Chetan B. Khadse

Abstract: This paper proposes conjugate gradient back-propagation based artificial neural network for real time power quality assessment. The novel real time voltage sag and swell detection, classification scheme using artificial neural network is presented. The ANN is trained in MATLAB using voltage sampled signal data and corresponding parameters of the neural network are utilized to implement in LabVIEW in real time sag swell detection and classification. The Levenberg Marquardt, the resilient back-propagation and conjugate back-propagation algorithm performance is evaluated in MATLAB to find the best neural network for real time application. Among these three back-propagation algorithms, conjugate gradient algorithm has better performance for real time power quality monitoring. The mathematical model of Conjugate gradient back-propagation neural network is implemented in LabVIEW. Real time voltage signals of sag and swell for different time duration and magnitude, intensity are acquired from hardware experimental setup. Hardware setup mainly consists of single phase 230 V voltage source, microcontroller, dimmerstat and solid state relays. Voltage signals of sag and swell are sensed using high precision voltage sensor. Data acquisition system is used to acquire the signal from voltage sensor. The output of data acquisition system is given to the personal computer with LabVIEW. The proposed monitoring system also detects odd and even harmonic components in the voltage signal acquired using FFT. Real time hardware results obtained using proposed power quality monitoring system for detection of voltage sag, swell and harmonics claims the suitability, robustness and adaptability to monitor power quality issues.

Abstract: In this paper, an electromagnetic compatibility estimator is proposed using an artificial neural network with a scaled conjugate gradient algorithm. Neural networks are trained with the help of seven different optimization algorithms in MATLAB. Their performance is evaluated on the basis of number of neurons, desired output, and mean-squared error in offline mode in MATLAB. Among seven algorithms, scaled conjugate gradient algorithm is found to be the best choice. Hence, it is implemented in LabVIEW for online assessment of electromagnetic compatibility issues. Voltage dip, swell, and harmonics are generated with the help of an experimental setup. It consists of 230 V, 50 Hz input voltage supply, microcontroller, variac, and solid-state relays. It is interfaced to the LabVIEW software with the help of an NI USB 6361 data acquisition system. It enabled the continuous online monitoring of various signals. Along with voltage dip and swell, harmonics are also evaluated with the help of spectrum analyzer in LabVIEW. The detailed description of a hardware setup and mathematical modeling of trained network is given in this paper.

Abstract: In this paper Levenberg-Marquardt, Conjugate gradient, Resilient back-propagation algorithms are compared for power quality monitoring. Three Networks are trained in MATLAB. Each network is trained with the single algorithm mentioned above. Data for training is generated with the help of numerical model of power quality events in MATLAB. Voltage sag and swell is taken into consideration. The networks are so trained that it should detect and classify the voltage sag/swell accurately. Training performance of each network is presented with the help of performance and validation graph. Trained networks are tested with the help of simulation model. Simulation model is made in MATLAB which can generate sag/swell of any magnitude for any time period. Algorithms are compared on the basis of the ability of trained network to detect as well as classify the sag/swell and the performance of training.

Abstract: In this paper, comparison of seven backpropagation algorithms used in neural network is made for the three phase power quality assessment. Voltage sag and swell are the two disturbances taken into account for comparing the algorithms. These disturbances are generated with the help of programming in MATLAB. The input data, to train the network is the generated sag and swell disturbances. The backpropagation algorithms are taken into account for comparison are conjugate gradient descent, Levenberg-Marquardt, one step secant, Baysian regularization, scaled conjugate gradient descent, gradient descent with momentum and gradient descent with adaptive learning rate. The seven algorithms are used to train the seven networks and tested using simulation model in MATLAB. After testing is done, the comparison is made on the basis of mean squared error, number of neurons, percent of accurate cases detected, number of iterations required for the training process. The testing results for the each algorithm and comparison are presented.

