Recent Advances in Electrical & Electronic Engineering - Volume 16, Issue 6, 2023

Background: When conventional petroleum-based liquid fuels are combusted, several hazardous pollutants are produced. These pollutants are noxious for both human beings and the environment. Objective: This study aims to analyze the stability of Hydrogen-CNG powered vehicles. Hydrogen has the potential to be one of the most sustainable fuels of the future, decrease the global dependence on fossil fuel resources, and lower the pollutant emissions from the transportation industry. Though we have alternative non-conventional sources of energy, the perfect one to use as the energy source for vehicles is hydrogen. The mixture of Hydrogen and CNG provides excellent properties as fuel for transportation. A fast and accurate control system is designed that allows the combustion of pure Hydrogen. Methods: Combination of Hydrogen with CNG can increase the power and efficiency of the vehicle. The vehicle powered by Hydrogen-CNG needs an electronic control system assuring the operation of its discrete components. MIMO system models having brakes and acceleration inputs are considered. Results: Results are presented for the vehicle transfer function in the form of Bode plots, Root locus, and Routh Hurwitz judging the stability of the vehicle. Conclusion: The main goals of the paper that is to analyze the stability of the vehicle and design an efficient control system are achieved. Obtaining the transfer function for governing equations for understanding the stability of the plant and generating a visual representation showing the relation between the variable quantities have been achieved in this paper.

Background: The Sensor Nodes (SNs) are deployed in an environment where human beings are not able to perform the tasks. Wireless Sensor Network (WSN) is used for applications in security, military surveillance, habitat monitoring, agriculture, etc. All these applications require SNs to have good battery backup so that they can perform for a long duration. Recent research shows that agent-based strategies increase the efficiency of WSN in comparison to conventional WSN in which every SN is static. During the current pandemic of Covid-19, various public resources, such as train, bus, hotels, etc., were used for isolating the Covid-19 patients. In a similar way, the ship helps to keep humans away from each other. Methods: In the current work, WSN has been deployed on a ship to monitor the health of Covid-19 patients. A 109 m long ship with 12.8 m altitude and 23 m width along with 6 decks has been considered. The SNs have been deployed on different floors of the modern ship. Six decks have been considered, and on each deck, 50 SNs have been deployed. A Drone-driven Mobile Agent (DMA) routing strategy has been proposed. DMA is a software program that moves across the network around the SNs and collects information from wearable sensors, such as body temperature, SpO2, etc. DMA is capable of aggregating and delivering the data packets to the base station for further processing. DMA performs information processing, local processing, and collaborative signaling. DMA can move randomly or in fixed locations. Results: Results have been compared with multi-sink and mobile sink strategies, which reveal that the proposed and simulated technique enhances the life span and throughput of the network to monitor Covid-19 patients effectively. Conclusion: Results revealed the proposed technique to enhance the lifetime (DMA-fixed: 11633 rounds and DMA-random: 11740 rounds) and throughput (DMA-fixed: 148788 packets sent and DMA-random: 150008 packets sent) of the network.

Background: Harmonic resonance is one of the apprehensive power quality problems in electric railway traction power supply systems. Harmonic resonances result in big harmonic voltage distortions or harmonic currents. Different harmonic resonance frequencies existing in a power system possess varying propagation areas and amplification levels. The frequency scan analysis technique and the resonance mode analysis technique are the two most commonly used methods. The frequency scan analysis technique can reveal whether resonance exists and determine the resonance frequency. Resonance mode analysis technique can further identify the location where a resonance can be most easily initiatedand observed. However, there are still few studies on the quantitative analysis of harmonic propagation and resonance amplification in traction power supply systems. Methods: The model of the all-parallel autotransformer-fed system in the high-speed railway is built accurately, then a quantitative analysis method based on harmonic electrical distance is used to study the harmonic propagation characteristics and propagation severities caused by electric trains, and analyses in detail the influence of the number of lines, the number of trains, and the location of the train on the severity of resonance amplification. Results: The results of case studies reveal the traction power supply system harmonic propagation characteristics and amplification severities and system parameter sensitivities in a quantitative way, validating the effectiveness and superiority of the quantitative analysis method. Conclusion: Compared with the resonance mode analysis method and the frequency scan analysis method, at a harmonic resonance frequency point, the quantitative analysis method can quantitatively reveal the harmonic propagation area and amplification level at each bus of the traction power supply system. Meanwhile, it is simple and practical.

