Data-Aware Retrodiction for Asynchronous Harmonic Measurement in a Cyber-Physical Energy System

Abstract

Cyber-physical energy systems provide a networked solution for safety, reliability and efficiency problems in smart grids. On the demand side, the secure and trustworthy energy supply requires real-time supervising and online power quality assessing. Harmonics measurement is necessary in power quality evaluation. However, under the large-scale distributed metering architecture, harmonic measurement faces the out-of-sequence measurement (OOSM) problem, which is the result of latencies in sensing or the communication process and brings deviations in data fusion. This paper depicts a distributed measurement network for large-scale asynchronous harmonic analysis and exploits a nonlinear autoregressive model with exogenous inputs (NARX) network to reorder the out-of-sequence measuring data. The NARX network gets the characteristics of the electrical harmonics from practical data rather than the kinematic equations. Thus, the data-aware network approximates the behavior of the practical electrical parameter with real-time data and improves the retrodiction accuracy. Theoretical analysis demonstrates that the data-aware method maintains a reasonable consumption of computing resources. Experiments on a practical testbed of a cyber-physical system are implemented, and harmonic measurement and analysis accuracy are adopted to evaluate the measuring mechanism under a distributed metering network. Results demonstrate an improvement of the harmonics analysis precision and validate the asynchronous measuring method in cyber-physical energy systems.

A new intelligent time series prediction technique for coherency identification performance enhancement

“…The proposed method is simple and accurate. The application of the NARX model has been reported in Guzman et al, (2017) and Liu et al, (2016). The method demonstrates promising performance in predicting the future time series of a nonlinear system.…”

Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

“…The problem becomes especially relevant in large-scale networks consisting hundreds to thousands of measuring devices, as the complexity of network communication increases and communication delays of data packages get bigger. 23 In the area of multi-sensor data fusion, the solutions for this problem focus mostly on enhancing filtering algorithms (e.g. Kalman filter or particle filter) that cope with measurements arriving only a single or a few steps later.…”

“…The delays in such networks can be much more unpredictable and longer and approaches considering filtering and state estimation are computationally more complicated and resource demanding. 23 Some early examples that consider out-of-order arrival of data for in-network processing in WSNs are Shi et al 25 and Xiaoliang et al 26 While both papers consider OOSM filtering approach with discrete step delays, the former handles mixed and bounded delays from a single sensor and the latter deals with delays from multiple sensors with delay length of a single sensor data refreshing period. These approaches are still in their early stages and not yet suitable for DIL and ad hoc WSNs where multi-sensor fusion is considered.…”