Computers & Electrical Engineering最新文献

In recent years, the dual-function radar and communication (DFRC) paradigm has emerged as a focal point in addressing spectrum congestion challenges. However, prevailing research heavily relies on computationally complex likelihood-based approaches for communication signals with an added Gaussian noise based single waveform. Note that, a single waveform for diverse scenarios e.g., presence of a communication receiver in the radar main lobe, side lobe, etc., may lead to a deteriorated detection performance in a DFRC design. Therefore, in this paper, we present a cognitive DFRC architecture that utilizes a diverse set of orthogonal waveforms at the transmitter. Specifically, based on a perception-action cycle, a QAM-based waveform is employed for communication when both the radar target and communication receiver are within the main lobe, while a PSK-based waveform is used when the radar target is in the main lobe and the communication receiver is in the side lobes. Furthermore, to enhance the feature-based estimation, the communication receiver integrates a Convolutional Neural Network (CNN) architecture designed to autonomously learn and extract features from received signals with different Signal-to-Noise ratio (SNR). Next, the adaptive nature of the system enables proficient discernment of the received signal type and its corresponding SNR value. Moreover, deep learning techniques are applied in realistic scenarios with various channel impairments to extract features from received signals, departing significantly from likelihood-based methods and reducing computational complexity. The proposed methodology’s effectiveness is validated through Monte Carlo simulations, underscoring its potential to address challenges associated with DFRC under real-world conditions.

Recently, the popularity of direct current (DC) transmission has surged due to a 30 % reduction in power electronics equipment costs over the last decade. High-Voltage Direct Current (HVDC) transmission lines experience fluctuations in both nominal and faulty states, posing challenges for fault location methods, particularly those based on traveling waves, resulting in imprecise outcomes with an average error margin of ±2 %. This paper investigates a novel fault detection and location approach utilizing wavelet and phaselet transforms. The proposed method focuses on traveling waves in HVDC transmission lines, where the phaselet transform effectively eliminates undesired fluctuations in line currents, improving fault detection accuracy by up to 15 %. By leveraging the traveling time in the faulty line, the exact fault location is calculated with an accuracy of 98.5 %, independent of transmission line parameters, making it applicable to any DC line. Additionally, the method's precision is minimally affected by fault resistance, with <0.5 % deviation even in nonlinear scenarios. Sensitivity analysis conducted on various parameters, including mother wavelet patterns, suggests the optimal solution for each fault type, enhancing detection speed by 20 %. The proposed method is validated using the Cigre HVDC benchmark, confirming its accuracy, speed, and robustness in fault detection and location in HVDC lines. The main contribution of this paper is an accurate, fast, and robust phaselet-based algorithm that reduces fault location errors to <1 % and is applicable to all types of bipolar and monopolar HVDC systems, delivering reliable results for both ground and line faults.

In biomedical Applications, Network plays a vital role to transmit the health-related information from patient to doctors which helps to save the human lives. During the data transmission, there is the possibility of data loss, delayed delivery, malicious attack and that leads to huge loss in human lives. To overcome this issue, a new technique Smart & secure Flying Adhoc Networks (FANET) architecture was proposed for the Quality-of-Service (QoS) improvement in biomedical applications. This research work helps society to save human lives during any emergency. With the integration of optimization and clustering techniques, the design configuration of the proposed architecture includes mobility management and secure transmission. The first phase includes sensing the patient's information by using Arduino IDE hardware. The sensed data will be transmitted through different types of networks including mobile ad-hoc networks, vehicular ad-hoc networks and flying ad-hoc networks. The routing protocol concentrates on clustering formation, mobility management, secure transmission and optimization. When compared to other type of networks, the proposed Smart & secure Flying Adhoc Networks (FANET) improves the network performance. The quality-of-service was achieved using the parameters such as network throughput, latency, delivery rate, routing overhead and control overhead.

In order to ensure the effectiveness of power grid scheduling decisions, ensure the stability, safety, and intelligence level of scheduling operations, and solve the semi persistent problem of station scheduling data loss in power grid scheduling, a Gaussian mixture clustering based method for compensating station scheduling data loss in power grid scheduling is proposed. Based on the role of the intermediate station and its semi persistent scheduling data generation mechanism in the control system of the large power grid, a Gaussian mixture model is constructed to calculate the conditional expected value of missing data as compensation value, and the final compensation result of the semi persistent scheduling data of the intermediate station is obtained. The experimental results show that in various types and degrees of scheduling data missing scenarios, this method performs well, and its Pearson correlation coefficient for compensating data is generally higher than 0.94, fully verifying the effectiveness and accuracy of this method. This achievement not only provides a practical and feasible solution to the problem of data loss in power grid scheduling, but also provides strong technical support for improving the accuracy of power grid regulation and ensuring the safe and stable operation of the power grid.

To address the challenging issue of automatic carrier landing with disturbances, this paper presents an active anti-disturbance control with integrated direct lift scheme. This control scheme is comprised of three controllers: the attitude controller for maintaining angle of attack and eliminating lateral tracking error, the integrated direct lift controller for eliminating longitudinal tracking error and the auto-throttle controller for maintaining the velocity. To estimate and compensate for the external disturbances, the extended state observer is designed, along with the implementation of sliding mode and nonlinear dynamic inversion methods. The stability of the controllers is proved by the Lyapunov theorem. Based on this control scheme, a novel automatic carrier landing system is developed, consisting of the glide path subsystem, the guidance subsystem, the aircraft subsystem and the control subsystem. To validate the efficacy of the proposed method, numerical simulations are conducted. The comparative results demonstrate that the proposed method is capable of achieving precise and safe carrier landing with external disturbances. Furthermore, the Monte Carlo test results provide compelling evidence of the superiority of the proposed method.

