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In this article, the amalgamation of two well-established meta-heuristic optimization methods is presented to solve the multi-objective distributed generation (DG) allocation problem of distribution systems. To overcome some of the shortcomings of newly developed elephant herding optimization (EHO), an improvement is suggested and then, a prominent feature of particle swarm optimization is introduced to the modified version of EHO. The suggested modifications are validated by solving a single objective DG integration problem where various performance parameters of the proposed hybrid method are compared with their individual standard variants. After validation, the proposed technique is exploited to solve a multi-objective DG allocation problem of distribution systems, aiming to minimize power loss and node voltage deviation while simultaneously maximizing the voltage stability index of three benchmark distribution systems namely, 33-bus, 69-bus and 118-bus. The obtained simulation results are further compared with that of the same available in the existing literature. This comparison reveals that the proposed hybrid approach is promising to solve the multi-objective DG integration problem of distribution systems as compared to many existing methods. Nomenclature NTotal number of buses in the system.x jk Reactance of branch between node j & k (Ω).L max , L min Max and min limits of clan. S M ax DGMaximum DG capacity allowed on a bus (kVA).Velocity of elephant i in iteration k. S DGjSuggested capacity of DG at bus j (kVA).Position of particle i in iteration k.Y jk Element of Y-bus matrix (Ω −1 ).c 1 , c 2 The acceleration coefficients.

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This paper presents a wide area damping control system using robust linear quadratic Gaussian (LQG) controller for damping the inter area oscillations in the power systems. The designed robust controller is capable of dealing with the problem of imperfect communication medium, which introduces communication noise, measurement noise and signal latency. The performance of the robust controller is verified by the non-linear time domain simulation in MATLAB/simulink and Software-In-Loop(SIL) validation is performed on real time digital simulator opal RT-lab.