Neuromorphic Power Grids Using Spiking Neural Synchronization
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P117Keywords:
Neuromorphic Computing, Spiking Neural Networks, Kuramoto Model, Power Grid Synchronization, Transient Stability, Loihi, TrueNorth, Spinnaker, Izhikevich Neuron, Spike-Timing-Dependent Plasticity, Event-Driven Control, Smart GridAbstract
The combination of neuromorphic computing architectures and power-grid control theory is a paradigm shift in intelligent energy management. In this paper, we review the basic and modern works on spiking neural networks (SNNs) and propose a distributed, event-driven framework for using SNNs as controllers in large-scale electrical power grids. The phase-coupling dynamics of synchronous generators are mapped onto the Kuramoto oscillator model, thus providing a direct mathematical bridge between the problem of synchronization of biological neural networks and the transient-stability conditions imposed by modern grid operators. Neuromorphic hardware platforms, such as Intel Loihi (2018), IBM TrueNorth (2014) and the SpiNNaker system (2014), are assessed as potential substrates for real-time deployment, and Loihi has shown energy consumption of only 23.6 pJ per synaptic operation and throughput efficiency of 3,200 GSOPS/W. The model of Izhikevich neuron is recognized as the tractable encoding mechanism which produces biologically realistic spike patterns with significantly lower computational cost compared to the classical Hodgkin–Huxley formalism. Comparative analysis shows that the latencies of SNN based disturbance classifiers are below 2ms and are around 15 times faster than the conventional digital-relay system, with up to 10000 times lower energy consumption per inference compared to alternatives based on GPUs. To provide context for the performance metrics reported entirely from the literature published until 2020, seven comprehensive tables and seven figures are presented, such as 3D surface plots, spike-raster diagrams and hardware-efficiency scatter plots. The proposed engineered blueprint provides practical solutions for self-organizing, fault-tolerant, energy efficient power grid networks.
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