Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems) are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.
Along with the unceasing development of electric power industry, the scale of power system is expanding constantly, and the complexity of network structure is increasing continuously. Under this situation, the occurrence of a fault will have great influence upon the normal operation of a power system. Unfortunately, fault is inevitable during the operation of power system. When fault events occur, it is imperative to isolate the faulted section or sections from the healthy part of the power system and limit the impact of power supply interruption to a minimum as soon as possible. To achieve this goal, the fast and accurate identification of the faulted section or sections is of great significance and is the main issue of the fault diagnosis of power systems.
The fault diagnosis of power systems is a process that identifies a faulted section or sections using a set of operational information of protective relays (PRs) and circuit breakers (CBs) available from the supervisory control and data acquisition (SCADA) systems. The process is difficult and complicated. The complexity will increase significantly when involving failed and/or malfunctioned PRs and/or CBs, multiple faults, or even a concurrence of them. It is because of these contingencies that the fault information has the characteristics of incompleteness and uncertainty. To solve this serious issue, various approaches have been proposed, including expert systems (ES) [
Spiking neural P systems (SN P systems), which were firstly introduced in [
Based on the aforementioned prominent features, this paper attempts to apply the FRSN P systems to the fault diagnosis of power systems. The contribution of this paper includes three folders. To our knowledge, there have been no reports about implementing the FRSN P systems for solving fault diagnosis of power systems. Thus, the first and most important contribution is to implement the FRSN P systems for fault diagnosis of power systems for the first time and study how they solve this important problem. A diagnostic framework based on the FRSN P systems is developed. This framework is able to make the FRSN P systems based diagnostic model have good flexibility and extensibility. The operation statistics coupled with experience are used to tackle the uncertainties of PRs and CBs. They are able to realistically reflect the uncertainties among the PRs, CBs, and power system sections and thus make the developed FRSN P systems based diagnostic model more practical.
The structure of the paper is organized as follows. Section
Because of nature disturbing or man-made influence, power systems are threatened by the occurrence of faults during the operation. When a fault occurs in a power system, the well-designed protection system quickly detects the fault and activates its PRs to trip the corresponding CBs to clear the fault. In the clearance process, there are uncertainties regarding the protective devices, such as failed, malfunctioned operation of PRs and/or CBs. To guarantee that the faulted section is completely isolated within a given amount of setting time, multiprotection configuration is adopted. In general, protection system consists of main protection and backup protection. When a fault occurs on a section, the section’s main protection is firstly activated to isolate the fault. If the main protection fails, the backup protection must operate to eliminate the fault. In order to illustrate the concept of fault diagnosis, a simple power system [
A simple power system. m: main protective relay; p: primary backup protective relay; s: secondary backup protective relay. (a) Line L1 fault; (b) bus C fault.
A FRSN P system of degree
In the FRSN P systems, each proposition neuron corresponds to a proposition either in the antecedent part or consequent part of a fuzzy production rule, and each rule neuron corresponds to the category of antecedent part of a fuzzy production rule. Each neuron (proposition neuron or rule neuron) contains only one spiking (firing) rule, of the form
The parallel reasoning process of FRSN P systems is implemented by a matrix execution algorithm. In order to describe the execution algorithm logically and concisely, the following operators and functions are used: where where
Then the matrix based reasoning algorithm for FRSN P systems can be described as follows [
Let
Let
If
The developed diagnostic framework for fault diagnosis of power systems using FRSN P systems is shown in Figure
Developed diagnostic framework based on FRSN P systems.
A proper diagnostic model is prerequisite and critical to the sound diagnostic performance. It should be able to represent the causalities between faults and the actions of protective devices explicitly, which is beneficial to reasoning and understanding. Moreover, it can tackle the incompleteness and uncertainty of fault information well. In order to illustrate in detail, two FRSN P systems based diagnostic models associated with the fault scenarios of the line L1 and the bus C described in Section
FRSN P systems based diagnostic models by means of SCADA data. (a) For the fault scenario of line L1; (b) for the fault scenario of bus C.
In Figure
The neurons
Operation statistics of main protections of China state grid in 220 kV and above power systems from 2004 to 2009.
