Artificial Intelligence of Things-Based Optimal Finite-Time Terminal Attractor and Its Application to Maximum Power Point Tracking of Photovoltaic Arrays in Smart Cities

Abstract

The combination of artificial intelligence of things (AIoT) and photovoltaic power generation can save energy and reduce carbon emissions and further promote the development of smart cities. In order to obtain the maximum power output from photovoltaic (PV) arrays, we can use optimal maximum power point tracking (MPPT) technique with AIoT sensing to improve system efficiency. The optimal MPPT technique is the finite-time terminal attractor (FTTA) based on the gradient particle swarm optimization (GPSO), which can be applied to track the maximum power of a PV array system. The FTTA not only provides fast finite-time convergence but also attenuates steady-state errors, making it ideal for nonlinear system applications. The GPSO is used to search the control parameters of the FTTA, which is able to find the global best solution. This avoids unmodeled dynamic behavior of the system excited by the quiver, which slows down the control convergence and prematurely traps the system into a local optimum. The MATLAB computer software is used to simulate the proposed PV maximum power point tracking system. The results show that more accurate and better tracking control of the PV array can be produced under partial shading conditions and then improve the steady-state and transient performance.

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

“…Another application found was the Maximum PowerPoint Tracking of photovoltaic panels, which is the point where solar PV panels produce the maximum energy possible, and the tracking of this point increases their energy efficiency; in [50], a Finite-Time Terminal Attractor (FFTA) is combined with Gradient Particle Swarm Optimization (GPSO) to track MPPT of a solar PV system. Another application in solar energy can be found in [10], in which an Artificial Neural Network (ANN) is applied to create a decision-making tool based on the generation and consumption of solar PV systems that can aid decision makers in creating strategies towards energy generation; these strategies include reducing costs and/or maximizing solar energy generation.…”

Optimization of energy consumption in smart homes using firefly algorithm and deep neural networks

“…In order to reduce the energy consumption of smart homes, the need and supply of electrical power must be synchronized. Since the development of smart cities, conventional grids have been replaced with smart grids, which generate electricity and monitor energy consumption [5], [6]. Prediction methods are typically used to anticipate energy in smart homes and smart grids, as well as day ahead (DA) energy pricing [7].…”