Voltage stability evaluation of power system with FACTS devices using fuzzy neural network

Voltage stability has been a major concern for power system utilities because of several events of voltage collapses in the recent past. With the developments of flexible ac transmission system (FACTS) devices, power system performance has improved. This paper proposes an approach based on fuzzy neural network to calculate loadability margin of the power system with static synchronous compensator (STATCOM). A multi-input, single output fuzzy neural network is developed. Kohonen self-organizing map is employed to cluster the real and reactive loads at all the buses to reduce the input features, thus limiting the size of the network and reducing computational burden. Uncertainties of real and reactive loads, real and reactive generations, bus voltages and STATCOM parameters are taken into account by transforming them into fuzzy domains using combination of different nonlinear membership functions. A three-layered feed-forward neural network with fuzzy input variables is developed to evaluate the loadability margin. All ac limits are considered. The proposed methodology is applied to IEEE-30 bus and IEEE-118 bus systems. The proposed methodology is fast and accurate as compared to the conventional techniques. This method can also be used for online calculation of the voltage stability of the large power systems.     

Arabic isolated word recognition system using hybrid feature extraction techniques and neural network

In this paper, we implemented a speaker-dependent speech recognition system for 11 standard Arabic isolated words. During the feature extraction phase, several techniques were used such as Mel frequency cepstral coefficients, perceptual linear prediction, relative perceptual linear prediction and their first order temporal derivatives. Principal component analysis was adopted in order to reduce the feature dimension. The recognition phase is based on the feed forward back-propagation neural network using two learning algorithms: the Levenberg–Marquardt “Trainlm” and the scaled conjugate gradient “Trainscg”. Hybrid approaches were used and compared in terms of computational time and recognition rates and have produced very interesting performances.     

Monitoring and control of power system voltage stability using an artificial neural network

This paper proposes a decentralized method for monitoring and control of power system voltage stability with an artificial neural network (ANN). One of the problems in applying neural networks to power systems is how to cope with a lot of variables in real-size power systems. Most of the conventional ANN-based approaches suffer from the curse of the dimensionality in power systems. As a result, it seems that the applications are far from the real world. However, as far as voltage problems are concerned, they possess peculiar local characteristics. It implies that the problem may be decomposed into subproblems. This paper focuses on the characteristics and considers more realistic ANN applications. The proposed method is tested in a 30-node system to demonstrate the effectiveness.     

Voltage stability assessment in power network using self organizing feature map and radial basis function

Self-organizing feature map (SOFM) in conjunction with radial basis function (RBF) has been applied in this paper to determine and classify the voltage stability states of a multi-bus power network. Simulations were carried out on a real 203-bus system of an Indian power utility considering load changes and contingencies. The data collected from simulations are then used as inputs to the SOFM which acts as a classifier to classify the voltage stability states of the system under test. To augment the effectiveness of the proposed method, the initial classification results were improved with the application of RBF technique. Studies show that the SOFM-RBF combination delivers high classification accuracy in the order of almost 100% and can be considered an effective soft-computing tool to ease the operation of large-multi bus power network under variable operating conditions.     

Intelligent control of SVC using wavelet neural network to enhance transient stability

In order to enhance transient stability in a power system, a new intelligent controller is proposed to control a Static VAR compensator (SVC) located at center of the transmission line. This controller is an online trained wavelet neural network controller (OTWNNC) with adaptive learning rates derived by the Lyapunov stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller. One of the proposed controller features is robustness to different operating conditions and disturbances. The test power system is a two-area two-machine system power. The simulation results show that the oscillations are satisfactorily damped out by the OTWNNC.     

Adaptive image interpolation using probabilistic neural network

This paper proposes an image interpolation model based on probabilistic neural network (PNN). The method adjusts automatically the smoothing parameters for varied smooth/edge image region, and takes into consideration both smoothness (flat region) and sharpness (edge region) characteristics at the same model. A single neuron, combined with PSO training, is used for sharpness/smoothness adaptation. Finally, we report the performance of these newly proposed methods in other image interpolation method.     

