ADAPTIVE DIGITAL DIFFERENTIAL RELAY OF PARABOLIC CHARACTERISTIC FOR TRANSFORMER PROTECTION

ABSTRACT

In the present paper a variable-bias digital differential relay for power transformers protection is proposed. The amount of bias is varied linearly with the value of through current (mean of the currents entering and leaving the transformer). To take care of mismatch between CT ratios on primary and secondary sides of the power transformer, secondary currents of CT are corrected using a precalculated correction factor for the power transformer tap in operation. In the event of an abnormal operation, a fault discriminant based on fundamental, second and fifth harmonic components of differential current is used. It provides discrimination between faults and magnetizing inrush /overexcitation copditions. Trip and indication signals arc issued in the former case, while only an indication is given in the latter case.

The relay scheme is simulated on a personal computer. The advantage of the proposed scheme is its ability to detect even low-level internal faults, which may not be detected by a fixed-bias relay. The relay is restrained from operations on magnetizing inrush and overexcitation. The proposed relay is stable on those heavy through faults on which a fixed-bias relay may operate.     

A Kalman filter based digital percentage differential and groundfault relay for a 3-phase power transformer

The design and real-time implementation of a Kalman-filter-based digital percentage differential and a ground-fault protection scheme for three-phase power transformers are presented. A set of eleven-state Kalman filters is used to estimate the fundamental and up to fifth harmonic components of the transformer current signals. The protective relay is equipped with an even harmonic restraint during magnetizing inrush conditions and a fifth harmonic restraint during overexcitation conditions. The restraint during external faults is provided by means of a percentage differential characteristic. The relay operates in half a cycle during internal faults. In order to achieve sensitive ground-fault protection, separate primary and secondary ground-fault protection are provided. The relay algorithm is implemented on a single TMS320 digital signal processor and tested in real time using a three-phase laboratory power transformer     

A current-based solution for transformer differential protection.I. Problem statement

This paper analyzes the problem of transformer differential protection. First, the authors review the concept of transformer differential protection. They then analyze magnetizing inrush, overexcitation and current transformer (CT) saturation phenomena as possible causes of relay misoperation. Finally, they summarize the existing methods for discriminating internal faults from inrush and overexcitation conditions     

Transformer Differential Protection Using Principal Component Analysis

This paper describe a new algorithm for transformer differential protection, based on pattern recognition of the differential current obtained as the phasor sum of the current-transformer secondary currents. The algorithm uses principal component analysis to preprocess data from the power system in order to eliminate redundant information and enhance hidden pattern in differential current to discriminate between internal faults (transformer differential protection zone) from inrush and overexcitation conditions. The algorithm was proven using PSCAD/EMTDC simulations in a three-phase power system considering critical fault cases. The results show the feasibility to implement this algorithm for transformer differential protection.     

Artificial neural network approach to distance protection oftransmission lines

A distance relay for the protection of transmission lines is usually designed on the basis of fixed settings. The reach of such relays is therefore affected by the changing network conditions. The implementation of a pattern recognizer for power system diagnosis can provide great advances in the protection field. This paper demonstrates the use of an artificial neural network as a pattern classifier for a distance relay operation. The scheme utilizes the magnitudes of three phase voltage and current phasors as inputs. An improved performance with the use of an artificial neural network approach is experienced once the relay can operate correctly, keeping the reach when faced with different fault conditions as well as network configuration changes     

Discriminating transformer large inrush currents from fault currents

One of the most widely used approaches for transformer protection is the low impedance differential relay, which can be adversely affected by inrush currents. Although the conventional inrush current detectors, i.e. the gap detection and second harmonic criteria, can block the differential relay in most of such cases, they are severely prone to maloperation in the case of large inrush currents. This paper presents a new strategy to enhance the security of the differential relay. The suggested approach exploits an intrinsic feature of large inrush currents in three-limb three-phase transformers with the widely used star-delta connection. Based on the feature, some novel criteria are proposed to improve the inrush current detection scheme. To evaluate the performance of the proposed approach for both inrush current and internal fault phenomena, a real 230/63-kV power transformer is modeled based on the time-based transient simulation. Extensive simulation studies and also evaluation using real data reveal that the proposed approach results in a more secure inrush current discrimination method.     

