Chapter 6 described the novel application of the fuzzy ontology in power system, that was capable of dealing with the uncertainty. The advantages of fuzzy ontology and some examples of its application for power transformer fault diagnosis were discussed. To investigate the improvement of the fuzzy ontology compared to the previously developed ontology, the fault diagnosis based on Roger’s method was applied. The accuracy of developed fuzzy ontology was evaluated using the same DGA samples as previously; then the overall accuracy was assessed. It was shown that the use of fuzzy ontology allowed to improve accuracy by over 22 %, compared to the other types of knowledge- based systems discussed earlier.
7.2
Future Research
Due to the time limitation and the broadness of the research field the work was carried out at, it was not possible to investigate all the possible methods in terms of this project. This section provides several suggestions that might be used for future work conducted in the relevant area.
• MAS decision maker based on fuzzy ontology in power system
Fuzzy ontology provides the semantic annotations based on logic for dealing with uncertain knowledge. The present thesis describes a limited number of the agents developed. Various types of behaviour, such as cyclic, parallel, etc., can be im- plemented for improving the agent system. However, one feature of the fuzzy ontology is to implement the relative importance of every criterion with decision alternatives, by assigning a weight to it. This feature can be applied for the power transformer fault diagnosis for the case of diagnosed various types of faults. In case of xdecision alternatives and a set of y criteria according to which the desir- ability of the fault type can be judged. The use of developed agent framework with knowledge-based system combined with the features of fuzzy ontology described above can provide an additional abilities to be used in power system.
7.2 Future Research 132
Agent technology is an impressive technique that can be applied in power system for condition monitoring and automation of the components. Depending on the application, the agent architectures may vary to be able to cooperate for achieving the goals. In this work the developed agent system for transformer fault diagnosis uses the reactive architecture. This type of architecture was chosen based on its ability to respond actively to the fault situation. However, in case of electricity marketing and utilities, long term decision makers based on agent system obser- vation and goal direction are required. The BDI agent architecture is a useful architecture can be applied for those type of applications in power system.
• Fault diagnosis with non-linear classification method
The interaction of the proposed multi-agent framework withKNN classifier were successful. A linear classifier method,KNN, was applied to evaluate the accuracy. For the purpose of increasing the accuracy of the fault diagnosis, various other methods can be applied. For instance, using non-liner classification, such as SVM, can improve the overall accuracy.
• Knowledge-based learning system
The complexity of real-world problems often require complicated methods and tools for building on-line, knowledge-based intelligent systems. The multi-agent framework described in this thesis does not possess the learning method. Therefore the proposed system can be potentially improved with introducing the learning ability to it. Thus, the learning ability of the knowledge base means that the system attempts to complete the missing knowledge and reduce non-reliability of the communication process between man and machine [115].
Appendix A
The Gaia Methodology Design for
the System
A.1
Role Schema of Gaia Methodology
Figures A.1 to A.7 represent the role schemas of Gaia methodology for the remain roles.
Role Schema: Description:
Protocols and Activities:
Responsibilities
Reporter
Receives messa e rom ser or aphical report, requests data from the data collector, draws data and replies to the report.
AwaitRequestReport, ExtractRequestDataDetail, SendRequestData, AwaitReceiveRequestData, DrawRequestData, SendReportRequestData Permission:
reads generates
supplied data createReport
// what data is required // draw data as a report
Liveness:
Safety:
Reporter = (AwaitRequestReport. GetData. Generate- Report)ʷ
GetData = (ExtractRequestDataDetail. SendRequestData. AwaitReceiveRequestData)
GenerateReport = (DrawRequestData. SendReportRequestData)
·
repliedRequestReport = trueA.1 Role Schema of Gaia Methodology 134
Role Schema: Description:
Protocols and Activities:
Permission:
Data_Collector
Receives messa e rom the data sender, extracts the new data from message and saves into database. The new data is sent for fault diagnosis by knowledge-based system, result is sent to be saved in database. This role is also able to get request for data and reply to this request.