An expert model for self-care problems classification using probabilistic neural network and feature selection approach

Self-care problems classification is one of the important challenges for occupational therapists. Extent and variety of disorders make the self-care problems classification process complex and time-consuming. To overcome this challenge, an expert model is proposed innovatively in this research. The proposed model is based on Probabilistic Neural Network (PNN) and Genetic Algorithm (GA) for classifying self-care problems of children with physical and motor disability. In this model, PNN is employed as a classifier and GA is applied for feature selection. The PNN is trained by using a standard ICF-CY dataset. Based on ICF-CY, occupational therapists must evaluate many features to diagnose self-care problems. According to the experiences of occupational therapists, these features have different effects on classification. Hence, GA is employed to select relevant and important features in self-care problems classification. Since the classification rules are important for occupational therapists, the self-care problems classification rules are extracted additionally by using the CART algorithm. The experimental results show that by using the feature selection algorithm, the accuracy and time complexity of classification are improved in comparison to other models. The proposed model can classify self-care problems of children with 94.28% accuracy by using only 16.5% of all features.     

Loadability margin calculation of power system with SVC using artificial neural network

Voltage stability has become a major concern among the utilities over the past decade. With the development of FACTS devices, there is a growing interest in using these devices to improve the stability. In this paper a method using parallel self-organizing hierarchical neural network (PSHNN) is proposed to estimate the loadability margin of the power system with static var compensator (SVC). Limits on reactive generations are considered. Real and reactive power injections along with firing angle of SVC and bus voltage at which SVC is connected, are taken as input features. To improve the performance of network, K-means clustering is employed to form the clusters of patterns having similar loadability margin. To reduce the number of input features in each cluster, system entropy information gain method is used and only those real and reactive power injections, which affect the loadability margin most, are selected. Separate PSHNN is trained for each cluster. The proposed method is implemented on IEEE-30 bus and IEEE-118 bus system. Once trained, the network produces the output, with accuracy and speed. The computation time is also independent of the system size and the load pattern.     

Performance evaluation system for probabilistic neural network hardware

The probabilistic neural network (PNN) is one of the most promising neural networks, and is now applied to some real-world applications. In order to speed up the PNN calculation considerably, we have developed a PNN hardware system for video image recognition. The performance of the PNN hardware cannot be evaluated precisely until the evaluation system is completed. In this study, we developed a performance evaluation system for the PNN hardware and demonstrated it using the developed evaluation system.This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

基于果蝇算法优化的概率神经网络在变压器故障诊断中的应用

传统的变压器故障诊断方法存在编码不全,容易错判漏判的缺点。随着变压器在线监测技术的发展和产品需求的增加,变压器故障诊断技术朝着智能化的方向发展。为提高故障诊断率,结合油中气体分析法,本文提出了一种基于果蝇算法优化的概率神经网络模型的变压器故障诊断方法。作为一种新型的启发式和进化式算法,果蝇优化算法具有易理解和快速收敛到全局最优解的优点。概率神经网络结构简单、训练简洁,具有强大的非线性分类能力,将样本空间映射到故障模式空间中,从而形成一有较强容错能力和机构自适应能力的诊断网络。采用果蝇算法对模型参数进行优化,减少人为因素对神经网络设计的影响。仿真实验证明这种基于果蝇优化算法的概率神经网络可以有效地运用到变压器故障诊断中,为变压器故障诊断供了一条新途径,具有良好的研究价值和发展前景。     

Fuzzy neural network based voltage stability evaluation of power systems with SVC

Voltage stability has become of major concern for the power utilities. In this paper, multi input, single output fuzzy neural network is developed for voltage stability evaluation of the power systems with SVC by calculating the loadability margin. Uncertainties of real and reactive loads, real and reactive generations, bus voltages and SVC parameters are taken into account. All ac limits are considered. In the first stage, Kohonen self-organizing map is developed to cluster the real and reactive loads at all the buses to reduce the input features, thus limiting the size of the network and reducing computational burden. In the second stage, combination of different non-linear membership functions is proposed to transform the input variables into fuzzy domains. Then a three-layered feed forward neural network with fuzzy input variables is developed to evaluate the loadability margin. The proposed methodology is applied to IEEE-30 bus and IEEE-118 bus systems.     

Structure damage diagnosis using neural network and feature fusion

A structure damage diagnosis method combining the wavelet packet decomposition, multi-sensor feature fusion theory and neural network pattern classification was presented. Firstly, vibration signals gathered from sensors were decomposed using orthogonal wavelet. Secondly, the relative energy of decomposed frequency band was calculated. Thirdly, the input feature vectors of neural network classifier were built by fusing wavelet packet relative energy distribution of these sensors. Finally, with the trained classifier, damage diagnosis and assessment was realized. The result indicates that, a much more precise and reliable diagnosis information is obtained and the diagnosis accuracy is improved as well.     

