Electromagnetic Torque Ripple in Multiple Three-Phase Brushless DC Motors for Electric Vehicles

This paper investigated an electromagnetic torque ripple level of BLDC drives with multiple three-phase (TP) permanent magnet (PM) motors for electric vehicles. For this purpose, mathematical models of PM machines of different armature winding sets-single (STP), dual (DTP), triple (TTP), and quadruple (QTP) ones of asymmetrical configuration and optimal angular displacement between winding sets were developed and corresponding computer models in the Matlab/Simulink environment were created. In conducted simulation, the influence of various factors on the electromagnetic torque ripple of the multiple-TP BLDC drives was investigated—degree of modularity, magnetic coupling between armature winding sets, and drive operation in open and closed-loop control systems. Studies have shown an increase of the electromagnetic torque ripple generated by one module in the multiple TP BLDC drives with magnetically coupled winding sets, due to additional current pulsations caused by magnetic interactions between the machine modules. However, the total electromagnetic torque ripples are much lower than in similar drives with magnetically insulated winding sets. Compared with the STP BLDC drive, the multiple TP BLDC drives with the same output parameters showed a reduction of the electromagnetic torque ripple by 27.6% for the DTP, 32.3% for the TTP, and 34.0% for the QTP BLDC drive

Study and Analysis of Dynamics and Energy Efficiency of Arc Steelmaking Furnace Electrical Mode with a Fuzzy Control Algorithm

A review of the control laws (models) of alternating current arc steelmaking furnaces’ (ASF) electric modes (EM) is carried out. A phase-symmetric three-component additive fuzzy model of electrode movement control signal formation is proposed. A synthesis of fuzzy inference systems based on the Sugeno model for the implementation of the proposed additive three-component fuzzy law of arc length control is performed. A structural computer Simulink model of the EM control system in a high-power arc steelmaking furnace of the DSP-200 type with an ARDM-T-12 arcs power regulator is created. Computer research into control dynamics indicators under the influence of deterministic perturbations and also integral indicators of energy efficiency when handling stationary random arc lengths fluctuations (corresponding to various technological stages of melting) are carried out. A comparative analysis of dynamics indicators, energy efficiency, and electromagnetic compatibility of the proposed fuzzy and known differential model of ASF arc lengths control is carried out. The implementation of the proposed fuzzy three-component additive control model in comparison with the existing deterministic differential one reduces the dispersion of voltages, currents, and arcs powers, reduces electrical losses in an arc furnace high-power network by 10–22% and increases the average arc power by 0.9–1.5%

Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task

Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods

This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification

Application of Selected Machine Learning Techniques for Identification of Basic Classes of Partial Discharges Occurring in Paper-Oil Insulation Measured by Acoustic Emission Technique

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established

Geospatial Assessment of the Territorial Road Network by Fractal Method

This paper proposes an approach to the geospatial assessment of a territorial road network based on the fractals theory. This approach allows us to obtain quantitative values of spatial complexity for any transport network and, in contrast to the classical indicators of the transport provisions of a territory (Botcher, Henkel, Engel, Goltz, Uspensky, etc.), consider only the complexity level of the network itself, regardless of the area of the territory. The degree of complexity is measured by a fractal dimension. A method for calculating the fractal dimension based on a combination of box counting and GIS analysis is proposed. We created a geoprocessing script tool for the GIS software system ESRI ArcGIS 10.7, and a study of the spatial pattern of the transport network of the Ukraine territory, and other countries of the world, was made. The results of the study will help to better understand the different aspects of the development of transport networks, their changes over time and the impact on the socioeconomic indicators of urban development

Development of Additive Fibonacci Generators with Improved Characteristics for Cybersecurity Needs

Pseudorandom sequence generation is used in many industries, including cryptographic information security devices, measurement technology, and communication systems. The purpose of the present work is to research additive Fibonacci generators (AFG) and modified AFG (MAFG) with modules p prime numbers, designed primarily for their hardware implementation. The known AFG and MAFG, as with any cryptographic generators of pseudorandom sequences, are used in arguments with tremendous values. At the same time, there are specific difficulties in defining of their statistical characteristics. In this regard, the following research methodologies were used in work: for each variant of AFG and MAFG, two models were created—abstract, which is not directly related to the circuit solution, and hardware, which corresponds to the proposed structure; for relatively small values of arguments, the identity of models was proved; the research of statistical characteristics, with large values of arguments, was carried out using an abstract model and static tests NIST. Proven identity of hardware and abstract models suggest that the principles laid down in the organization of AFG and MAFG structures with modules of prime numbers ensure their effective hardware implementation in compliance with all requirements for their statistical characteristics and the possibility of application in cryptographic information security devices

A New Approach to the Development of Additive Fibonacci Generators Based on Prime Numbers

Pseudorandom number and bit sequence generators are widely used in cybersecurity, measurement, and other technology fields. A special place among such generators is occupied by additive Fibonacci generators (AFG). By itself, such a generator is not cryptographically strong. Nevertheless, when used as a primary it can be quite resistant to cryptanalysis generators. This paper proposes a modification to AGF, the essence of which is to use prime numbers as modules of recurrent equations describing the operation of generators. This modification made it possible to ensure the constancy of the repetition period of the output pseudorandom pulse sequence in the entire range of possible values of the initial settings–keys (seed) at specific values of the module. In addition, it has proposed a new generator scheme, which consists of two generators: the first of which is based on a modified AFG and the second is based on a linear feedback shift register (LFSR). The output pulses of both generators are combined through a logic element XOR. The results of the experiment show that the specific values of modules provide a constant repetition period of the output pseudorandom pulse sequence in a whole range of possible values of the initial settings–keys (seed) and provide all the requirements of the NIST test to statistical characteristics of the sequence. Modified AFGs are designed primarily for hardware implementation, which allows them to provide high performance