جستجوی مقالات مرتبط با کلیدواژه « Fault detection » در نشریات گروه « برق »

Due to the widespread use of electric motors in various industries, checking the conditions and diagnosing its possible faults in the early stages is one of the most important goals of intelligent industrial monitoring equipment in modern factories. Bearings are one of the mechanical parts that often fail during operation. The mechanical faults of the electric motor show themselves as vibration in the motor, which can be used to diagnose the fault, especially in the early stages. In addition, noise in industrial environments usually has less impact on vibration because vibration is extracted directly from the motor body or its base. According to this explanation, in this research, a bearing fault detection method is proposed using wavelet transform of the vibration signal in induction motors, which can detect the defect with very high accuracy. The proposed method was implemented on two different databases using Matlab 2021. The obtained results, compared with the latest articles in this field, confirmed the effectiveness of proposed method based on criteria such as accuracy and correctness. In the meantime, the advantages of proposed method, such as high speed, low calculations and its robustness to noise, were also shown.

In this paper, a new method for detection and fault location and classification in MTDC solar microgrid is presented. Some issues such as expanding renewable energy sources and DC loads and efforts to increase power quality and reduce the environmental impact of electricity generation have led to the expansion of solar networks. Identifying the types and locations of faults is important to ensure service continues and to prevent further breakdowns and the increasing the protection’s selectivity characteristic. In this method, an orbital kit is connected to the network. In the fault occurrence time in the network, the fault is detected by passing a current through the connected kits and measuring the traveling waves derived from the fault current, and applying it to a mathematical morphological filter The location of the error is determined using orbital equations and flow calculations. Mathematical morphology filter output and signal energy analysis were used to determine the type of faults. The method presented in an MTDC microgrid connected to energy storage and renewable sources was tested with many faults. The results indicate the accuracy of the proposed method. This method is resistant to changes in arcs resistance (up to 100 ohms), and has a very good performance in high impedance faults conditions(up to 1000 ohms).

The extensive penetration of microgrids in the distribution network poses challenges to the control system, coordination with the network, and especially the traditional current-based protection systems despite many advantages such as reducing power outages, increasing reliability and resiliency, enhancing the flexibility of the power system in supplying loads, and improving the power quality. The main reason for the incorrect performance of current protection schemes is the change in the network fault level due to the connection and disconnection of the distributed generator or the microgrid operating mode change from the grid connected to the islanded and vice versa. To improve the performance of the protection system in the presence of microgrids, some schemes have been presented in two general categories: schemes based on modifying the network behavior and schemes based on modifying the protection system. In the first category, the network behavior during the faults is modified by external equipment to operate the conventional protection schemes properly. In the second category, the protection schemes are modified to correct performance according to the network behavior change during the short circuit faults. Due to the limitations of the schemes in the first category, the schemes of the second ones are more practical. Among the second category schemes, the impedance-based ones can operate in both grid-connected and islanded modes of the microgrid due to their directional nature and independence of the fault level of the network.This article rearranges and reforms the conventional sequence equivalent circuits of the lines during the short circuit faults and presents new equivalent circuits. Also, a protection scheme is proposed using these circuits for low-voltage and medium-voltage overhead and cable lines in smart AC microgrids. The basis of the proposed scheme is the large change in impedance in the proposed delta sequence equivalent circuit. This scheme employs the voltage and current data at the relay location and the magnitude of the positive sequence voltage at the other line end. Therefore, minimum data exchange between two ends of the line and low sampling rate are the features of the proposed scheme. This scheme is independent of the configuration of the microgrid, its operating mode, and uncertainties in the microgrid. Also, it can detect high resistance and high impedance short circuit faults in the grid-connected and islanded mode with a detection time of fewer than two cycles in low voltage lines and about three cycles in medium voltage lines. A case study microgrid is simulated in PSCAD software, and the proposed scheme is implemented on it by MATLAB software. The results show the accurate performance of the proposed protection scheme in detecting short circuit fault types in different conditions.

In this paper, a new method for fault detection, location, and classification in multi-terminal DC microgrid (MTDC) is proposed. MTDC grids have expanded due to some issues such as the expansion of DC resources, loads, and aims to increase power quality. Diagnosing the types and location of faults is important to continue the service and prevent further outages. In this method, a circuit kit is connected to the grid. In fault time, the fault in the network is detected by passing the current through the connected kits and measuring the traveling waves derived from the fault current as well as applying it to a mathematical morphological filter. Determining the location of the fault is done using circuit equations and current calculations. Phaselet output and fuzzy inference systems are used to determine the type of faults. The presented method was tested in an MTDC microgrid connected to renewable and energy storages with many faults. The results show the ability of the proposed method. The error of the proposed fault location method is less than 7%. This method is resistant towards the change in sampling frequency (between 500 Hz and 50 kHz), fault resistance (up to 125 ohms), and loading (up to 120% of the nominal load); it acts very well in high impedance faults.

