A Survey of Islanding Detection Methods for Microgrids and Assessment of Non-Detection Zones in Comparison with Grid Codes

Detection of unintentional islanding is critical in microgrids in order to guarantee personal safety and avoid equipment damage. Most islanding detection techniques are based on monitoring and detecting abnormalities in magnitudes such as frequency, voltage, current and power. However, in normal operation, the utility grid has fluctuations in voltage and frequency, and grid codes establish that local generators must remain connected if deviations from the nominal values do not exceed the defined thresholds and ramps. This means that islanding detection methods could not detect islanding if there are fluctuations that do not exceed the grid code requirements, known as the non-detection zone (NDZ). A survey on the benefits of islanding detection techniques is provided, showing the advantages and disadvantages of each one. NDZs size of the most common passive islanding detection methods are calculated and obtained by simulation and compared with the limits obtained by ENTSO-E and islanding standards in the function of grid codes requirements in order to compare the effectiveness of different techniques and the suitability of each one.

A reliable islanding identification mechanism for DC microgrid using PCC transient signal

The islanding detection is a major problem for both AC and DC Microgrids. Failure to do so may result in problems such as system instability, increased non-detection zone, out-of-phase reclosing, personnel safety, and power quality deterioration. To address this issue, this paper presents a reliable island identification method for DC Microgrids that employs a Cumulative Sum of Rate of change of Voltage (CSROCOV) to reduce the non-recognition region. The proposed islanding protection scheme employs point of common coupling (PCC) transient signal to detect islands events. The voltage, power, and current sampling are accumulated from the PCC of the distributed generation terminals. The proposed scheme detects islanding in three test cases with varying power mismatching conditions, while non-islanding events are classified as capacitor switching and faults. The system is modelled and simulated in the MATLAB/Simulink environment, then islanding conditions are applied by turning off the main circuit breaker. Simulation results are presented to verify the methodology under different test cases. The robustness of the proposed scheme is also validated against measurement noise.

Hemagglutination Detection with Paper–Plastic Hybrid Passive Microfluidic Chip

Hemagglutination is a critical reaction that occurs when antigens expressed on red blood cells (RBCs) react with the antibodies used for blood typing. Even though blood typing devices have been introduced to the market, they continue to face several limitations in terms of observation by the eye alone, blood manipulation difficulties, and the need for large-scale equipment, particularly process automated machines. Thus, this study aimed to design, fabricate, and test a novel hybrid passive microfluidic chip made of filter paper and polymer using a cost-effective xurography manufacturing technique. This chip is referred to as the microfluidic paper–plastic hybrid passive device (PPHD). A passive PPHD does not require external sources, such as a syringe pump. It is composed of a paper-based component that contains dried antibodies within its porous paper and a polymer component that serves as the detection zone. A single blood sample was injected into the chip’s inlet, and classification was determined using the mean intensity image. The results indicated that embedded antibodies were capable of causing RBC agglutination without a saline washing step and that the results could be classified as obviously agglutination or nonagglutination for blood typing using both the naked eye and a mean intensity image. As a proof-of-concept, this study demonstrated efficiency in quantitative hemagglutination measurement within a passive PPHD for blood typing, which could be used to simplify blood biomarker analysis.

Islanding Detection Methods for Microgrids: A Comprehensive Review

Microgrids that are integrated with distributed energy resources (DERs) provide many benefits, including high power quality, energy efficiency and low carbon emissions, to the power grid. Microgrids are operated either in grid-connected or island modes running on different strategies. However, one of the major technical issues in a microgrid is unintentional islanding, where failure to trip the microgrid may lead to serious consequences in terms of protection, security, voltage and frequency stability, and safety. Therefore, fast and efficient islanding detection is necessary for reliable microgrid operations. This paper provides an overview of microgrid islanding detection methods, which are classified as local and remote. Various detection methods in each class are studied, and the advantages and disadvantages of each method are discussed based on performance evaluation indices such as non-detection zone (NDZ), detection time, error detection ratio, power quality and effectiveness in multiple inverter cases. Recent modifications on islanding methods using signal processing techniques and intelligent classifiers are also discussed. Modified passive methods with signal processing and intelligent classifiers are addressing the drawbacks of passive methods and are getting more attention in the recently published works. This comprehensive review of islanding methods will provide power utilities and researchers a reference and guideline to select the best islanding detection method based on their effectiveness and economic feasibility.

