Induction motors (IMs) experience power losses when a portion of the input power is converted to heat instead of driving the load. The combined effect of copper losses, core losses, and mechanical losses results in IM power losses. Unfortunately, the core losses in the motor, which have a considerable impact on its energy efficiency, are not taken into account by the generally employed dynamic model in the majority of the studies. Due to this, the motor rating often corresponds to the worst-case load in applications, but the motor frequently operates below rated conditions. A hybridized model reference adaptive system (MRAS) with sliding mode control (SMC) is used in this study for sensorless speed control of an induction motor with core loss, allowing the motor to operate under a variety of load conditions. As a result, the machine can run at maximum efficiency while carrying its rated load. By adjusting the ��-axis current in the �� - �� reference frame in vector-controlled drives, the system’s performance is enhanced by running the motor at its optimum flux. Regarding the torque and speed of both induction motors with and without core loss, the Adaptive Observer Sliding Mode Control (AOSMC) has been constructed and simulated in this case. The AOSMC with core loss produced good performance when the proposed controller was tested.

The paper presents a honey badger algorithm (HB) based on a modified backwardforward sweep power flow method to determine the optimal placement of droop-controlled dispatchable distributed generations (DDG) corresponding to their sizes in an autonomous microgrid (AMG). The objectives are to minimise active power loss while considering the reduction of reactive power loss and total bus voltage deviation, and the maximisation of the voltage stability index. The proposed HB algorithm has been tested on a modified IEEE 33-bus AMG under four scenarios of the load profile at 40%, 60%, 80%, and 100% of the rated load. The analysis of the results indicates that Scenario 4, where the HB algorithm is used to optimise droop gains, the positioning of DDGs, and their reference voltage magnitudes within a permissible range, is more effective in mitigating transmission line losses than the other scenarios. Specifically, the active and reactive power losses in Scenario 4 with the HB algorithm are only 0.184% and 0.271% of the total investigated load demands, respectively. Compared to the base scenario (rated load), Scenario 4 using the HB algorithm also reduces active and reactive power losses by 41.86% and 31.54%, respectively. Furthermore, the proposed HB algorithm outperforms the differential evolution algorithm when comparing power losses for scenarios at the total investigated load and the rated load. The results obtained demonstrate that the proposed algorithm is effective in reducing power losses for the problem of optimal placement and size of DDGs in the AMG.

This study describes a method that allows the modelling of magnetisation processes in transformer steel sheets for any direction of the magnetic field strength. In the proposed approach, limiting hysteresis loops for the rolling and transverse directions were used. These loops are modified depending on the magnetisation angle between the direction of the field strength vector and rolling direction. For this purpose, unique correction coefficients, which are functions of the magnetisation angle, were applied for both hysteresis loops. An algorithm for determining the limiting hysteresis loops for any magnetisation angle is presented herein. The calculation results for several cases of magnetisation were compared with the measured hysteresis loops.

Manufacturing errors (MEs) are unavoidable in product fabrication. The omnipresence of manufacturing errors (MEs) in product engineering necessitates the development of robust optimization methodologies. In this research, a novel approach based on the morphological operations and interval field (MOIF) theory is proposed to address MEs in the discrete-variable-based topology optimization procedures. On the basis of a methodology for deterministic topology optimization (TO) based on the Min-Cut, MOIF introduces morphological operations to generate geometrical variations, while the dimension of the structuring element is dynamically set by the interval field function’s output. The effectiveness of the proposed approach as a powerful tool for accounting for spatially uneven ME in the TOs has been demonstrated.

The paper focusses on the analysis of the demagnetisation process of permanent magnets in line-start synchronous motors in dynamic states related to start-up and resynchronisation. A field-circuit model of electromagnetic phenomena was used to analyse the demagnetisation process, taking into account the influence of temperature on the properties of permanent magnets and their resistance to demagnetisation. The results of the conducted research have shown, among other things, that the process of resynchronisation of the motor is much more dangerous from the standpoint of the risk of demagnetisation than the start-up itself.