The SCADA system is a vital system that monitors and controls industrial processes. In these systems, a cyber attack occurs by infiltrating the communication channels between sensors, actuators and servers. In recent years, cyber-attacks have created problems for industrial control systems. A cyber attack has a function similar to a fault in terms of system malfunction. Having instantaneous network parameters such as latency, packet loss, and network traffic can probably make the difference between a fault and a cyber attack. The purpose of this initial research is to detect and then isolate the fault from the cyber attack of the SCADA system using different networks. To do this, a fluid passage system with a controller is modeled. A fault detection filter (BFDF) was designed, which detects various anomalies of the system, If at the time of detection of the anomaly by the filter, the network parameters also show abnormal conditions, a cyber attack is detected. The simulations were performed in Omnet ++ software. The research results showed the effectiveness of this method in distinguishing between fault and cyber attack.

In this paper we use SVR and MLP Neural Network for modelling and fault detection of KAHAK wind turbines. Electrical subsystems is our purpose for modelling and fault detection. We have actual data and real parameters about 2.5 MW wind turbines that installed in KAHAK wind farme. In first step we modelled and fault datect with two approach, Support Vector Regresion (SVR) and Multi Layer Perceptron (MLP), in second step we compare model error and accurase of SVR and MLP approach, that we can see SVR approach is better than MLP approach according modelling error. We use MATLAB software for this work.

Fault detection has always been important in aviation systems to prevent many accidents. This process is possible in different ways. In this paper, we first identify the longitudinal axis plane model using neural network approach. Then based on the obtained model and using fuzzy logic, the aircraft status sensor fault detection unit was designed. The simulation results show that the fault detection system is able to work well, with additional alarms averaging 1 alert per 4-hour flight and miss alert rates averaging 1 alert per 2 hours. The results are confirmed by the experts from the UAV system.

One of the most common types of wind turbines (WTs) is the Doubly-Fed Induction Generator (DFIG) with a back-to-back converter. The power converter with an Insulated Gate Bipolar Transistor (IGBT) switch is essential equipment for power regulation and grid connection in WTs. The converter IGBT open-circuit causes a drawback in output current and as a result, the production performance of the turbine is reduced. Condition Monitoring (CM), Fault Diagnostic (FD), and Fault-Tolerant Control (FTC) of a WT increase the reliability and availability of the turbine and are typical methods to reduce energy production costs and WT downtime. The IGBT switch’s open-circuit failure rate is significant compared to the overall fault rate in WTs. This paper discusses IGBT open-circuit FTC in back-to-back DFIG converters. In the proposed structure, the converter switch fault is first diagnosed using a fast and numerical method. In the first part, a method based on normalized phase currents, the absolute of the phase currents, and an adaptive threshold is used. The FTC system can be divided into two classes of redundancy and non-redundancy or into active and inactive classes. In this article, the proposed method is active and non-redundancy. In the second step, the FTC process is activated to compensate for the system degradation. In this method, the faulty leg is removed with the gate opening of both IGBTs in one leg by the controller, and one of the legs is positioned as a common leg between the two grid side and rotor side converters. The conventional method in this structure is to use Pulse Width Modulation (PWM) with a zero sequence signal. The novelty of the paper is the use of the proposed SVM vector control. The proposed structure is inexpensive. Furthermore, the fault diagnostic structure operates without additional sensors and the FTC structure operates with minimal hardware. To evaluate the proposed structure, a 2.5 MW turbine software simulator based on real data is used. The simulations show that the proposed method is effective and robust in FD and FTC and that the proposed method is successful in detecting multiple defects of the grid-side and rotor-side converters, as well as the defects of one leg. The proposed method can compensate for the open switch fault to construct three-phase output currents by comparing the numerical parameters. The ability of the proposed method is specified.

Dissolved gases in transformer oil indicate the occurrence of a fault in the transformer. Many standards use the ratio of dissolved gases in transformer oil to detect faults in transformers. These traditional methods cannot, however, be used in cases in which the transformer needs to be immediately removed from service in case of a serious error such as electric arcs to prevent the error from spreading. For this purpose, this paper uses signal processing methods that analyze the signal online. The paper assumes that CO and CO2 dissolved gases in transformer oil are measured online by a sensor and then some signal processing methods are applied to the measurement data. These methods include Fast Fourier Transform, Discrete Wavelet Transform, Hilbert Transform, Gabor Transform, the combination of discrete wavelet and Hilbert transform, the combination of discrete wavelet and Gabor transform, and spectrogram. These methods are continuously applied to these two gases that are soluble in transformer oil to determine their variations in the transformer oil at a certain time or a certain frequency. The gases are also modified and then the performance of each of the processing methods mentioned in these changes is investigated. Fault detection reference is the CIGER 761-2019 standard. The purpose of this paper is to find out the samples of gases change in a frequency interval or timeframe together irrespective of the faults in the transformer and changes in the volume of dissolved gases in transformer oil, analyze the signal processing methods, and detect the type of fault using CIGRE 761-2019 standard. The fast Fourier transform method analyzes signal power by frequency. The discrete wavelet transform method extracts high-frequency components of gases and detects faults based on the largest components. The Hilbert transform method converts the signal into two real and imaginary parts. Then, it uses the imaginary part that represents the signal phase to detect faults. The Gabor transform method extracts instantaneous frequencies in the time-frequency plane and uses this method to detect faults. In the methods that combine discrete wavelet transform and Hilbert or Gabor transform, high-frequency components are extracted by discrete wavelet transform, and then Hilbert or Gabor transform methods are applied. The spectrogram method also indicates the size of the short-time Fourier transform, which is used to analyze the signal. These signal processing methods are compared in several features, and the discrete wavelet transform method is introduced as the best method for fault detection.