Modeling And Simulation Of The Switched Reluctance Motor

This Paper summarizes the study conducted on the techniques used and implemented to minimize the torque ripple of the Switched reluctance Motors. These motors although offering the advantages of higher speeds, reliability and phase independence, have the limitations of the torque ripple and non-linearity in the magnetic characteristics. Thus in order to have the good understanding of the Motor, it is simulated in the MATLAB/SIMULINK environment. This paper describes details on modeling of two different configurations of Switched Reluctance Motor concentrating only on the linear model by obeying all of its characteristics. The two configurations of motors are applied with two different control techniques and the results are calculated and tabulated. Load and No load analysis are also performed to understand the behavior of motor with load. Through out the analysis, various values of turn-on and turn-off angles are selected and finally the optimum values are calculated based on the performance parameters of Average torque, speed and torque ripple. All simulations are documented through this paper including its block models and initializations performed. Finally a control technique is recommended which produces the best results with smallest torque ripple

Modeling And Simulation Of The Switched Reluctance Motor

This Paper summarizes the study conducted on the techniques used and implemented to minimize the torque ripple of the Switched reluctance Motors. These motors although offering the advantages of higher speeds, reliability and phase independence, have the limitations of the torque ripple and non-linearity in the magnetic characteristics. Thus in order to have the good understanding of the Motor, it is simulated in the MATLAB/SIMULINK environment. This paper describes details on modeling of two different configurations of Switched Reluctance Motor concentrating only on the linear model by obeying all of its characteristics. The two configurations of motors are applied with two different control techniques and the results are calculated and tabulated. Load and No load analysis are also performed to understand the behavior of motor with load. Through out the analysis, various values of turn-on and turn-off angles are selected and finally the optimum values are calculated based on the performance parameters of Average torque, speed and torque ripple. All simulations are documented through this paper including its block models and initializations performed. Finally a control technique is recommended which produces the best results with smallest torque ripple

MODELING AND SIMULATION OF THE SWITCHED RELUCTANCE MOTOR

This Paper summarizes the study conducted on the techniques used and implemented to minimize the torque ripple of the Switched reluctance Motors. These motors although offering the advantages of higher speeds, reliability and phase independence, have the limitations of the torque ripple and non-linearity in the magnetic characteristics. Thus in order to have the good understanding of the Motor, it is simulated in the MA TLAB/SIMULINK environment. This paper describes details on modeling of two different configurations of Switched Reluctance Motor concentrating only on the linear model by obeying all of its characteristics. The two configurations of motors are applied with two different control techniques and the results are calculated and tabulated. Load and No load analysis are also performed to understand the behavior of motor with load. Through out the analysis, various values of turn-on and turn-off angles are selected and finally the optimum values are calculated based on the performance parameters of Average torque, speed and torque ripple. All simulations are documented through this paper including its block models and initializations performed. Finally a control technique is recommended which produces the best results with smallest torque ripple

Mitigation of Power Losses and Enhancement in Voltage Profile by Optimal Placement of Capacitor Banks With Particle Swarm Optimization in Radial Distribution Networks

The prime purpose of placing a capaci- tor bank in a power system is to provide reactive power, reduce power losses, and enhances voltage profile. The main challenge is to determine the optimum capacitor position and size that reduces both system power losses and the overall cost of the sys- tem with rigid constraints. For this purpose, different optimization techniques are used, for example Particle Swarm Optimization (PSO) which converges the com- plex non-linear problem in a systematic and method- ological way to find the best optimal solution. In this paper, the standard IEEE 33-bus and 69-bus systems are used to find the optimum location and size of the capacitors bank. These power networks are simu- lated in Siemens PSS®E software. For the optimum solution of capacitor banks, the PSO algorithm is used. The PSO fitness function is modelled in such a way which contains the high average bus voltage, the small size of capacitor banks, and low power losses. The fitness function used is a weighted type to reduce the computation time and multi-objective function complexity

Security analysis of network anomalies mitigation schemes in IoT networks

The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed

A DDoS attack mitigation framework for IoT networks using fog computing

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks

A hybrid dual-mode trust management scheme for vehicular networks

Vehicular ad-hoc networks allow vehicles to exchange messages pertaining to safety and road efficiency. Building trust between nodes can, therefore, protect vehicular ad-hoc networks from malicious nodes and eliminate fake messages. Although there are several trust models already exist, many schemes suffer from varied limitations. For example, many schemes rely on information provided by other peers or central authorities, for example, roadside units and reputation management centers to ensure message reliability and build nodes’ reputation. Also, none of the proposed schemes operate in different environments, for example, urban and rural. To overcome these limitations, we propose a novel trust management scheme for self-organized vehicular ad-hoc networks. The scheme is based on a crediting technique and does not rely on other peers or central authorities which distinguishes it as an economical solution. Moreover, it is hybrid, in the sense it is data-based and entity-based which makes it capable of revoking malicious nodes and discarding fake messages. Furthermore, it operates in a dual-mode (urban and rural). The simulation has been performed utilizing Veins, an open-source framework along with OMNeT++, a network simulator, and SUMO, a traffic simulator. The scheme has been tested with two trust models (urban and rural). The simulation results prove the performance and security efficacy of the proposed scheme

TQ-Model: A New Evaluation Model for Knowledge-Based Authentication Schemes

Many user authentication schemes are developed to resolve security issues of traditional textual password scheme. However, only Android unlock scheme gets wide acceptance among users in the domain of smartphones. Although Android unlock scheme has many security issues, it is widely used due to usability advantages. Different models and frameworks are developed for evaluating the performance of user authentication schemes. However, most of the existing frameworks provide ambiguous process of evaluation, and their results do not reflect how much an authentication scheme is strong or weak with respect to traditional textual password scheme. In this research paper, an evaluation model called textual passwords-based quantification model (TQ-Model) is proposed for knowledge-based authentication schemes. In the TQ-Model, evaluation is done on the basis of different features, which are related to security, usability and memorability. An evaluator needs to assign a score to each of the feature based on some criteria defined in the model. From the evaluation result, the performance difference between a knowledge-based authentication scheme and textual password scheme can be measured. Furthermore, evaluation results of Android unlock scheme, picture gesture authentication scheme and Passface scheme are presented in the paper using the TQ-Model

An anomaly mitigation framework for IoT using fog computing

The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates