Topology identification method of distribution network based on branch active power

Because of low measurement redundancy and frequent switch changes, it is difficult to identify the correct topology structure. In this paper, a topology recognition method of distribution network based on branch active power is proposed. Firstly, branch active power residual algorithm is used to identify the topological structure. The topology obtained by this method has the highest matching degree with the real-time measured data. Then genetic algorithm is used to optimize the inverse recognition of power grid topology. The numerical example shows that the method is reasonable, effective, rapid and simple. It also has good adaptability with a large number of measurement errors.

Deep Residual Reinforcement Learning (Extended Abstract)

We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in commonly used benchmarks. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD(k) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.

On the global convergence of a new spectral residual algorithm for nonlinear systems of equations

We present a derivative-free method for solving systems of nonlinear equations that belongs to the class of spectral residual methods. We will show that by endowing a previous version of the algorithm with a suitable new linesearch strategy, standard global convergence results can be attained under mild general assumptions. The robustness of the new method is therefore potentially improved with respect to the previous version as shown by the reported numerical experiments.

Biconjugate residual algorithm for solving general Sylvester-transpose matrix equations

The present paper is concerned with the solution of the coupled generalized Sylvester-transpose matrix equations {A1XB1 + C1XD1 + E1XTF1 = M1, A2XB2 + C2XD2 + E2XTF2 = M2, including the well-known Lyapunov and Sylvester matrix equations. Based on a variant of biconjugate residual (BCR) algorithm, we construct and analyze an efficient algorithm to find the (least Frobenius norm) solution of the general Sylvester-transpose matrix equations within a finite number of iterations in the absence of round-off errors. Two numerical examples are given to examine the performance of the constructed algorithm.