Showing 28 results for Detection
J. Sadeh, E. Kamyab,
Volume 8, Issue 4 (12-2012)
Abstract
Islanded operation of distributed generators is a problem that can take place when they are connected to a distribution system. In this paper an islanding detection method is presented for inverter based distributed generation (DG) using under/over voltage relay. The method is an adaptive one and is based on the change of DG active power reference (Pref) in inverter control interface. The active power reference has a fixed value in normal condition, whereas, if the point of common coupling (PCC) voltage changes, Pref has determined as a linear function of voltage. The slope of Pref is dependent to the load active power (Pload) and should be changed if Pload changes. The non-detection zone (NDZ) of the proposed method is dependent on the accuracy of the voltage measurement equipment if changing of the PCC voltage is sensed, then, islanding will be detected if it is occurred. Also it does not have any negative effects on the distribution system in normal conditions. Moreover, the proposed technique can be applied when two-DG is in the island. The proposed method is evaluated according to the requirements of the IEEE 1547 and UL 1741 standards, using PSCAD/EMTDC software.
M. Bakhshi, R. Noroozian, G. Gharehpetian,
Volume 9, Issue 2 (6-2013)
Abstract
Identification of intentional and unintentional islanding situations of dispersed generators (DGs) is one of the most important protection concerns in power systems. Considering safety and reliability problems of distribution networks, an exact diagnosis index is required to discriminate the loss of the main network from the existing parallel operation. Hence, this paper introduces a new islanding detection method for synchronous machineโbased DGs. This method uses the average value of the generator frequency to calculate a new detection index. The proposed method is an effective supplement of the over/under frequency protection (OFP/UFP) system. The analytical equations and simulation results are used to assess the performance of the proposed method under various scenarios such as different types of faults, load changes and capacitor bank switching. To show the effectiveness of the proposed method, it is compared with the performance of both ROCOF and ROCOFOP methods.
A. Soofiabadi, A. Akbari Foroud,
Volume 10, Issue 1 (3-2014)
Abstract
This paper proposes an index for nodal market power detection in power market under locational marginal pricing (LMP). This index is an ex-ante technique to detect the market power. More precisely, this criterion detects the potential of exercising market power regardless of detecting the actual market power. Also it is obvious that pricing and market clearing method affect the potential of exercising market power. Different potential of market power exists in different pricing methods. This index has been analyzed under LMP method which seems to be a desirable environment to exercise market power. In LMP method by load growth, in some determined load levels which is called Critical Load Levels (CLLs), locational marginal prices have step change. This step change in locational marginal prices causes step change in revenue and benefit of Gencos. So it is significant to detect the behavior of Gencos in the CLLs. The proposed criterion has been tested on constant system load and CLLs of system.
F. Namdari, M. Parvizi, E. Rokrok,
Volume 12, Issue 1 (3-2016)
Abstract
Integration of distributed generations (DGs) in power grids is expected to play an essential role in the infrastructure and market of electrical power systems. Microgrids are small energy systems, capable of balancing captive supply and requesting resources to retain stable service within a specific boundary. Microgrids can operate in grid-connected or islanding modes. Effective islanding detection methods are essential for realizing the optimal operation of microgrids. In this paper, a new passive islanding detection method is presented according to the change rate of DG’s voltage over active power index. This technique has been applied on inverter-based and synchronous-based microgrids. The efficiency of the proposed method is verified through a comprehensive set of simulation studies carried out in Matlab/Simulink.
