Iranian Journal of Science and Technology-Transactions of Electrical Engineering最新文献

Abstract

This paper studies an overlay network with non-orthogonal multiple access (NOMA) wherein the far-licensed NOMA user is assisted by either the near-licensed NOMA user or the unlicensed source to maintain its communications with the licensed NOMA transmitter. Both the near-licensed NOMA user and the unlicensed source implement practical nonlinear energy harvesters to meliorate energy efficiency. The performance metrics of the proposed overlay network with NOMA and EH (ONwNOMAEH) such as energy efficiency, system throughput, and outage probability are analysed in an explicit form. Multiple results expose the effectiveness and the flexibly adjusted performance of the proposed ONwNOMAEH.

This paper proposes a soft-switched high-gain interleaved coupled inductor-based boost converter for renewable energy systems. The interleaved configuration, at the source side, reduces the current ripple of input and enhances the converter’s power capacity. At the output side, the voltage multiplier circuit increases the gain by means of coupled inductors (CIs). Zero voltage switching turn ON of switches minimizes the switching losses. The active-clamping nullifies the voltage spikes generated at the main switches. The leakage inductance of CI mitigates the reverse recovery issue in diodes. The proposed converter builds high gain with a reasonable turns ratio of CI and duty ratio. The leakage energy recycling and soft-switching can enhance the converter’s efficiency. The prototype of the 200 W proposed converter is developed to validate the performance.

In order to meet the communication requirements of multi-band base station antennas, an ultra-wideband dual-polarized 5G base station antenna integrating n77/n78/n79 frequency bands is proposed. The main radiator of the antenna is composed of two pairs of arrow cross-dipoles placed at ± 45°, and the antenna impedance bandwidth is guaranteed by adding four rectangular parasitic patches and opening rectangular slots. The designed parasitic patch expands the bandwidth while enhancing the dual-polarization characteristics of the antenna. Furthermore, a box-type reflector is employed, which facilitates obtaining high gain and good half-power beamwidth. Finally, a sample of the antenna is fabricated and tested. As shown in the measurement results, the − 10 dB impedance bandwidth reaches up to 51.9% (3.0–5.1 GHz) with the antenna isolation in the working frequency band better than 24 dB. In the frequency range of 3.3–5 GHz, the antenna achieves a high gain of 9 dB and a HPBW of 64 ± 2°, and the cross-polarization ratio is better than 21 dB overall. Compared to traditional base station antennas, this antenna has significant advantages in terms of gain and HPBW.

A kind of wireless network called a “mobile ad hoc network” (MANET) can transfer data without the aid of any infrastructure. Due to its short battery life, limited bandwidth, reliance on intermediaries or other nodes, distributed architecture, and self-organisation, the MANET node is vulnerable to many security-related attacks. The Internet of Things (IoT), a more modern networking pattern that can be seen as a superset of the paradigms discussed above, has recently come into existence. It is extremely difficult to secure these networks due to their scattered design and the few resources they have. A key function of intrusion detection systems (IDS) is the identification of hostile actions that impair network performance. It is extremely important that an IDS be able to adapt to such difficulties. As a result, the research creates a deep learning-based feature extraction to increase the machine learning technique's classification accuracy. The suggested model uses outstanding network-constructed feature extraction (RNBFE), which pulls structures from a deep residual network's many convolutional layers. Additionally, RNBFE's numerous parameters cause a lot of configuration issues because they require manual parameter adjustment. Therefore, the integration of the Rider Optimization Algorithm (ROA) and the Spotted Hyena Optimizer (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA), is used to adjust the RNBFE’s settings. Attack classification is performed on the resulting feature vectors using fuzzy neural classifiers (FNC). The experimental analysis uses two datasets that are publicly accessible.

Maximum power point (MPP) technique in photovoltaic (PV) systems implements a tracking controller, which is utilized to optimize the energy production under variable atmospheric conditions. The tracking process becomes more difficult due to appearance of many peaks owing to partial shading conditions. Although conventional and soft computing technologies are frequently used to solve MPP tracking issues, their performance is constrained by the fixed step size of conventional methods. However, once soft computing methods reach a certain MPP, they are constrained by a lack of randomness. The novel adaptive flower pollination algorithm (AFPA) optimization technique proposed in this work, proceeds with global and local searching in a single step, which is very crucial for the success of the MPP tracking with this method. The robustness of the approach is examined by conducting zero, weak, moderate, and strong shading patterns to a complete performance assessment via simulation, and that performance is compared with traditional flower pollination algorithm (FPA) and particle swarm optimization (PSO) techniques. This newly proposed method has the following advantages over the conventional FPA: a) risk of failure is zero; b) oscillation of power, voltage, and current across the load is minimized; c) produced energy is increased by 0.5 to 2.5% with respect to FPA; d) MPP is tracked smoothly and e) reduced MPP tracking time by average 45%. This advantage is especially noticeable in the dynamic variation of the shading patterns.

