ANN-based Scheme Research Articles

Performance of EWMA and ANN-based Schemes in Detection of Denial of Service Attack

To ensure successful implementation of cyber-physical systems, industries require computer networks to be protected from malicious attacks. Despite various intrusion detection techniques being proposed by researchers, computer networks are still vulnerable to attacks. As new attacks becoming more complicated, more research is needed to develop more effective and reliable intrusion detection schemes. This study investigated the exponentially weighted moving average control charting technique for detection of malicious denial of service (DoS) trafic and compared it with artificial neural network (ANN) based scheme. Eight features from the Benchmark KDD Cup99 computer network datasets were extracted and their respective ARL1 and false alarm rate were evaluated. The results suggest that EWMA technique is effective only for selective features and the ANN-based scheme is relatively consistent in handling variability in traffic data. This study opens new opportunities for further investigation to enhance performance of the proposed schemes.

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

The increasing number of distributed generators connected to distribution grids requires a reliable monitoring of distribution grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First approaches using a limited number of measurements to monitor distribution grids exist, some of which use artificial neural networks (ANN). The current ANN-based approaches, however, are limited to static topologies, only estimate voltage magnitudes, do not work properly when confronted with a high amount of distributed generation and often yield inaccurate results. These strong limitations have prevented a true applicability of ANN for distribution system monitoring. The objective of this paper is to overcome the limitations of existing approaches. We do that by presenting an ANN-based scheme, which advances the state-of-the-art in several ways: Our scheme can cope with a very low number of measurements, far less than is traditionally required by the state-of-the-art weighted least squares state estimation (WLS SE). It can estimate both voltage magnitudes and line loadings with high precision and includes different switching states as inputs. Our contribution consists of a method to generate useful training data by using a scenario generator and a number of hyperparameters that define the ANN architecture. Both can be used for different power grids even with a high amount of distributed generation. Simulations are performed with an elaborate evaluation approach on a real distribution grid and a CIGRE benchmark grid both with a high amount of distributed generation from photovoltaics and wind energy converters. They demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors when comparing voltage magnitude and line magnitude estimation errors.

Neural Networks for Stable Control of Nonlinear DFIG in Wind Power Systems

This paper presents an Artificial Neural Network (ANN) based Direct Power Control (DPC) strategy for controlling power flow, and synchronizing Double Fed Induction Generator (DFIG) with grid and Voltage Oriented Control (VOC). In order to cope with the complex calculations required in DPC, the proposed ANN system employs the individual training strategy with fixed-weight and supervised models. The ANN controller is divided into five subnets: 1) real and reactive power measurement sub-net (fixed-weight) with dynamic neurons; 2) reference real and reactive calculation sub-net (fixed-weight) with square neurons; 3) reference stator current calculation sub-net (supervised) with log-sigmoid neurons and tan-sigmoid neurons; 4) reference rotor current calculation sub-net (fixed-weight) with recurrent neurons; and 5) reference rotor voltage calculation sub-net (fixed-weight or supervised). The results obtained demonstrate the feasibility of ANN–DPC. The proposed ANN-based scheme incurs much shorter execution times and, hence, the errors caused by control time delays are minimized.

Comparison of MLP-ANN Scheme and SDSM as Tools for Providing Downscaled Precipitation for Impact Studies at Daily Time Scale

Statistical downscaling has become an important part in most of the watershed scale climate change investigations. It is usually performed using multiple regression-based models. Basic working principle of such models is to develop a suitable relationship between the large scale (predictors) and the local climatic parameters called predictands. The development of such relationships using linear regression becomes very challenging when the local parameter to be downscaled is complex in nature such as precipitation. For this reason, use of nonlinear data driven techniques including Artificial Neural Networks (ANNs) is becoming more and more popular. Therefore, an attempt has been made in the study presented here to introduce a new Multi-Layer Perceptron (MLP) ANN-based scheme to develop a robust predictors-predictand relationship to be used as a downscaling model at daily time scale. The efficiency of this model has been compared with a popularly used model called Statistical Down Scaling Model (SDSM), for daily precipitation at the Clutha watershed in New Zealand. The results show that the model developed based on ANN scheme exhibits better performance than the SDSM. Hence, it is concluded that the use of artificial intelligence techniques such as ANN can greatly help in developing more efficient predictor-predictand models for even for precipitation being the toughest climate variable to model

A Novel Approach for Protection and Condition Monitoring of Power Transformer Using MRBFNN

This article presents differential relaying and condition monitoring of power transformer using minimal radial basis function neural network (MRBFNN). This type of RBF neural network uses a sequential learning procedure to determine the optimum number of neurons in the hidden layer without resorting to trial and error. This ANN-based scheme monitors operating conditions of the transformer and detects the fault and issues the trip signal in case of an internal fault. It has been realized through two different ANN structures using the minimal radial basis function (MRBF) learning algorithm. The proposed protection scheme has been evaluated using simulated data obtained through EMTP/ATP package. The results amply demonstrate the capabilities of the proposed fault detector (FD) and condition monitor (CM) in terms of accuracy and speed with respect to detection of fault, classification and pattern recognition of different events of power transformer. The number of training patterns and training time are drastically reduced and significant accuracy is achieved.