Optimizing the aerodynamic structure of composite insulators can guarantee the safe operation of power systems. In this study, we construct a simulation model for composite insulator contaminant deposition using the COMSOL simulation software, and the rationality of the simulation model and method is verified through wind tunnel experiments. Taking the FXBW4-110/100 composite insulator as an example, we adopt a progressive optimization plan to explore the impacts of shed spacing s, and shed inclination angles α and β on its contaminant deposition characteristics under DC and AC voltages. Based on the numerical simulation results, we analyze the antifouling performance of insulators before and after structural optimization. The results indicate the following: 1) The contaminant deposition of the insulator under AC and DC voltages is negatively correlated with the shed spacing s, but positively correlated with the lower inclination angle β. 2) Under AC voltages, the contaminant deposition of the insulator increases with the upper inclination angle α, while under DC voltages, the contaminant deposition shows an uptrend first, then a downtrend and then an uptrend again with the increase of the upper inclination angle α. 3) Compared with the original model, the AC-optimized model ( α = 6°, β = 2° and s = 98 mm) with a larger shed spacing s, and smaller shed inclination angles α and β showed superior antifouling performance at wind speeds of no less than 2 m/s, and under the typical conditions ( v = 2.5 m/s, d = 20 μm, and ρ = 2 200 kg/m ³), its contaminant deposition is 15% less than that of the original model ( α = 10°, β = 2° and s = 80 mm).

A novelty dual-stator brushless doubly-fed generator (DSBDFG) with magneticbarrier rotor structure is put forward for application in wind power. Compared with a doublyfed induction generator, the DSBDFG has virtues of high reliability and low maintenance costs because of elimination of brush and sliprings components. Therefore, the proposed structure has tremendous potential as a wind power generator to apply in wind power. According to the operating principle of electric machine, the DSBDFG is studied in wind power application. At first, the topology, the winding connecting, the rotor structure, the power flow chart of different operating models and the variable speed capability of electric machine are discussed and analyzed. Then, a 50 kW DSBDFG is designed. Based on the principal dimension of the design electric machine, the electromagnetic characteristics of the DSBDFG with different running modes are analyzed and calculated to adopt the numerical method. From the result, it meets the requests of electromagnetic consistency and winding connecting in the design electric machine. Meanwhile, it confirms the proposed DSBDFG has the strong ability of speed regulation.

Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.

In response to the inability of the flexible DC transmission system connected to the AC grid under conventional control strategies to provide inertia to the system as well as to participate in frequency regulation, a virtual synchronous generator (VSG) control strategy is proposed for a voltage source converter (VSC)-based multi-terminal high-voltage direct current (VSC-MTDC) interconnection system. First, the virtual controller module is designed by coupling AC frequency and active power through virtual inertia control, so that the VSC-MTDC system can provide inertia response for AC grid frequency. Second, by introducing the power margin of the converter station into the droop coefficient, the unbalanced power on the DC side is reasonably allocated to reduce the overshoot of the DC voltage in the regulation process. Finally, the power regulation capability of the normal AC system is used to provide power support to the fault end system, reducing frequency deviations and enabling inter-regional resource complementation. The simulation model of the three-terminal flexible DC grid is built in PSCAD/EMTDC, and the effectiveness of the proposed control strategy is verified by comparing the conventional control strategy and the additional frequency control strategy.

The abundant use of solar energy in Indonesia has the potential to become electrical energy in a microgrid system. Currently the use of renewable energy sources (RESs) in Indonesia is increasing in line with the reduction of fossil fuels. This paper proposes a new microgrid DC configuration and designs a centralized control strategy to manage the power flow from renewable energy sources and the load side. The proposed design uses three PV arrays (300 Wp PV module) with a multi-battery storage system (MBSS), storage (200 Ah battery). Centralized control in the study used an outseal programmable logic controller (PLC). In this study, the load on the microgrid is twenty housing, so that the use of electrical energy for one day is 146.360 Wh. It is estimated that in one month it takes 4.390.800 Wh of electrical energy. The new DC microgrid configuration uses a hybrid configuration, namely the DC coupling and AC coupling configurations.The results of the study show that the DC microgrid hybrid configuration with centralized control is able to alternately regulate the energy flow from the PV array and MBSS. The proposed system has an efficiency of 98% higher than the previous DC microgrid control strategy and configuration models.

