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Neural-Network Security-Boundary Constrained Optimal Power Flow

Neural-Network Security-Boundary Constrained Optimal Power Flow,10.1109/TPWRS.2010.2050344,IEEE Transactions on Power Systems,Victor J. Gutierrez-Mart

Neural-Network Security-Boundary Constrained Optimal Power Flow   (Citations: 2)
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This paper proposes a new approach to model sta- bility and security constraints in optimal power flow (OPF) prob- lems based on a neural network (NN) representation of the system securityboundary(SB).Thenoveltyofthisproposalisthataclosed form, differentiable function derived from the system's SB is used to represent security constraints in an OPF model. The procedure involves two main steps: First, an NN representation of the SB is obtained based on back-propagation neural network (BPNN) training. Second,adifferentiable mappingfunctionextracted from the BPNN is used to directly incorporate this function as a con- straintintheOPFmodel.Thisapproachensures thattheoperating pointsresultingfromtheOPFsolutionprocessarewithinafeasible and secure region, whose limits are better represented using the proposed technique compared to typical security-constrained OPF models. The effectiveness and feasibility of the proposed approach is demonstrated through the implementation, as well as testing and comparison using the IEEE two-area and 118-bus benchmark sys- tems, of an optimal dispatch technique that guarantees system se- curity in the context of competitive electricity markets.
Journal: IEEE Transactions on Power Systems - IEEE TRANS POWER SYST , vol. 26, no. 1, pp. 63-72, 2011
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    • ...Given the unpredictability of VSIs at voltage collapse when reactive power generation limits are taken into account in system modeling, a differentiable function that represents the security boundary of a power system was proposed in [9]...
    • ...The effectiveness and feasibility of the technique presented in [9] has led to the idea that VSIs at voltage collapse may be successfully represented by means of NNs as well...

    Guilherme G. Lageet al. Neural-network representation of Voltage Stability Indices at the volt...

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