Modern power system with renewables in distribution network has made the optimal sizing and location of reactive power support crucial and essential. By optimal locating and sizing of reactive power support resources causes a power loss reduction, improvement in voltage profile and maximizes techno-economic benefits to consumers and system operators while improving overall system performance and reliability.
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Research in the field of IDS has been going on since long time; however, there exists a number of ways to further improve the efficiency of IDS. This paper investigates the performance of Intrusion detection system using feature reduction and EBPA. The first step involves the reduction in number of features, based on the combination of information gain and correlation. In the next step, error back propagation algorithm (EBPA) is used to train the network and then analyze the performance. EBPA is commonly used due to its ease of use, high accuracy and efficiency. The proposed model is tested over the KDD Cup 99 and NSL-KDD datasets. Results show that the proposed IDS model with reduced feature set outperforms the other models, both in terms of performance metrics and processing time.
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Biometric information is used in a variety of industrial fields. Heart rate and respiration rate, in particular, are widely applied not only in medical institutions but also in life safety. However, a sensor must be worn or directly attached to the human body to obtain a bio signal, which is inconvenient and limits its application. In this study, a 24 GHz radar sensor module is developed, and an algorithm is implemented by analyzing the frequency and peak values of a human participant’s heartbeat and respiration signals in an unconstrained state. In the experiment, the existing ECG equipment (MP150) and radar sensor module are compared. The results indicate that the average value of MP150 is higher than that of the Doppler sensor in terms of all parameters; however, the deviation of the Doppler sensor is small, and the bias is low. Furthermore, it is confirmed that the HRV decreases in the drowsy state compared to that in the wakeful state in both devices. These results confirm that bio-signals change during drowsiness, and conversely, drowsiness can be detected through changes in bio-signals, which is a significant finding.
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In this work, an attempt is made to improve the accuracy of accident severity. To achieve the same, a particle swarm optimization-based algorithm is applied for evaluating the accident severity. Prior to implement the PSO, two modification are incorporated into PSO algorithm, called improved PSO. These modifications can be described as mutation operator and trail candidate generation. The performance of improved PSO is examined over accident traffic severity dataset and results are evaluated using accuracy, recall@5 and precision@5 metrics. Several existing techniques are considered for comparing the results of IPSO algorithm. It is revealed that IPSO achieves more accurate results among all techniques.
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In this research a secured data access control is proposed using the Advanced Encryption Standard (AES) combined with a weighted attribute-based Encryption (AES-WABE). To encrypt the data, the access control policies are used and weight is assigned according to its significance of each attribute. The outsourced data is stored by the cloud service provider and the attribute authority based on the weight that updates the attributes. To minimize the computational overload the data file is accessed by the receiver corresponding to its weight. The proposed procedure provides resistance for collusion, multiple user security with control of fine-grained access based on protection, reliability and efficiency. On concerning the data collaboration and confidentiality, the performance rating is done related with the Cipher-text Policy–Attribute-based Encryption (CP-ABE) and the hybrid attribute-based encryption (HABE) scheme, access control flexibility, limited decryption, full delegation, verification and partial signing.
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This paper presents a summary of recent research in the domain of integration of renewable energy sources with electric vehicles (EVs) under the smart grid environment. Electric vehicles-smart grid integrated systems face several issues related to communication, grid infrastructure and control in the future power system. Feasibility of integration of solar and wind energy systems with electric vehicles is discussed in section 2. The existing research articles in this area are classified into two based on the purpose: EVs integration into the electric grid and Vehicle to grid services. The function of V2G in the electricity market, as well as its management concerns, are investigated in section 3. Finally, the research gaps and future scope of incorporating electric vehicles with renewable energy sources and the Smart grid are highlighted.
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With increased penetration of renewables since last decade has evolved measures from the regulator for robust distribution network mostly catering to residential load. With increasing future demand of commercial and industrial load (CIL) due to aspiring GDP growth and insistence of popular governments to encourage renewable use, large chunk of the CIL will be served by renewables. So increasing the robustness alone on the renewable supply side will be in vain unless effective Demand response with rationalized tariff system for CIL which is more profitable energy market than subsidized residential tariff in India .
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The paper describes winding failures in Permanent Magnet Synchronous Motor (PMSM) which are preferred in Electric Vehicles. Drive can be designed with fault tolerance to continue to operate under occurrence of faults. Winding insulation failure is responsible for some of the most serious faults. A method for detecting open circuit of winding, short circuit of winding or turn to turn short circuit in winding may result as a failure of winding insulation is presented. Further a technique for continued post-fault operation of the drive is discussed based upon mathematical model. The detection method operates in real time without the use of additional sensors and is sensitive enough to detect the presence of an air-gap between turns.
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A solar pumping model is proposed in this paper using a Brushless DC (BLDC) motor. Zeta converter is used as a DC-DC link between the voltage source inverter and the PV array. Zeta converter enables soft starting of the BLDC motor and the speed control is achieved by simple variation of DC link voltage, thereby eliminating the need for complex switching circuitry. Zeta converter belongs to the class of buck-boost converter hence offers a wide range of operating voltage. The proposed model is simple and cost-effective so it can be practically implemented with minimum cost. The proposed model is tested for its suitability in the MATLAB/Simulink environment.
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