P. K. Dash

Input-Output Linearization and Robust Sliding-Mode Controller for the VSC-HVDC Transmission Link

This paper presents a robust nonlinear controller for VSC-HVDC transmission link using input-output linearization and sliding mode-control strategy. The feedback linearization is used to cancel nonlinearities and the sliding mode control offers invariant stability to modeling uncertainties due to converter parameter changes, changes in system frequency, and exogenous inputs. Comprehensive computer simulations are carried out to verify the proposed control scheme under several system disturbances, such as changes in short-circuit ratio, converter parametric changes, and faults on the converter and inverter buses. Based upon the time-domain simulations in the MATLAB/SIMULINK environment, the proposed controller is tested.

A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices

This paper presents an integrated functional link interval type-2 fuzzy neural system (FLIT2FNS) for predicting the stock market indices. The hybrid model uses a TSK (Takagi-Sugano-Kang) type fuzzy rule base that employs type-2 fuzzy sets in the antecedent parts and the outputs from the Functional Link Artificial Neural Network (FLANN) in the consequent parts. Two other approaches, namely the integrated FLANN and type-1 fuzzy logic system and Local Linear Wavelet Neural Network (LLWNN) are also presented for a comparative study. Backpropagation and particle swarm optimization (PSO) learning algorithms have been used independently to optimize the parameters of all the forecasting models. To test the model performance, three well known stock market indices like the Standards & Poors 500 (S&P 500), Bombay stock exchange (BSE), and Dow Jones industrial average (DJIA) are used. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are used to find out the performance of all the three models. Finally, it is observed that out of three methods, FLIT2FNS performs the best irrespective of the time horizons spanning from 1 day to 1 month.

Power quality time series data mining using S-transform and fuzzy expert system

This paper presents a new approach for power quality time series data mining using S-transform based fuzzy expert system (FES). Initially the power signal time series disturbance data are pre-processed through an advanced signal processing tool such as S-transform and various statistical features are extracted, which are used as inputs to the fuzzy expert system for power quality event detection. The proposed expert system uses a data mining approach for assigning a certainty factor for each classification rule, thereby providing robustness to the rule in the presence of noise. Further to provide a very high degree of accuracy in pattern classification, both the Gaussian and trapezoidal membership functions of the concerned fuzzy sets are optimized using a fuzzy logic based adaptive particle swarm optimization (PSO) technique. The proposed hybrid PSO-fuzzy expert system (PSOFES) provides accurate classification rates even under noisy conditions compared to the existing techniques, which show the efficacy and robustness of the proposed algorithm for power quality time series data mining.

Measurement and Classification of Simultaneous Power Signal Patterns With an S-Transform Variant and Fuzzy Decision Tree

This paper proposes a new scheme for measurement, identification, and classification of various types of power quality (PQ) disturbances. The proposed method employs a fast variant of S-Transform (ST) algorithm for the extraction of relevant features, which are used to distinguish among different PQ events by a fuzzy decision tree (FDT)-based classifier. Various single as well as simultaneous power signal disturbances have been simulated to demonstrate the efficiency of the proposed technique. The simulation result implies that the proposed scheme has a higher recognition rate while classifying simultaneous PQ faults, unlike other methods. The Fast dyadic S-transform (FDST) algorithm for accurate time-frequency localization, Decision Tree algorithms for optimal feature selection, Fuzzy decision rules to complement overlapping patterns, robust performance under different noise conditions and a relatively simple classifier methodology are the strengths of the proposed scheme.

Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier

This paper proposes fast variants of the discrete S-transform (FDST) algorithm to accurately extract the time localized spectral characteristics of nonstationary signals. Novel frequency partitioning schemes along with band pass filtering are proposed to reduce the computational cost of S-transform significantly. A generalized window function is introduced to improve the energy concentration of the time-frequency (TF) distribution. An application of the proposed algorithms is extended for detection and classification of various nonstationary power quality (PQ) disturbances. The relevant features required for classification were extracted from the time-frequency distribution of the nonstationary power signal patterns. An automated decision tree (DT) construction algorithm was employed to select optimal set of features based on a specified optimality criterion for extraction of the decision rules. The set of decision rules thus obtained were used for identification of the PQ disturbance types. Various single as well as simultaneous power signal disturbances were considered in this paper to prove the efficiency of proposed classification scheme. A comparison of the classification accuracies with techniques proposed earlier, clearly demonstrates the improved performance. The major contributions of this manuscript are new FDST algorithms for fast and accurate time-frequency representation and an efficient classification algorithm for identifying PQ disturbances. The advantages of the classification algorithm are (i) accurate feature derivation from the TF distribution and optimum feature selection by the DT construction algorithm, (ii) robust performance at different signal-to-noise ratios, (iii) simple decision rules for classification, and (iv) recognition of simultaneous PQ events.

Fast Tracking of Power Quality Disturbance Signals Using an Optimized Unscented Filter

This paper presents a hybrid approach for tracking the amplitude, phase, frequency, and harmonic content of power quality disturbance signals occurring in power networks using an unscented Kalman filter (UKF) and swarm intelligence. The UKF is a novel extension of the well-known extended Kalman filter (EKF) using an unscented transformation to overcome the difficulties of linearization and derivative calculations of signals with a low signal-to-noise ratio (SNR). Further, the model and measurement error covariance matrices Q and R, along with the UKF parameters, are selected using a modified particle swarm optimization (PSO) algorithm for accurate tracking of signal parameters. To circumvent the problem of premature convergence and local minima in conventional PSO, a dynamically varying inertia weight based on the variance of the population fitness is used. This results in a better local and global searching ability of the particles, which improves the convergence of the velocity, and in a better accuracy of the UKF parameters. Various simulation results for nonstationary sinusoidal signals occurring in power networks with varying amplitudes, phases, and harmonic contents corrupted with noise having a low SNR reveal significant improvements in noise rejection and speed of convergence and accuracy.