电力变压器的励磁涌流判据及其发展方向

励磁涌流与内部故障电流的判别一直是伴随电力变压器差动保护的关键问题，围绕这一主题，世界各国的科技工作者先后提出了许多方法，但仍不能很好地满足当前电力变压器保护的需求——可靠（不拒动）、安全（不误动）及快的动作速度。文中对各种判别方法的原理、优缺点、技术关键及研究和应用现状进行了较详细的分析与客观评价，对比研究后给出了今后励磁涌流判别方法的发展方向。     

A new transformer differential protection approach on the basis of space-vectors examination

Transformer differential protection is exposed to faulty operation related to abnormal operation conditions (inrush, overexcitation, etc.). In this paper a new approach to improve the relay security and dependability is proposed, based on the space-vector analysis of the differential signal, and on their time characteristic shapes in Parks plane. The approach is supported by an introductory formulation and later validated. The technique has been shown to be robust and very simple for the purpose of detecting internal faults.     

用模糊集贴近度法识别变压器的故障电流和励磁涌流

根据电力变压器差动保护中故障电流和励磁涌流波形的对称情况,提出了用波形对称及模糊集贴近度原理来辨别故障电流和励磁涌流的新方法。数字仿真结果表明,本方法可准确识别故障电流和励磁涌流,即使在空载合闸于内部故障时,保护继电器也能迅速可靠动作,避免了采用二次谐波制动导致的误动问题。     

基于小波神经网络的变压器励磁涌流和内部故障电流识别

小波神经网络 (WaveletNeuralNetwork)结合了小波变换及神经网络的优点 ,即具有良好的时频局部性质 ,又有较好的自学习能力和容错能力。针对变压器励磁涌流与内部故障电流的识别问题 ,提出了一种基于小波神经网络的解决方案 ,阐述了基本的思想方法和具体的算法过程 ,EMTP仿真实验表明了该方案的有效性与可行性。     

基于最小二乘矩阵束的励磁涌流识别方案

准确、快速地切除变压器故障直接影响电力系统能否持续安全稳定运行,励磁涌流的鉴别正是变压器保护中的重要一环。为了有效辨识励磁涌流与故障电流,避免变压器差动保护的不正确动作,利用变压器励磁涌流与短路故障电流频域信息的差异,提出了一种基于最小矩阵束算法的励磁涌流识别新方案,以变压器两侧的差动电流作为信号量,通过矩阵束算法分析采样信号中不同频率的分量,计算含衰减因数的电流能量信息熵识别励磁涌流。仿真试验证明了新判据具有原理清晰、抗干扰能力强等优点,能够正确区分励磁涌流和故障电流,为后续研究及工程应用提供借鉴和参考。     

基于小波变换和概率神经网络的励磁涌流识别

针对变压器的励磁涌流问题,提出了一种基于小波变换和概率神经网络的新的变压器差动保护方案,用以实现励磁涌流与其它内部短路电流诸如单相短路、两相接地短路、三相接地短路、匝间短路的鉴别。利用小波变换进行信号分解,提取各尺度高频部分的能量,作为神经网络的输入特征向量,概率神经网络是为了进行模式识别。利用Matlab/Simulink平台上的仿真建模,获取励磁涌流和内部故障电流的数据。大量仿真结果显示,该方案可以有效地识别励磁涌流。     

A Morphological Scheme for Inrush Identification in Transformer Protection

This paper presents a novel morphological scheme for the identification of transformer magnetizing inrush. The scheme decomposes a current signal into multiresolution levels based on synthesis and analysis operators of mathematical morphology. It is able to discriminate between inrush and internal fault currents even in the case of an inrush with a low second harmonic component and an internal fault current with a high second harmonic component. Simulation studies have been undertaken to evaluate the scheme based on a three-phase high-voltage power transformer operating under different conditions, which is simulated with an Alternative Transients Program. The testing results show that the proposed scheme is able to identify inrush currents reliably and provides an alternative solution for transformer differential protection.      

Performance of Power Differential Relay With Adaptive Setting for Line Protection

In this paper, proposed setting and response evaluations of power differential relay scheme for line protection are presented. Mathematical expressions describing the adaptive features of both active and reactive power settings are given. The union action of these two detectors is adopted to provide sensitive detection for high impedance internal faults and avoid maloperation for all power swings and external fault conditions. Response is computed for both active and reactive power detectors under different operation and fault conditions. The results corroborate the applicability and the immunity of the proposed relay scheme against the sampling misalignment and practical frequency drift. High sensitivity for high impedance internal faults is verified     

Power differential method for discrimination between fault andmagnetizing inrush current in transformers

To avoid the needless trip by magnetizing inrush current, the second harmonic component is commonly used for the blocking differential relay in power transformers. However, the second harmonic component in fault current is increased by the introduction of underground 500 kV lines. This paper describes a new method to discriminate internal fault from inrush current by the sum of active power flowing into transformers from each terminal. The average power is almost zero for energizing, but an internal fault consumes large power. To check the performance of this method, actual inrush current and voltage waveforms of a 500/154 kV transformer are accurately measured by digital equipment. The usefulness is confirmed by applying the method to the measured inrush and simulated fault data     

基于分布式神经网络的变压器励磁涌流鉴别的研究

针对目前变压器保护中普遍采用的利用二次谐波原理识别变压器励磁涌流存在的缺陷,提出利用分布式人工神经网络实现基于半波叠加原理鉴别励磁涌流的新方案。经仿真实验,该方案能准确地判别出变压器内部故障和励磁涌流,不受系统谐波影响,具有很高的可靠性。     

基于电子式互感器的变压器励磁涌流识别方法

随着电子式互感器研究的日趋成熟及智能变电站的快速推进,智能变电站已广泛采用电子式互感器获取电流及电压信号。传统的变压器保护采用励磁涌流中的二次谐波含量区分励磁涌流和故障电流,从而对差动保护进行闭锁。但在空投或故障切除后恢复供电时,变压器内部发生轻微匝间故障,受非故障相励磁涌流的影响,差动保护可能延迟动作甚至拒动。针对上述原理的缺陷,提出了二次谐波“或”闭锁原理加故障开放的励磁涌流识别方法。试验结果表明,改进方案能明显提高变压器内部故障时差动保护的动作速度。     

A novel algorithm for discrimination between inrush current and internal faults in power transformer differential protection based on discrete wavelet transform

This paper proposes a novel methodology for transformer differential protection, based on wave shape recognition of the discriminating criterion extracted of the instantaneous differential currents. Discrete wavelet transform has been applied to the differential currents due to internal fault and inrush currents. The diagnosis criterion is based on median absolute deviation (MAD) of wavelet coefficients over a specified frequency band. The proposed algorithm is examined using various simulated inrush and internal fault current cases on a power transformer that has been modeled using electromagnetic transients program EMTDC software. Results of evaluation study show that, proposed wavelet based differential protection scheme can discriminate internal faults from inrush currents.     

GA trained parallel hidden layered ANN based differential protection of three phase power transformer

Transformer protection is an established area of research to find the fastest and efficient differential relay algorithm that isolates the transformer from remaining system causing least damage. Algorithm should also avoid mal-operation when differentiating between the operating conditions. Various differential algorithms were proposed in the past, allowing a scope for further research. In this paper, ANN is used as a pattern classifier which discriminates among normal, magnetizing inrush, over-excitation and internal fault currents in a power transformer. The proposed scheme has been realized through different ANN architectures including a new customized parallel-hidden layered design, which originates to be more accurate in differentiating between the normal wave and faulty wave despite the shape similarity. A combination of two ANNs in Master–Slave mode has also been discussed. Back Propagation (BP) and Genetic Algorithm (GA) are used to train the multi-layered feed forward neural network and their simulated results are compared. The neural network trained by GA gives more accurate results (in terms of mean square error) than by BP Algorithm. Simulated data are used as an input to the ANN to verify the accuracy of the algorithm. Thus, GA trained Master–Slave ANN based differential protection scheme provides faster, accurate, more secured and dependable relay for power transformers.     

基于广义瞬时功率的新型变压器保护原理

在差有功法的基础上,提出一种利用广义瞬时功率实现变压器保护的新原理。该原理通过消去变压器回路方程中直接体现主磁通的非线性项,构造了仅含漏电感和绕组电阻的二端网络。根据输入端口的广义瞬时功率直流分量的大小,来区分励磁涌流和内部故障电流。该原理避开了励磁涌流和铁损带来的不利影响,计算量小,易于工程实现。经仿真和动模试验验证,新原理能够快速、可靠的切除变压器内部故障,对空投轻微故障也有足够的灵敏度。