In this paper, a creative dung beetle optimization (CDBO) algorithm is proposed and applied to the offline parameter identification of permanent magnet synchronous motors. First, in order to uniformly initialize the population state and increase the population diversity, a strategy to improve the initialization of the dung beetle population using Singer chaotic mapping is proposed to improve the global search performance; second, in order to improve the local search performance and enhance the convergence accuracy of the algorithm, a new dung beetle position update strategy is designed to increase the spatial search range of the algorithm. Simulation results show that the proposed optimization algorithm can quickly and accurately identify parameters such as resistance, inductance, and magnetic chain of the PMSM, with significant improvements in convergence algebra, identification accuracy and stability.

This paper aims to address the problems of inaccurate location and large computation in hybrid transmission line traveling wave detection methods. In this paper, a new fault location method based on empirical Fourier decomposition (EFD) and the Teager energy operator (TEO) is proposed. Firstly, the combination of EFD and the TEO is used to detect the time difference between the arrival of the initial traveling wave of the fault at the two measurement ends of the hybrid line. Then, when the fault occurs at the midpoint of each line segment and at the connection point of the hybrid line, the time difference between the arrival of the fault traveling wave at the two measurement ends of the line is calculated according to the line parameters. By comparing the obtained time differences, it is determined whether the fault occurs in the first or second half of the line. Finally, the fault distance is calculated using the double-ended traveling wave method according to the fault section. The model was built on PSCAD and the proposed algorithm was simulated on MATLAB platform. The results demonstrate that the proposed method achieves an average fault location accuracy of 98.88% by adjusting transition resistances and fault distances and comparing with other location methods. After validation, the proposed method for locating faults has a high level of accuracy in location, computational efficiency, and reliability. It can accurately identify fault segments and locations in hybrid transmission line systems.

The topology identification of low-voltage distribution networks is an important foundation for the intelligence of low-voltage distribution networks. Its accuracy fundamentally determines the effectiveness of functions such as power system state estimation, operational control, optimization planning, and intelligent electricity consumption. The low-voltage distribution network is composed of transformers, lines, and end users. The key task of topology identification is to distinguish the connection relationship between distribution transformers, low-voltage lines, and phase sequence with end users, which can be divided into transformer user relationship, line user relationship, and phase user relationship. At present, the main methods of low-voltage network topology identification can be divided into signal injection method and data analysis method. The signal injection method requires a large number of additional terminal devices and is difficult to promote. The data analysis method combines the characteristics of switch state, voltage, current, electrical energy, and other data to perform topology analysis. The commonly used methods include correlation analysis and feature learning. Finally, typical problems that urgently need to be solved in topology recognition and representation were proposed, providing a reference for the research and development of low-voltage distribution network topology automatic recognition technology.

Current drive control systems tend to push control loops to the limits of their performance. One of the ways of doing so is to use advanced optimization algorithms, usually related to model-based off-line calculations, such as genetic algorithms, the particle swarmoptimisation or the others. There is, however, a simpler way, namely to use predictive control formalism and by formulation of a simple linear programming problem which is easy to solve using powerful solvers, without excessive computational burden, what is a reliable solution, as whenever the optimization problem has a feasible solution, a global minimizer can be efficiently found. This approach has been deployed for a servo drive system operated by a real-time sampled-data controller, verified between model-in-the-loop and hardwarein- the-loop configurations, for a range of prediction horizons, as an attractive alternative to classical quadratic programming-related formulation of predictive control task.

Dynamic characteristics for three types of railgun constructions were simulated and measured in this work. The simplest construction is the iron-less (IL) railgun, while the two other ones (IC and ICPM) have an iron-core. The iron-core permanent magnet (ICPM) railgun additionally has permanent magnets. To compare their characteristics, similar dimensions of the rails and iron cores were adopted, and the same power supply system was used. Numerical magnetic field analyses and our analytical models have been used to determine the electromagnetic parameters. They were verified experimentally. The transient states of the railguns were studied with our field-circuit mathematical model, and their results were also verified by experiments.

This paper proposes an augmented speed control scheme of dual induction motors fed by a five-leg voltage source inverter (VSI) with a common/shared-leg. An additional control loop is proposed here and based on the mutual flux angle – the difference between flux angular positions of the IMs. The main purpose of this research is to minimize the energy losses in the common inverter leg by controlling the mutual flux angle, at equal angular speeds of both motors. Simulation and experimental studies were carried out and the effectiveness of the proposed control method was proven. The PLECS software package was used for the simulation tests. The laboratory prototypewas prepared for the experimental validation. All results were provided and discussed in this paper.