Sandeep Kaur

An integrated approach for power loss reduction in primary distribution system

Power loss reduction is issue of main concern to the network operators. Various techniques such as capacitor placement, network reconfiguration and DG placement are considered to minimize the distribution system losses. DG and capacitor placement in the presence of network reconfiguration may rejoice optimal benefit in terms of loss reduction, voltage improvement, network up-gradation cost deferral, etc. Therefore, in this paper, various combinations of methods for power loss reduction are considered. It is concluded that simultaneous placement and sizing of DG and capacitor in the presence of network reconfiguration is most optimal combination. The mixed integer non-linear optimization problem of simultaneous placement of DG and capacitor units in the presence of network reconfiguration is solved. Due to its non-convex nature, this Mixed Integer Non-Linear Programming (MINLP) problem is solved using Harmony Search algorithm. The proposed formulation is tested on IEEE 33-bus distribution system. Loss reduction with capacitor, DG and network reconfiguration is compared with the independent placement of DG, capacitor, and network reconfiguration options. Power loss reduction and flat voltage profile with lesser computational time proves the importance of integrated model.

Optimal placement and sizing of DG comparison of different techniques of DG placement

The increasing power demand and the deficiency in generating capacities have set the way towards Distributed Generation. Distributed Generation (DG) is assuming widespread popularity owing to its potential to solve numerous issues like the power system de-regulation; meeting the rising power demand; improving voltage profile; reduction in power losses etc. The Optimum Location of DG and optimum DG Sizing are the two critical issues in the integration of DG with the electric grid because the improper placement and improper sizing of DG in power system can not only leads to the enhanced total power losses but can also damage the normal operation of power system. Optimal placement of DG units is a nonlinear optimization problem. This paper proposes a methodology to calculate the optimal location and effective optimal size. The influence of variation in location of DG with respect to the total power losses and voltage in the system is also carried out. The proposed methodology is tested on 33 bus radial distribution network. The obtained results are exhibited in graphical manner. The results achieved from the proposed methodology are paralleled with that of the exhaustive load flows.

Robust finite time integral sliding mode tracker for nth-order non-affine non-linear system with uncertainty and disturbance estimator

Abstract This paper presents a novel control technique for a class of uncertain and time varying n th-order non-affine non-linear systems with an integral terminal sliding mode augmented with uncertainty and disturbance estimator (UDE). These non-linear systems are difficult to control due to non-affine nature of their inputs, failure of feedback linearization approach and control singularity problems. Therefore, an integral terminal sliding surface is chosen to ensure the faster and finite time convergence of the system dynamics to the desired dynamics. UDE provides a chatter-free auxiliary controller for eliminating the impacts of complex system non-affine uncertainties. Moreover, the system uncertainties and external disturbances are tackled without requiring information about their upper bounds. Furthermore, the superior tracking performances and stability are guaranteed through Lyapunov’s direct method. Also, three simulation examples are presented to illustrate the efficacy of the proposed methodology by comparing with the previous methods in literature.

Fractional order uncertainty estimator based hierarchical sliding mode design for a class of fractional order non-holonomic chained system

This paper proposes a novel fractional order sliding mode control approach to address the issues of stabilization as well as tracking of an N-dimensional extended chained form of fractional order non-holonomic system. Firstly, the hierarchical fractional order terminal sliding manifolds are selected to procure the desired objectives in finite time. Then, a sliding mode control law is formulated which provides robustness against various system uncertainties or external disturbances. In addition, a novel fractional order uncertainty estimator is deduced mathematically to estimate and mitigate the effects of uncertainties, which also excludes the requirement of their upper bounds. Due to the omission of discontinuous control action, the proposed algorithm ensures a chatter-free control input. Moreover, the finite time stability of the closed loop system has been proved analytically through well known Mittag-Leffler and Fractional Lyapunov theorems. Finally, the proposed methodology is validated with MATLAB simulations on two examples including an application of fractional order non-holonomic wheeled mobile robot and its performances are also compared with the existing control approach.

A hybrid approach for promoting low carbon technologies in distributed generation planning

The greatest challenge for power utilities is to meet exponentially increasing energy demand subjected to the constraints of sustainable development with clean energy apart from economic viability. Renewable DGs, in spite of high investment cost and intermittent generation, are compulsive choice for environment friendly planning and sustainable growth. Clean energy DG technologies can provide solution to ever increasing power demand in sustainable and cost effective manner by adopting appropriate incentive mechanism. Clean energy technologies can also be encouraged by penalizing the conventional resources for harmful emissions. The proposed method minimizes the annual cost by maximizing the emission reduction and carbon credit revenue. The proposed formulation yields solution in terms of type, optimal size and location while fulfilling the criterion in terms of economic, technical or techno-economic. The objective function comprises of energy purchase, losses, capital, operational and GHG emission costs. Importance of each objective is mapped with optimal weight allocation, thereby maintaining the consistency among all objectives. A hybrid optimization technique based on Harmony Search integrated with Teaching-Learning is used to enhance the search process. The merit of the proposed algorithm is dynamic tuning of control parameters which enhances the convergence property of the solution algorithm. Results indicate that renewable DG technologies can become financially viable with appropriate price mechanism depending on planners objective.

Demand side management in a smart grid environment

The demand side management (DSM) comprises techniques and policies which aim at equalizing energy consumption levels over the day. As opposed to the supply side management involving the addition of new generation units and total installed capacity, the idea here is not only to increase the energy to be supplied, but also to control the shape of consumption by applying energy management techniques. The main challenge in the implementation of a DSM program is the quest for knowledge of the daily behavior of loads in the electrical system, which is generally not available from the systems based on conventional electromechanical meters. In such a scenario, the emergence of novel technologies like Smart Grid technology, creates an environment for convergence between the infrastructures of generation, transmission, distribution, information technology and digital communication infrastructure which enables the exchange of information and control actions among the various segments of the power grid. The paper throws light on the research trends within the area of demand side management in a smart grid environment and proposes a scheduling scheme using genetic algorithm for load management. Simulation results confirm that the proposed algorithm efficiently reduces the PAR and electricity consumption cost.