M. J. Abbasi, H. Yaghobi,
Volume 12, Issue 4 (12-2016)
Abstract
The doubly fed induction generator (DFIG) is one of the most popular technologies used in wind power systems. With the growing use of DFIGs and increasing power system dependence on them in recent years, protecting of these generators against internal faults is more considered. Loss of excitation (LOE) event is among the most frequent failures in electric generators. However, LOE detection studies heretofore were usually confined to synchronous generators. Common LOE detection methods are based on impedance trajectory which makes the system slow and also prone to interpret a stable power swing (SPS) as a LOE fault. This paper suggests a new method to detect the LOE based on the measured variables from the DFIG terminal. In this combined method for LOE detection, the rate of change of both the terminal voltage and the output reactive power are utilized and for SPS detection, the fast Fourier transform (FFT) analysis of the output instantaneous active power has been used. The performance of the proposed method was evaluated using Matlab/Simulink interface for various power capacities and operating conditions. The results proved the method's quickness, simplicity and security.
S. Mohammad Nejad, H. Arab, N. Ronagh Sheshkelani,
Volume 14, Issue 3 (9-2018)
Abstract
In this paper, after a brief overview on laser warning system (LWS), a new structure for an optical array that is used in its optical subsystem is introduced. According to the laser threats’ wavelengths (0.5 – 1.6 µm) and our desirable field of view (FOV), we used 6 lenses for gathering the incident radiation and then optimized the optical array. Lenses’ radius, their semi diameter, their distance from each other, their thickness and the kind of glass used in them was chosen in which we access a very high transmission coefficient. Also the optical reflection and absorption of the array decreases at the same time. After optimization, the obtained optical transmission in our desirable FOV is up to 82% and the obtained optical reflection and absorption is less than 15%. Total aberration of the incident ray decreased notably and the results showed that this parameter is less than 2µm. The laser spot diameter which is focused on the detector is smaller than 400 µm in the worst case which is the laser radiation with 1.54 µm wavelength and field of 10 degrees. Total track of the array is 66.819 mm and effective focal length and F/# parameter are as small as possible which leads to high quality of the light’s focus on the detector and smaller dimension and lighter weight for the receiver. Using optical devices with such appropriate arrangement and very good optical transmission coefficient, the offered structure has a remarkable signal to noise ratio (SNR) which is up to 160 dB. The receiver’s operation in far distances from laser sources (beyond 15 km) and in hazy conditions and low temperatures is quite suitable as well.
S. Dolatabadi, S. Tohidi, S. Ghasemzadeh,
Volume 14, Issue 4 (12-2018)
Abstract
In this paper, a new active method based on traveling wave theory for islanding detection is presented. A standard power grid that combines a distributed generation source and local loads is used to test the proposed method. Simulations are carried out in MATLAB/Simulink and EMTP/rv which demonstrate fast response and zero non-detection zone (NDZ) of the method along with low perturbation.
M. Dodangeh, N. Ghaffarzadeh,
Volume 16, Issue 2 (6-2020)
Abstract
In this paper, a new fast and accurate method for fault detection, location, and classification on multi-terminal DC (MTDC) distribution networks connected to renewable energy and energy storages presented. MTDC networks develop due to some issues such as DC resources and loads expanding, and try to the power quality increasing. It is important to recognize the fault type and location in order to continue service and prevent further damages. In this method, a circuit kit is connected to the network. Fault detection is performed with the measurement of the current of the connected kits and the traveling-waves of the derivative of the fault current and applying to a mathematical morphology filter, in the Fault time. The type and location of faults determinate using circuit equations and current calculations. DC series and ground arc faults are considered as DC distribution network disturbances. The presented method was tested in an MTDC network with many faults. The results illustrate the validity of the proposed method. The main advantages of the proposed fault location and classification strategy are higher accuracy and speed than conventional approaches. This method robustly operates to changing in sampling frequency, fault resistance, and works very well in high impedance fault.