AwaitNewMessage, ExtractDataDetails, SaveRawData, SendNew- DataForFaultDiagnose, AwaitReceiveDiagnosedData, Extract- DiagnosedData, SaveDataInDatabase, AwaitRequestData, GetRequest- Data, ReplayRequestData reads generates supplied newData supplied diagnosedData supplied requestData saveInDatabase messageWithNewData saveDiagnosedData getRequestData
// new data information
// new diagnosed data information // what data required
// save raw data in database
// create a message with data content // save data fault diagnosis
// get the information of required data Responsibilities
Liveness:
Safety:
Data_Collector = (AwaitNewMessage. SaveData. DiagnosedData. RepliedData)ʷ
SaveData = (ExtractDataDetails. SaveRawData) DiagnosedData= (SendNewDataForFaultDiagnose. Await-
ReceiveDiagnosedNewData. ExtractDiagnosed- Data. SaveDataInDatabase)
RepliedData = (AwaitRequestData. GetRequestData. Replay- RequestData)
·
dataSaved = true·
diagnosedDataSaved = true·
repliedRequestData = trueA.1 Role Schema of Gaia Methodology 135
Role Schema: Description:
Protocols and Activities:
Permission:
User
his role receives request from user interface for data or report, and gets the reply. It also requests applying some action and informs whether the action is done.
RequestData, ReplyRequestData, RequestReport, ReplyRequestReport, RequestPerformAction, InformActionDone reads generates supplied data supplied dataReport supplied actionApplied messagePerformingAction // data information
// report data in form of graph // inform the action is completed // create message for performing action Responsibilities
Liveness:
Safety:
User = (GetData | GetReport | PerformAction)+ GetData = (RequestData. ReplyRequestData) GetReport = (RequestReport. ReplyRequestReport) PerformAction = (RequestPerformAction. InformActionDone)
·
repliedDataReport = true·
repliedRequestReport = true·
actionPerformed = trueFigure A.3: The “User” role schema
Role Schema: Description:
Protocols and Activities:
Controller
his role receives an action per ormance and also in orms the ser hat action is applied
ait ormAction, PerformAction, InformAppliedAction Permission:
reads generates
supplied performAction messageWithNewData
// which equipment is activated // create a message with applied
action Responsibilities
Liveness: Safety:
Controller = (AwaitInformAction. ActionDone)+ ActionDone = (PerformAction. InformAppliedAction)
·
actionPerformed = trueA.1 Role Schema of Gaia Methodology 136
Role Schema: Description:
Protocols and Activities:
Knowledge-based
This role receives data from data collector and uses the knowledge-based system to diagnose the fault. It is also able to send message to relevant controlling devices.
AwaitReseiveData, DiagnoseFault, AssignAction, SendDiagnosedData, SendPerformAction reads generates supplied newData usesKnowledge-basedSystem messageDiagnosedFault messageActionPerformance
// new data information
// connect to knowledge-based system // create message with defined fault // create message for performing
action Permission:
Responsibilities Liveness: Safety:
Knowledge-based = (AwaitReseiveData. DiagnoseFault. Send-
DiagnosedData. SendPerformAction)ʷ
·
faultDiagnosed = true·
actionPerformed = trueA.1 Role Schema of Gaia Methodology 137
Role Schema: Description:
Protocols and Activities:
Analyser
This role receives data, connects to MATLAB, fault diagnosis and finally informs the data collector about the results.
AwaitNewMessage, ExtractDataDetail, ConnectToMATLAB,
DiagnoseFault, SendDiagnosedData Permission: reads generates supplied newData supplied diagnosedData messageWithDiagnosedData
// new data information
// new diagnosed data information // create a message with diagnosed
data Responsibilities
Liveness:
Safety:
Analyser = (AwaitNewMessage. DiagnosedFaults )ʷ
DiagnosedFaults = (ExtractDataDetail. ConnectToMATLAB. DiagnoseFault. SendDiagnosedData)
·
faultDiagnosed = true·
repliedDiagnosedData = trueFigure A.6: The “Analyser” role schema
Role Schema: Description:
Protocols and Activities:
Coordinator
This role provides coordination et een roles y checking their reports.
AwaitInformDataCollected, AwaitInformDataReported, AwaitInform- GraphReported, AwaitInformActionDone, AwaitInformFaultDiagnosed Permission:
reads Supplied reportTaskDone // reporting task completed Responsibilities
Liveness:
Safety:
Coordinator = (AwaitInformeDataCollected. AwaitInformData- Reported. AwaitInformGraphReported. Await- InformActionDone. AwaitInformFaultDiagnosed)ʷ