The problem of lithium-ion cells, which degrade in time on their own and while used, causes a significant decrease in total capacity and an increase in inner resistance. So, it is important to have a way to predict and simulate the remaining usability of batteries. The process and description of cell degradation are very complex and depend on various variables. Classical methods are based, on the one hand, on fitting a somewhat arbitrary parametric function to laboratory data and, on the other hand, on electrochemical modelling of the physics of degradation. Alternative solutions are machine learning ones or nonparametric ones like support-vector machines or the Gaussian process (GP), which we used in this case. Besides using the GP, our approach is based on current knowledge of how to use non-parametric approaches for modeling the electrochemical state of batteries. It also uses two different ways of dealing with GP problems, like maximum likelihood type II (ML-II) methods and the Monte Carlo Markov Chain (MCMC) sampling.

Different buried permanent magnet arrangements in rotors are compared based on electrical machines found in literature regarding high-speed capability. An analytical approach is presented to analytically calculate mechanical stresses in the bilateral and central bridge of V arrangements in order to determine the achievable circumferential velocity of a rotor geometry. The mechanical model is coupled to an analytical model which can determine the flux density in the main air gap under consideration of flux leakage within the rotor. The multi-domain model enables the analytical design of high-speed rotors with buried permanent magnets in V-arrangement.

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.

Commutation reactance is an important component in the voltage-source converter- based high-voltage direct current (VSC–HVDC) transmission system. Due to its connection to the converter, when there is a fault occurring on the valve-side bushing of a converter transformer, the nonlinearity operation of the converter complicates the characteristics of current flowing through commutation reactance, which may lead to maloperation of its overcurrent protection. It is of great significance to study the performance of commutation reactance overcurrent protection under this fault condition and propose corresponding improvement measures to ensure the safe and stable operation of AC and DC systems. In the VSC–HVDC system with the pseudo-bipolar structure of a three-phase two-level voltage source converter, the valve has six working periods in a power frequency cycle, and each period is divided into five working states. According to the difference between the fault phase and non-fault phase of the conductive bridge arms at the time of fault occurrence, these five working states are merged into two categories. On this basis, various faults of the valve-side bushing of a converter transformer are analyzed, and the conclusion is drawn that the asymmetric fault of valve-side bushing can lead to the maloperation of the commutation reactance overcurrent protection. Based on the characteristics that the current flowing through the commutation reactance after the asymmetric fault of the valve-side bushing contains decaying aperiodic components in addition to the fundamental frequency wave, a scheme to prevent the maloperation of commutation reactance overcurrent protection is proposed, which uses the unequal of two half cycle integral values with different starting points to realize the blocking of commutation reactance overcurrent protection, and it makes up the deficiency of existing protection in this aspect. Finally, this paper builds a VSC–HVDC system simulation model in the PSCAD/EMTDC platform to verify the effectiveness of the scheme.

The effective design of energy-saving electric motors with efficiency class IE4 and higher requires the use of material characteristics that take into account the core shaping process. Therefore, it becomes necessary to use analytical or numerical models that take into account the change of local properties of Fe-Si material. The aim of the work is to indicate a useful analytical model for estimating the local magnetic permeability of the material, as well as to understand the reasons for these changes. For this purpose, low-loss ferromagnetic materials cut with a guillotine and a laser were tested. Rectangular samples, cut at an angle of 0 degrees in relation to the rolling direction, were subjected to macroscopic and microscopic examinations. Finally, the main reasons for changes in material characteristics for both cutting technologies were indicated. Therefore, the proposed model takes into account not only the cutting technology used, but also the current width of the tested strip, for which the material characteristics are to be determined. The parameters of the analytical model are determined on the basis of a limited number of measurements carried out on samples of a simple geometric shape.

Gapped magnetic components are inherent to applications where conversion of power would force magnetic flux density beyond the saturation point of magnetic materials. A physical discontinuity in a magnetic path, which an air gap represents, signifies a drastic change in its reluctance to magnetic flux. This gives rise to a phenomenon referred to as the fringing effect, which impacts the performance of magnetic components. The fringing flux also affects the physical properties of magnetic components, such as magnetic reluctance and inductance. Since inductance of gapped magnetic components is a function of the size of the air gap, a relatively simple change to the configuration of the air gap or splitting a single gap into a plurality of gaps entails, frequently, a radical change to the magnetic circuit of the component. This paper examines the way the air-gap configuration affects the distribution of the fringing flux and, by extension, magnetic reluctance and inductance. A method to aid the design of multigap inductors is presented based on 3-D electromagnetic modelling as well as measurements. An analytic expression, which closely approximates the required length of quasi-distributed gaps substituting a single gap, is developed.

This paper presents a novel fault detection algorithm for a three-phase interleaved DC–DC boost converter integrated in a photovoltaic system. Interleaved DC–DC converters have been used widely due to their advantages in terms of efficiency, ripple reductions, modularity and small filter components. The fault detection algorithm depends on the input current waveform as a fault indicator and does not require any additional sensors in the system. To guarantee service continuity, a fault tolerant topology is achieved by connecting a redundant switch to the interleaved converter. The proposed fault detection algorithm is validated under different scenarios by the obtained results.

Fault diagnosis and condition monitoring of synchronous machines running under load is a key determinant of their lifespan and performance. Faults such as broken rotor bars, bent shafts and bearing issues lead to eccentricity faults. These faults if not monitored may lead to repair, replacement and unforeseen loss of income. Researchers who attempted to investigate this kind of machine stopped at characterizing and deduced ways, types and effects of rotor eccentricity fault on the machine inductances using the winding function method. A modified closed-form analytical model of an eccentric synchronus reluctance motor (SynRM) is developed here taking into cognizance the machine dimensions and winding distribution for the cases of a healthy and unhealthy SynRM. This paper reports the study the SynRM under static rotor eccentricity using the developed analytical model and firming up the model with finite element method (FEM) solutions. These methods are beneficial as they investigated and presented the influence of the degrees of static eccentricity on the machine performance indicators such as speed, torque and the stator
current and assess the extent to which the machine performance will deteriorate when running with and without load. The results show that static eccentricity significantly affects the machine’s performance as the degree of eccentricity increases.

A microgrid is an appropriate concept for urban areas with high penetration of renewable power generation, which improves the reliability and efficiency of the distribution network at the consumer premises to meet various loads such as domestic, industrial, and agricultural types. Microgrids comprising inverter-based and synchronous generator-based distribution generators can lead to the instability of the system during the islanded mode of operation. This paper presents a study on designing stable microgrids to facilitate higher penetration of solar power generation into a distribution network. Ageneralized small signal model is derived for a microgrid with static loads, dynamic loads, energy storages, solar photovoltaic (PV) systems, and diesel generators, incorporating the features of dynamic systems. The model is validated by comparing the transient curves given by the model and a transient simulator subjected to step changes. The result shows that full dynamic models of complex systems of microgrids can be built accurately, and the proposed microgrid is stable for all the considered loading situations and solar PV penetration levels according to the small signal stability analysis.

This paper studies the influence of different cooling technologies on the power density of a traction machine for heavy-duty distribution transport. A prototype induction machine is built with a housing cooling jacket, potted end-windings, entire winding cooling, and shaft cooling. Electromagnetic finite element and thermal lumped-parameter models are parameterized and verified using test bench measurements. The influence of each thermal resistance along the heat paths is studied and discussed. The results are used for studying different cooling technologies. The results indicate an improvement of the continuous power density up to 108% using shaft cooling and up to 15.6% using entire winding cooling.

The problem of large speed loss exists in the traditional passing through the electric phase-separation method of trains, which is more prominent when trains pass through an electric phase-separation zone in the uphill section of long ramps and may lead to the trains not passing through the phase-separation zone safely. In order to solve this problem, based on the energy storage type railroad power conditioner, a train uninterrupted phase-separation passing system based on the energy storage type railroad power conditioner is proposed. The energy storage railroad power conditioner can realize the recovery and utilization of regenerative braking energy of the electrified railroad. In the structure of the energy storage railroad power conditioner, the single-phase inverter is led from the middle DC side of the energy storage railroad power conditioner and connected to the neutral line through theLCLfilter and the step-up transformer, which constitutes an uninterrupted phaseseparation passing system. The single-phase inverter is controlled using virtual synchronous generator technology, which allows the single-phase inverter to have external characteristics similar to those of a synchronous generator, providing support for the voltage and frequency in the neutral zone. The power required by the train to pass the electric phase-separation is provided by the power supply arm or the energy storage system, which not only improves the utilization rate of regenerative braking energy but also realizes the uninterrupted phaseseparation passing of the train through the control of the voltage in the neutral region.

Transmission lines’ live working is one of an effective means to ensure the reliable operation of transmission lines. In order to solve the unsafe problems existing in the implementation of traditional live working, the paper uses ground-based lidar to collect point cloud data. A tile based on the pyramid data structure is proposed to complete the storage and calling of point cloud data. The improved bidirectional filtering algorithm is used to distinguish surface features quickly and obtain a 3D model of the site. Considering the characteristics of live working, the speed of data reading and querying, the nearest point search algorithm based on octree is used to acquire a real- time calculation of the safe distance of each point in the planned path, and the safety of the operation mode is obtained by comparing with the value specified in the regulation, and assist in making decisions of the operation plan. In the paper, the simulation of the actual working condition is carried out by taking the “the electric lifting device ascending” as an example. The experimental results show that the established three-dimensional model can meet the whole process control of the operation, and has achieved practical effect.

The grid integration of large-scale wind power will alter the dynamic characteristics of the original system and the power distribution among synchronous machines. Meanwhile, the interaction between wind turbines and synchronous machines will affect the damping oscillation characteristics of the system. The additional damping control of traditional synchronous generators provides an important means for wind turbines to enhance the damping characteristics of the system. To improve the low frequency oscillation characteristics of wind power grid-connected power systems, this paper adds a parallel virtual impedance link to the traditional damping controller and designs a DFIG-PSS-VI controller. In the designed controller, the turbine active power difference is chosen as the input signal based on residual analysis, and the output signal is fed back to the reactive power control loop to obtain the rotor voltage quadrature component. With DigSILENT/PowerFactory, the influence of the controller parameters is analyzed. In addition, based on different tie-line transmission powers, the impact of the controller on the low-frequency oscillation characteristics of the power system is examined through utilizing the characteristic root and time domain simulation analysis.