基于角微特征的神经网络识别人脸特征

提出一种利用人脸角微特征几何特性的图像预处理,建立BP神经网络识别人脸特征模型的方法。研究了角微特征提取和具体算法,讨论了BP网络结构的设计,输入、输出层设计和隐藏层节点选取问题。微特征提取,可以降低网络输入维度,对于识别不同角度、不同表情的人脸图像提供了可能性。利用ORL人脸图像数据库做实验,结果表明此方法有效。     

Controlling a robot manipulator with fuzzy voice commands using a probabilistic neural network

Natural language commands are generated by intelligent human beings. As a result, they contain a lot of information. Therefore, if it is possible to learn from such commands and reuse that knowledge, it will be a very efficient process. In this paper, learning from such information rich voice commands for controlling a robot is studied. First, new concepts of fuzzy coach-player system and sub-coach are proposed for controlling robots with natural language commands. Then, the characteristics of the subjective human decision making process are discussed and a Probabilistic Neural Network (PNN) based learning method is proposed to learn from such commands and to reuse the acquired knowledge. Finally, the proposed concept is demonstrated and confirmed with experiments conducted using a PA-10 redundant manipulator.     

Conception of complex probabilistic neural network system for classification of partial discharge patterns using multifarious inputs

Pattern recognition has a long history within electrical engineering but has recently become much more widespread as the automated capture of signal and images has been cheaper. Very many of the application of neural networks are to classification, and so are within the field of pattern recognition and classification. In this paper, we explore how probabilistic neural networks fit into the earlier framework of pattern recognition of partial discharge patterns since the PD patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely used representation is phase resolved PD (PRPD) patterns. Also this paper describes a method for the automated recognition of PRPD patterns using a novel complex probabilistic neural network system for the actual classification task. The efficacy of composite neural network developed using probabilistic neural network is examined.     

Classification trees with neural network feature extraction

The ideal use of small multilayer nets at the decision nodes of a binary classification tree to extract nonlinear features is proposed. The nets are trained and the tree is grown using a gradient-type learning algorithm in the multiclass case. The method improves on standard classification tree design methods in that it generally produces trees with lower error rates and fewer nodes. It also reduces the problems associated with training large unstructured nets and transfers the problem of selecting the size of the net to the simpler problem of finding a tree of the right size. An efficient tree pruning algorithm is proposed for this purpose. Trees constructed with the method and the CART method are compared on a waveform recognition problem and a handwritten character recognition problem. The approach demonstrates significant decrease in error rate and tree size. It also yields comparable error rates and shorter training times than a large multilayer net trained with backpropagation on the same problems.     

Fuzzy controller design by using neural network techniques

This paper investigates the relationship between the piecewise linear fuzzy controller (PLFC), in which the membership functions for fuzzy variables and the associated inference rules are all in piecewise linear forms, and a Gaussian potential function network based controller (GPFNC), in which the network output is a weighted summation of hidden responses from a series of Gaussian potential function units (GPFU's). Systematic procedures are proposed for transformation from a PLFC to its GPFNC counterpart, and vice versa. Based on these transformation principles, a series of systematic and feasible steps is presented for the design of an optimized PLFC (PLFC*) by using neural network techniques. In the design procedures, the simplified PLFC is used as the initial controller structure, then a GPFNC, which gives the approximate control response to the initially given PLFC, is found for further optimization. A neutralization process is used to demonstrate the feasibility and the potential applicability of these intelligent controllers on the regulation of highly nonlinear chemical processes     

基于联合层特征的卷积神经网络在车标识别中的应用

针对现有智能交通系统仅仅通过车牌信息获取车辆信息存在不准确的情况,提出一种基于联合层特征的卷积神经网络(Multi-CNN)进行车标识别。该方法将通过卷积神经网络中不同层提取的特征联合起来,一起作为全连接层的输入,训练获得分类器。通过理论分析和实验表明,与传统的卷积神经网络训练获得的分类器相比,Multi-CNN方法能够减少训练所需计算量,同时将车标识别准确率提升至98.7%。