High impedance fault protection in transmission lines using a WPT-based algorithm

This paper proposes a new algorithm for High Impedance Fault (HIF) protection, in high voltage transmission lines, with the aid of Wavelet Packet Transform (WPT). The new scheme uses the HIF-induced distortion of voltage and current waveforms to detect HIF and discrimination of the fault location, respectively. The algorithm is based on a recursive method, which adds up the absolute values of high frequency signal coefficients, generated over one last cycle. Application of the proposed algorithm to the pilot protection schemes is also discussed. The proposed method is evaluated by Electro Magnetic Transients Program (EMTP) simulation studies. Several simulations, which are performed using an appropriate HIF model, bring about results which assess the proposed technique accuracy in identifying HIF in overhead transmission lines. A comprehensive simulation study shows the efficiency of the proposed protection scheme from the viewpoints of dependability and security.     

基于故障分量原理的配电网高阻接地故障检测方法

配电网中发生高阻接地故障时,短路电流小于传统过流保护的阈值,无法被常规保护装置检测和清除。若不及时消除短路电路,极易演化成严重故障。针对该问题,文中首先分析发生高阻接地故障时配电网的故障分量特征和基于母线处的正序电压故障分量与其相连接的各馈线正序电流故障分量的相位差特征,给出适用于配电网高阻接地故障检测的故障判据。然后,为解决配高阻接地故障检测过程中系统不平衡引起的一系列问题,制定了相应的故障检测启动判据。基于该故障检测判据和启动判据,制定基于故障分量原理的配电网高阻接地故障检测方法。最后,在PSCAD/EMTDC仿真软件中建立含架空线路的中压配电网模型,仿真结果验证了所提高阻接地故障检测方法的正确性。     

Real time evaluation of DWT-based high impedance fault detection in EHV transmission

It is possible to capture the required travelling wave information contained in fault transients using wavelet transform. This paper presents practical real time testing for the high impedance fault (HIF) detection algorithm based on real time accidents data. The proposed scheme is implemented for HIF detection in extra high voltage transmission lines. The classifier is based on an algorithm that uses recursive method to sum the absolute values of the high frequency signal generated over one cycle and shifting one sample. Characteristics of this scheme are analyzed by extensive real time studies that clearly reveal that this technique can accurately detect HIFs in the EHV transmission lines within only half a cycle from the instant of fault occurrence. The reliability of this scheme is not affected by different fault conditions such as fault distance and fault inception angle.     

Transmission lines distance protection using artificial neural networks

This work presents the development and implementation of an artificial neural network based algorithm for transmission lines distance protection. This algorithm was developed to be used in any transmission line regardless of its configuration or voltage level. The described ANN-based algorithm does not need any topology adaptation or ANN parameters adjustment when applied to different electrical systems. This feature makes this solution unique since all ANN-based solutions presented until now were developed for particular transmission lines, which means that those solutions cannot be implemented in commercial relays.     

Internal fault current identification based on wavelet transform in power transformers

This paper presents a novel approach for differential protection of power transformers. This method uses wavelet transform (WT) and adaptive network-based fuzzy inference system (ANFIS) to discriminate internal faults from inrush currents. The proposed method has been designed based on the differences between amplitudes of wavelet transform coefficients in a specific frequency band generated by faults and inrush currents. The performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software. Also the proposed algorithm is tested off-line using data collected from a prototype laboratory three-phase power transformer. The test results show that the new algorithm is very quick and accurate.     

Applying wavelet entropy principle in fault classification

The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropies of such decompositions are analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault.     

