Automatic adaptation of a Solar Plant Intelligent Control System

Abstract

The article continues the authors’ scientific research in the field of solar plant intelligent control system. This paper presents a developed on-line and off-line automatic adaptation methods which allow solar plant intelligent control system to adapt to rapid dynamic of insolation in real time and to seasonal insolation fluctuations during night time. The experimental results show that the proposed solar plant intelligent self-adaptive control system produced robust and rapid maximum power point tracking, as compared to a classical solar plant MPPT system based on Perturb & Observe or particle swarm optimization under seasonal fluctuations or rapid dynamic of insolation.

A Review on Machine Learning Applications for Solar Plants

“…Thus, ML methods have been proposed to approximate this complex dynamic. The recent studies [ 1 , 2 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] prove that ML technologies for a solar plant’s design, forecasting, maintenance, and control increase the effectiveness and reliability of the solar plant as compared to conventional methods. In smart sensor systems of solar plants, ML methods are crucial units to increase the quality of datasets processing the solar plant’s design, forecasting, maintenance, and control.…”

“…With the goal to develop intelligent models for sizing, forecasting, and control of a solar plant system and to make an RNN more adaptive with regard to a task’s complexity and overfitting problem, we developed an MFNN [ 5 , 6 , 7 , 8 ]. The MFNN includes RNNs with fuzzy units and/or a convolutional block to process images.…”

“…An RNN approximates a membership function in contrast to an Adaptive Network-Based Fuzzy Inference System (ANFIS). We combined the modified multidimensional quantum-behaved particle swarm optimization (PSO) with the Levenberg–Marquardt algorithm (MD QPSO) and developed a hierarchical encoder of the particle’s dimension component [ 5 , 6 , 7 , 8 ] to automatically create an optimal architecture of an MFNN and improve the convergence.…”

“…In Table 11 , we summarize the ML models for MPPT of a solar plant from studies [ 8 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 ].…”

“…Second, we reviewed resources where researchers can find open datasets, source code, and ML framework and simulation environments to create ML technologies for a solar plant system. Third, in contrast with other review articles, our review proposes a simple but effective pipeline scheme for an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on integrated, cutting-edge technologies; estimates the impact of the ML technologies based on the proposed scheme on a solar plant value chain; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system based on the comparative studies’ results summaries; and briefly summarizes our self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant based on a modified fuzzy neural net (MFNN) that is automatically created with regard to tasks’ complexities and overfitting problems [ 5 , 6 , 7 , 8 ]. Our research provides a mapping of the recently reported ML methods and quantification of their advantage and shortcomings as compared to classical methods, which are usually employed in the sensor system, hardware, and software of solar plants; an effective integration scheme of ML technologies into the sensor systems and software of solar plants; a future prospect of the integrated cutting-edge technologies, including ML for digital transformation of solar energy into smart solar energy.…”