Recurrent neural networks models for analyzing single and multiple transient faults in combinational circuits

Farjaminezhad, Rasoul; Safari, Saeed; Moghadam, Amir Masood Eftekhari

doi:10.1016/j.mejo.2021.104993

Cited by 5 publications

(1 citation statement)

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“…It is well known that the traditional time-series analytical methods (such as auto-correlation) need to identify the seasonality and stability from the time-series data. The effectiveness of identification may vary according to the network structure and the calculation speed, and it needs to be adjusted for each simulation (Chen et al, 2018;Farjaminezhad et al, 2021). The characteristic of the RNN is to create a closed-loop calculation in the hidden layer, which forms a circulating adaptive model to capture the internal hidden historical state features in the way of iterative update, and thus to complete the process of error level accumulation in the training stage.…”

Section: Introductionmentioning

confidence: 99%

Electricity Theft Detection in Power Consumption Data Based on Adaptive Tuning Recurrent Neural Network

et al. 2021

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Electricity theft behavior has serious influence on the normal operation of power grid and the economic benefits of power enterprises. Intelligent anti-power-theft algorithm is required for monitoring the power consumption data to recognize electricity power theft. In this paper, an adaptive time-series recurrent neural network (TSRNN) architecture was built up to detect the abnormal users (i.e., the electricity theft users) in time-series data of the power consumption. In fusion with the synthetic minority oversampling technique (SMOTE) algorithm, a batch of virtual abnormal observations were generated as the implementation for training the TSRNN model. The power consumption record was characterized with the sharp data (ARP), the peak data (PEA), and the shoulder data (SHO). In the TSRNN architectural framework, a basic network unit was formed with three input nodes linked to one hidden neuron for extracting data features from the three characteristic variables. For time-series analysis, the TSRNN structure was re-formed by circulating the basic unit. Each hidden node was designed receiving data from both the current input neurons and the time-former neuron, thus to form a combination of network linking weights for adaptive tuning. The optimization of the TSRNN model is to automatically search for the most suitable values of these linking weights driven by the collected and simulated data. The TSRNN model was trained and optimized with a high discriminant accuracy of 95.1%, and evaluated to have 89.3% accuracy. Finally, the optimized TSRNN model was used to predict the 47 real abnormal samples, resulting in having only three samples false predicted. These experimental results indicated that the proposed adaptive TSRNN architecture combined with SMOTE is feasible to identify the abnormal electricity theft behavior. It is prospective to be applied to online monitoring of distributed analysis of large-scale electricity power consumption data.

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Section: Introductionmentioning

confidence: 99%