Majlesi Journal of Electrical Engineering
Volume:18 Issue: 4, Dec 2024

Large scale requirement engineering needs automated precise and efficient capability modeling and analyzing methods formally to interoperate with the evolving and goal driven requirements. The proposed capability driven requirement engineering framework presents a two-layer framework for the automation of requirements engineering. In the first layer, a meta-model is proposed to define a fault-free model instantiating of the requirements model, and thereby ensuring consistency in the process of requirement execution and in the second layer the analysis algorithms are provided for discovering and querying the boundaries and capabilities of the system at the abstract level. The proposed capability driven requirement framework provides the ability to specify, decomposition, and identification of the requirement traces to execute the activities in terms of available capacities and resources. We also provide the applicability of the approach from various points of view including quality and stability of bounded contexts, average precision and query assessment. As a running example, we highlight the essential role of electrical features in achieving seamless integration and operation, encompassing power distribution, automation systems, energy efficiency and safety measures. The proposed capability-driven requirement framework is crucial for effective smart home engineering in this context. The proposed structured, formal description of software requirement capabilities may increase the precision and recall of module discovery mechanisms for large-scale software engineering.

An innovative Synchronous Buck Converter (SBC) with a wide input voltage range is presented in this article for use in Electric Vehicle (EV) applications. The disadvantages of higher losses in an Asynchronous Buck Converter (ABC) are intended to be addressed by the Synchronous Buck Converter. Fewer losses occur in the circuit when MOSFET (or any controlled switch) is used in place of diode. To enhance system performance, the control approach known as Emulated Peak Current Mode (EPCM) is employed. The design of a broad range input SBC and the investigation of power loss calculations using different control techniques, which are implemented using PSIM, are the primary contributions made in this paper. The buck converter employed in this paper has an output voltage of 12 V and a wider input voltage range of 40–75 V. It is utilized by electric vehicles' light and horn systems. Using PSIM software, the SBC using the EPCM, Current Mode Control (CMC), and Voltage Mode Control (VMC) techniques is simulated. Hardware results coincide with the simulation results and thus the results are validated.

Sulfur hexafluoride (SF6) is extensively utilised as an essential insulating material in high-voltage industries owing to its outstanding electrical characteristics. However, the use of SF6 gas faces two significant challenges: its substantial impact on global warming and the high toxicity of its breakdown byproducts. This study aims to analyse the breakdown properties of various gas and gas mixtures, including, pure Nitrogen (N2), pure Oxygen(O2), O2+N2 gas mixture, and SF6+N2 gas mixture as alternative insulation media under positive standard lightning impulses. The experiments involve sphere and plate electrodes with varying gap distances. Following the BS EN 60060-1 standard, the breakdown voltage is measured using the Up and Down method, with a voltage interval between levels set between 1.5% and 3%. Based on the findings, N2 gas has been determined to be a highly efficient insulator, outperforming other options. The reason for this is its superior breakdown voltage, which reaches 37.415 kV, surpassing the values of other gas and gas mixtures.

Jet engines consume a lot of power at start time which makes necessary to use a ground power supply unit (GPU). The GPU must provide the aircraft's demanded power at the start point only. One of the most important factors in supplying the required power for jet engines is to provide the necessary quality for the transmission of electricity to the aircraft. There are a lot of standards to check the quality of the starter among them MIL-STD-704 military standard is one of the most frequently used ones. This standard governs two principles of the power quality and transmitted power to flying vehicle. A GPU system is designed and implemented in this paper to meet the requirements of MIL-STD-704.This paper proposes a new approach to implement a starter system for turbo-shaft engines. Transformer tap changer mechanism is used to voltage control and parameters of transformer are also optimized using Maxwell software to reduce losses. The power quality value is achieved based on an adaptive smart filtering system implemented at the output terminal of the starter system. This smart filter is designed to be controlled based on SVM machine learning scheme. Experimental results are provided to illustrate the successful and high-quality supply of power needed to start a helicopter.

