Distributed Fast Fault Diagnosis for Multimachine Power Systems via Deterministic Learning

In this paper, a distributed fast fault diagnosis approach is proposed for multimachine power systems based on deterministic learning (DL) theory. First, a learning estimator is constructed to accumulate the knowledge of transient fault dynamics in power systems. Through DL, a partial persistent excitation condition of the local neurons is satisfied and convergence of the neural weights is achieved. Thus, a knowledge bank can be established and gradually updated. To deal with the high dimensional data, only the neurons centered in a neighborhood of the system trajectory are activated. In this manner, the computation load is mitigated. Second, by using the learnt knowledge, a distributed fault diagnosis scheme is designed to monitor the power system. The memory of the transient fault dynamics can be quickly recalled and a fast fault diagnosis decision is made. Finally, based on the concepts of mismatch interval and duty ratio, the diagnosis capabilities of the proposed scheme are investigated. The effectiveness of the proposed diagnosis scheme is demonstrated by computer simulation and the hardware-in-loop experimental test based on the RT-LAB realtime simulator.

I. Introduction

Fast FAULT diagnosis is of critical importance for the operation of power systems [1]. An untimely response to a fault may trigger a cascading failure, and even a large-scale blackout. Over the past decades, the development of fault diagnosis schemes for power systems has received significant attention [2]–[14]. A wide range of techniques have been used to address the diagnosis problem of faults in transmission lines, which can be classified into three categories [8]: Data-driven [2]–[10], knowledge-based [11], [12], and optimization techniques [13], [14]. Most of these methods are based on sensor information from protective relays and circuit breakers [5]. On the other hand, with developments of information and communication technology, a huge amount of data are gathered from various electronic devices [10], [16]. How to process the huge data in a timely and distributed manner has become an active topic in the literature.