Indrani Kar

Influence of EV on grid power quality and optimizing the charging schedule to mitigate voltage imbalance and reduce power loss

The penetration of Electric Vehicle (EV) on the Indian grid and its positive impact can be seen if the EVs are co-ordinated. The co-ordinate charging and discharging of EVs can improve the voltage profile and reduce the power transmission loss. Primary distribution of Guwahati City is simulated using actual data. Voltage profile and transmission loss have been analyzed considering various levels of EV penetration and charging patterns. It is shown that coordinated charging and discharging of EVs on the grid will flatten the voltage profile of a bus as well as reduce the power loss.

Variable-Gain Controllers for Nonlinear Systems Using the T–S Fuzzy Model

This correspondence proposes two novel control schemes with variable state-feedback gain to stabilize a Takagi-Sugeno (T-S) fuzzy system. The T-S fuzzy model is expressed as a linear plant with nonlinear disturbance terms in both schemes. In controller I, the T-S fuzzy model is expressed as a linear plant around a nominal plant arbitrarily selected from the set of linear subsystems that the T-S fuzzy model consists of. The variable gain then becomes a function of a gain parameter that is computed to neutralize the effect of disturbance term, which is, in essence, the deviation of the actual system dynamics from the nominal plant as the system traverses a specific trajectory. This controller is shown to stabilize the T-S fuzzy model. In controller II, individual linear subsystems are locally stabilized. Fuzzy blending of individual control actions is shown to make the T-S fuzzy system Lyapunov stable. Although applicability of both control schemes depends on the norm bound of unmatched state disturbance, this constraint is relaxed further in controller II. The efficacy of controllers I and II has been tested on two nonlinear systems

A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets

A theoretical quantum neural network model is proposed using a nonlinear Schrödinger wave equation. The model proposes that there exists a nonlinear Schrödinger wave equation that mediates the collective response of a neural lattice. The model is used to explain eye movements when tracking moving targets. Using a recurrent quantum neural network(RQNN) while simulating the eye tracking model, two very interesting phenomena are observed. First, as eye sensor data is processed in a classical neural network, a wave packet is triggered in the quantum neural network.This wave packet moves like a particle. Second, when the eye tracks a fixed target, this wave packet moves not in a continuous but rather in a discrete mode. This result reminds one of the saccadic movements of the eye consisting of ‘jumps’ and ‘rests’. However, such a saccadic movement is intertwined with smooth pursuit movements when the eye has to track a dynamic trajectory. In a sense, this is the first theoretical model explaining the experimental observation reported concerning eye movements in a static scene situation. The resulting prediction is found to be very precise and efficient in comparison to classical objective modeling schemes such as the Kalman filter.

Real-Time Coordination of Electric Vehicles to Support the Grid at the Distribution Substation Level

In this paper, the charging stations (CSs) of electric vehicles (EVs) and their coordination at the substation level are presented. It is considered that the EVs of a particular area arrive at the CS in their idle time to charge their batteries. Fuzzy logic controllers (FLCs) have been designed at the substation and the CS level. The FLC at the substation level decides the amount of power to be compensated by the entire CSs, and the FLC at the CS level determines the power to be exchanged by individual CS. The aggregator at the substation level will distribute the power among the CSs connected to different subfeeders. Also, every subfeeder has an aggregator which distributes the power among different CSs connected to the same subfeeder. Batteries of EVs have been modeled which can handle the capacity loss at different charging/discharging rates (Crate). The Crate of the battery is controlled to achieve the desired rate of power flow between the grid and the EV battery. The main focus of this paper is to coordinate the EVs, present at the CSs, and support the grid by peak shaving and valley filling.

Analysis of vehicle to Grid concept in Indian scenario

There seems to be a huge potential of electric vehicles in India by 2020, thus Vehicle to Grid concepts can be easily implemented in Indian scenario. In this paper, various possibilities and their impacts on power scenario for implementing Vehicle to Grid (V2G) have been explored in Indian context by considering the actual data. It has been established that electric vehicles can meet the peak demand by taking the power at off peak time and supplying the power back to the grid in peak hours.