This study investigates the efficacy of FOPI regulators as a substitute for conventional proportional integral (PI) controllers in grid-connected PV inverters. The research's objective is to improve the dynamic efficiency of these systems by integrating intelligent optimization techniques that utilize both PI and FOPI controllers and employing different metaheuristic optimization procedures. The study introduces four new optimization algorithms: the Tyrannosaurus Optimization Algorithm (TROA), the Nutcracker Optimization Algorithm (NOA), the Golden Eagle Optimizer (GEO), and the Jellyfish Search Optimizer (JSO). These are made to meet the needs of multi-objective optimization. The proposal suggests using two PI/FOPI regulators to regulate both voltage and amperage provided by the multilayer inverter. The proposal employs a T-type three-level inverter, known for its superior conversion efficiency over conventional inverters. Matlab-Simulink simulations demonstrate that the Nutcracker optimization algorithm (NOA) outperforms other metaheuristic techniques for optimizing dynamic behavior, such as overshoot, settling time, execution, and rising time. Comparisons with existing methods, such as the Manta Rays Foraging Optimization (MRFO) and Grey Wolf Optimizer (GWO), show that all four new algorithms consistently outperform these existing algorithms. The data suggest that using NOA improves stability, efficiency, and power factor while reducing the inverter's THD.

Rapid and accurate short-term load forecasting for distribution network is beneficial to ensure the safe and stable operation of power grid, reduce operating costs and improve the utilization rate of energy. Initially, through the data preprocessing minimizes the impact of outlier data on predictions. Subsequently, using variational mode decomposition and sample entropy methods separate modal components into high-frequency and low-frequency periodic sequences. Pearson correlation coefficient and principal component analysis are then employed to analyze feature parameter correlations, constructing distinct feature matrices for each Sub-sequence. High-frequency sequences are inputted into a prediction model combining time convolutional and bidirectional long short-term memory networks, while low-frequency periodic sequences are fed into a model combining auto regressive integral moving average and support vector regression. An illustrative analysis using January data from a Chinese province. Results indicate that compared with the 13-dimensional eigenmatrix, the proposed method saves 63 s in prediction time and improves the efficiency by 23.6 %. Mean absolute percentage error only decreased by 0.143 %, indicating that the method can ensure the prediction accuracy without losing robustness. Additionally, case analyses for different prediction durations (1 day and 1 week) exhibit promising results with mean absolute percentage error indices of 1.982 % and 2.022 %, indicating strong predictive performance.

This paper aims to develop a nested cubature rule for probabilistic power flow (PPF) computation. In the case that marginal distributions of PPF inputs are unknown, a polynomial cumulative distribution function (CDF) model is derived to recover distribution functions of PPF inputs. With a percentile matching method, parameters of the polynomial CDF model are easily obtained by solving a system of linear equations. If PPF inputs include correlated random variables, a Liouville–Gaussian copula is proposed to map the PPF problem to a homogeneously correlated normal space, then, a suitable elliptical copula or Archimedean copula is employed to relate PPF inputs to an independent standard normal vector, whereby it can reproduce the asymmetric dependence structure of PPF inputs. In order to accurately calculate statistical moments of PPF outputs, a nested cubature rule is developed in the framework of Kronecker product and Hadamard matrix, it can capture interactive uncertainties among PPF inputs, and has a computational burden linear with the number of PPF inputs. Finally, case studies are performed to check the polynomial CDF model and Liouville–Gaussian copula for fitting correlated PPF inputs, a PPF computation is conducted on IEEE 118-bus system to demonstrate the efficiency and accuracy of the nested cubature rule.

In order to effectively improve the accuracy and efficiency of unstructured environment navigation and trajectory calibration, a multi data fusion based unstructured environment navigation and trajectory calibration method was studied. Described the basic principles of the Strapdown Inertial Navigation System and Doppler Log. After introducing DVL velocity information and relative position information of underwater robots into SINS, the Kalman filter fusion algorithm was used to obtain the system's state equation and measurement equation. The navigation parameter error and device error parameters of the system were selected as the state variables. The difference between SINS output speed and DVL measurement speed, as well as the difference between SINS output position and relative position, are used as the speed and position measurements of the measurement system. Error estimation is obtained through indirect filtering, and the accuracy of navigation and positioning is improved through output correction. The experimental results show that the proposed method has a good calibration effect and can effectively improve the accuracy and efficiency of trajectory calibration in unstructured environments.

Silicon carbide (SiC) inverters and interior permanent magnet synchronous motors (IPMSMs) are widely employed in electric vehicles (EVs) due to their superior efficiency and high power density. However, the harmonics generated by the high switching frequency and the dead-time effect in SiC inverters present a significant challenge. The time-varying nature of on–off delays and conduction voltage drops in power devices makes it difficult to establish a precise nonlinear model for inverters, thus limiting the effectiveness of traditional dead-time compensation methods. This paper introduces a dead-time compensation approach for voltage source inverters (VSIs) that leverages a nonlinear observer, thereby circumventing the need for a complex inverter model while enhancing compensation accuracy. The method determines the three-phase stator current polarity of the IPMSM by deriving the relationship between the current vector angle and the rotor position angle. Along with duty cycle variations computed by the PI controller, the inverter’s nonlinearity is adaptively compensated. Simulation and experimental results indicate that the proposed method outperforms the traditional method based on the error voltage model, effectively reducing the total harmonic distortion (THD) of the phase current.