International Journal of Engineering and Technology Innovation

Fault Identification, Classification, and Location on Transmission Lines Using Combined Machine Learning Methods

2022-02-24T01:24:08+00:00

This study develops a hybrid method to identify, classify, and locate electrical faults on transmission lines based on Machine Learning (ML) methods. Firstly, Wavelet Transform (WT) technique is applied to extract features from the current or voltage signals. The extracted signals are decomposed into eleven coefficients. These coefficients are calculated to the energy level, and the data of teen fault types are converted to the RGB image. Secondly, GoogLeNet model is applied to classify the fault, and Convolutional Neural Network (CNN) method is proposed to locate the fault. The proposed method is tested on the four-bus power system with the 220 kV transmission line via time-domain simulation using Matlab software. The conditions of the fault resistance random values and the pre-fault load changes are considered. The simulation results show that the proposed method has high accuracy and fast processing time, and is a useful tool for analyzing the system stability in the field of electricity.

Enhanced Kalman Filter Navigation Algorithm Based on Correntropy and Fixed-Point Update

2022-02-24T01:24:08+00:00

The accuracy of position estimation plays a key role in many of the precise positioning applications such as category I (CAT-I) aircraft landings, survey work, etc. To improve the accuracy of position estimation, a novel kinematic positioning algorithm designated as correntropy Kalman filter (CKF) is proposed in this study. Instead of minimum mean square error (MMSE), correntropy criterion (CC) is used as the optimality criterion of CKF. The prior estimates of the state and covariance matrix are computed in CKF and a novel fixed-point algorithm is then used to update the posterior estimates. The data of a dual-frequency global positioning system (GPS) receiver located at Indian Institute of Science (IISc), Bangalore (13.021°N/77.5°E) is collected from Scripps Orbit and Permanent Array Centre (SOPAC) to implement the proposed algorithm. The results of the proposed CKF algorithm are promising and exhibit significant improvement in position estimation compared to the conventional methods.

Evaluation on Mechanical Deterioration of the Asphalt Mixtures Containing Waste Materials When Exposed to Corrosion Solutions

2022-02-24T01:24:07+00:00

This research investigates the effect of corrosion solutions on the mechanical properties of asphalt concrete mixtures. A control asphalt mixture (CM) and five polymer-modified (PM) or filler-modified (FM) mixtures containing waste materials are prepared, namely PM high-density polyethylene plastic (PM-PL), PM crumb rubber (PM-CR), FM Para wood ash (FM-PA), FM palm empty fruit bunch ash (FM-EA), and FM rice husk ash (FM-RA). The experiment is conducted by immersing the mixture specimens in four types of water solutions (i.e., distilled water, alkaline solution, sulfate solution, and acid solution), followed by the splitting tests. Finally, the corrosion resistance factor (f_c) is computed to assess the corrosive effect of the corrosion solutions. The results show that the degree of reduction in tensile strength mainly depends on the type of corrosion solutions, type of mixtures, and immersion time. FM-EA provides better resistance under the alkaline and acid solutions, while PM-PL exhibits the greatest f_c under the sulfate solution. Among all the mixtures, PM-PL shows the greatest ability in withstanding the corrosion solutions.

Evaluation of Activation Energy for Agricultural Residues with Ignition Temperature

2022-02-24T01:24:07+00:00

The objective of this study is to evaluate the activation energy of agricultural residues with their ignition characteristics. The ignition temperature of agricultural residues (peanut shell, rice hull, and rice straw) is determined by measuring particle temperature, particle luminosity, and gas temperature for samples weighing 2.0, 2.5, and 3.0 grams. The maximal slope of the particle temperature versus furnace temperature is used to determine the occurrence of ignition. Values of activation energy are analyzed by the Semenov model with the measured ignition temperature. Results show that the particle ignition temperature is 317, 324, and 330°C for rice straw, peanut shell, and rice hull, respectively. The results also indicate that the particle ignition temperature reduces as the volatile content increases and the sample amount decreases. The value of activation energy is 157.2, 170.3, and 192.8 kJ/mole for rice straw, peanut shell, and rice hull, respectively.

Effectiveness of Pozzolanic Leaf Ashes and Plastics on Geotechnical Characteristics

2022-02-24T01:24:07+00:00

This study aims to investigate the geotechnical characteristics of three soils by adding waste plastics and a mixture of leaf ashes. The soil stabilizers used in the study are the plastics strips from waste plastic file folders and a mixture of ashes from five naturally occurring pozzolanic leaves in Sri Lanka. The plastics used in this study have a width of 5 mm and aspect ratios of 1, 2, 3, and 4 in the weight percentages 0.5, 1, 2, 4, and 8. The mixture of leaf ashes used is in the weight percentages 2, 4, 6, 8, and 10. The investigated geotechnical characteristics of the soils include the improvement of maximum dry density (MDD), optimum moisture content (OMC), soaked California bearing ratio (CBR), shear strength parameters, plastic index (PI), and Atterberg limits. The results suggest that the optimum improvement in soaked CBR and MDD can be achieved by adding 2% plastics and 6% leaf ash mixture into the soils. Shear strength parameters and PI can also be improved.

Lightweight Compressive Sensing for Joint Compression and Encryption of Sensor Data

2022-02-24T01:24:08+00:00

The security and energy efficiency of resource-constrained distributed sensors are the major concerns in the Internet of Things (IoT) network. A novel lightweight compressive sensing (CS) method is proposed in this study for simultaneous compression and encryption of sensor data in IoT scenarios. The proposed method reduces the storage space and transmission cost and increases the IoT security, with joint compression and encryption of data by image sensors. In this proposed method, the cryptographic advantage of CS with a structurally random matrix (SRM) is considered. Block compressive sensing (BCS) with an SRM-based measurement matrix is performed to generate the compressed and primary encrypted data. To enhance security, a stream cipher-based pseudo-error vector is added to corrupt the compressed data, preventing the leakage of statistical information. The experimental results and comparative analyses show that the proposed scheme outperforms the conventional and state-of-art schemes in terms of reconstruction performance and encryption efficiency.