A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement

Abstract

The widespread popularity of renewable and sustainable sources of energy such as solar and wind calls for the integration of renewable energy sources into electrical power grids for sustainable development. Microgrids minimize power quality issues in the main grid by linking with an active filter and furnishing reactive power compensation, harmonic mitigation, and load balancing at the point of common coupling. The reliability issues faced by standalone DC microgrids can be managed by interlinking microgrids with a power grid. An artificial intelligence-based Icosϕ control algorithm for power sharing and power quality improvement in smart microgrid systems is proposed here to render grid-integrated power systems more intelligent. The proposed controller considers various uncertainties caused by load variations, state of charge of the battery of microgrids, and power tariff based on the availability of power in microgrids. This paper presents a detailed analysis of the integration of wind and solar microgrids with the grid for dynamic power flow management in order to improve the power quality and to reduce the burden, thereby strengthening the central grid. A smart grid system with multiple smart microgrids coupled with a renewable energy source with tariff control and judicious power flow management was simulated for power-sharing and power quality improvement. A hardware prototype of the artificial intelligence-based Icosϕ control algorithm with nonlinear load was also implemented successfully. Furthermore, the economic viability was investigated to ensure the feasibility of the smart microgrid system with the proposed controller design for power flow management and power quality improvement.

A review on the state of the art of dynamic voltage restorer: topologies, operational modes, compensation methods, and control algorithms

“…The economic value of power quality is significant, as it directly impacts utilities, consumers, and load device vendors, all of whom may experience financial consequences as a result. For many users, power quality may have an immediate economic effect [18].…”

“…Under typical grid circumstances, the phase angle of the reference load-voltage has a significant impact on the battery mode. When in standby mode, inappropriate selection of this angle could result in continuous battery drain [15,18,103]. DVR Standby mode illustrated in figure 14.…”

“…In the event of a voltage sag, the Dynamic Voltage Restorer (DVR) will switch to injection mode and utilize a booster transformer to inject a compensated voltage. This injection of the compensated voltage is triggered in response to any disturbance or disruption in the supply voltage [18].…”

Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)

“…The threshold emerged through the application of the isolation forest anomaly scoring method, intrinsically embedded within PyCaret, a methodological approach pioneered by Nair et al [25]. Concurrently, the Mean Squared Error (MSE) (as per Equation 1) of the reconstruction on real-time data served as a metric to determine the deviation from the threshold, ensuring consistent accuracy and data fidelity.…”

An Adaptive Neuro-Fuzzy Control Strategy for Improved Power Quality in Multi-Microgrid Clusters

“…The suggested controller is based on a hybrid neural network with differential evolution optimization. Later in [15], to make grid-integrated power systems more intelligent, a suggested φ cos I control algorithm for power sharing and power quality enhancement in smart microgrid systems is based on artificial intelligence. The presented controller takes into account several variables brought on by changes in load, the microgrid battery's state of charge, and power tariffs based on the availability of electricity in microgrids.…”