This paper shows the design and implementation of a multimode optical source in the VPIphotonics software. Considering all the powerful features this software provides, the absence of a device such as a multimode source in its library constitutes a limitation for particular simulations. The use of single‐mode optical sources available in the library allows for obtaining a multimode spectrum. The mathematical relationship that describes a multimode optical source is numerically evaluated in MATLAB. Afterward, this graphical result is compared with the obtained in the VPIphotonics software. The importance of using this type of optical source resides in its applications for modern optical communications systems, microwave photonic filters (MPFs), opto‐electronic oscillators (OEOs), and sensors. This implementation aims to drive virtual systems where required, and by setting its parameters similar to the real ones, it allows good studying and understanding of system responses. Even more, it predicts or ensures the results of experimental systems by having the possibility to vary different parameters. A practical application of the obtained multimode optical spectrum is shown with its use in the implementation of an MPF. This last simulation is experimentally corroborated. This work presents a technique for designing a multimode optical source in this particular software and provides also academics and students, with a methodology for the use of this software for this kind of photonic design.

Clock distribution systems are used in many applications requiring accurate time basis: integrated circuits, computer networks, satellite communications, and Global Position System (GPS). Trying to automatize the design of clock distribution systems, this work presents a general formulation considering the possible topologies and parameters, building a computational tool allowing to design and evaluate the performance of any case to be studied. The developed tool enables to simulate networks setting the number of nodes, internal node parameters, topology, perturbations, and signal propagation delays. The simulation results include response times, synchronization quality, and stability for all types of arrangements: mutually connected, chains, rings, stars, and mixed architectures.

The electricity sector deregulation has led to the formation of short‐term power markets where the consumers can purchase electricity by bidding at the electricity market. The electricity market price is volatile and changes are due to change in electricity demand and the bid price at different span of time during the day. The availability of the electricity price forecast is essential for the electricity market participants to make informed decisions. In this paper, the modified LSTM approach, wavelet‐LSTM, and Hilbert‐LSTM are proposed to predict the electricity price for bidding in the short‐term electricity market. The objective is to improve the precision and adaptability of electricity price predictions by utilizing the temporal dependence identification capability of LSTM and the multiresolution analysis capability of the transforms. The proposed models combine these two effective methods in order to capture both the long‐term trends and short‐term variations present in electricity price time series data. In this approach, the 8‐year dataset is used for training the models, and based on this the day‐ahead price is calculated and compared with the testing data. The proposed techniques show better performance in terms of rank correlation, mean square error, and root mean square error compared to the existing algorithms of LSTM and CNN‐LSTM. The prediction results achieved with wavelet‐LSTM and Hilbert‐LSTM (1‐month dataset of 8 years) are rank correlation 0.9746 and 0.9749, MSE 0.2962 and 0.1363, and RMSE 0.5443 and 0.3692, respectively. The results achieved with the proposed methods are better than the existing forecasting models, and the RMSE for Hilbert‐LSTM and wavelet‐LSTM techniques is improved by 61% and 43%, respectively, compared to the LSTM method. Also, results are calculated for the complete 8 years all 12 months with Hilbert‐LSTM, and the results achieved are rank correlation 0.9645, MSE 0.3876, and RMSE 0.6225. The results achieved with the proposed models are improved in terms of performance parameters compared to the conventional approaches. The proposed models can be used in the day‐ahead electricity price forecasting to bid for electricity accurately in the day‐ahead electricity market.

The development of society and the economy depends on the wise use of renewable energy sources and reduced reliance on fossil fuels. In both industrialized and developing nations, solar energy, which is a significant source of renewable energy, is emerging as a dependable and cost‐effective replacement for fossil fuels. In several industries, including residential, commercial, and agricultural, there is an increasing demand for solar photovoltaic (PV) modules. However, dust buildup on solar panels can limit energy transmission and result in power loss in regions with significant amounts of sunlight. The solar cells are shaded by dirt, debris, and soil carried by the wind, which reduces the energy they receive and raises cell temperature, which further reduces solar cell efficiency. The natural accumulation of dust and heat significantly lowers the panel’s ability to produce electricity. This article is intended to develop an automatic self‐cleaning mechanism to solve this problem, which seeks to increase panel efficiency, monitor and control cell temperature, and provide a more affordable solution than the current alternatives. The experimental evaluation of cleaning system performance shows a 14.81% increase in output efficiency, demonstrating its effectiveness in preventing solar degradation. For PV modules, the suggested technique provides an accessible and low‐cost automatic self‐cleaning alternative.

The complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low‐frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low‐frequency oscillation phenomena. To address matter that the traditional S‐transform time‐frequency window function has a fixed scale and cannot be applied to the specific local characteristics of different signals, an adaptive generalized S‐transform algorithm based on a bat algorithm is proposed in this paper. It uses adjustment parameters to control the generalized Gaussian window function. The parameters are automatically adjusted by a bat algorithm adaptive optimization to find the best time‐frequency characterization. Secondly, the PMU data waveform with implicit low‐frequency oscillation information is converted into a two‐dimensional time‐frequency figure including the onset moment, frequency, and amplitude. The system enables identification and visual monitoring of low‐frequency oscillations. After that, simulation experiments of New England system are conducted. The superiority of the proposed method is verified, which can greatly improve the time‐frequency resolution of PMU active power data signal and has effective noise immunity.

Switched reluctance motors’ (SRMs) implementation in electric vehicle drives are widely increasing due to several merits, such as a wide speed range, high torque density, and rare earth‐free characteristics, but it also has some drawbacks, such as more torque ripples, acoustic noise, and vibration, that hinder its usage in electrical vehicle applications. This paper introduces a novel geometry of rotor teeth of SRM, where two slots are inserted per each tooth of rotor to create rotor‐slotted tooth SRM (RST‐SRM). The novel topology aims to enhance the performance of SRMs, especially at high speeds. The overall performance of the RST‐SRM was carried out in addition to comparing it with performance of both stator‐slotted tooth SRM (SST‐SRM) and conventional SRM. The design parameters of the three models are the same and carried out by finite element tools (FETs) to obtain the magnetic characteristics, and the static analysis of the three models is compared. Dynamic performance was achieved by implementing the MATLAB Simulink package. The steady‐state and start‐up performance of the three models were obtained and compared. The performance parameters are estimated at various slot dimensions of the novel technique, in addition to studying the effect of conduction angles for three models. Finally, the efficiency, iron losses, and torque ripple maps of the three models are compared to simulate the scope of the work of electric vehicles.