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 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.

Purpose: The purpose of this paper is to compute the values of induced voltages and currents in an underground gas pipeline, which shares the same right of way with a high voltage power line (HVPL) in unbalanced phase currents regime, in order to detect the possibility of the AC corrosion occurring in the pipeline.Design/methodology/approach: The analysis of the induced voltages in metallic structures (gas pipeline) was done using a professional analysis and modeling software which uses the electromagnetic field method.Findings: It was shown that during fault conditions, large currents and voltages are induced on the pipelines, which may pose a threat to operating personnel and equipment. Induced voltages also occur in cases of unbalanced phase currents of the HVPL, which may pose a threat voltage that could lead to damages of the underground gas pipeline due to corrosion phenomena.Practical implications: Because the electromagnetic field approach involves complicated analytical expression and introduces serious computational difficulties, among which matrix of coefficients representing the set of simultaneous linear equations, the use of professional analysis and modeling software is justified.Originality/value: The paper reveals that even in unbalanced phase currents regime of the HVPL, the nearby metallic structures that share the same right of way with the transmission line are in danger of degradation because of the induced voltages, whose values exceed the values imposed by regulation.

An artificial intelligence (AI) based approach has been applied in order to investigate the electromagnetic interference problems between high voltage overhead power lines (HV OPL) and nearby underground metallic pipelines (MP). The implemented artificial neural network (ANN) solution evaluates the inductive coupling matrix describing the OPL-MP electromagnetic interference problem in case of different problem geometries and multi-layer soil structures. The ANN provided results were compared to data obtained through a finite element method (FEM) based analysis, considered as reference. This artificial intelligence technique, proposed by the authors, has the advantage of a simplified mathematical solver compared to FEM, and implicitly a lower required computing time. Finally the ANN provided inductive coupling data was used to evaluate the induced AC currents and voltages induced in an underground gas pipeline.