Recent Advances in Electrical & Electronic Engineering - Volume 17, Issue 2, 2024

Introduction: In recent years, the frequent occurrence of network security attacks in the power field has brought huge risks to the production, transmission, and supply of power systems, and Advanced Persistent Threat (APT) is a covert advanced network security attack, which has become one of the network security risks that cannot be ignored in the construction of new power systems. Objective: This study aims to resist the increasing risk of APT attacks in the construction of new power systems, this paper proposes an attack detection model based on Deep Packet Inspection (DPI) and Transformer. Methods: Firstly, we extracted 606 traffic characteristics from the original traffic data through the extended CIC Flowmeter and used them all to train the Transformer network. Then, we used the DPI-Transformer model and traffic labels to perform feature analysis on the traffic data and finally obtained the APT-Score. If the APT-Score is greater than the threshold, the alarm module is triggered. Results: By analyzing the headers and payloads of the network traffic in the APT-2020 dataset, the experimental results show that the detection accuracy of APT attacks by the DPI-Transformer detection model is significantly higher than that of the current mainstream APT attack detection algorithms. Conclusion: Combined with the characteristics of the new power system and APT attacks, this paper proposes an attack detection model DPI-Transformer, which proves that the model has greatly improved the detection accuracy.

Aims: The significant aim of the proposed work is to develop an adaptive method to compute the threshold during run-time and update it adaptively for each pixel in the testing phase. It classifies motion-oriented pixels from the scene for moving objects using background subtraction and enhances using post-processing. Background: According to the huge demand for surveillance system, society is looking towards an intelligent video surveillance system that detect and track moving objects from video captured through a surveillance camera. So, it is very crucial and highly recommended throughout the globe in numerous domains such as video-based surveillance, healthcare, transportation, and many more. Practically, this research area faces lots of challenging issues such as illumination variation, cluttered background, camouflage, etc. So, this paper has developed an adaptive background subtraction method to handle such challenging problems. Objective: To focus and study the problematic video data captured through the camera sensor. To handle challenging issues available in real-time video scenes. To develop a background subtraction method and update the background model adaptively for moving object detection. Methods: The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background model Results: The qualitative analysis of the proposed work is experimented with publicly available datasets and compared with considered state-of-the-art methods. In this work, library sequence (thermal data) of CDNET and other color video frame sequences Foreground aperture, Waving Tree and Camouflage are considered from Microsoft’s Wallflower. The quantitative values depicted in Table- 1. This work demonstrate the better performance of the proposed method as compared to state-ofthe- art methods. It also generates better outcomes and handles the problem of a dynamic environment and illumination variation. Conclusion: Currently, the world is demanding computer vision-based security and surveillancebased applications for society. This work has provided a method for the detection of moving information using an adaptive method of background subtraction approach for moving object detection in video scenes. The performance evaluation depicts better average results as compared to considered peer methods.

Background: The locomotive traction motor has gradually shifted from DC motors to AC motors in high-speed railways, and the traction motor needs to be regularly maintained and tested frequently. Objective: The aim of this study was to test the related motor performance by obtaining and analyzing motor data from the test according to the standard "Three-phase Asynchronous Motor Test Method (GB-T 1032-2012)". The performance of the tested motor has been evaluated to meet the relevant requirements of the application. Thus, reasonable and scientific references have been offered for the maintenance and repair of the motor. Methods: A three-phase AC asynchronous motor test system based on LabVIEW and an AC power dynamometer was constructed based on the needs of the factory and type test of a three-phase AC asynchronous motor. Ambient temperature measurement, insulation resistance measurement, DC resistance measurement, no-load characteristics test, overspeed test, blocking characteristics test, load test, and temperature rise test have been carried out. The data on voltage, current, speed, power, and so on, have been collected. A 125 kW three-phase asynchronous motor was tested with the designed system, and the parameters obtained from the system were compared with those from the motor labels. Results: The three-phase AC asynchronous motor test system was designed based on LabVIEW and AC dynamometer operating on an industrial computer with a precision measuring instrument. The most advanced virtual instrument technology was used to combine the powerful data computing and processing ability with the measurement and control ability of instrument hardware. The software proved to be able to acquire data display, control, storage, and analysis simultaneously. In addition, a high degree of intelligence, an automatic motor start and stop control, automatic synchronous acquisition of test data, automatic data processing and calculation, and automatic test report generation and printing function were covered in this system. The simulated results of the system agreed well with the actual performance of the three-phase asynchronous motor, and helped the motor to operate well. Conclusion: The designed testing system exhibited a high automation ability, reliability, and accuracy. It proved to be a time and manpower-saving technical method, improving the actual test efficiency, and helped to reduce labor intensity dramatically.

