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All Journal International Journal of Electrical and Computer Engineering International Journal of Applied Power Engineering (IJAPE) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Wazir Mustafa
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Electricity theft detection framework based on universal prediction algorithm Abdulrahaman Okino Otuoze; Mohd Wazir Mustafa; Ibim Ebianga Sofimieari; Abdulhakeem Mohd Dobi; Aliyu Hamza Sule; Abiodun Emmanuel Abioye; Muhammad Salman Saeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp758-768

Abstract

Electricity theft has caused huge losses over the globe and the trend of its perpetuation constantly evolve even as smart technologies such as smart meters are being deployed. Although the smart meters have come under some attacks, they provide sufficient data which can be analysed by an intelligent strategy for effective monitoring and detection of compromised situations. So many techniques have been employed but satisfactory result is yet to be obtained for a real-time detection of this electrical fraud. This work suggests a framework based on Universal Anomaly Detection (UAD) utilizing Lempel-Ziv universal compression algorithm, aimed at achieving a real-time detection in a smart grid environment. A number of the network parameters can be monitored to detect anomalies, but this framework monitors the energy consumption data, rate of change of the energy consumption data, its date stamp and time signatures. To classify the data based on normal and abnormal behaviour, Lempel-Ziv algorithm is used to assign probability of occurrence to the compressed data of the monitored parameters. This framework can learn normal behaviours of smart meter data and give alerts during any detected anomaly based on deviation from this probability. A forced aggressivemeasure is also suggested in the framework as means of applying fines to fraudulent customers.
Fuzzy Neural Network for Classification Fault In Protection System Azriyenni Azriyenni; Mohd Wazir Mustafa; Naila Zareen
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp5969-5975

Abstract

Novel intelligent technique is a combination of fuzzy and neural network techniques that can be used to classify faults in electric power system protection. There have two problems in the protection system, which are: undesired tripping and fail to operate. Loss of power supply to relays and circuit breakers or failure in protective devices may cause failures in protection system. Construction of neural networks to explore fact to identify fault component is from control center. The objective of this paper is to develop novel concept for classification failures protection system are using Fuzzy Neural Network technique. Methodology consists of Neural Network and Fuzzy. The Neural network is also conscientious for estimating degree of membership in system components from corresponding area in classification of disorders. The input variables of neural network formed of binary numbers. Value of 1 indicates if fault occurs and value of 0 indicates no-fault occurs. Fuzzy relations will represent by fuzzy. These Fuzzy relations can be represented by fuzzy diagram consisting of three sets of node that would be considered to represent components, relays and circuit breakers.
Co-Authors Abdulhakeem Mohd Dobi Abdulrahaman Okino Otuoze Abdulrahaman Okino Otuoze Abiodun Emmanuel Abioye Ahsanullah Memon Aliyu Hamza Sule Azriyenni Azhari Zakri Daw Saleh Sasi Mohammed Ibim Ebianga Sofimieari Jafaru Usman Mamunu Mustapha Mamunu Mustapha Muhammad Salman Saeed Nabila Ahmad Rufa’i Naila Zareen Oladimeji Ibrahim Olatunji Obalowu Mohammed Raja Masood Larik Saddam Hussain Sajid Hussain Qazi Sandy Ahmad Sani Salisu Sani Salisu Sani Salisu Syukri Darmawan