2010) received his Ph.D. degree from Tsinghua University, Beijing, China, in 1985, in electrical engineering. From 1985 he has progressed from a lecturer to an associate professor and finally to a professor in the Department of Electrical Engineering of Tsinghua University. His research interests include power system analysis and control, especially the EMS advanced applications in EPCC. He is a steering member of CIGRE China State Committee and Int. Workshop of EPCC.