High‐impedance fault location using matching technique and wavelet transform for underground cable distribution network

Locating the faulty section of a high‐impedance fault (HIF) is quite challenging for the underground distribution network of a power system. The complexity of the distribution network, such as branches, nonhomogenous lines, and HIF, contributes to the difficulties in locating the faulty section. In this paper, the shortest distance (SD) technique and a database approach have been proposed to determine the faulty section. A multiresolution analysis based on discrete wavelet transforms is chosen to extract the unique features from voltage signals during the HIF event. The output coefficients from the decomposition process is stored in a database and used as the input data for the SD algorithm. The first, second, and third level of detailed coefficients of the post‐disturbance voltage signal were utilized for the identification of the faulty section using the proposed method. A ranking analysis was created to provide a number of possibilities of faulty section. In this paper, a 38‐node underground distribution network system in a national grid in Malaysia was modeled using the PSCAD software. The proposed method was able to successfully determine the faulty section. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于小波变换与BP网络的舰船磁场信号检测

针对水中兵器探测舰船磁场信号时信噪比较低的问题,提出了一种小波变换结合反向传播(backpropagation,BP)神经网络的检测方法.根据舰船磁场信号的时频特征,首先对信号进行小波分解,提取最后一层的低频分量,滤除高频噪声;再采用BP神经网络对低频分量进行学习,提取舰船目标特征信号.将此算法应用于船模实测实验,结果...     

航拍绝缘子卷积神经网络检测及自爆识别研究

无人机巡检通过搭载的高清相机和图传设备可获取大量详实的巡检影像。绝缘子是输电线路中极其重要且用量庞大的部件,在图像视频中快速准确地检测出绝缘子可为无人机贴近铁塔和输电线路进行细节巡视的测距和避障飞行提供可靠的依据;同时绝缘子为故障多发元件严重威胁电网的安全,需充分利计算机技术对其进行故障诊断。通过搭建卷积神经网络,在由5个卷积池化模块和2个全连接模块组成的经典架构的基础上,对网络进行改进,实现在复杂航拍背景中绝缘子检测。同时在训练的网络模型中抽取绝缘子的特征融入自组织特征映射网络中实现显著性检测,结合超像素分割和轮廓检测等图像处理方法对绝缘子进行数学建模,提出一种针对绝缘子自爆故障的识别算法,取代人工分析,降低由人为经验判断可能造成的误差。经测试,复杂航拍背景下的绝缘子检测精度达90%以上,自爆识别准确率达到85%以上,均满足工程需求,有效提升巡检的效率和智能化水平。     

Electricity price forecasting using artificial neural networks

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to “fit” and “extrapolate” the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price.     

小波变换在电力系统谐波检测方面的应用

针对傅里叶变换的谐波检测方法无法同时实现时-频变域分析这一缺点,提出了小波变换这一新方法对谐波进行分析。通过小波变换对电力系统中的谐波电流进行分解,得到信号的基波分量和高次谐波分量。针对电力系统中的突变信号,提出了基于小波变换的模极大值的奇异性检测方法,通过小波变换模的极值点在多尺度上的综合表现,来表示信号的突变特征,并通过仿真实例验证该算法的有效性。     

Fast fault detection and classification based on a combination of wavelet singular entropy theory and fuzzy logic in distribution lines in the presence of distributed generations

This paper proposes a new method of fault detection and classification in asymmetrical distribution systems with dispersed generation to detect islanding and perform protective action based on applying a combination of wavelet singular entropy and fuzzy logic. In this method, positive components of currents at common coupling points are decomposed to adjust detailed coefficients of wavelet transforms and singular value matrices, and expected entropy values are calculated via stochastic process. Indexes are defined based on the wavelet singular entropy in positive components and three phase currents to detect and classify the fault. This protection scheme is put forward for fault detection and is investigated in different types of faults such as single-phase to ground, double-phase to ground, three-phase to ground and line to line in distribution lines in the presence of distributed generations, and different locations of faults are verified when the distributed generation is connected to the utility. The major priority of the proposed protection scheme is its reduction in time (10 ms from the event inception) in distinguishing islanding and protection transmission lines in the presence of distributed generations.     

