Constantly growing distributed energy generation based on renewable sources creates a number of new challenges for electrical power system operation. One of the challenges is islanding detection. Unintentional islanding, which can cause health and safety hazards for the personnel, is currently being experienced by a growing number of consumers/prosumers especially in the case of photovoltaic inverters. This work presents a new islanding detection method based on synchrophasor measurements. The proposed method works in either a passive or hybrid mode. In a passive mode, a single phasor measurement unit (PMU) in the island region is used. In a hybrid mode, one PMU in the island and another one outside the island are exploited. The proposed method was verified in conducted laboratory tests that confirmed the applicability of PMUs data for effective detection and monitoring of unintentional islanding.

The article presents a concept for a new design of the well-known Transverse Flux Machine (TFM) made with the use of a flat core used in classical electrical machines. The proposed design was first analytically verified and was subsequently verified using the finite element method, which fully corroborated the results. The simulations show that a set of three single-phase TFM machines with slotted flat rotor yokes generates a torque over three times greater than that of an induction motor and twice as large as Fractional Slot Concentrated Winding—Permanent Magnet Synchronous Machines (FSCW-PMSM). The performed comparative calculations confirmed that the torque generated by machines operating on principles similar to TFM can generate a torque much greater than those currently in common use.

An essential component of guaranteeing the stability and safety of electricity distribution networks is islanding detection. In this work, a novel method for islanding detection which combined both phasor measurement units (PMU) and artificial neural network (ANN) is proposed. Using PMU measurements, the technique extracts features including phasor voltage, voltage frequency, and voltage rate of change of frequency (ROCOF), which later are fed into an ANN classifier. Using a huge dataset of more than a hundred thousand observations of both islanding and non‐islanding scenarios, testing was done on 24 distinct types of inverters in compliance with PN‐EN 62116 protocol criteria. The tests were carried out using Regenerative Grid Simulator Chroma 61815‐powered system which was connected in parallel to adjusting RLC load; the tested inverters were linked to a Photovoltaic Panels Simulator, the National Instruments cRIO‐9024 measuring equipment was used to carry out the measurements, MATLAB and LabVIEW were used for analyzing the data and results. With a testing accuracy of 99.05% and a training accuracy of 99.34%, the results demonstrate a high degree of accuracy. This work offers a practical solution for problems that occurred due to islanding phenomenon in power networks which can enhance the system dependability and security.

The cogging machine is a new design of a machine with permanent magnets, which combines the advantages of a machine with transverse flux (TFM), eliminating its disadvantages. The biggest advantage of TFM machines is their high torque density, which varies with the design parameters. The developed machine retains these advantages while simplifying the construction of the stator core, which is the biggest disadvantage of TFM machines. This article presents the results of an analysis of the power density of the developed cogging machine as a function of a number of pole pairs and the basic design parameters. Analyses were carried out on the basis of the derived analytical equations, which make the generated torque depend on the construction parameters. Thanks to the derived dependencies, one can easily examine the effects of modifying the machine structure. The results presented in the article prove that the developed design of the cogging machine is competitive in relation to machines with permanent magnets existing on the market.