Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network

Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality.In this paper,an attention-based independently recurrent neural network(IndRNN)for sag source location and sag type recognition in sparsely monitored power system is proposed.Specially,the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system,and the desired outputs simultaneously contain the following information:the located lines where sag occurs;the corresponding sag types,including motor starting,transformer energizing and short circuit;and the fault phase for short circuit.In essence,the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible.A favorable feature of the proposed method is that it can be realized without system parameters or models.The proposed method is validated by IEEE 30-bus system and a real 134-bus system.Experimental results demonstrate that the accuracy of sag source location is higher than 99%for all lines,and the accuracy of sag type recognition is also higher than 99%for various sag sources including motor starting,transformer energizing and 7 different types of short circuits.Furthermore,a comparison among different monitor placements for the proposed method is conducted,which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.

Independently recurrent neural network for remaining useful life estimation

In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining useful life(RUL) of the equipment can be accurately predicted, the equipment can be maintained in time to avoid the downtime caused by equipment failure and greatly improve the production efficiency of enterprises. This paper aims to use independently recurrent neural network(IndRNN) to learn health degradation of turbofan engine and make accurate predictions of its RUL, which not only effectively solves the problem of gradient explosion and vanishing, but also increases the interpretability of neural networks. IndRNN can be used to process longer time series which matches the scene with high frequency sampling sensor in industrial practical applications. The results demonstrate that IndRNN for RUL estimation significantly outperforms traditional approaches, as well as convolutional neural network(CNN) and long short-term memory network(LSTM) for RUL estimation.

Towards Fast and Efficient Algorithm for Learning Bayesian Network

Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence（CI） tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC（FEPC）, for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm.