Weak Fault Detection of Rotor Winding Inter-Turn Short Circuit in Excitation System Based on Residual Interval Observer

Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system,an interval observer based on current residual is designed.Firstly,the mechanism of the inter-turn short circuit of the rotor winding in the excitation system is modeled under the premise of stable working conditions,and electromagnetic decoupling and system simplification are carried out through Park Transform.An interval observer is designed based on the current residual in the two-phase coordinate system,and the sensitive and stable conditions of the observer is preset.The fault diagnosis process based on the interval observer is formulated,and the observer gain matrix is convexly optimized by linear matrix inequality.The numerical simulation and experimental results show that the inter-turn short circuit weak fault is hardly detected directly through the current signal,but the fault is quickly and accurately diagnosed through the residual internal observer.Compared with the traditional fault diagnosis method based on excitation current,the diagnosis speed and accuracy are greatly improved,and the probability of misdiagnosis also decreases.This method provides a theoretical basis for weak fault identification of excitation systems,and is of great significance for the operation and maintenance of excitation systems.

A fault warning for inter-turn short circuit of excitation winding of synchronous generator based on GRU-CNN

Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.

The Detection of Inter-Turn Short Circuits in the Stator Windings of Sensorless Operating Induction Motors

This work proposes an alternative strategy to the use of a speed sensor in the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-gain observer gives very similar results to those using the speed sensor. The estimation errors in the cases of speed variation and load torque are almost identical. Variations in stator and rotor resistances influence the performance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.

A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.

Short-TermWind Power Prediction Based on Combinatorial Neural Networks

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.

Fault Diagnosis Based on ANN for Turn-to-Turn Short Circuit of Synchronous Generator Rotor Windings

Rotor winding turn-to-turn short circuit is a common electrical fault in steam turbines. When turn-to-turn short circuit fault happens to rotor winding of the generator, the generator terminal parameters will change. According to these parameters, the conditions of the rotor winding can be reflected. However, it is hard to express the relations between fault information and generator terminal parameters in accurate mathematical formula. The satisfactory results in fault diagnosis can be obtained by the application of neural network. In general, the information about the severity level of the generator faults can be acquired directly when the faulty samples are found in the training samples of neural network. However, the faulty samples are difficult to acquire in practice. In this paper, the relations among active power, reactive power and excitation current are discovered by analyzing the generator mmf with terminal voltage constant. Depending on these relations, a novel diagnosis method of generator rotor winding turn-to-turn short circuit fault is proposed by using ANN method to obtain the fault samples directly, without destructive tests.

Research on Numerical Simulation of 3D Leakage Magnetic Field and Short-circuit Impedance of Axial Dual-low-voltage Split-winding Transformer

It is difficult to accurately calculate the short-circuit impedance, due to the complexity of axial dual-low-voltage split-winding transformer winding structure. In this paper, firstly, the leakage magnetic field and short-circuit impedance model of axial dual-low-voltage split-winding transformer is established, and then the 2D and 3D leakage magnetic field are analyzed. Secondly, the short-circuit impedance and split parallel branch current distribution in different working conditions are calculated, which is based on field-circuit coupled method. At last, effectiveness and feasibility of the proposed model is verified by comparison between experiment, analysis and simulation. The results showed that the 3D analysis method is a better approach to calculate the short-circuit impedance, since its analytical value is more closer to the experimental value compared with the 2D analysis results, the finite element method calculation error is less than 2%, while the leakage flux method maximum error is 7.2%.

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

The Fractional Power Law of Wind Wave Growth in Deep Water for Short Fetch

Combining the 3/2 power law proposed by Toba with the significant wave energy balance equation for wind waves, wave growth in deep water for short fetch is investigated. It is found that the variations of wave height and period with fetch have the form of power function with fractional exponents 3/8 and 1/4 respectively. Using these exponents in the power functions and through data fitting, the concise wind wave growth relations for short fetch are obtained.

Analysis of electromagnetic characteristics of typical faults in permanent magnet wind generators

Due to the harsh actual operating environment of the permanent magnet wind turbine,it is easy to break down and difficult to monitor.Therefore,the electromagnetic characteristics identification of major fault types of large-scale permanent magnet wind turbines is studied in this paper.The typical faults of rotor eccentricity,stator winding short circuit and permanent magnet demagnetization of permanent magnet wind turbines are analyzed theoretically.The wavelet analysis algorithm is used to decompose and reconstruct the abnormal electromagnetic signal waveform band,and the characteristic frequency of the electromagnetic signal is obtained when the fault occurs.In order to verify the effectiveness of the proposed method,a 3.680MW permanent magnet wind turbine was taken as the research object.Its physical simulation model was established,and an external circuit was built to carry out field co-simulation.The results show that the motor fault type can be determined by detecting the change rule of fault characteristic frequency in the spectrum diagram,and the electromagnetic characteristic analysis can be applied to the early monitoring of the permanent magnet wind turbine fault.

Analysis of Short-circuit Characteristics and Calculation of Steady-state Short-circuit Current for DFIG Wind Turbine

Large-scale doubly-fed induction generator(DFIG)wind turbines are connected to the grid and required to remain grid-connection during faults,the short-circuit current contributed by the generation has become a significant issue.However,the traditional calculation methods aiming at synchronous generators cannot be directly applied to the DFIG wind turbines.A new method is needed to calculate the short-circuit current required by the planning,protection and control of the power grid.The short-circuit transition of DFIG under symmetrical and asymmetric short-circuit conditions are mathematically deduced,and the short-circuit characteristics of DFIG are analyzed.A new method is proposed to calculate the steady-state short-circuit current of DFIG based on the derived expressions.The time-domain simulations are conducted to verify the accuracy of the proposed method.

Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

Wind speed forecasting is important for wind energy forecasting.In the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed accurately.The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overfitting and non-linearity issues.The curse of dimensionality and overfitting issues are handled by using Boruta feature selec-tion.The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed forecasting.The model identifies relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time steps.The proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and R2.The BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of 0.8766.The experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

Short-circuit Current Characteristics of Wind Generators

To study the effects of wind generators on distribution system protection,the short-circuit current(SCC) characteristics of wind generators is important.Although there are many researches on the issue,a clear agreement has not been reached so far.The SCC characteristics for different wind generators are studied.PSCAD simulation is performed in the same system integrated with different kinds of wind generators,and their results are compared with those reported in IEEE papers.The detection possibility by overcurrent relay(OCR)is discussed based on the simulation results.

Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis

In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.

Rolling Generation Dispatch Based on Ultra-short-term Wind Power Forecast

The power systems economic and safety operation considering large-scale wind power penetration are now facing great challenges, which are based on reliable power supply and predictable load demands in the past. A rolling generation dispatch model based on ultra-short-term wind power forecast was proposed. In generation dispatch process, the model rolling correct not only the conventional units power output but also the power from wind farm, simultaneously. Second order Markov chain model was utilized to modify wind power prediction error state (WPPES) and update forecast results of wind power over the remaining dispatch periods. The prime-dual affine scaling interior point method was used to solve the proposed model that taken into account the constraints of multi-periods power balance, unit output adjustment, up spinning reserve and down spinning reserve.

基于CEEMD-SE的CNN&LSTM-GRU短期风电功率预测

为进一步提升短期风电功率的预测精度,提出了一种基于互补集合经验模态分解-样本熵(complementary ensemble empirical mode decomposition-sample entropy,CEEMD-SE)的卷积神经网络(convolutional neural network,CNN)和长短期记忆-门控循环单元(longshorttermmemory-gatedrecurrentunit,LSTM-GRU)的短期风电功率预测模型。首先,利用互补集合经验模态分解将原始风电功率序列分解为若干本征模态函数(intrinsic mode function,IMF)分量和一个残差(residual,RES)分量,利用样本熵算法将相近的分量进行重构;其次,搭建卷积神经网络和长短期记忆网络的并行网络结构,提取数据的局部特征和时序特征,并将特征融合后输入门控循环单元网络中进行学习预测;最后,通过算例进行验证,结果表明采用该模型后预测精度得到了有效提升,其均方根误差降低了15.06%、平均绝对误差降低了15.22%、决定系数提高了1.91%。

计及短路比提升与暂态过电压抑制的含高比例风电送端电网两阶段式调相机优化配置

随着送端电网中大规模风电机组的接入及常规同步机组的置换淘汰,电网短路比水平下降,电压支撑能力削弱,故障时易导致新能源机组暂态过电压而发生脱网事故。面向含高比例风电的送端电网,研究综合计及短路比提升和暂态过电压抑制的调相机优化配置方法。首先,利用新能源多场站短路比(multiple renewable energy stations’short-circuit ratio,MRSCR)分析了调相机接入对风电场短路比的提升作用,并基于调相机暂态快速无功响应特性揭示了其对风电机组暂态过电压的抑制机理;然后,提出一种同时计及短路比提升和暂态过电压抑制的两阶段式调相机优化配置策略,第一阶段以风电场短路比提升为核心约束优化配置调相机位置与基础容量,第二阶段以暂态过电压安全为关键约束进行调相机配置容量修正,并引入量子遗传算法(quantum genetic algorithm,QGA)进行优化求解。最终,基于PSD-BPA在含高比例风电的送端电网进行算例分析,验证了所提出的调相机优化配置方法能够显著提升风电机组多场站短路比水平并有效抑制风电机端暂态过电压。

星-三角接法的分数槽永磁电机匝间短路故障分析

为了分析永磁电机绕组采用星-三角(Y-△)接法时发生匝间短路故障后对电机的影响,建立了绕组在Y-△接法下分别在星形(Y)接法部分和角形(△)接法部分发生匝间短路故障的电路模型,推导得到故障发生后电机三相电流。以10极12槽永磁电机为例进行二维有限元仿真,对传统的Y接法双层绕组永磁电机和Y-△接法4层绕组永磁电机发生匝间短路故障的情况进行对比分析。仿真结果表明:在Y-△接法下匝间短路故障位置的不同对电流会有很大影响;在发生匝间短路故障后Y-△接法能降低电机的转矩脉动,降低故障发生后故障相电流中的3次谐波幅值。

基于特征选择及ISSA-CNN-BiGRU的短期风功率预测

针对风电功率随机性大、平稳性低,以及直接输入预测模型往往难以取得较高精度等问题,提出了一种基于特征选择及改进麻雀搜索算法(ISSA)优化卷积神经网络-双向门控循环单元(CNN-BiGRU)的短期风电功率预测方法。首先,利用变分模态分解(VMD)将原始功率分解为一组包含不同信息的子分量,以降低原始功率序列的非平稳性,提升可预测性,同时通过观察中心频率方式确定模态分解数。其次,对每一分量采用随机森林(RF)特征重要度的方法进行特征选择,从风速、风向、温度、空气密度等气象特征因素中,选取对各个分量预测贡献度较高的影响因素组成输入特征向量。然后,建立各分量的CNN-BiGRU预测模型,针对神经网络算法参数难调、手动配置参数随机性大的问题,利用ISSA对模型超参数寻优,自适应搜寻最优参数组合。最后,叠加各分量的预测值,得到最终的预测结果。以中国内蒙古某风电场实际数据进行仿真实验,与多种单一及组合预测方法进行对比,结果表明,本文所提方法相比于其他方法具有更高的预测精度,其平均绝对百分比误差值达到2.644 0%;在其他4个数据集上进行的模型准确性及泛化性验证结果显示,模型平均绝对百分比误差值分别为4.385 3%、3.174 9%、1.576 1%和1.358 8%,均保持在5.000 0%以内,证明本文所提方法具有较好的预测精度及泛化能力。

基于CBAM-LSTM的风电集群功率短期预测方法

风电功率的精准预测对我国实现“碳达峰”、“碳中和”的目标具有重要意义。传统的风电功率预测方法往往忽视了时间序列数据中的长期依赖关系和空间相关性,导致预测结果不准确。为了解决这个问题,文中提出了了卷积块注意力机制(Convolutional Block Attention Module, CBAM)和长短时记忆网络(Long Short-Term Memory, LSTM)相结合的模型。首先,使用CBAM对风电功率时间序列数据特征和数值天气预报中蕴含的空间特性进行提取,该模块能够自适应地学习时间和空间上的重要特征;然后,将提取的特征输入到LSTM层结构中进行功率预测。为了验证所提方法的有效性,使用中国吉林省某风电场的数据集进行验证,实验结果表明,与其他功率预测方法相比,文中所提方法平均绝对误差(Mean Absolute Error, MAE)平均降低2.67%;决定系数(R-Square, R2)平均提高23%;均方根误差(Root Mean Square Error, RMSE)平均降低2.69%。