This paper focuses on designing and modeling multiple-output wireless chargers for electric vehicles (EVs). This paper uses a closed loop buck converter to obtain variable output dc voltage of a wireless electric vehicle charger (WEVC). For designing a closed-loop buck converter, a PI controller is used. The tuning of a PI controller for a nonlinear system is a typical task; therefore, in the paper, three optimization techniques such as Krill Herd Optimization (KHO), Harris Hawks Optimization (HHO), and Sparrow Search Optimization (SSO) utilized to find the optimum value of a PI gain parameters (Kp and Ki). Background: Wired charging system is famous but contains problems like unkempt wires and protection concerns in a damp environment. Then a solution to this problem is a wireless charger because it does not contain any exposed wires. Hence it is convenient for charging and intrepid transmission of power in antagonistic environmental conditions. A wireless charger can be used in unmanned electric vehicles. Objective: The objective of this study is to develop a wireless electric vehicle charger at the variable output voltage. The performance analysis by different optimization techniques such as SSO, KHO, and HHO for variable reference output voltage. Methods: Three optimization techniques are used, such as SSO, KHO, and HHO, for obtaining smooth output dc voltage, reducing harmonics, and improving power transfer capability. Results: SSO gives better results for the variable output voltage of the wireless charger. Conclusion: Wireless charger is designed at a variable reference output voltage and SSO-optimized PI-gain parameters of the charger and provides better performance than other optimization techniques, such as HHO and KHO.

Background: In this paper, a new frequency-selective structure (FSS) for 3 to 4 GHz frequency band of fifth generation (5G) is proposed as a result of an analytical mode-matching method. Methods: A new periodic structure with stepped rods is designed using a closed-form equation derived by the analytical mode-matching method. Performance of the structure is simulated by different numerical packages. Results: The analytical and simulation results demonstrate that the designed structure transmits incident waves in 3.4 to 3.9 GHz frequency range with return loss lower than 10 dB and insertion loss of about 0.5 dB. The structure reflects the frequencies out of this range, especially wireless local area network (WLAN) 5 GHz, which is adjacent to this band. Furthermore, the performance of the proposed structure is independent of the TE and TM polarization of the incident wave and relative to the angle of the incident wave up to 60 degrees from perpendicular to the FSS surface, it has minor variations of about 8% in the transmitted frequency bandwidth. In addition, the average value of maximum field enhancement factor (MFEF) as the ratio of maximum field magnitude on the FSS surface to the magnitude of the incident field, used for assessing power handling capability of the structure, is about 4.5. Conclusion: Therefore, these features make the proposed structure suitable for 5G communication and high power systems.

Background: The research in this paper aims to address the effect of large-span AC lines on ion currents in parallel DC lines. This paper proposes an algorithm for solving mixed ion flow fields based on meshless radial point interpolation, which can accurately solve the ion flow field of large-span AC-DC parallel lines. Methods: The shape function of the traditional meshless method is modified by polynomial radial point interpolation so that the shape function can satisfy the Kronecker-δ function property at the point where the boundary constraints are to be imposed so as to meet the requirements of directly applying the boundary constraints; Combined with the influencing factors of the AC line on the mixed ion flow field, the mixed ion flow field and the ion current density value was obtained. Results: Using the proposed algorithm, the relative error of ion current density can be reduced to 4.5%, and the relative error of ion flow field can be reduced to 24%. Conclusion: The simulation results are consistent with the measurement results, which proves the correctness and practicability of the mixed ion flow field solution algorithm based on meshless radial point interpolation.