T-S fuzzy model based controller and observer design for a Twin Rotor MIMO System

This paper deals with the design of fuzzy controller and observer for a Twin Rotor MIMO System (TRMS). Twin Rotor MIMO System is the prototype of a helicopter with two degrees of freedom which involves significant cross coupling between the main rotor and the tail rotor. A Takagi-Sugeno (T-S) fuzzy model is used to approximate the nonlinear dynamics of the system. With the derived T-S fuzzy model, a fuzzy controller is designed that guarantees not only stability but also satisfies the specified performance criteria of the closed-loop control system. A set of inequalities is formed based on the Lyapunov sufficient condition to ensure stability of the T-S fuzzy model using a parallel distributor fuzzy compensator. The controller gains are obtained by solving the set of inequalities. A fuzzy observer is also designed to estimate the states of the system.

Quantum stochastic filtering

This paper presents a new paradigm for stochastic filtering by modeling the unified response of a neural lattice using the Schroedinger wave equation. The model is based on a novel concept that a quantum object mediates the collective response of a neural lattice. The model is referred as recurrent quantum neural network (RQNN). The RQNN model has been simulated in two different ways. In one case the potential field of the Schroedinger wave equation is linearly modulated and in the other case the potential field of the Schroedinger wave equation is nonlinearly modulated. It is shown that the proposed quantum stochastic filter can efficiently denoise signals such as DC, sinusoid, amplitude modulated sinusoid and speech signals embedded in very high Gaussian and non-Gaussian noises. Performance of linearly modulated RQNN compares well with traditional techniques such as Kalman filter and wavelet filter. However, preliminary results show that nonlinearly modulated RQNN performs much better when compared with traditional techniques. For example, nonlinearly modulated RQNN model denoises a DC signal 1000 times more accurately in comparison to a traditional Kalman filter. The most important fact is that the proposed quantum stochastic filter does not make any assumption about the shape and nature of the signal and noise when denoising a signal. In a sense, the proposed quantum stochastic filter is a step forward towards intelligent filtering.

Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network

A fuzzy self-organizing map (SOM) network is proposed in this paper for visual motor control of a 7 degrees of freedom (DOF) robot manipulator. The inverse kinematic map from the image plane to joint angle space of a redundant manipulator is highly nonlinear and ill-posed in the sense that a typical end-effector position is associated with several joint angle vectors. In the proposed approach, the robot workspace in image plane is discretized into a number of fuzzy regions whose center locations and fuzzy membership values are determined using a Fuzzy C-Mean (FCM) clustering algorithm. SOM network then learns the inverse kinematics by on-line by associating a local linear map for each cluster. A novel learning algorithm has been proposed to make the robot manipulator to reach a target position. Any arbitrary level of accuracy can be achieved with a number of fine movements of the manipulator tip. These fine movements depend on the error between the target position and the current manipulator position. In particular, the fuzzy model is found to be better as compared to Kohonen self-organizing map (KSOM) based learning scheme proposed for visual motor control. Like existing KSOM learning schemes, the proposed scheme leads to a unique inverse kinematic solution even for a redundant manipulator. The proposed algorithms have been successfully implemented in real-time on a 7 DOF PowerCube robot manipulator, and results are found to concur with the theoretical findings.

Recurrent Quantum Neural Network and its Applications

Although the biological body consists of many individual parts or agents, our experience is holistic. We suggest that collective response behavior is a key feature in intelligence. A nonlinear Schrodinger wave equation is used to model collective response behavior. It is shown that such a paradigm can naturally make a model more intelligent. This aspect has been demonstrated through an application — intelligent filtering — where complex signals are denoised without any a priori knowledge about either signal or noise. Such a paradigm has also helped us to model eye-tracking behavior. Experimental observations such as saccadic and smooth-pursuit eye-movement behavior have been successfully predicted by this model.

Designing a multi charging station for Electric Vehicles and its utilization for the grid support

A new method of charging the Electric Vehicles (EVs) at the charging station (CS) has been proposed. A CS modeled in this work is a place where EVs of a particular area will come together to charge their batteries in accordance to grid norms. A sufficiently large number of aggregated parked EVs in a CS could provide several important services to the grid such as voltage regulation, peak power shaving, spinning reserve and other ancillary services. In this paper, a multi charging station (MCS) located at three different places under a single power distribution node has been modeled. Furthermore, Vehicle owners preferred minimum state of charge (SOC) and maximum allowable charging/discharging rate (Crate) has been taken into account while delivering power to the grid. Based on the qualitative description of the MCS, a fuzzy logic controller (FLC) has been developed to control the flow of power between the grid and the EVs battery. Finally, the MCS developed is verified on a typical distribution network of a city and mainly the voltage stability and peak shaving is presented.