Yiming Han

If a failure in the components of a photovoltaic (PV) system, such as PV module, controller, inverter, load, cable, etc. goes undetected and uncorrected, it can seriously affect the efficiency, safety, and reliability of the entire PV power plant. In addition, fires can occur if specific faults, such as arc, ground, and line-to-line faults remain unresolved. Therefore, PV system (PVS) fault diagnoses (FD) are crucial for PV power plant reliability, efficiency, and safety. Many fault diagnosis methods and techniques for PVS components have been developed. In addition, with the development of PV devices, more advanced and intelligent diagnostic technologies are continuously being researched and developed. However, a systematic and thorough analysis, summary, and conclusion are still urgently required. Thus, this paper introduces the types, causes, and impacts of PVS faults, and reviews and discusses the methods proposed in the literature for PVS fault diagnosis, and in particular, failures in PV arrays (PVA). Special attention is paid to the optimization direction of various fault diagnosis methods under different priorities, and their limitations, feasibility, complexity, and cost-effectiveness. Finally, challenges and suggestions are put forward for future research.

This paper designs a novel adaptive fractional-order PID (AFOPID) control of a permanent magnetic synchronous generator (PMSG)-based wind energy conversion system, which attempts to extract the maximum wind power by using a linear perturbation observer. The combinatorial effect of generator nonlinearities and parameter uncertainties, unmodelled dynamics, and stochastic wind speed variation is aggregated into a perturbation, which is then estimated in real time by a linear extended-state observer called high-gain state and perturbation observer. Besides, the perturbation estimate is used as an auxiliary control signal which is fully compensated by a fractional-order PID (FOPID) controller to achieve a globally robust control consistency, simple structure and high reliability, as well as an improved tracking performance compared to that of PID control. In addition, AFOPID does not require an accurate PMSG model while only the measurement of d-axis current and mechanical rotation speed is required, in which parameter is optimally tuned by particle swarm optimization. Four case studies are carried out, including step change of wind speed, low-turbulence stochastic wind speed, high-turbulence stochastic wind speed, and generator parameter uncertainties, respectively. Simulation results verify the effectiveness and superiority of AFOPID compared to that of PID, FOPID, and nonlinear control.

With the improvement in the integration of solar power generation, photovoltaic (PV) power forecasting plays a significant role in ensuring the operation security and stability of power grids. At present, the widely used backpropagation (BP) and improved BP neural network algorithm in short-term output prediction of PV power stations own the drawbacks of neglection of meteorological factors and weather conditions in inputs. Meanwhile, the existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. Therefore, based on the PV power plant in Lijiang, considering the related factors that influence PV output such as solar irradiance, environmental temperature, atmospheric pressure, wind velocity, wind direction, and historical generation data of the PV power station, three neural network algorithms (i.e., BP, GA-BP, and PSO-BP) are utilized respectively in this work to construct a short-term forecasting model of PV output. Simulation results show that GA-BP and PSO-BP network forecasting models both obtain high prediction accuracy, which indicates GA and PSO methods can effectively reduce the prediction errors in contrast to the original BP model. In particular, PSO owns better applicability than GA, which can further reduce the errors of the PV power prediction model.

OPINION article Front. Energy Res., 15 March 2022Sec.Process and Energy Systems Engineering https://doi.org/10.3389/fenrg.2022.875790