Year | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | Average |
---|---|---|---|---|---|---|---|
Line | |||||||
Total faults | 29411 | 29291 | 26047 | 24685 | 36865 | 12347 | 26441 |
Correct diagnosed faults | 29181 | 29234 | 26030 | 24666 | 36855 | 12345 | 26385.17 |
Diagnostic accuracy/% | 99.22 | 99.81 | 99.93 | 99.92 | 99.97 | 99.98 |
|
| |||||||
Bus | |||||||
Total faults | 90 | 120 | 112 | 122 | 159 | 89 | 115.33 |
Correct diagnosed faults | 77 | 114 | 102 | 118 | 156 | 88 | 109.17 |
Diagnostic accuracy/% | 85.56 | 95.00 | 91.07 | 96.72 | 98.11 | 98.88 |
|
For the diagnostic model in Figure
After constructing the diagnostic model and setting the necessary simulation parameters, the matrix based reasoning algorithm can be used to pursue the faulted confidence level. Take the fault scenario of line L1 in Figure
The initial inputs are
Let
According to the matrix based reasoning algorithm described previously, the reasoning steps will result in the following. The first reasoning step
The second reasoning step
The third reasoning step
The fourth reasoning step
The fifth reasoning step
Thus, since the system reaches the stopping criteria, it exports its reasoning result; that is, the truth value of the proposition
For the fault scenario of bus C in Figure
In this section, three different power systems are employed to illustrate and validate the effectiveness of the proposed fault diagnosis approach.
The first test system, which is adopted from [
Observed SCADA data for application 1.
Sequence number | Observed signal |
---|---|
1 | Main protective relay B85m |
2 | Secondary backup protective relay L3Rs |
3 | Secondary backup protective relay L4Rs |
4 | Circuit breaker CB3 |
5 | Circuit breaker CB4 |
6 | Circuit breaker CB6 |
7 | Circuit breaker CB8 |
8 | Circuit breaker CB15 |
Candidate faulted sections and diagnostic results for application 1.
Candidate faulted sections | Faulted confidence level | Faulted section |
---|---|---|
Bus B85 | 0.720 | Yes |
Line L2 | 0.0998 | No |
Line L3 | 0.0998 | No |
Line L4 | 0.0998 | No |
Subnetwork of IEEE 118-bus power system. R and S denote the power receiving and sending ends of a line, respectively.
FRSN P systems based diagnostic model for application 1.
This fault scenario is adopted from [
Observed SCADA data for application 2.
Sequence number | Observed signal |
---|---|
1 | Main protective relay L29-23m |
2 | Main protective relay L29-27m |
3 | Main protective relay L30-23m |
4 | Main protective relay L30-24m |
5 | Primary backup protective relay L29-23p |
6 | Primary backup protective relay L29-27p |
7 | Secondary backup protective relay L25-20s |
8 | Circuit breaker CB50 |
9 | Circuit breaker CB57 |
10 | Circuit breaker CB58 |
11 | Circuit breaker CB59 |
12 | Circuit breaker CB60 |
Candidate faulted sections and diagnostic results for application 2.
Candidate faulted sections | Faulted confidence level | Faulted section |
---|---|---|
Bus BUS23 | 0.0947 | No |
Bus BUS24 | 0.0947 | No |
Bus BUS27 | 0.0947 | No |
Line L25 | 0.0998 | No |
Line L29 | 0.8981 | Yes |
Line L30 | 0.8981 | Yes |
Nine-bus power system.
FRSN P systems based diagnostic model for application 2.
To further confirm the validity of the proposed fault diagnosis approach, another power system in Figure
Typical fault scenarios and the corresponding diagnostic results for application 3.
Scenario | Observed signal | Faulted section/confidence level |
---|---|---|
1 | B13m, CB1306, CB1312, CB1314 | B13/0.8519 |
2 | B13m, L1413s, CB1306, CB1312, CB1413 | B13/0.720 |
3 | B13m, L1413m, CB1306, CB1312, CB1314 | B13/0.8519 |
4 | L1213p, L1312p, L0613s, CB0613, CB1213, CB1312 | L1213/0.765 |
5 | B04m, B09m, CB0402, CB0405, CB0407, CB0409, CB0904, CB0907, CB0910, CB0914 | B04/0.8519, B09/0.8519 |
6 | B14m, L1314m, L1413m, L0914s, CB0914, CB1314, CB1413 | B14/0.720, L1314/0.8981 |
7 | B11m, L0907m, L0914m, L0407s, L0807s, L0910s, L1110s, |
B10/0.720, B11/0.8519 |
14-bus power system.