基于小波变换与BP神经网络相结合的配电网单相接地故障定位方法

提出一种基于小波变换与BP神经网络相结合的方法来实现小电流接地系统单相接地故障定位。由于利用暂态故障电流和暂态母线电压的模极大值的实部和虚部作为BP神经网络的输入,提高了识别故障能力和可靠性,通过对BP神经网络的特别处理,大大地减小过渡电阻对故障定位的影响。仿真结果表明,该故障定位方法准确可靠。     

High impedance fault detection based on wavelet transform and statistical pattern recognition

A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.     

A new artificial neural network based method for islanding detection of distributed generators

This paper presents an artificial neural network (ANN) based method for islanding detection of distributed synchronous generators. The proposed method takes advantage of ANN as pattern classifiers. It is capable of identifying the islanding condition based on samples of the voltage waveform measured at the distributed generator terminals only, which is an important advantage over other ANN-based anti-islanding methods. Moreover, the proposed method is robust against false operation. In order to create a training data set for the ANN, a data selection procedure has been proposed, so that the ANN could be trained more effectively, which has contributed positively to the good performance of the method. The concept of the time-performance region has been introduced to assess the method performance, as well as the non-detection zones. A detailed discussion about the data sampling rate to feed the proposed method has also been conducted, so that the computational burden can be faced as an important factor to assess its performance.     

小波变换在配电网单相接地故障选线中的应用

小波变换克服了傅里叶分析的缺陷，能同时对时频局部化，具有很强的信号特征提取功能，可将其应用到小电流接地电网单相接地故障检测中。对故障后暂态电气量进行分解，利用小波变换系数作为判据实现故障选线，仿真实验表明该方法可以快速准确地检测出故障线路。     

Wavelet and PDD as fault detection techniques

Motor current signature analysis has been successfully used for fault diagnosis in induction machines. However, this method does not always achieve good results with variable load torque. This paper proposes a different signal processing method, which combines wavelet and power spectral density techniques giving the power detail density as a fault factor. The method shows good theoretical and experimental results.     

Hybrid classifier for fault location in active distribution networks

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation, and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types, fault points, and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.     

A novel wavelet transform based voltage sag/swell detection algorithm

This paper proposes a novel sag/swell detection algorithm based on wavelet transform (WT) operating even in the presence of flicker and harmonics in source voltage. The developed algorithm is the hybrid of Daubechies wavelets of order 2 (db2) and order 8 (db8) to detect voltage sag/swell with and without positive/negative phase jumps. The hybrid detection algorithm can detect the start and end times of voltage sag/swell with and without phase jumps within 0.5 ms and 1.15 ms, respectively. The performance of the proposed voltage sag/swell detection method is compared with the results of dq-transformation, Fast Fourier Transform (FFT) and Enhanced Phase Locked Loop (EPLL) based voltage sag/swell detection methods. The good robustness and faster processing time to detect balanced and unbalanced voltage sag/swell are provided using proposed method. With the proposed hybrid detection algorithm consisting of db2 and db8 wavelet functions, a robust sag/swell detection is achieved which can give precise and quick response. The performance of proposed hybrid algorithm is validated and confirmed through simulation studies using the PSCAD/EMTDC analysis program.     

Distortions identification and compensation based on artificial neural networks using symmetrical components of the voltages and the currents

The problem of harmonics identifying and compensating has been of great interest in recent years. A new neural identification scheme for an active power filter (APF) is proposed. This scheme identifies the direct, inverse and zero sequence components of both the voltages and the currents of the power network. The components result from a new and generic decomposition of a three-phase signal which can be either the voltage or the current. For one signal, the direct components extraction requires two independent Adaline networks, and the inverse components extraction two other Adalines. The voltage and current components are used to on-line compute the instantaneous direct, inverse and zero sequence powers. The proposed decomposition is a new formulation of the instantaneous powers and is also appropriate for unbalanced systems. The reference compensation currents can be determined according to different compensation objectives. The resulting compensation currents are then re-injected phase-opposite through the APF in real-time. The performance is evaluated through several simulation examples and through different experiments. The results show that the proposed neural method outperforms other methods, such as the conventional instantaneous power theory.