Protection and Conditions Monitoring of Power Transformer Using ANN

This paper presents differential protection and condition monitoring scheme for power transformer using ANN. This ANN-based scheme monitors different operating conditions of the transformer, detects the fault, and issues the trip signal in case of internal fault only. The proposed protection scheme is accurate, fast, efficient, and reliable. This scheme has been realized through two different ANN structures using the Radial Basis Function (RBF) learning algorithm. The proposed protection scheme has been evaluated using simulated data obtained through EMTP/ATP package. The results amply demonstrate the capabilities of Fault Detector (FD) and Condition Monitor (CM) in terms of accuracy and speed with respect to detection of fault, classification, and pattern recognition of different events of power transformer.

Direct self control of induction motor based on neural network

This paper presents an artificial-neural-network-based direct-self-control (ANN-DSC) scheme for an input inverter-fed three-phase induction motor. In order to cope with the complex calculations required in direct self control (DSC), the proposed artificial-neural-network (ANN) system employs the individual training strategy with fixed-weight and supervised models. A computer simulation program is developed using Matlab/Simulink together with the Neural Network Toolbox. The simulated results obtained demonstrate the feasibility of ANN-DSC. Compared with the classical digital-signal-processor-based DSC, the proposed ANN-based scheme incurs much shorter execution times and, hence, the errors caused by control time delays are minimized.

An intelligent pressure sensor using neural networks

In this paper, we propose a scheme of an intelligent capacitive pressure sensor (CPS) using an artificial neural network (ANN). A switched-capacitor circuit (SCC) converts the change in capacitance of the pressure-sensor into an equivalent voltage. The effect of change in environmental conditions on the CPS and subsequently upon the output of the SCC is nonlinear in nature. Especially, change in ambient temperature causes response characteristics of the CPS to become highly nonlinear, and complex signal processing may be required to obtain correct readout. The proposed ANN-based scheme incorporates intelligence into the sensor. It is revealed from the simulation studies that this CPS model can provide correct pressure readout within /spl plusmn/1% error (full scale) over a range of temperature variations from -20/spl deg/C to 70/spl deg/C. Two ANN schemes, direct modeling and inverse modeling of a CPS, are reported. The former modeling technique enables an estimate of the nonlinear sensor characteristics, whereas the latter technique estimates the applied pressure which is used for direct digital readout. When there is a change in ambient temperature, the ANN automatically compensates for this change based on the distributive information stored in its weights.

Incorporation of a set enumeration trees-based classifier into a hybrid computer-assisted diagnosis scheme for mass detection

The authors evaluated whether a hybrid classifier of two independent computer-aided diagnosis (CAD) schemes, the set enumeration (SE) trees approach and an artificial neural network (ANN), could improve the detection of masses on digitized mammograms. The potential benefits resulting from the interpretability of the SE trees model was also explored. Two hundred thirty verified mass regions and 230 negative but suspicious regions were randomly selected from 618 digitized mammograms. Each region was represented by a 24-parameter feature vector. These features were used as input data for the SE trees and ANN-based schemes. After the positive and negative regions were randomly segmented into five exclusive partitions, a fivefold cross-validation method was applied to evaluate and compare the performance of the SE trees, ANN, and hybrid system in the identification of masses. The performance of the SE trees approach was comparable to that of the ANN. The average area under the receiver operating characteristic (ROC) curves for all five partitions was 0.88 (standard deviation, 0.04). Owing to the relatively low correlation between the region-based results of the SE trees and ANN methods, the hybrid classifier yielded a significantly improved performance, with an area under the ROC curve of 0.94 (standard deviation, 0.02; P < .05). The hybrid CAD scheme significantly improved performance. The amenability of the SE trees models to interpretation may aid in the assessment of the importance of specific features.

Online self-tuning ANN-based speed control of a PM DC motor

This paper presents an online self-tuning artificial-neural-network (ANN)-based speed control scheme of a permanent magnet (PM) DC motor. For precise speed control, an online training algorithm with an adaptive learning rate is introduced, rather than using fixed weights and biases of the ANN. The complete system is implemented in real time using a digital signal processor controller board (DS1102) on a laboratory PM DC motor. To validate its efficacy, the performances of the proposed ANN-based scheme are compared with a proportional-integral controller-based PM DC motor drive system under different operating conditions. The comparative results show that the ANN-based speed control scheme is robust, accurate, and insensitive to parameter variations and load disturbances.

Robust radar target classifier using artificial neural networks

In this paper an artificial neural network (ANN) based radar target classifier is presented, and its performance is compared with that of a conventional minimum distance classifier. Radar returns from realistic aircraft are synthesized using a thin wire time domain electromagnetic code. The time varying backscattered electric field from each target is processed using both a conventional scheme and an ANN-based scheme for classification purposes. It is found that a multilayer feedforward ANN, trained using a backpropagation learning algorithm, provides a higher percentage of successful classification than the conventional scheme. The performance of the ANN is found to be particularly attractive in an environment of low signal-to-noise ratio. The performance of both methods are also compared when a preemphasis filter is used to enhance the contributions from the high frequency poles in the target response.