Short-term electrical load forecasting plays a pivotal role in modern energy systems, addressing the need for accurate predictions of electricity demand within a time frame ranging from a few hours to a few days. The implications of inaccurate predictions extend beyond operational challenges to potential economic and environmental consequences, emphasizing the critical role that short-term electrical load forecasting plays in the modern energy landscape. The purpose of this research is to address the aforementioned consequences by developing an optimally configured Long Short-Term Memory (LSTM) model for predicting short-term electrical load forecasting in Tamil Nadu, specifically focusing on India's Villupuram region. While LSTM models are recognized for their overall effectiveness, their performance in short-term electrical load forecasting necessitates a tailored approach. Hyperparameter optimization is the appropriate choice for configuring the LSTM model for short-term electrical load forecasting. The manual or trial-and-error process in hyperparameter tuning is time-consuming and complex to compute. To address this, the research integrates the Cauchy-distributed Harris Hawks Optimization (Cd-HHO) approach for the optimal configuration of the LSTM model. The optimally configured LSTM through Cd-HHO consistently achieves lower Mean Squared Error (MSE) compared to other state-of-the-art methods, which is 0.7225 in the 2017 database, 0.974 in the 2018 database, and 0.116 in the 2019 database.

In today's society, the importance of creating highly reliable distribution networks cannot be overstated. Utilities face challenges in planning and developing these systems effectively, aiming to decrease costs and meet consumer demands. This research proposes a coordinated architecture that focuses on the integration of a Demand Response Program (DRP) to improve the reliability of power distribution networks. Specifically, in this paper the reliability improvement is presented through finding optimal price, location, and amount of participated load in demand response program considering automatic switches and ESUs in service restoration process in electrical distribution systems. Also, uncertainty of repair time for faulted equipment is considered in this paper. suggested objective is to minimize the Total Cost of the system (TC) by optimizing the placement of the price, location, and amount of participation loads. The TC includes the cost of customer interruption, energy not supplied, ESU participation, and DRP. To illustrate the applicability and efficiency of the suggested approach, it is applied to three cases on a test case. Additionally, a sensitivity study is conducted. The results demonstrate that optimizing the incentive and penalty costs leads to significantly reduced SAIDI index and total costs. Moreover, the value of the incentive and penalty costs is lower than the fixed ones in this study, resulting in increased participation of sensitive load points in DRP.

Unbalanced voltage is a prevalent issue in power networks that can have significant adverse effects. Voltage unbalance arises when the three-phase voltages in the power system differ in magnitude or phase angle, leading to waveform distortion. This imbalance can increase energy losses, thereby elevating costs for energy consumers. Additionally, it can negatively impact the power factor, which measures the efficiency of power usage in the system. To improve power quality and mitigate the detrimental effects, this paper proposes an innovative control strategy using a Static Synchronous Compensator (STATCOM) based on the multivariable filter (MVF) method within a utility-connected microgrid. The MVF configuration effectively separates the positive and negative components, which are then utilized in control loops. First, the voltage control loop builds the references for current control loop. Then, the current control unit sends the appropriate commands to the STATCOM in order compensate the votage amplitude. The primary advantage of the proposed strategy is its precision in reference tracking and ease of implementation. The MVF-based STATCOM can compensate for voltage imbalances by injecting reactive power and regulating the DC-link voltage. The results demonstrate that the proposed control structure effectively eliminates negative components and enhances the voltage profile of the studied microgrid.

An efficient and remarkable automatic modulation classification (AMC) technique is essential with the advent of sixth-generation (6G) communication systems. Using the pre-trained convolutional neural network (CNN), a deep learning (DL) approach to classify eight types of digital modulated signals. National Instrument LabVIEW NXG is used to build the modulation transceivers at 100 GHz, a 6G carrier frequency. The dataset was collected in a complicated environment, including carrier frequency offset (CFO), phase noise (PN), and distinct signal-to-noise ratios (SNR). Through experimental simulation, an improvement in the classification accuracies was achieved. In particular, the outstanding accuracy rates achieved are 98.68% and 96.05% using ResNet18 and ResNet101, respectively. Furthermore, these models can classify the modulated signals at lower SNRs. These innovative models are suitable and effective to utilize for 6G wireless communication networks.