This paper proposes a neural-network-based state estimation（NNSE） method that aims to achieve higher time effi ciency, improved robustness against noise, and extended observ ability when compared with the conventional weighted leas squares（WLS） state estimation method. NNSE consists of two parts, the linear state estimation neural network（LSE-net） and the unobservable state estimation neural network（USE-net）The LSE-net functions as an adaptive approximator of linear state estimation（LSE） equations to estimate the nominally ob servable states. The inputs of LSE-net are the vectors of syn chrophasors while the outputs are the estimated states. The USE-net operates as the complementary estimator on the nomi nally unobservable states. The inputs are the estimated observ able states from LSE-net while the outputs are the estimation of nominally unobservable states. USE-net is trained off-line to approximate the veiled relationship between observable states and unobservable states. Two test cases are conducted to vali date the performance of the proposed approach. The first case which is based on the IEEE 118-bus system, shows the compre hensive performance of convergence, accuracy, and robustness of the proposed approach. The second case study adopts real world synchrophasor measurements, and is based on the Jiang su power grid, which is one of the largest provincial power sys tems in China.
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