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Variable speed wind turbine for maximum power capture using adaptive fuzzy integral sliding mode control

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Journal of Modern Power Systems and Clean Energy 2014年第2期2卷 114-125页

作者： Saravanakumar RAJENDRAN debashisha jena National Institute of Technology Karnataka SurathkalIndia

This paper presents a nonlinear control approach to variable speed wind turbine(VSWT)with a wind speed *** dynamics of the wind turbine(WT)is derived from single mass *** this work,a modified Newton Raphson estimator has been considered for exact estimation of effective wind *** main objective of this work is to extract maximum energy from the wind at below rated wind speed while reducing drive train *** order to achieve the above objectives,VSWT should operate close to the optimal power *** generator torque is considered as the control input to achieve maximum energy *** the literature,it is clear that existing linear and nonlinear control techniques suffer from poor tracking of WT dynamics,increased power loss and complex control *** addition,they are not robust with respect to input *** order to overcome the above drawbacks,adaptive fuzzy integral sliding mode control(AFISMC)is proposed for VSWT *** proposed controller is tested with different types of disturbances and compared with other nonlinear controllers such as sliding mode control and integral sliding mode *** result shows the better performance of AFISMC and its robustness to input *** this paper,the discontinuity in integral sliding mode controller is smoothed by using hyperbolic tangent function,and the sliding gain is adapted using a fuzzy technique which makes the controller more robust.

关键词： Variable speed wind turbine Integral sliding mode controller(ISMC) Sliding mode control(SMC) Adaptive fuzzy integral sliding mode control(AFISMC)

An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identification

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International Journal of Automation and computing 2009年第2期6卷 137-144页

作者： Bidyadhar Subudhi debashisha jena Center for Industrial Electronics & Robotics Department of Electrical Engineering National Institute of Technology

This paper presents an improved nonlinear system identification scheme using di？erential evolution （DE）, neural network （NN） and Levenberg Marquardt algorithm （LM）. With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE＋NN on a number of examples including a practical case study. The identification results obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.

关键词： Differential evolution neural network (NN) nonlinear system identification Levenberg Marquardt algorithm

Combined Cumulant and Gaussian Mixture Approximation for Correlated Probabilistic Load Flow Studies:A New Approach

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CSEE Journal of Power and Energy Systems 2016年第2期2卷 71-78页

作者： B Rajanarayan Prusty debashisha jena Department of Electrical and Electronics Engineering National Institute of Technology KarnatakaSurathkalIndia 575025

In this paper,a probabilistic load flow analysis technique that combines the cumulant method and Gaussian mixture approximation method is *** technique overcomes the incapability of the existing series expansion methods to approximate multimodal probability distributions.A mix of Gaussian,non-Gaussian,and discrete type probability distributions for input bus powers is *** distributions of multimodal bus voltages and line power flows pertaining to these inputs are precisely obtained without using any series expansion *** the same time,multiple input correlations are *** of the proposed method is demonstrated in IEEE 14 and 57 bus test *** are compared with cumulant and Gram Charlier expansion,cumulant and Cornish Fisher expansion,dependent discrete convolution,and Monte Carlo *** of different correlation cases on distribution of bus voltages and line power flows are also studied.

关键词： Correlation cumulant Gaussian mixture approximation photovoltaic generation probabilistic load flow

Cumulant-based correlated probabilistic load flowconsidering photovoltaic generation and electric vehiclecharging demand

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Frontiers in Energy 2017年第2期11卷 184-196页

作者： Nitesh Ganesh BHAT B. Rajanarayan PRUSTY debashisha jena Department of Electrical and Electronics Engineering National Instituteof Technology Karnataka Surathkal Mangalore 575025 India

This paper applies a cumulant-based analytical method for probabilistic load flow (PLF) assessment in transmission and distribution systems. The uncertainties pertaining to photovoltaic generations and aggregate bus load powers are probabilistically modeled in the case of transmission systems. In the case of distribution systems, the uncertainties pertaining to plug-in hybrid electric vehicle and battery electric vehicle charging demands in residential community as well as charging stations are probabilistically modeled. The probability distributions of the result variables (bus voltages and branch power flows) pertaining to these inputs are accurately established. The multiple input correlation cases are incorporated. Simultaneously, the performance of the proposed method is demonstrated on a modified Ward-Hale 6-bus system and an IEEE 14-bus transmission system as well as on a modified IEEE 69-bus radial and an IEEE 33-bus mesh distribution system. The results of the proposed method are compared with that of Monte-Carlo simulation.

关键词： battery electric vehicle extended cumulant method photovoltaic generation plug-in hybrid electric vehicle probabilistic load flow