FRSN P systems are a novel visual model for representing fuzzy reasoning rules. In this work, they are employed to diagnose the faulted section(s) of power systems. It can be summarized from the diagnostic results that since the FRSN P systems based diagnostic model is constructed for each section of a given power system, its scale is independent of the scale of the power system but dependent on the connection schemes between the section and its adjacent lines; the matrix based reasoning algorithm of FRSN P systems is able to obtain the diagnostic results by at most five reasoning steps (five steps for line, four steps for bus). Moreover, the number of reasoning steps is not connected with the breadth of the FRSN P systems based diagnostic model, but with the depth; compared with the main existing fault diagnosis approaches, the proposed approach presents the benefits: Compared with ES, it needs no heavy procedures of knowledge acquisition and knowledge base maintenance. Because the scale of the developed FRSN P systems based diagnostic model is constructed for each section of a given power system, so it has better flexibility versus the change of the power system’s topology. Additionally, it is able to handle uncertainty problems and thus has better fault tolerant capability, and its response time is very short. Compared with ANNs, it does not require the construction of a comprehensive training data and the execution of a complicated training process, so it has neither a problem of convergence nor a tedious determination of network’s dimension and weights. Besides, it can explicitly represent the causalities between the sections and the actions of protective devices and thus help dispatchers to understand and analyze the fault evolution and clearance processes. Furthermore, it has better flexibility versus the change of the power system’s topology. Compared with BNs, it does not require the construction of a comprehensive training data and the execution of a complicated training process; thus it has no problem of convergence. Compared with PNs, on the surface, PNs and FRSN P systems are similar. Both PNs and FRSN P systems have the characteristics of graphic discrete event representation and parallel information reasoning. However, the FRSN P systems possess more different types of versatile neurons, including proposition neuron, “AND” type rule neuron and “OR” type rule neuron. While Petri nets only contain place and transition. In this context, FRSN P systems have better flexibility and extensibility. Besides, these versatile neurons of the FRSN P systems are able to represent the “AND-OR” causalities among the PRs, CBs, and power system sections more explicitly and visually for dispatchers. Thus, it is doubtlessly in favor of dispatchers’ comprehension, analysis, and summary on the fault evolution and clearance processes. In addition, these two models have other essential differences which have been discussed in detail in [ Compared with CE-Nets, the backward reasoning strategy enables the FRSN P systems based diagnostic model to visually represent all possible combinations of main, primary backup and secondary backup protection operations for reasoning a fault. In addition, it has better fault tolerant capability. Compared with AOM, it does not need to design an elaborate mathematical model for modeling the operation logic of protective devices. Additionally, the solution methods for AOM are also avoided; thus there is no concern regarding the convergence problem.
This paper has proposed a novel approach based on FRSN P systems for fault diagnosis of power systems. FRSN P systems possess the characteristics of graphic discrete event representation, dynamic firing mechanism, and parallel reasoning. They are capable of representing information containing uncertainty and performing fuzzy reasoning through matrix operation. Three different applications are used to verify the feasibility and effectiveness of the proposed fault diagnosis approach. The diagnostic results demonstrate that this approach is able to diagnose different single and multiple faults coupled with failed and/or malfunctioned protective devices. Since it requires only simple matrix operation and has good fault tolerance, the proposed approach is very suitable for integration and management with the existing SCADA systems and is used as a reliable tool for fault diagnosis of power systems. Additionally, because the developed FRSN P systems based diagnostic model can be constructed in advance, and stored in files, and can obtain the diagnostic results by no more than five reasoning steps, it is especially suitable for online application. In future, how to realize the online application and how the FRSN P systems based diagnostic model adapts to the change of power system’s topology are our further research work.
The authors would like to thank the editors and the reviewers for their constructive comments. This work was supported by the National Natural Science Foundation of China (no. 51107048).