In this study, the design optimisation of a synchronous reluctance machine for light electric vehicles is proposed, to increase efficiency and reduce torque ripples. The existing machine was structurally optimised, using dedicated genetic algorithms, replacing only the rotor and keeping the stator and it's winding untouched. Starting from the original design of the rotor implemented in Flux2D, a finite element analysis software, and the genetic algorithm optimisation implemented in Matlab, a complex co‐simulation was accomplished to obtain a rotor architecture that increases the machine's performances and decreases the torque ripples. By this, performing rotor skewing is not needed any more, hence the torque loss due to it was cancelled. The optimised rotor design increases the machine performances by higher mean torque, no skewing, <8% torque ripples, higher efficiency and better inductance characteristics. Comparative results obtained both in simulations and experimental measurements prove positive outcomes of the optimisation process.

Purpose: The purpose of this paper is to make a study of electromagnetic interference between electrical power lines and nearby underground metallic pipelines.Design/methodology/approach: The equivalent electrical circuit of the studied electromagnetic interference problem between electrical power lines and nearby metallic pipelines is created and solved using a loop currents technique based on a hybrid method. The used circuit solving technique was implemented in a software application developed by the authors.Findings: The authors have identified the influence of phase sequence on induced voltage level in an underground pipeline for a double circuit electrical power line. Also the effect of different normal operation and phase to earth fault currents have been revealed.Practical implications: The study has been made through a research project with the Romanian gas transportation company, in order to find the proper protection techniques for underground metallic pipelines.Originality/value: The paper reveals the influence of some electrical and geometrical parameters that have not been studied in detail previously.

The lumped‐voltage‐source model proposed by Baba and Rakov [2005b] for studying the interaction of lightning with tall objects was used to examine the origin of earlier zero‐crossings observed in electric field signatures produced by lightning strikes to towers. Different return‐stroke models of transmission‐line type were used and model parameters were varied in wide ranges. Lightning channel was assumed to be straight and vertical. It was found that the observed narrow field signatures cannot be reproduced by traditional models and require a narrower input current waveform or/and its faster decay with height. Contribution to the total electric field peak from a tower whose height exceeds 100 m or so is greater than that from the lightning channel. At distances of 2 to 50 km, the electric field signature due to the tower current was found to be bipolar, while that due to the lightning‐channel current was unipolar. The narrow bipolar electric field waveforms produced by lightning striking the 257‐m tower in Florida were reproduced using two approaches. In the first one, we employed as input a typical channel‐base current waveform and the MTLE (exponential current decay with height) model with a very small decay height constant. In the second approach, we used a narrow pulse followed by a steady‐level tail as the input current waveform and the MTLL (linear current decay with height) model. In both approaches, the computed electric field waveforms matched well with the corresponding measured waveforms, at least for the initial half‐cycle and opposite‐polarity overshoot.

The following paper presents an overview of the actual forecasting methods and models used in renewable energy systems. The purpose of the study is to summarize the available forecasting tools in order to select the most efficient approach. The paper showcases the forecasting of production and consumption of electrical energy for local and global systems and also the topic associated with it.

The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO₂ emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.