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As the wind power installations are increasing in number, Wind Turbine Generators (WTG) are required to have Fault Ride-Through (FRT) capabilities. Lately developed grid operating codes demand the WTGs to stay connected during fault conditions, supporting the grid to recover faster back to its normal state. In this paper, the generator side converter incorporates the maximum power point tracking algorithm to extract maximum energy from wind turbine system. A hybrid control scheme for energy storage systems (ESS) and braking choppers for fault ride-through capability and a suppression of the output power fluctuation is proposed for permanent-magnet synchronous generator (PMSG) wind turbine systems. During grid faults, the dc-link voltage is controlled by the ESS instead of the line-side converter (LSC), whereas the LSC is exploited as a STATCOM to inject reactive current into the grid for assisting in the grid voltage recovery. A simple model of the proposed system is developed and simulated in MATLAB environment. The effectiveness of the system is validated through extensive simulation results

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The paper describes an effective method to reconfigure a power distribution system using optimization techniques. Here genetic algorithm is used for the reconfiguration to enhance reliability and to reduce losses. The reliability at the load points is evaluated using probabilistic reliability approach. For finding minimal cut sets and losses different algorithms are used. To maximise the reliability and to reduce the losses, the status of the switch is controlled using genetic algorithm. The effectiveness of the system is tested in 33 bus distribution system.

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In deregulated electricity market, Congestion Management (CM) is one of the most significant issues in order to maintain the system in secure state and to get the reliable system operation. While addressing Congestion Management voltage stability should also be taken into account. This paper elucidates an Improved Differential Evolution (IDE) algorithm to alleviate Congestion in transmission line by rescheduling of generators while considering voltage stability. Differential Evolution (DE) is one of the heuristic, population based algorithm which is well suited for solving complex and non-linear optimization problems. A Double Best Mutation Operator (DBMO) is proposed to improve DE algorithm’s convergence rate. In order to validate suitability of the suggested approach, it has been evaluated on the IEEE-30 bus test system on both base case loading as well as 10% increased load. The test system has been also examined under critical line outages. The results and discussions clearly depicts the effectiveness of the projected approach in solving Congestion Management Problem.

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Power quality is certainly a major concern in the present era. This paper proposes a flexible D-STATCOM with a new controller scheme. And it supplies power to sensitive loads under Islanding conditions. This paper introduces the performance of FD-STATCOM system to mitigate power quality problems under all types of system related disturbances such as L-L &DLG faults. A 12 pulse IGBT based D-STATCOM is designed using MATLAB. Here the super capacitor is used as the storage device. The realibility of the control scheme in the system response to the voltage disturbances caused by LL&DLG faults and Islanded operating conditions are obviously proved in the simulation results.

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Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Metaheuristic optimization techniques especially Improved Particle Swarm Optimization (IPSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of IPSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. This paper illustrates successful implementation of the Improved Particle Swarm Optimization (IPSO) to Economic Load Dispatch Problem (ELD). Power output of each generating unit and optimum fuel cost obtained using IPSO algorithm has been compared with conventional techniques. The results obtained shows that IPSO algorithm converges to optimal fuel cost with reduced computational time when compared to PSO and GA for the three, six and IEEE 30 bus system.

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