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

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.

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

This paper presents a nonlinear control approach to variable speed wind turbine(VSWT)with a wind speed estimator.The dynamics of the wind turbine(WT)is derived from single mass model.In this work,a modified Newton Raphson estimator has been considered for exact estimation of effective wind speed.The main objective of this work is to extract maximum energy from the wind at below rated wind speed while reducing drive train oscillation.In order to achieve the above objectives,VSWT should operate close to the optimal power coefficient.The generator torque is considered as the control input to achieve maximum energy capture.From 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 law.In addition,they are not robust with respect to input disturbances.In order to overcome the above drawbacks,adaptive fuzzy integral sliding mode control(AFISMC)is proposed for VSWT control.The proposed controller is tested with different types of disturbances and compared with other nonlinear controllers such as sliding mode control and integral sliding mode control.The result shows the better performance of AFISMC and its robustness to input disturbances.In 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.

Backstepping sliding mode control of a variable speed wind turbine for power optimization

To optimize the energy capture from the wind,wind turbine(WT)should operate at variable speed.Based on the wind speed,the operating regions of the WT are divided into two parts:below and above the rated wind speed.The main aim at below rated wind speed is to maximize the energy capture from the wind with reduced oscillation on the drive train.At above rated wind speed,the aim is to maintain the rated power by using pitch control.This paper presents the control of WT at below rated wind speed by using backstepping sliding mode control(BSMC).In BSMC,generator torque is considered as the control input that depends on the optimal rotor speed.Usually,this optimal rotor speed is derived from effective wind speed.In this paper,effective wind speed is estimated from aerodynamic torque and rotor speed by using the modified Newton Rapshon(MNR)algorithm.Initially,a conventional sliding mode controller(SMC)is applied to the WT,but the performance of the controller was found to be less robust with respect to disturbances.Generally,WT external disturbance is not predictable.To overcome the above drawback,BSMC is proposed and both the controllers are tested with mathematical model and finally validated with the fatigue,aerodynamics,structures,and turbulence(FAST)WT simulator in the presence of disturbances.From the results,it is concluded that the proposed BSMC is more robust than conventional SMC in the presence of disturbances.

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

In this paper,a probabilistic load flow analysis technique that combines the cumulant method and Gaussian mixture approximation method is proposed.This 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 considered.Probability distributions of multimodal bus voltages and line power flows pertaining to these inputs are precisely obtained without using any series expansion method.At the same time,multiple input correlations are considered.Performance of the proposed method is demonstrated in IEEE 14 and 57 bus test systems.Results are compared with cumulant and Gram Charlier expansion,cumulant and Cornish Fisher expansion,dependent discrete convolution,and Monte Carlo simulation.Effects of different correlation cases on distribution of bus voltages and line power flows are also studied.

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

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.