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Application of Composite Method for Determining Fault Location on Electrical Power Distribution Lines
Distribution line is one of the most important components of the distribution system. Troubleshooting faults on these lines are often a tedious task requiring service vehicles and personnel moving from one place to another in order to locate the fault and fix the problem. The study, therefore, is on how a composite fault location technique can be applied to predict the location of faults on the distribution lines. The calculations for the estimation of the fault location are performed using one terminal voltage and current data of the distribution line. A composite method that combines the impedance-based method and the fuzzy inference system method is used in the fault location algorithm. The presented algorithm has been extensively tested using the MATLAB-Simulink model of a 33 KV 40-kilometer distribution line. The simulation result demonstrates good accuracy and robustness of the algorithm.
Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine
Fault detection for turbine engine components is becoming increasingly important for the efficient running of commercial aircraft. Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. In this paper, an adaptive weighted one-class SVM-based fault detection method coupled with incremental and decremental strategy is proposed, which can efficiently solve the time series data stream drifting problem. To update the efficient training of the fault detection model, the incremental strategy based on the new incoming data and support vectors is proposed. The weight of the training sample is updated by the variations of the decision boundaries. Meanwhile, to increase the calculating speed of the fault detection model and reduce the redundant data, the decremental strategy based on the k-nearest neighbor (KNN) is adopted. Based on time series data stream, numerical simulations are conducted and the results validated the superiority of the proposed approach in terms of both the detection performance and robustness.
Enhanced Magnetic Wireless Sensor Network Algorithm for Traffic Flow Monitoring in Low-Speed Congested Traffic
Traffic flow monitoring using magnetic wireless sensor networks in chaotic cities of developing countries represents an emergent technology. One of the challenges facing such deployment is the development of effective detection signal-processing algorithm in low-speed congested traffic based on the Earth’s magnetic fields. The proposed algorithm is the performance improvement of the previous algorithm known as the Scanning and Decision Algorithm (SDA). The novel algorithm based on the moving-average model includes an addition of a two-pass moving-average filter to improve the signal-to-noise ratio after analog-to-digital conversion. The improved mathematical capabilities enable us to capture additional features of vehicular direction and classification. Other outputs of the model include vehicular detection, count, speed, and travel time index (TTI). The performance evaluation of a proposed algorithm is conducted through on-site real-time experiments at the designated road segment. The results indicated that the roadside magnetic sensor improved vehicular detection, count, travel time index, and classification during low-speed congested traffic state.
Intelligent Medical Auxiliary Diagnosis Algorithm Based on Improved Decision Tree
In order to address the problem of low ability of intelligent medical auxiliary diagnosis (IMAD), an IMAD based on improved decision tree is proposed. Firstly, the constraint parameter model of IMAD is constructed. Secondly, according to the physiological indexes of IMAD, the independent variables and dependent variables of auxiliary diagnosis are constructed, the quantitative recurrent analysis of IMAD is carried out by using regression analysis method, the data analysis model of IMAD is constructed, and the adaptive classification and recognition of IMAD are carried out. Finally, the attribute feature quantity of IMAD with pathological characteristics is extracted, and the improved decision tree model is used to realize intelligent medical auxiliary, assist in the optimal decision of diagnosis, and realize the effective classification and recognition of pathological characteristics. The results show that this method has better decision-making ability and better classification performance for IMAD, which improves the intelligence and accuracy of intelligent medical auxiliary diagnosis.
Management of Voltage Profile and Power Loss Minimization in a Grid-Connected Microgrid System Using Fuzzy-Based STATCOM Controller
The expansion of renewable energy is continuing powerfully. Electrical system ought to transmit power with diminished loss, improved power quality, and reliability while pleasing the need of customer’s load demand. Nevertheless, owing to the exhaustion of fossil fuels and their environmental impact, the availability of quality, stable, and reliable power in developing countries is worrying. Integrating a solar-wind based microgrid to the distribution network is the more feasible and best alternative solution to gratify the customer intensifying power demand while seeing the strict environmental regulations of generating power. However, the microgrid system connected in a distribution network has diverse problems and challenges. The problems comprise the development of voltage sag and swell, voltage unbalance, and power losses because of the intermittent nature of PV and wind resources. The objective of this study is to integrate microgrid system with STATCOM (static synchronous compensator) controller to ensure the higher power flow with enhanced voltage profile and reduced power loss. MATLAB/PSAT is used to model microgrid and STATCOM controller connected to the grid. Proportional integral (PI) and fuzzy logic controllers (FLC) are also applied to control the STATCOM. The effectiveness of STATCOM with microgrid integration is tested by connecting to the main distribution system using standard IEEE 30-bus system. Finally, it was observed that STATCOM raises the capacity of the distribution line and contributes to voltage profile improvements and power loss reduction.
Distributed Monitoring Based on -Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network
This paper falls under the problems of the monitoring of a Discrete Event System (DES) with time constraints. Among the various techniques used for online and distributed monitoring, we are interested in the chronicle recognition. Chronicles are temporal patterns that represent the system’s possible evolutions. The proposed models are based on -time Petri nets that are suitable to represent with accuracy and modularity the Tunisian railway network. These models are scalable and may be used to represent a large variety of railway networks. Then, monitoring is based on the generation of chronicles that are suitable to detect and isolate traffic incidents in a distributed setting. Consequently, the proposed approach is tractable for large networks. Finally, to demonstrate the effectiveness and accuracy of the approach, an application to the case study of the Tunisian railway network is outlined.