Background: In order to achieve the social goal of low carbon and high efficiency, integrated energy system (IES) with deep integration of cyber-physical systems (CPS) are being developed rapidly. The reliability evaluation is an important basis for IES planning and operation. Considering the deep coupling relationship between various systems, energy networks, and cyber networks, and the impact of multiple uncertainties on the integrated energy system, the traditional single index is difficult to quantify the reliability of the system, and the current evaluation methods rely on mechanism analysis and accurate modeling. It is difficult to deal with the coupling relationship between integrated energy systems, energy networks and information networks. So, the quantitative evaluation of the reliability level index of integrated energy systems under the cyber physical system is established, and a data-driven evaluation method is proposed. Methods: In this article, the energy entropy and information entropy theory are combined to establish the reliability evaluation index of integrated energy system integration of cyber-physical systems, and a data-driven evaluation method based on random matrix theory (RMT) is proposed. Results: A reliability evaluation model is established through RMT, and the single-ring law is used to analyze the statistical properties of the matrix model to identify the abnormal state of the system. Then the reliability of cyber and physical layers are evaluated based on energy entropy and information entropy, respectively. In addition, sensitivity analyses on the impact of node interruption probability, network topology, access load rate, and other factors are carried out. Conclusion: The examples of IES are given to verify the feasibility and effectiveness of the proposed method.

Background: With the large-scale grid connection operation of new or renewable energy and the access of active loads such as electric vehicles and air conditioners, the electric energy trading business in the power market faces problems such as the rapid expansion of the number of market settlement subjects, explosive growth, various subjects responsible for deviation assessment, various electric energy trading methods and so on. Objective: This paper focuses on the medium and long-term generation side power trading in the new power market. Through cause analysis, induction and summary, algorithm design and case analysis, the problem of generation side deviation prediction is solved and power waste is reduced. Methods: This paper puts forward the reasons for the imbalance of medium and long-term power trading in the new power market dominated by new energy, as well as the deviation prediction algorithm based on multi-layer LSTM, which brings the total historical deviation, total planned deviation, total measurement deviation, new energy consumption and other data into the M-LSTM deep learning network for testing in each provincial power market center. Results: We use the neural network prediction algorithm. Compared with a single LSTM, the multi-layer LSTM can better maintain the characteristics of the sample time series and reduce the prediction error. Compared with BPNNM-BPNN and Cooperative game theory, LSTM has a better memory effect. Conclusion: The experiment shows that the more accurate prediction deviation of this method can better arrange the generation plan, reduce the loss caused by excessive deviation, reduce the "price trampling" of the power market, and ensure the fair and efficient development of the power market.

Introduction: In order to solve the problems of time-consuming and poor effects of traditional mechanical vibration control methods for the relay, the mechanical characteristics and vibration control of railway signal relays are studied in this paper. Based on the analysis of the mechanical characteristics of railway signal relays, the mechanical characteristic parameters of the relay, such as contact force, initial pressure, contact clearance, and overtravel are explored. On this basis, mechanical vibration control is completed based on particle swarm optimization. Methods: First, sensors are used to collect the data on the railway signal relay, and the mechanical vibration control model of the railway signal relay is built. Then, the structure of the PID vibration controller and LQR vibration controller in the model is analyzed. Finally, the controller parameters are adjusted through particle swarm optimization to improve the mechanical vibration control effect of the relay. Results: The simulation results show that the average signal-to-noise ratio of the method is 67dB, the collected data has low noise, and the control time is short, which is 1.4 s. Conclusion: The displacement of the railway signal relay controlled by the method is always less than 0.15 mm, and the control effect is good, which can be widely used in practice.

Background: Facial emotion recognition (FER) is a vital research area in machine vision and artificial intelligence due to its application in academics and industry. Although FER can primarily be conducted using multiple sensors, research shows that using facial images/videos to recognize facial expressions is a better way to convey emotions because visual expressions carry essential information. Objective: This paper focuses on implementing learning frameworks that combine machine learning and deep learning for detecting 50 classes of compound emotions using the iCV Multi- Emotion Facial Expression Dataset (iCV-MEFED). Methods: In the proposed methodology, we used a deep learning Inception v3 CNN-based model to extract features for each image, and a Multi-Class Support Vector Machine (mSVM) classifier was used to detect the corresponding 50 classes of basic and compound emotions. Results: The proposed learning framework for the iCV-MEFED dataset has an accuracy of 26%, outperforming the state-of-the-art results. Conclusion: Moreover, the results got are compared with competition results in terms of misclassification results, which shows our methodology gives the best result of 74.00%.