Abstract: Solid State relay based single phase power quality disturbance generator is proposed in this paper. Digital control system based on microcontroller 89C51 is implemented to control the duration of power quality disturbances. Voltage sag, swell and interruption are generated. Dimmerstat is used to change the voltage levels and so to control the intensity of disturbances. Design of proposed generator is discussed. Simple design and use of easily available components makes this generator economic. It is highly suitable in power quality laboratory for experimental work. Students working on voltage sag/swell compensation and monitoring of power quality events can use this generator. Proposed generator is used as an application for PC based real time power quality monitoring system. Monitoring system is designed in LabVIEW. For fetching the real time signal, voltage sensor and data acquisition system are used. Results of real time detection of disturbances and classification of disturbances into momentary, temporary, instantaneous and under voltage or over voltage are done by monitoring system and presented in this paper.

Abstract: In this paper, the existing recurrent neural network (RNN) models for solving zero-finding (e.g., matrix inversion) with time-varying parameters are revisited from the perspective of control and unified into a control-theoretical framework. Then, limitations on the activated functions of existing RNN models are pointed out and remedied with the aid of control-theoretical techniques. In addition, gradient-based RNNs, as the classical method for zero-finding, have been remolded to solve dynamic problems in manners free of errors and matrix inversions. Finally, computer simulations are conducted and analyzed to illustrate the efficacy and superiority of the modified RNN models designed from the perspective of control. The main contribution of this paper lies in the removal of the convex restriction and the elimination of the matrix inversion in existing RNN models for the dynamic matrix inversion. This work provides a systematic approach on exploiting control techniques to design RNN models for robustly and accurately solving algebraic equations.

Abstract: In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production steadily. Thus, Small Hydropower Plant (SHP) includes a Kaplan turbine was verified to explore its applicability. This paper concentrates on applying on Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation to make performance predictions of the hydropower plant at the Himreen lake dam-Diyala in terms of net turbine head, flow rate of water and power production that data gathered during a research over a 10 year period. The model studies the uncertainties of inputs and output operation and there's a designing to network structure and then trained by means of the entire of 3570 experimental and observed data. Furthermore, ANN offers an analyzing and diagnosing instrument effectively to model performance of the nonlinear plant. The study suggests that the ANN may predict the performance of the plant with a correlation coefficient ( R ) between the variables of predicted and observed output that would be higher than 0.96.

Abstract: This paper proposes a new method to distinguish power quality events based on the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). We examine energy quality events such as sag, interruptions, swell, harmonic, transient, notch and flicker. The proposed method calculates numerous power quality disturbances such as flickering with harmonics, intrusion with harmonics, and sagging with harmonics. It has less processing time than the previous methods due to multiple events occurring at same time. Numerical experiments performed on a real database of power quality disturbances show that there is less calculation in the proposal in comparison with the wavelet change, S-transform and Hilbert change. Recognition with the assistance of HOG gives better and precise outcome in time area with faster reaction.

Abstract: Nonlinear operators for KANSEI evaluation dataset were significantly developed such as uncertainty reason techniques including rough set, fuzzy set and neural networks. In order to extract more accurate KANSEI knowledge, rule-based presentation was concluded a promising way in KANSEI engineering research. In the present work, variable precision rough set was applied in rule-based system to reduce the complexity of the knowledge database from normal item dataset to high frequent rule set. In addition, evidence theory's reliability indices, namely the support and confidence for rule-based knowledge presentation, were proposed by using back propagation neural network with Bayesian regularization algorithm. The proposed method was applied in shoes KANSEI evaluation system; for a certain KANSEI adjective, the key form features of products were predicted. Some similar algorithms such as Levenberg---Marquardt and scaled conjugate gradient were also discussed and compared to establish the effectiveness of the proposed approach. The experimental results established the effectiveness and feasibility of the proposed algorithms customized for shoe industry, where the proposed back propagation neural network/Bayesian regularization approach achieved superior performance compared to the other algorithms in terms of the performance, gradient, Mu, Effective number of parameter, and the sum square parameter in KANSEI support and confidence time series prediction.