Background: The control system of the vehicle regulates parameters like fuel flow control, vehicle speed control, tracking, etc. Objective: The main objective of the paper is to monitor and determine an efficient, and automated control system for an H-CNG-powered vehicle. Using neural networks and machine learning, we would develop an algorithm for the controller to regulate the speed of the car with the help of variables involved during the runtime of the vehicle. Methods: Initially, Generating a dataset with the help of formulation and computation for training. Further, analysing different supervised machine learning algorithms and training the Artificial Neural Network (ANN) using the generated dataset to predict and track the gains of the H-CNG vehicle accurately. Results: Analysis of the gains of the H-CNG vehicle are presented to understand the precision of the trained Neural Network. Conclusion: The final verdict of the paper is that the Neural Network is successful in tracking the gains of the H-CNG vehicle with the help of the dataset presented for training using the Random Forest Regression technique for machine learning.

Background: Microgrids conquer a significant role in the evolution of distributed and modern grids from the traditional electricity system. However, microgrids with renewable energy sources connected to them often incur grid instability issues, due to the intermittent nature of these sources. Objective: This work aims to study Microgrids with Electric vehicles as a backup energy source and maintain the system’s frequency that can overcome this issue. Methods: This paper uses an autonomous control algorithm in an islanded ac microgrid to regulate the active power depending on the irradiation and load scenarios, thereby maintaining the system frequency and stability. The controller also keeps track of the battery's charge level, keeping it from overcharging or over-discharging conditions. The PI (Proportional Integral) and Fractional Order Proportional Integral (FOPI) controllers were compared, with the best controller utilized for system simulations. Results: Simulations are presented with MATLAB/Simulink for an Islanded Photo Voltaic AC microgrid system with the electric vehicle's battery connected to it as a source of backup energy. The system's effect is exhibited under varied irradiations and load levels, and the findings demonstrate the control algorithm's adaptability. Conclusion: This work attempts to discover the capability of the control technique to maintaining the stability of an AC islanded microgrid system under diverse irradiation and load situations, thereby maintaining the system's frequency and the State of Charge (SoC) of the battery of an electric vehicle under specified levels.

Aims: A typical microgrid network sourced by renewable energy encounters a technical setback owing to the voltage imbalance across the source integration and load power dissemination. Transformers employed to stabilize the potential may deteriorate the network efficiency and increases the cost and size of the system as well. Introduction: Photovoltaic based transformerless high gain DC-DC converter (THG-DC) is proposed here to aid the microgrid infrastructure. Microgrid fuelled by renewable energy sources demands the high gain converter interface to boost low voltage generation. The proposed THG-DC is employed with four switched inductors and three active power switches (IGBT) which are brought together under dual leg configurations. Methods: The proposed topology offers dual-duty cycle modes of regulating the active switches to realize the desired output voltage. Moreover, it is reliable to drive the proposed THG-DC with lower values of duty cycles to achieve a higher gain. The voltage stress across the switches is minimized and the magnitude of inductor current ripples is quashed to an extent. The proposed THGDC is simple in architecture and easy to control in all three operating modes. Results: The operating characteristics and performance investigation of the novel converter during the continuous and discontinuous modes are elucidated briefly and the comparative analysis on switching stress, gain, and efficiency are executed to justify the standards of the proposed THGDC. Conclusion: Finally, the miniature prototype model is experimented with in the laboratory (0.3 kW) and the obtained results are in agreement with the theory. It is evident from the investigations that the proposed THG-DC shows its dominance over other converters on the voltage gain, switching stress, number of components, and overall efficiency.

Background: This paper proposes a defect identification method for vehicle-mounted cable terminals in electric multiple units (EMUs) based on the improved Graham angle field and residual network to address the issue of low recognition accuracy caused by the lack of partial discharge (PD) and identification data for Ethylene Propylene Rubber (EPR) cable terminal defects. Methods: The improved Gramian angular field (IGAF) characteristic transformation method was used to transform the PD one-dimensional time-series signal into a two-dimensional one after cable terminals with four common insulation defects were constructed, and a PD detection platform was built. Finally, an anti-aliasing downsampling module and attention mechanism were added to the residual network ResNet101 model. The Center loss and Softmax loss functions were integrated to increase accuracy for training and recognition classification. Topological feature images improved the distinguishability of defect categories. Results: The test results showed that the diagnostic method has an accuracy rate of 97.3% for identifying PD at the cable terminal. Conclusion: The proposed diagnosis model has higher recognition accuracy and better balance than other conventional fault diagnosis methods, making it suitable for diagnosing high-voltage cable faults in EMU trains.

Background: The Internet of Things (IoT) is widely used because of the connectivity of devices with the Internet which provides accessibility, quick transmission, and broader coverage. IoT networks provide vast connectivity but finding the best path for sharing information is a big challenge because of limited resources like limited power and limited bandwidth. The routing protocol for low power lossy network (RPL) is standard protocol but it selects a node that has already been selected in a busty network. Methods: The fog computing technique is combined with RPL and the new objective function is used to design FOG-RPL which is the optimum routing protocol that reduces the network load using the fog computing principle and selects the right node using the new objective function. Results: The simulation is performed and experimental results show that FOG-RPL gives better results in terms of improvement and in terms of performance parameters. Conclusion: The FOG-RPL protocol uses the fog computing principle with a new objective function and performance analysis shows that as compared to the existing routing protocol, it is more efficient.

