Related papers: Bio-inspired Microgrid Management based on Brain's…
Our brain consists of biological neurons encoding information through accurate spike timing, yet both the architecture and learning rules of our brain remain largely unknown. Comparing to the recent development of backpropagation-based…
Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spiking neural networks (SNN) have drawn massive attention for…
Microgrids (MGs) rely on networked control supported by off-the-shelf wireless communications. This makes them vulnerable to cyber-attacks, such as denial-of-service (DoS). In this paper, we mitigate those attacks by applying the concepts…
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop real-time processing of biosignals. As in biology, to minimize power…
This paper proposes a novel model-free secondary voltage control (SVC) for microgrids using nonlinear multiple models adaptive control. The proposed method is comprised of two components. Firstly, a linear robust adaptive controller is…
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to digital systems. Though many devices have emerged as candidates for…
Synaptic plasticity is metabolically expensive, yet animals continuously update their internal models without exhausting energy reserves. However, when artificial neural networks are trained, the network parameters are typically updated on…
As robotic arm applications expand beyond traditional industrial settings into service-oriented domains such as catering, household and retail, existing control algorithms struggle to achieve the level of agile manipulation required in…
An increasing number of smart devices controlling loads opens a potential pathway for false data attacks which could alter the loads. The presence of energy storage with its ability to quickly respond to discrepancies in loads offers a…
Microgrids have emerged as a pivotal solution in the quest for a sustainable and energy-efficient future. While microgrids offer numerous advantages, they are also prone to issues related to reliably forecasting renewable energy demand and…
Network reconfiguration (NR) has recently received significant attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation…
In this paper, a neural network predictive controller is proposed to regulate the active and the reactive power delivered to the grid generated by a three-phase virtual inertia-based inverter. The concept of the conventional virtual…
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could…
Recent years have seen increased interest in the design and deployment of smart grid devices and control algorithms. Each of these smart communicating devices represents a potential access point for an intruder spurring research into…
Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of…
Perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however they are not as efficient as their biological counterparts. Visual attention is a set…
Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low…
A neural network-based energy management system (NN-EMS) has been proposed in this paper for islanded ac microgrids fed by multiple PV-battery based distributed generators (DG). The stochastic and unequal irradiation results in unequal PV…
The pursuit of sustainability motivates microgrids that depend on distributed resources to produce more renewable energies. An efficient operation and planning relies on a holistic framework that takes into account the interdependent…
Neuromorphic systems seek to replicate the functionalities of biological neural networks to attain significant improvements in performance and efficiency of AI computing platforms. However, these systems have generally remained limited to…