S. Shadpey, M. Sarlak,
Volume 16, Issue 4 (12-2020)
Abstract
This paper presents a pattern recognition-based scheme for detection of islanding conditions in synchronous- based distributed generation (DG) systems. The main idea behind the proposed scheme is the use of spatial features of system parameters such as the frequency, magnitude of positive sequence voltage, etc. In this study, the system parameters sampled at the point of common coupling (PCC) were analyzed using reduced-noise morphological gradient (RNMG) tool, first. Then, the spatial features of the RNMG magnitudes were calculated. Next, to optimize and increase the ability of the proposed scheme for islanding detection, the best features with a much discriminating power were selected based on separability index (SI) calculation. Finally, to distinguish the islanding conditions from the other normal operation conditions, a support vector machine (SVM) classifier was trained based on the selected features. To investigate the power of the proposed scheme for islanding detection, the results of examinations on the various islanding conditions including system loading and grid operating state were presented. These results show that the proposed algorithm reliably detect the islanding condition within 32.7 ms.
M. Mohiti, S. Sabzevari, P. Siano,
Volume 17, Issue 3 (9-2021)
Abstract
Islanding detection is essential for reliable and safe operation of systems with distributed generations (DG). In systems with multiple DGs, the interaction between DGs can make the islanding detection process more challenging. To address this concern, this paper proposes a two-stage islanding detection method for power systems equipped with multiple-DGs through estimation of high frequency impedance (Zf) and determination of the total harmonic distortion (THD). The impedances of the DGs are estimated at distinct frequencies to avoid interval overlaps. The concept of different frequency bands makes the proposed method applicable to multiple DG systems. To evaluate the effectiveness of the proposed method, a test system with multiple DGs is simulated through several case studies in PSCAD/EMTDC. The simulation results demonstrate the accuracy of the proposed islanding detection method in both single and multi-DG systems. It is also shown that the proposed method remains robust under different operating conditions and events.
F. Asghariyehlou, J. Javidan,
Volume 18, Issue 2 (6-2022)
Abstract
This paper deals with the optimization of the CORDIC-based modified Gram-Schmidt (MGS) algorithm for QR decomposition (QRD) and presents a scalable algorithm with maximum throughput, the least possible latency, and hardware resources. The optimized algorithm is implemented on Xilinx Virtex 6 FPGA using ISE software as a fixed point with selected accuracy based on the results of MATLAB simulation. Using the loop unrolling technique with different coefficients, an attempt is made to reduce the latency and increase the throughput. In contrast, increasing the unrolling factor leads to a decrease in the frequency of the CORDIC unit as well as a decrease in the number of resources. As a result, there is a trade-off between the unrolling factor and the frequency of the CORDIC unit. By investigating the different unrolling factors, it is shown that the loop unrolling technique with a factor of 4 has the highest throughput with the value of 5.777 MQRD/s and the lowest latency with the value of 173 ns. Moreover, it is shown that throughput and latency are improved by 42.52% and 73.74% respectively compared to the not optimized case. The proposed method is also scalable for different sizes of m×m complex channel matrices, where log2 m ∈ N.
A. Ataee, S. J. Kazemitabar,
Volume 19, Issue 1 (3-2023)
Abstract
We propose a real-time Yolov5 based deep convolutional neural network for detecting ships in the video. We begin with two famous publicly available SeaShip datasets each having around 9,000 images. We then supplement that with our self-collected dataset containing another thirteen thousand images. These images were labeled in six different classes, including passenger ships, military ships, cargo ships, container ships, fishing boats, and crane ships. The results confirm that Yolov5s can classify the ship's position in real-time from 135 frames per second videos with 99 % precision.
Das P. Chennamsetty, Sravana K. Bali,
Volume 19, Issue 2 (6-2023)
Abstract
Symmetrical nature of mean of electrical signals during normal operating conditions is used in the fault detection task for dependable, robust, and simple fault detector implementation is presented in this work. Every fourth cycle of the instantaneous current signal, the mean is computed and carried into the next cycle to discover nonlinearities in the signal. A fault detection task is completed using a comparison of two sub cycle means, and the same concept is extended to faulty phase classification. Under various fault and system operating situations, the suggested technique is assessed for regular faults, remote end faults, high resistive faults, and high impedance arcing faults. This paper's extensive case studies illustrate the suggested scheme's simplicity, computational flexibility, speed, and reliability. The suggested approach yields 100% consistent results in 4-8 msec detection time.
