Observer-Based Control for a New Stochastic Maximum Power Point Tracking for Photovoltaic Systems With Networked Control System

This study discusses the new stochastic
maximum power point tracking
control approach toward the
photovoltaic cells
(PCs). A PC generator is isolated from the grid, resulting in a
direct current
microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed
networked control systems
beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the
Takagi–Sugeno
(T--S) fuzzy model is another option. The T--S fuzzy model is presented in a unified framework, for which 1) the fuzzy observer based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer-based controller can be created using the same premises being the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulation in the PC generator model for the real-time climate data obtained in China demonstrates the importance of our method. In addition, by using a new
Lyapunov–Krasovskii functional
for combining with the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the tensor-product (T-P)
transformation
, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators.