Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Abstract

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

Reduced Voltage-Dependency by Categorical Location Information and Distance Along Street Metric for Meter-Transformer Mapping in Distribution Systems

“…There are many methods that can be used as a preprocessing step before our algorithm to detect smart meter malfunction, [44], [45]. In case of a smart meter malfunction, our method can work by excluding the malfunctioning meters, and recovering the topology for the working meters.…”

Design and Implementation of a Smart Energy Meter Using a LoRa Network in Real Time

“…Bañales et al [43] proposed a clustering method based on two-stage K-medoids clustering with normalised load profiles organised in time series. Zhao et al [44] addressed the problem of improving the efficiency of malfunction detection in SMs using deep learning techniques.…”