A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Abstract Stock market prediction is of great interest to stock traders and investors due to high profit in trading the stocks. A successful stock buying/selling generally occurs near price trend turning point. Thus the prediction of stock market indices and its analysis are important to ascertain whether the next days closing price would increase or decrease. This paper, therefore, presents a simple IIR filter based dynamic neural network (DNN) and an innovative optimized adaptive unscented Kalman filter for forecasting stock price indices of four different Indian stocks, namely the Bombay stock exchange (BSE), the IBM stock market, RIL stock market, and Oracle stock market. The weights of the dynamic neural information system are adjusted by four different learning strategies that include gradient calculation, unscented Kalman filter (UKF), differential evolution (DE), and a hybrid technique (DEUKF) by alternately executing the DE and UKF for a few generations. To improve the performance of both the UKF and DE algorithms, adaptation of certain parameters in both these algorithms has been presented in this paper. After predicting the stock price indices one day to one week ahead time horizon, the stock market trend has been analyzed using several important technical indicators like the moving average (MA), stochastic oscillators like K and D parameters, WMS%R (William indicator), etc. Extensive computer simulations are carried out with the four learning strategies for prediction of stock indices and the up or down trends of the indices. From the results it is observed that significant accuracy is achieved using the hybrid DEUKF algorithm in comparison to others that include only DE, UKF, and gradient descent technique in chronological order. Comparisons with some of the widely used neural networks (NNs) are also presented in the paper.

A New Real-Time Fast Discrete S-Transform for Cross-Differential Protection of Shunt-Compensated Power Systems

This paper presents a cross-differential protection scheme for power transmission systems inclusive of a STATCOM. The measurement of the energy of the prominent frequency components of the current signals is done using a computationally fast version of the discrete S Transform suitable for power system signal analysis. The energy thus obtained is used for cross-differential purposes. The practical implementation of the new fast discrete S-transform is performed on the TMS320C6713 digital signal processor for verification of real-time operation of the relaying scheme. The computational load of the processor to perform the transform in online mode is reduced by a cross-differential check using a cumulative difference technique. The results obtained from the extensive experimentations show the feasibility and speed of the new approach.

An improved cuckoo search based extreme learning machine for medical data classification

Abstract Machine learning techniques are being increasingly used for detection and diagnosis of diseases for its accuracy and efficiency in pattern classification. In this paper, improved cuckoo search based extreme learning machine (ICSELM) is proposed to classify binary medical datasets. Extreme learning machine (ELM) is widely used as a learning algorithm for training single layer feed forward neural networks (SLFN) in the field of classification. However, to make the model more stable, an evolutionary algorithm improved cuckoo search (ICS) is used to pre-train ELM by selecting the input weights and hidden biases. Like ELM, Moore–Penrose (MP) generalized inverse is used in ICSELM to analytically determines the output weights. To evaluate the effectiveness of the proposed model, four benchmark datasets, i.e. Breast Cancer, Diabetes, Bupa and Hepatitis from the UCI Repository of Machine Learning are used. A number of useful performance evaluation measures including accuracy, sensitivity, specificity, confusion matrix, Gmean, F-score and norm of the output weights as well as the area under the receiver operating characteristic (ROC) curve are computed. The results are analyzed and compared with both ELM based models like ELM, on-line sequential extreme learning algorithm (OSELM), CSELM and other artificial neural networks i.e. multi-layered perceptron (MLP), MLPCS, MLPICS and radial basis function neural network (RBFNN), RBFNNCS, RBFNNICS. The experimental results demonstrate that the ICSELM model outperforms other models.

A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction

Abstract This paper proposes a hybrid learning framework called Self Adaptive Differential Harmony Search Based Optimized Extreme Learning Machine (SADHS-OELM) for single hidden layer feed forward neural network (SLFN). The new learning paradigm seeks to take advantage of the generalization ability of extreme learning machines (ELM) along with the global learning capability of a self adaptive differential harmony search technique in order to optimize the fitting performance of SLFNs. SADHS is a variant of harmony search technique that uses the current to best mutation scheme of DE in the pitch adjustment operation for harmony improvisation process. SADHS has been used for optimal selection of the hidden layer parameters, the bias of neurons of the hidden-layer, and the regularization factor of robust least squares, whereas ELM has been applied to obtain the output weights analytically using a robust least squares solution. The proposed learning algorithm is applied on two SLFNs i.e. RBF and a low complexity Functional link Artificial Neural Networks (CEFLANN) for prediction of closing price and volatility of five different stock indices. The proposed learning scheme is also compared with other learning schemes like ELM, DE-OELM, DE, SADHS and two other variants of harmony search algorithm. Performance comparison of CEFLANN and RBF with different learning schemes clearly reveals that CEFLANN model trained with SADHS-OELM outperforms other learning methods and also the RBF model for both stock index and volatility prediction.