Outlier detection approaches for wireless sensor networks

Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis

This research work put forward an intelligent method for diagnosis and classification of power transformers faults based on the instructive Dissolved Gas Analysis Method (DGAM) attributes and machine learning algorithms. In the proposed method, 14 attributes obtained through DGAM are utilized as the initial and unprocessed inputs of Adaptive Neuro-Fuzzy Inference System (ANFIS). In this method, attribute selection and improved learning algorithm are utilized to enhance fault detection and recognition precision . In the propounded fault detection and classification method, the most instructive attributes obtained by DGAM are selected by association rules learning technique (ARLT). Using efficient enlightening attributes and eliminating tautological attributes lead to higher accuracy and superior operation. Furthermore, appropriate training of ANFIS has significant effect on its precision and robustness . Therefore, Black Widow Optimization Algorithm (BWOA) is applied to train the ANFIS. Having excellent exploration and extraction capability, fast convergence speed and simplicity is the main reason for choosing the BWOA as the learning algorithm. Two industrial datasets are utilized to test and evaluate the performance of the put forward method. The results show that the propounded diagnosis system has high accuracy, robust performance and short run time. Selecting the most educative attributes of DGAM, training ANFIS optimally, improving the robustness of ANFIS and increasing the classification accuracy are the main contribution of this paper in the field of power transformer fault detection and classification.

An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis.

Accurate diagnosis of incipient faults in oil-filled power transformers is important in preventive maintenance of transformers. Dissolved gas analysis (DGA) is an effective tool to diagnose incipient transformer faults. The majority of the methods reported in literature to analyze DGA results lay more emphasis on user experience rather than mathematical formulation/justification. Furthermore, sometimes DGA results for a certain fault do not belong to any of the IEC/IEEE standard and cannot be categorized/diagnosed. To address these issues, we propose a new approach for DGA interpretation using gene expression programming (GEP). The proposed approach is employed for analysis of 552 DGA samples collected from transformers of Himachal Pradesh State Electricity Board, India, in conjunction with samples extracted from reliable literature. We use the aforementioned dataset to test and validate our proposed GEP model. We also compare the performance of our approach against other artificial intelligence-based techniques such as artificial neural network, fuzzy-logic, and support vector machine. Results and comparison against other soft computing approaches show relative superiority of GEP-based DGA interpretation in terms of classification accuracy.

Application of Gene Expression Programming (GEP) in Power Transformers Fault Diagnosis Using DGA

Wireless Sensor Networks (WSNs) have been applied in agriculture monitoring to monitor and collect various physical attributes within a specific area. It is important to detect data anomalies to determine a suitable course of action. The underlying aim of this paper is therefore to propose an anomaly detection scheme which is able to detect anomalies accurately by means of exploiting both time and frequency characteristics of the data signals. The contribution of this paper centers on anomaly detection by using Discrete Wavelet Transform (DWT) combined with a competitive learning neural network called self-organizing map (SOM) in order to accurately detect abnormal data readings. Experiment results from synthetic and real data collected from a WSN show that the proposed algorithm outperforms the SOM algorithm by up to 18% and DWT algorithm by up to 35% in presence of bursty faults with marginal increase of false alarm rate.

http://www.wseas.us/e-library/conferences/2010/Japan/ACS/ACS-59.pdf

Anomaly detection in wireless sensor networks using self-organizing map and wavelets

This paper describes a remote-controlled underwater drone developed using Arduino mega 2560 microcontroller and proposes utilization of this underwater drone for maritime security and surveillance. The Internet of Things system is proposed for providing a smart solution to marine security issues. The system that proposed would detect the submarine which crosses the maritime border line and would help inform the concerned authorities. Thus, the system enhances overall security of coastline and reduces the intermittent patrolling of sea by the coast guard. The Internet-of-Things-based system has been developed for any violations in maritime border which is immediately notified to security personnel, and an alert system is also created for remote access.

Nagireddy Ravi

Papers

Development of IoT Based Underwater Drone for Maritime Security and Surveillance

Electric vehicle charging method and impact of charging and discharging on distribution system: a review