Fault Identification Technology Research Articles

Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area

Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area

Research on lighting fault identification technology of transmission lines based on non-contact sensing

According to field operation records, lightning stroke accounts for 60% of transmission line failures. Therefore, it is of great significance to strengthen lightning protection of the power system. However, there are several lightning faults, and the corresponding protection methods differ. Consequently, identifying lightning stroke faults will be beneficial to take corresponding lightning protection measures. This paper investigates the mechanism of different lightning strike faults, and simulates them by a 110kV transmission line EMTP-ATP model. Analysis and simulation show that the direction of tower current represents lightning’s polarity; the insulator voltage’s direction differs when shielding failure or back striking occurs. If insulator flashovers, the voltage of the insulator drops down to zero, and as the transient process comes to an end, the voltage of the insulator on the nearby tower decreases to zero as well; after the occurrence of back striking flashover, the direction of insulator voltage on nearby tower alters. Based on those features, insulator voltage and tower current are introduced as a characteristic signal, and their direction and rms of them are formed as recognition parameters for lightning stroke identification. The EMTP-ATP simulations demonstrate that the proposed method is correct and effective, and the recognition rate of different lightning faults is 100% under the abovementioned method.

Fault Identification Technology of 66 kV Transmission Lines Based on Fault Feature Matrix and IPSO-WNN

Due to the barely resonant earthed system used in the transmission line, it is more challenging to identify faults at a 66 kV voltage level because of insufficient fault identification techniques. In this paper, a 66 kV transmission line fault identification method based on a fault characteristic matrix and an improved particle swarm optimization (IPSO)-wavelet neural network (WNN) is proposed to address the difficulties in extracting and detecting characteristic parameters. The maximum matrix of the dbN wavelet was used to determine its decomposition scale and construct the fault characteristic matrix based on the energy values of frequency bands. The decomposition scale of the dbN wavelet was determined by the modulus maximum matrix to ensure the integrity of fault information. The fault feature matrix was then constructed based on the energy values of frequency bands and the fault feature was accurately extracted. In this research, aiming at the problems such as slow convergence speed and a tendency to fall into local minima, the WNN algorithm is enhanced with the IPSO algorithm. This significantly increased the convergence speed of the identification model and its ability to discover the global optimal solution. The simulation results demonstrate that this method can effectively and accurately identify the fault type with high identification accuracy, quick identification, and robust adaptability. Under challenging working conditions, it is capable of accurately identifying the fault type of 66 kV transmission lines.

Application of Deep Learning in Photovoltaic Array Fault Identification

If a photovoltaic power station needs to operate normally, it cannot do without the blessing of the photovoltaic array, which also takes up about half of the cost of the photovoltaic system. The failure and low efficiency of photovoltaic power plants are the main two reasons for the loss of energy. Among them, the failure of photovoltaic arrays is undoubtedly one of the important reasons. In this article, the author uses the superiority of deep neural network in fault identification technology to complete the fault identification model of photovoltaic array.

An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.

Research on fault identification technology of electricity information acquisition terminal

There are many kinds of faults in electricity information acquisition system, and the magnitude of acquisition terminal is more than 10,000, so it is necessary to find the faults of acquisition terminal actively by technical means. Starting from the analysis of the reasons for the low success rate of acquisition, this paper further studies how to discover the fault of acquisition terminal and the process of troubleshooting. Thus, it can help the operation and maintenance personnel of the acquisition system to identify the fault of the acquisition terminal remotely, and improve the quality and efficiency of the acquisition and maintenance.

Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm

Multiscale Permutation Entropy (MPE) is a presented nonlinear dynamic technology for measuring the randomness and detecting the nonlinear dynamic change of time sequences and can be used effectively to extract the nonlinear dynamic wear fault feature of gear tooth surface from vibration signals of gear set. To solve the subjectivity drawback of threshold parameter selection process in MPE method, a joint calculation method based on the Mutual Information (MI) and improved False Nearest Neighbor (FNN) principle for calculating threshold parameters for MPE method was presented in this article. Then, the influence of threshold parameters on the identification accuracy of fault features with the MPE was studied by analyzing simulation data. Through the simulation analysis, the effectiveness of the proposed MPE method is validated. Finally, the wear failure test of spur gear was carried out, and the proposed method was applied to analyze the experimental data of fault signal. Meanwhile, the vibration characteristics of the fault signal are acquired. The analysis results show that the proposed method can effectively realize the fault diagnosis of gear box and has higher fault identification accuracy than the existing methods.

Research on intelligent fault identification technology of wind turbine supported by fault knowledge base

Research on intelligent fault identification technology of wind turbine supported by fault knowledge base

Research on Fault Identification Technology of Jumper Windage Yaw Flashover

jumper has been weakness of protection of windage yaw flashover in Grid, timely and effective identification method of windage yaw flashover is lacked in the existing power system; focusing on the point that less jumper windage yaw flashover, the phenomenon of jumper windage yaw flashover is explored, by checking windage yaw angle and the minimum air space under strong wind, to confirm the possibility of jumper windage yaw flashover caused by strong wind; by mining the law of the jumper windage yaw flashover, and applying the fractal theory calculation, the electromagnetic transient characteristics of transient current travelling waves is studied, to propose a jumper windage yaw fault identification method based on the similarity of transient current travelling waves, which is successfully applied to Hainan Power Grid’s Fuguan line jumper windage yaw flashover in 2011.