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Passivity based control of DC motor in sensorless configuration is proposed in this paper. Exact tracking error dynamics passive output feedback control is used for stabilizing the speed of Buck converter fed DC motor under various load torques such as constant type, fan type, propeller type, and unknown load torques. Under load conditions, sensorless online algebraic approach is proposed, and it is compared with sensorless reduced order observer approach. The former produces better response in estimating the load torque. Sensitivity analysis is also performed to select the appropriate control variables. Simulation and experimental results fully confirm the superiority of the proposed approach suggested in this paper.

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This article proposes a new strategy for Congestion Management (CM) through Generation Rescheduling (GR) with Distributed Slack Bus (DSB) model using Improved Teaching Learning Based Optimization (ITLBO) algorithm. Most of the previous research mainly focuses on rescheduling the existing generators for CM and least concerned about the consequence of this rescheduling on the slack bus. As a result, the slack bus is supposed to carry the entire residual effect of all other generators which leads to increased Congestion Management Cost (CMC). This work proposes a distributed slack bus model for reducing the excess burden on the slack bus and a twofold contribution is made to develop the proposed technique. First, is the selection of the participating generator for CM by means of Incremental Generator Sensitivity Factor (IGSF), which adopts the generator sensitivity as well as the bidding cost. Next, CM is formulated as an optimization problem with the objective function involving congestion management cost and solved using ITLBO algorithm, which incorporates the self-motivated learning concept with the basic TLBO operators. For evaluation, the standard IEEE 30-bus and IEEE 118-bus systems are used and the obtained results are compared with the other CM systems reported through literature.

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