A Methodology for Spikes and Transients Detection and Removal in Power Signals Using Chebyshev Approximation

Sreelakshmi, K.; Sasidhar, T Tulasi; Mohan, Neethu; Soman, K. P.

doi:10.1109/icacc48162.2019.8986152

Cited by 3 publications

(1 citation statement)

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“…The parameters A + , A − , r + and r − are adjusted according to the specific neuron being modeled, while window W is usually time asymmetric, i.e., A + = A − and r + = r − . It should be noted that the various options of the above parameters can lead to different inherent operating patterns, depending on the type of attacks that this model is called to resolve; depending on the configuration of these parameters, a wide variety of neural characteristics can be modeled [50].…”

Section: The Proposed Artificial Immune Systemmentioning

confidence: 99%

An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection

et al. 2022

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The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks’ efficiency. However, when an additional attack surface is made available, the possibility of an increase in attacks is an unavoidable result. The energy ecosystem’s significant variety draws attackers with various goals, making any critical infrastructure a threat, regardless of scale. Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats. As a result, robust artificial intelligence cyber-defense solutions are more important than ever. Based on the above challenge, this paper proposes an ensemble transfer learning spiking immune system for adaptive smart grid protection. It is an innovative Artificial Immune System (AIS) that uses a swarm of Evolving Izhikevich Neural Networks (EINN) in an Ensemble architecture, which optimally integrates Transfer Learning methodologies. The effectiveness of the proposed innovative system is demonstrated experimentally in multiple complex scenarios that optimally simulate the modern energy environment. The most significant findings of this work are that the transfer learning architecture’s shared learning rate significantly adds to the speed of generalization and convergence approach. In addition, the ensemble combination improves the accuracy of the model because the overall behavior of the numerous models is less noisy than a comparable individual single model. Finally, the Izhikevich Spiking Neural Network used here, due to its dynamic configuration, can reproduce different spikes and triggering behaviors of neurons, which models precisely the problem of digital security of energy infrastructures, as proved experimentally.

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Section: The Proposed Artificial Immune Systemmentioning

confidence: 99%