S. Prasad Tiwari,
Volume 19, Issue 3 (9-2023)
Abstract
In spite of the numerous benefits over the traditional power distribution system, protection of the microgrid is a challenging and complex task. The varying fault resistances due to dissimilar grounding conditions can affect the performance of the protection scheme. Under such conditions, the magnitude of the fault current can vary from lower to higher level. In addition to the above, the dissimilar magnitude of fault current during grid connected and islanded mode demands a protection scheme that can easily discriminate the mode of operation. The magnitude of fault current in grid-connected and islanded modes needs a robust protection scheme. In this regard, an ensemble of subspace kNN based robust protection scheme has been proposed to detect the faulty conditions of the microgrid. The tasks of the mode detection, fault detection/classification as well as faulty line identification has been carried out in the proposed work. In the proposed protection scheme, discrete wavelet transform (DWT) has been used for processing of the data. After recording the voltage and current signals at bus-1, the protection scheme has been validated. The validation of the protection scheme in Section 6 reveals that the protection scheme is efficiently working.
Mohamed Hussien Moharam, Aya W. Wafik,
Volume 20, Issue 0 (12-2024)
Abstract
High peak-to-average power ratio (PAPR) has been a major drawback of Filter bank Multicarrier (FBMC) in the 5G system. This research aims to calculate the PAPR reduction associated with the FBMC system. This research uses four techniques to reduce PAPR. They are classical tone reservation (TR). It combines tone reservation with sliding window (SW-TR). It also combines them with active constellation extension (TRACE) and with deep learning (TR-Net). TR-net decreases the greatest PAPR reduction by around 8.6 dB compared to the original value.
Shankarshan Prasad Tiwari,
Volume 20, Issue 1 (3-2024)
Abstract
In recent years, due to the widespread applications of DC power-based appliances, the researchers attention to the adoption of DC microgrids are continuously increasing. Nevertheless, protection of the DC microgrid is still a major challenge due to a number of protection issues, such as pole-to-ground and pole-to-pole faults, absence of a zero crossing signal, magnitude of the fault current during grid-connected and islanded mode, bidirectional behaviour of converters, and failure of the converters due to enormous electrical stress in the converter switches which are integrated in the microgrid. Failure of the converter switches can interrupt the charging of the electrical vehicles in the charging stations which can affect transportation facilities. In addition to the above mentioned issues protection of the DC microgrid is more challenging when fault parameters are varying due to dissimilar grounding conditions and varying operational dynamics of the renewable sources of energy. Motivated by the above challenges a support vector machine and ensemble of k-nearest neighbor based protection scheme has been proposed in this paper to accurately detect and classify faults under both of the modes of operation. Results in the section 5 indicate that performance of the protection scheme is greater as compared to other algorithms.
Pampa Debnath, Diptadip Barai, Rajorshi Mandal, Ayeshee Sinha, Jeet Saha, Arpan Deyasi,
Volume 20, Issue 2 (6-2024)
Abstract
A novel architecture is proposed in the present paper for detection and monitoring of air pollution at real-time condition following industrial standard, embedded with gas sensors which are able to identify both organic as well as inorganic hazardous contents. A vis-à-vis comparative analysis is carried out with existing literature highlighting cons of most referred circuits, both in component, system and power consumption levels, and a generalized drawback is reported citing their inefficacy for real-time data collection and accuracy level. Detailed review is reported based on qualitative assessments also, and henceforth, justifies the significance of the proposed design; where not only higher ranges of detection are possible, however is also associated with lower power consumption (26.41% and 10.71% respectively compared to the two latest circuits) and finer detection of dust particles even at extremely low concentration. The architecture will help to implicate precautionary steps at real-time condition for controlling the harmful effect in Society.
Raheel Jawad, Rawaa Jawad,
Volume 20, Issue 3 (9-2024)
Abstract
Fire accidents are a disaster that can cause loss of life, property damage and permanent disability to the affected victim. Firefighting is a very important and dangerous job. Firefighters must extinguish the fire quickly and safely to prevent further damage and destruction. Detecting and extinguishing fires is a dangerous task that always puts the lives of firefighters at risk. One of the most effective tools for early fire extinguishing is the firefighting robot. Fire sensing in most industries is absolutely essential to prevent catastrophic losses. Robots with this type of embedded system can save the lives of engineers in industrial sites with hazardous conditions. This project aims to design and implement a solar-powered with artificial intelligent of mobile fire detection robot to detect fires in disaster-prone areas and thus reduce human work effort and level of destruction. Design a robot capable of moving using a rotary motor, finding a flame using a flame sensor, and extinguishing a fire using a water spray using a pump, all of which is controlled by an Arduino Uno microcontroller and programmed using an artificial intelligence (fuzzy) logic technology) using MATLAB, the inputs It has two variations:: flame and gas with three organic functions, each of which has a gas variable (low, medium, high), flame sensor (small, normal, large), and the output is a pump, (pump off , pump on ) with 9 rules. In addition to the experimental setup of the proposed system which demonstrates the performance of sensors (gas, flame) using fuzzy and implemented logic tools. The performance of the solar panels was first tested using MATLAB software as well as experimentally under different weather conditions. The pump's performance is being tested experimentally, and the robot is also being tested to detect and extinguish fires. The process of designing and implementing robotics involves creating mechanical and electrical systems. The results showed the effect of temperature change on the solar panel, as when it increases, the panel’s production capacity decreases, as well as the effect of decreased solar radiation resulting from clouds and other things, and the extent of its effect. Impact on the performance efficiency of solar panels, and observing the pump performance in terms of flow rate and height. Hence, it can be noted that the robot designed in the project is capable of discovering fire sources and extinguishing them using fire-fighting systems equipped with a water tank and a controllable pump to spray the water necessary for the process. From this study, can be concluded that the designed model is able to work according to its initial design with artificial intelligence with the least amount of errors, and therefore it can be applied in industrial applications, avoiding fire damage and extinguishing it when it occurs for the first time.
M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi,
Volume 20, Issue 4 (11-2024)
Abstract
Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.
๐๐ข๐ซ๐๐ฃ๐ฎ๐ฌ ๐๐๐ฅ๐๐ก๐ข๐ง, Shakila Rahman, ๐๐จ๐ก๐๐ฆ๐ฆ๐๐ ๐๐ฎ๐ซ, ๐๐ก๐ฆ๐๐ ๐๐ฌ๐ข๐, ๐๐จ๐ก๐๐ฆ๐ฆ๐๐ ๐๐ข๐ง ๐๐๐ซ๐ฎ๐ง, Jia Uddin,
Volume 20, Issue 4 (11-2024)
Abstract
Abnormal activity detection is crucial for video surveillance and security systems, aiming to identify behaviors that deviate from normal patterns and may indicate threats or incidents such as theft, vandalism, accidents, and aggression. Timely recognition of these activities enhances public safety across various environments, including transportation hubs, public spaces, workplaces, and homes. In this study, we focus on detecting violent and non-violent activities of humans using a YOLOv9-based deep learning model considering the above issues. A diverse dataset has been built of 9,341 images from various platforms, and then the dataset has been pre-processed, i.e., augmentation, resizing, and annotating. After pre-processing, the proposed model has been trained which demonstrated strong performance, achieving an F1 score of 95% during training for 150 epochs. It was also trained for 200 epochs, but early stopping was applied at 148 epochs as there was no significant improvement in the results. Finally, the results of the YOLOv9-based model have been analyzed with other baseline models (YOLOv5, YOLOv7, YOLOv8, and YOLOv10) and it performed better compared with others.