Background: SMES systems as power compensation devices can effectively improve the transient stability of the power system. Due to the nonlinear and strongly coupled characteristics of the compensation device, an effective transfer function cannot be established, such that the traditional PI control by the linearization cannot accurately describe the complex nonlinear system. Objective: In this paper, a passive control strategy is introduced for the SMES System based on CLLC resonant converter to solve the problems that the traditional PI control cannot accurately describe the complex nonlinear system and the parameters’ settings are complicated. Methods: First, according to KVL and KCL, the mathematical model of the SMES system based on the CLLC resonant converter in the (d, q) coordinates is derived and established. Second, based on passive control theory, the port-controlled dissipation Hamiltonian model of CLLC-SMES is given. Third, combined with the passivity of SMES, the energy equation is established and the active and reactive power are analyzed respectively for the balanceable expectation, and then the energy equation is solved to obtain the drive signal of the switch tube. Fourth, the stability of the passive controller is verified by the Lyapunov equation, and the feasibility of the passive control strategy of CLLC-SMES is verified by simulation. Results: The results show that compared with the traditional PI control strategy, the power compensation system based on the passive control strategy does not need to establish the transfer function and the parameters are simple to adjust. Conclusion: It can not only track the active and reactive power commands quickly and accurately but also improve the transient state of the power system effectively.

Background: A series sixfold-circuit "phase-hopping" AC-AC frequency converter (SSCPH-AAFC) is an extension of traditional AC-AC frequency converters. Compared to traditional frequency converters, it has the advantages of energy-saving, low cost, and simple control. Additionally, it can increase the upper limit of the frequency conversion to the power frequency. Through multiplexing processing, it reduces the harmonic content of the output waveform, making it suitable for applications, such as large-capacity fan or pump speed control. Objective: Although SSCPH-AAFC reduces harmonic content, the harmonic content of the single- phase and three-phase grid-side currents still reach 14.65% and 10.20%, respectively. To meet national standards for practical application, a compensation circuit needs to be designed to further reduce grid-side current harmonics. Methods: To address harmonic problems, a targeted single-phase current open-loop compensation method was designed based on the analysis of the current waveform defect. The method compensated for harmonics by selecting appropriate trigger angles and reducing the harmonic distortion rate of the power grid to meet the necessary application standards. Results: The method was simulated and analyzed using MATLAB, and the results showed that after single-phase open-loop compensation, the total harmonic distortion (THD) of the grid-side current for a single-phase SSCPH-AAFC was 2.64%, and for a three-phase SSCPH-AAFC, it was 1.80%. Conclusion: This method has a good effect and can meet the standard requirements.

Background: With the development of power transmission system, the problem of small transmission capacity and low power quality of traditional AC power distribution methods is becoming increasingly prominent. The DC power distribution method promotes its own rapid development because of its suitability of connection between distributed generation and grid, high power quality, large power supply capacity, low line cost and high reliability. Objective: In order to improve the transient performance when the system power fluctuates, a control method that can perform voltage regulation to the DC system is designed in the process of calculating the power command value of each converter station at the system level control. Simulation results show that the designed control method can effectively improve the inertial characteristics and transient response of DC transmission system. Methods: Aiming at the shortcomings of slow response speed and poor stability when the traditional dual closed-loop PI controller is used as a converter level controller, a sliding mode controller is designed. Simulation results show that the designed sliding mode controller can improve transient characteristics of voltage regulation. Results: Voltage control strategy of the DC transmission network based on the converter station is verified. Conclusion: A voltage control strategy based on distributed generation and secondary load switching is designed. The simulation verifies the effectiveness of the designed control strategy.

Computer vision is one of the prime domains that enable to derive meaningful and crisp information from digital media, such as images, videos, and other visual inputs. Background: Detection and correctly tracking the moving objects in a video streaming is still a challenging problem in India. Due to the high density of vehicles, it is difficult to identify the correct objects on the roads. Methods: In this work, we have used a YOLO.v5 (You Only Look Once) algorithm to identify the different objects on road, such as trucks, cars, trams, and vans. YOLO.v5 is the latest algorithm in the family of YOLO. To train the YOLO.v5, KITTY dataset was used having 11682 images having different objects in a traffic surveillance system. After training and validating the dataset, three different models have been constructed setting various parameters. To further validate the proposed approach, results have also been evaluated on the Indian traffic dataset DATS_2022. Results: All the models have been evaluated using three performance metrics, such as precision, recall, and mean average precision (MAP). The final model has attained the best performance on KITTY dataset as 93.5% precision, 90.7% recall, and 0.67 MAP for different objects. The results attained on the Indian traffic dataset DATS_2022 included 0.65 precision, 0.78 recall value, and 0.74 MAP for different objects. Conclusion: The results depict the proposed model to have improved results as compared to stateof- the-art approaches in terms of performance and also reduce the computation time and object loss.