Power system contingency is an operational state that can happen as a result of a system discontinuity and results in overall system voltage instability. The system has seen intermittent power outages in previous years, according to Ethiopia Electric Power. Parts of the grid system were disrupted during the event, and as a result, there was a partial blackout of the system. Therefore, research proposes identifying the system's exposure to lessen the likelihood of such accidents happening. The proposed method demonstrated on 15 bus system Northwest region of Ethiopia Electrical Power Network using DigSILENT software. System’s exposure was evaluated by using the voltage performance index and the overload performance index. The indices enable the evaluation of two distinct system stress signs, including voltage instability and overloads. Performance index data show that the BDII230 bus to the GoII bus line has highest performance index value (4.9488) from other for N-1 line outage circumstances. This indicates that BDII230 bus to the GoII is most dangerous line for interruption of Northwest region of Ethiopian Electric Power network. But the most sensitive bus for voltage limit violations on this condition are Gondar load bus and Metema load bus. Their voltage profile is 0.908 and 0.923 per unit, respectively. Also, for 50% load demand increasing conditions, Gondar load bus has highest performance index value (20.2352) compared to the other load buses and its voltage profile is 0.8879 per unit. So, applying performance index for Northwest region of Ethiopia Electric Power network is used to know the system performance such as voltage instability and overloading, for forecasting future operation and expansion planning of this area.

Fire/flame detection from images or videos is very important for early fire warning systems. In this way, fires can be intervened early and extinguished before they grow. Recently, many studies have been published on early fire warning systems based on image processing and machine learning. These studies are generally color space-based image segmentation applications. The given images are first transferred to another color space, and the fire/flame regions are determined by using color segmentation. In this study, a segmentation technique using deep network architecture for fire/flame detection is presented. The proposed method is a segmentation network structure in which the attention gate module is integrated. In the presented method, the success of the deep network architecture is evaluated by using the dice, Tversky, and focal Tversky loss functions. A data set containing 500 images was used for experimental studies, with the fivefold cross-validation criterion, and the success achieved was presented depending on the mean dice and Jaccard similarity criteria. The calculated results were compared with some studies in the literature. The comparison results were shown that the presented technique produced more successful results.

This paper investigates the merging and splitting maneuvers of multiple heterogeneous platoons in the presence of actuator saturation and faults. At first, the vehicles of interest are divided into separate platoons, where vehicles within each platoon synchronize within themselves. However, vehicles belonging to different platoons follow distinct target trajectories. Subsequently, a decentralized adaptive fixed-time control law is formulated to estimate the unknown faults and to compensate the impact of actuator saturation. In the proposed network, the communication exchange among vehicles of different platoons uses matrices that encode the orientation of the new platoon with respect to the other one. This makes it suitable for achieving consensus when tackling a large variety of vehicle formations. The control law guarantees that multi-leader platooning achieves fixed-time convergence even in the presence of actuator saturation, faults and external disturbances. The effectiveness of this proposed approach is demonstrated through numerical simulations.

The small frequency offset across the array elements in frequency diverse array (FDA) radars leads to the range-angle-time dependent beampattern of FDA radars which is different from the angle-only dependent beampattern of the traditional phased arrays. In this paper, a simple signal model for the received signal in pulsed FDA radars is derived and it is shown that the transmitted signal is amplitude modulated by a digital sinc function which depends on the angle of the scatterer. Then, a signal design framework is presented based on the derived model in which it is represented that how the angular coverage zone and range resolution depend on pulsed FDA radar parameters. After that an iterative low complexity constant false alarm rate (CFAR) Generalized Likelihood Ratio Test (GLRT)-based detector for general FDA radars is derived to detect multiple targets in the presence of clutter and noise. The performance of the proposed detection algorithm is verified by simulation results.

A current recycling structure is used to boost the tail current source of an operational transconductance amplifier (OTA), improving the slew rate (SR). The proposed current recycling circuit detects the magnitude of the differential input voltage and adjusts the tail current source value in proportion to the absolute value of the detected differential input voltage. When a small input signal is applied, the quiescent current of the OTA does not change; therefore the proposed tail current boosting circuit does not degrade the voltage gain and bandwidth. The proposed boosted tail current source has been used to drive a two-stage class A-AB OTA to evaluate its performance. Post-layout simulation results using standard 0.18 µm 3.3 V CMOS process technology show SR improvement of approximately 396% at the cost of 21% increase in static power consumption compared to a two-stage class A-AB primary OTA. The proposed AB-AB OTA achieves 65.7 V/μs at a static power consumption of 0.38 mW when driving a 10 pF capacitive load.