A fuzzy rule based approach for islanding detection in grid connected inverter systems

<span lang="EN-US">Islanding is when an area of the electrical distribution system is isolated from the electrical system while being powered by distributed generators. An important condition for the interconnection of power plants and distribution systems is the ability of the power plant to detect islands. The presented and proposed method is a combination of best active sandia frequency shift (SFS) method with the intelligent fuzzy logic controller, which has been tested in distributed production using the island detection function. And the choice to improve the method by fuzzy logic control (FLC) is retained, as this process is more effective in decreasing the non-</span><span lang="EN-US">detection zone (NDZ) and in further improving the efficiency of the islanding detection system. This paper proposes a new active islanding detection technique controlled by a fuzzy logic controller, for grid connected photovoltaic (PV) inverters. In addition, the efficiency and performance of the proposed method strategy for islanding detection has been analyzed and tested in the various situations of the network. In addition, the results of the simulations with the <span lang="EN-US">power </span><span lang="EN-US">simulation (</span>PSIM) software will be provided to illustrate the main conclusions and the development of the control. Thus, will be used to show the feasibility and validity of the proposed new algorithm.</span>

A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

In recent years, vehicle detection and classification have become essential tasks of intelligent transportation systems, and real-time, accurate vehicle detection from image and video data for traffic monitoring remains challenging. The most noteworthy challenges are real-time system operation to accurately locate and classify vehicles in traffic flows and working around total occlusions that hinder vehicle tracking. For real-time traffic monitoring, we present a traffic monitoring approach that overcomes the abovementioned challenges by employing convolutional neural networks that utilize You Only Look Once (YOLO). A real-time traffic monitoring system has been developed, and it has attracted significant attention from traffic management departments. Digitally processing and analyzing these videos in real time is crucial for extracting reliable data on traffic flow. Therefore, this study presents a real-time traffic monitoring system based on a virtual detection zone, Gaussian mixture model (GMM), and YOLO to increase the vehicle counting and classification efficiency. GMM and a virtual detection zone are used for vehicle counting, and YOLO is used to classify vehicles. Moreover, the distance and time traveled by a vehicle are used to estimate the speed of the vehicle. In this study, the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring data set (GRAM-RTM), and our collection data sets are used to verify the proposed method. Experimental results indicate that the proposed method with YOLOv4 achieved the highest classification accuracy of 98.91% and 99.5% in MAVD and GRAM-RTM data sets, respectively. Moreover, the proposed method with YOLOv4 also achieves the highest classification accuracy of 99.1%, 98.6%, and 98% in daytime, night time, and rainy day, respectively. In addition, the average absolute percentage error of vehicle speed estimation with the proposed method is about 7.6%.

Evaluation of Passive Islanding Detection Methods for Line to Ground Unsymmetrical Fault in Three Phase Microgrid Systems: Microgrid Islanding Detection Method

Distributed Generators (DGs) are incorporated in the power distribution systems to develop green energies in microgrids. Islanding is a challenging task in a microgrid. Different types of islanding methods, e.g. local and remote methods, have been developed for handling this task, with local methods being easier to implement, while remote methods are communication-based and costly. The local methods are classified as passive, active, and hybrid, out of which the passive methods are more simple and economical. In this paper, a passive islanding detection method is proposed to detect single line to ground fault. This fault is considered to represent the 60 to 70% of the total un-intentional faults of this category. The available passive methods cannot detect islanding at lower power mismatches as the variations in voltage and frequency fall within thresholding values. In this method, the voltage signals are first retrieved at the targeted DG output and then the phase angle is estimated. Finally, the phase angle is differentiated to get Rate Of Change Of Voltage Phase Angle (ROCOVPA) to detect islanding, and then it is compared with the Rate Of Change Of Frequency (ROCOF) at zero percent power mismatch. Simulation results depict that the ROCOVPA is more effective than ROCOF. The proposed method not only reduces detection time and Non-Detection Zone (NDZ) but is also stable during non-islanding cases like load connection and disconnection to avoid nuisance tripping.

FORMATION OF THE OPTOELECTRONIC SYSTEM DIRECTIONAL DIAGRAM USING FIBER-OPTIC CABLE

The method of forming directional diagrams (RD) with the possibility of controlling it in space is described. The method of forming of the object location detection zone of complex shape in the transmitter-receiver optical system with the help of fiber-optic cables (OIC) is substantiated and created. The problem of a circular field of view of a multi-channel optoelectronic system (ECO) creating, designed for advanced high-speed objects at short distance detection has been solved. According to the results of laboratory tests of the developed ECO model is established that in the working range of distances of a location of 0,5 ... 10,0 m reliable detection of the target high-speed and high-precision registration of object in all DS directions is reached.