This paper reports the results of an investigation into the use of magnetic sensors for measuring AC currents and subsequently, estimating AC current phasors in low‐ and medium voltage AC power systems. Tunnelling magnetoresistive (TMR) sensor of high sensitivity and a wide range was used for the magnetic field measurement around AC conductor. The sensor was calibrated to overcome the inequality in the sensed magnetic field due to various aspects such as the distance from the source, minute structural variations, the magnitude of the source current, and presence of harmonics. Performance was tested for accuracy at lower frequencies such as 1, 2, 5 and 10 Hz as well as at higher frequencies such as 2nd, 3rd, 4th and 5th harmonics of the fundamental frequency. The percentage total vector error (TVE) was calculated for current phasors with input current magnitudes varying from 5 to 1500 A of various frequencies and was compared with the actual current as well as with the outputs of a high accuracy conventional core‐wound donut type current transformer (CT). The measurement accuracy corresponding to magnitude, phase and TVE during laboratory and field applications validated the suitability of TMR sensor for contactless and non‐invasive AC current measurement.

This paper reports the results of an investigation into the use of magnetic sensors for measuring AC currents and subsequently, estimating AC current phasors in low‐ and medium voltage AC power systems. Input current magnitudes varying from 5 to 1500 A of various frequencies and was compared with the actual current as well as with the outputs of a high accuracy conventional core‐wound donut type current transformer (CT). Results show that the proposed sensors can be effectively used for contactless and non‐invasive AC current measurement in power systems.image

Impulsive noise poses a significant challenge to single input multiple output wireless communication systems. This paper assesses the effectiveness of hybrid combining techniques, namely selection combining‐equal gain combining and selection combining‐maximal ratio combining, in mitigating impulsive noise impact caused by both man‐made and natural phenomena. The evaluation considers three different noise models: Additive white Gaussian noise, Middleton class‐A impulsive noise, and symmetric alpha stable impulsive noise, across Rayleigh and Rician fading channels. Bit error rate is employed to gauge channel performance. Our research contribution lies in the comprehensive assessment of hybrid combining techniques under diverse noise models, addressing a critical research gap in wireless communication. Findings indicate that impulsive noise has a more pronounced impact on channel performance than additive white Gaussian noise, and hybrid combining techniques prove more effective in mitigating these effects. The results hold significant implications for single input multiple output wireless communication systems, guiding the development of more robust and reliable communication systems in impulsive noise scenarios.

The article evaluates hybrid combining techniques (selection combining‐equal gain combining and selection combining‐maximal ratio combining) under various impulsive noise models (Middleton Class‐A, symmetric alpha stable, and additive white Gaussian noise) in both Rayleigh and Rician fading channels, using bit error rate as the metric. It highlights the significant impact of impulsive noise on channel performance and demonstrates the effectiveness of hybrid combining in mitigating it, offering practical insights for single input multiple output wireless communication systems operating in impulsive noise environments.image

Carrier phase‐shifted sine pulse width modulation is a common modulation strategy for medium‐ and low‐voltage cascaded H‐bridges (CHB). This paper proposes a novel jittered‐carrier phase‐shifted sine pulse width modulation (JCPS‐SPWM) to reduce the total harmonic distortion (THD) of the converter. It makes the carrier jitter regularly while the total switching times remain unchanged, which reduces the THD of the bridge arm voltage and current by moving the low‐order output harmonics of the bridge arm voltage and current to filterable high‐order harmonics. Since the total number of switching times remains unchanged, this modulation strategy will not cause any increase in switching loss. A seven‐level CHB simulation model and an experimental prototype are built to verify the effectiveness of the approach. The results show that harmonic content can be reduced by 47.5% compared with the traditional method, thus verifying the effectiveness of the JCPS‐SPWM.

This paper proposes a novel jittered‐carrier phase‐shifted sine pulse width modulation to reduce the total harmonic distortion (THD) of the converter. It makes the carrier jitter regularly while the total switching times remain unchanged, which reduces the THD of the bridge arm voltage and current by moving the low‐order output harmonics of the bridge arm voltage and current to filterable high‐order harmonics.image

This study proposes an optimal energy management strategy for dual‐source fuel cell hybrid electric vehicles (FCHEV) utilizing the support vector machine (SVM) classifier. The goal is to optimize power distribution between fuel cells and batteries to enhance vehicle's performance and efficiency. The SVM classifier is trained using a dataset of driving conditions and corresponding optimal power distribution (OPD) values obtained through simulation. The trained classifier predicts real‐time OPD based on driving conditions. In comparison to existing literature, this study conducts a comparative analysis of energy management control strategies like model predictive control (MPC), fuzzy, equivalent consumption minimization strategy (ECMS), proportional‐integral (PI) control, and state machine control (SMC) strategy for FCHEVs using the MATLAB/SIMULINK platform and real‐world driving dataset. The proposed strategy is then tested in a real‐time EV simulator to verify its efficacy. Additionally, this study introduces the SVM classifier technique for selecting the optimal energy management strategy for FCHEVs. Performance analysis using SVM reveals that the MPC control strategy offers the highest efficiency compared to other techniques based on selected features, achieving an average performance of 95% through cross‐validation. This analysis demonstrates the most cost‐effective and fuel‐efficient utilization of electricity flow in a modern energy‐efficient environment.

This paper presents an optimal energy management strategy for dual‐source fuel cell hybrid electric vehicles (FCHEV) using support vector machine (SVM) classifier, aiming to enhance vehicle performance and efficiency. Through comparative analysis, the study identifies model predictive control (MPC) as the most efficient strategy, achieving 95% performance on average, demonstrating its cost‐effectiveness and fuel efficiency in modern energy‐efficient environments.image

With the growing recognition of the unique advantages of reinforcement learning and genetic algorithms in addressing combinatorial optimization problems, this study aims to integrate these two methods to collectively tackle the classic combinatorial optimization challenge of the travelling salesman problem (TSP). The TSP stands as a quintessential combinatorial optimization challenge, tasked with determining the shortest path among designated cities. This paper introduces an innovative approach by amalgamating reinforcement learning's path selection prowess with genetic algorithms' global search strategy, aiming to uncover superior solutions in TSP. Specifically, the experiment employs a dual Q‐learning algorithm within reinforcement learning to identify multiple optimal paths, serving as progenitors for the genetic algorithm to further enhance performance. The paper meticulously outlines the problem modelling process, elucidating TSP instance definitions, descriptions, and precise objective function definitions. Experimental findings underscore the substantial enhancements achievable in TSP optimization through this comprehensive approach, offering a fresh perspective and methodology for tackling combinatorial optimization challenges.

By merging the path selection capability of reinforcement learning with the global search strategy of genetic algorithms, an innovative approach aimed at finding more superior solutions in travelling salesman problems is proposed.image