Nowadays, the integration of distributed generators with the main utility grid is highly increasing due to the benefits which can be obtained, such as increasing the system efficiency and reliability. Apart from that, many technical and safety issues appear in the system due to this integration. One of these issues is the islanding condition, which has to be detected effectively and quickly before having any detrimental effects on the protection, stability, and security of the system. This study provides a detailed overview of several islanding detection approaches, which are divided into traditional methods, including local and remote methods, and modern methods, including methods based on signal processing and computational intelligence. Moreover, a comparison between each method based on various criteria, such as non-detected zone, quality factor, response time, implementation cost, degrading power quality, reliability, suitability for the type of distributed generators, suitability for multi-distributed generators system, and sensitivity to cyber-attacks, is carried out. Therefore, this review will offer a solid background in order to help researchers interested in this field distinguish between islanding detection methods and their relative advantages and disadvantages, as well as to be able to choose the most suitable islanding detection method among the others to be implemented in the network.

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.

In Jordan, there are many extensive challenges that the country faces against the national economy growth. This is mainly because Jordan has limited primary energy resources and imports more than 90% of natural gas and crude oil from neighboring countries which negatively affect its economy. However, the geographical location of Jordan is one of the benefits that make the country rich in renewable energy resources particularly solar and wind energy resources. Therefore, to fulfill the high growth of electricity demand in the coming years; Jordan is working on increasing the contribution percentage of renewable energy resources in the total energy mix. In this paper, the status of the electricity supply system and renewable energy resources in Jordan are discussed. Furthermore, the investigation of implementing the prospect scenario of increasing the share of electric generation from renewable energy resources shows that the generated power from solar and wind are significantly enhancing the national economy growth.

One of the problems that are associated to power systems is islanding condition, which must be rapidly and properly detected to prevent any negative consequences on the system's protection, stability, and security. This paper offers a thorough overview of several islanding detection strategies, which are divided into two categories: classic approaches, including local and remote approaches, and modern techniques, including techniques based on signal processing and computational intelligence. Additionally, each approach is compared and assessed based on several factors, including implementation costs, non-detected zones, declining power quality, and response times using the analytical hierarchy process (AHP). The multi-criteria decision-making analysis shows that the overall weight of passive methods (24.7%), active methods (7.8%), hybrid methods (5.6%), remote methods (14.5%), signal processing-based methods (26.6%), and computational intelligent-based methods (20.8%) based on the comparison of all criteria together. Thus, it can be seen from the total weight that hybrid approaches are the least suitable to be chosen, while signal processing-based methods are the most appropriate islanding detection method to be selected and implemented in power system with respect to the aforementioned factors. Using Expert Choice software, the proposed hierarchy model is studied and examined.

The increasing integration of inverter-based resources in smart grid systems has deepened the necessity to understand the difference between grid-following and grid-forming inverters’ operational performance, particularly under abnormal conditions such as unintentional islanding events. This work provides a comparative assessment, showing that while grid-following inverters perform well under strong grids, their stability degrades under weak grids due to their dependence on the grid reference voltage. On the other hand, grid-forming inverters improve the system stability under weak grids, as they operate as an independent voltage source. However, the widespread misconception in academia and industry that grid-forming inverters are always good and grid-following inverters are generally bad is challenged by this work’s results. Despite the stability advantages of grid-forming inverters, they significantly increase the size of non-detected zones and extend the detection time of unintentional islanding events, with various cases failing to meet standards, while grid-following inverters offer quicker and more expectable responses. A Random Forest-based islanding detection scheme is proposed to address the protection difficulties allied with both inverter types. The findings prove that this model can reduce the size of the non-detected zone and the detection time, emphasizing the necessity of intelligent protection schemes in future microgrid applications and the significance of performance-based inverter selection.