A hierarchical architecture for increasing efficiency of large photovoltaic plants under non-homogeneous solar irradiation

Under non-homogeneous solar irradiation, photovoltaic (PV) panels receive different solar irradiance, resulting in a decrease in efficiency of the PV generation system. There are a few technical options to fix this issue that goes under the name of mismatch. One of these is the reconfiguration of the PV generation system, namely changing the connections of the PV panels from the initial configuration to the optimal one. Such technique has been widely considered for small systems, due to the excessive number of required switches. In this paper, the authors propose a new method for increasing the efficiency of large PV systems under non-homogeneous solar irradiation using Series-Parallel (SP) topology. In the first part of the paper, the authors propose a method containing two key points: a switching matrix to change the connection of PV panels based on SP topology and the proof that the SP-based reconfiguration method can increase the efficiency of the photovoltaic system up to 50%. In the second part, the authors propose the extension of the method proposed in the first part to improve the efficiency of large solar generation systems by means of a two-levels architecture to minimize the cost of fabrication of the switching matrix

MPPT Control for Solar Splash Photovoltaic Array

This thesis demonstrates the ability to model and simulate the operation of Maximum Power Point Tracking, MPPT. Moreover, the MPPT technology is contextualized within the confines of the Solar Splash competition to provide the foundation for future model development and simulation for optimal competition performance. MatLab Simulink was used to model the solar panel\u27s operation. A MPPT algorithm was written using the perturb and observe method and was implemented in the model using a buck DC to DC converter. The performance of the model with hardware in the loop using Typhoon and dSPACE, which demonstrated how the actual hardware would operate in real time. The results showed that in Simulink, an idealized environment, the MPPT operates as expected. However, hardware simulation revealed inaccuracies of MPPT at lower irradiance values. For all cases, the driving force for changes in power is the value of irradiance

A Sliding Mode Control for a Sensorless Tracker: Application on a Photovoltaic System

The photovoltaic sun tracker allows us to increase the energy production. The sun tracker considered in this study has two degrees of freedom (2-DOF) and especially specified by the lack of sensors. In this way, the tracker will have as a set point the sun position at every second during the day for a period of five years. After sunset, the tracker goes back to the initial position (which of sunrise). The sliding mode control (SMC) will be applied to ensure at best the tracking mechanism and, in another hand, the sliding mode observer will replace the velocity sensor which suffers from a lot of measurement disturbances. Experimental measurements show that this autonomic dual axis Sun Tracker increases the power production by over 40%

Modeling a Grid-Connected PV/Battery Microgrid System with MPPT Controller

This paper focuses on performance analyzing and dynamic modeling of the current grid-tied fixed array 6.84kW solar photovoltaic system located at Florida Atlantic University (FAU). A battery energy storage system is designed and applied to improve the systems stability and reliability. An overview of the entire system and its PV module are presented. In sequel, the corresponding I-V and P-V curves are obtained using MATLAB-Simulink package. Actual data was collected and utilized for the modeling and simulation of the system. In addition, a grid- connected PV/Battery system with Maximum Power Point Tracking (MPPT) controller is modeled to analyze the system performance that has been evaluated under two different test conditions: (1) PV power production is higher than the load demand (2) PV generated power is less than required load. A battery system has also been sized to provide smoothing services to this array. The simulation results show the effective of the proposed method. This system can be implemented in developing countries with similar weather conditions to Florida.Comment: 6 pages, 14 figures, PVSC 201

Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities

Global maximum power tracking of PV system under partial shading

The increased usage of electrical energy in the recent times leads to a greater demand. It invites large development in the production of electrical energy from renewable energy sources. It involves more evolving technologies. Out of all energy extraction from solar would be abundant. Photovoltaic (PV) are one such components helps in deriving large amounts of energy, this has become more easiest method due to its economic liabilities and the world has aimed its interest in developing the PV technology, which gives clean energy. This paper objective is to implement various Maximum Power Point Tracking (MPPT) algorithms, mainly Cuckoo Search Algorithm, fuzzy logic control (FLC) and conventional perturb and observe (P&O), incremental conductance (INC) on solar PV systems. These controlled MPPT algorithms helps in driving DC-DC boost converter, which helps to obtain maximum output from the PV Panels/cells/modules/Arrays. The obtained results are compared with each other under several operating conditions. The operating conditions include change in irradiance, change in temperature dynamically, and partial shading on PV panels. The implemented MPPT algorithms require only the PV array voltage and current to control DC-DC converter, which makes them economically feasible and attractive. From the results, it can be observed that Cuckoo search algorithm gives better results under partial shading situations

Advanced Control for Energy Management of Grid-Connected Hybrid Power Systems in the Sugar Cane Industry

This work presents a process supervision and advanced control structure, based on Model Predictive Control (MPC) coupled with disturbance estimation techniques and a finite-state machine decision system, responsible for setting energy productions set-points. This control scheme is applied to energy generation optimization in a sugar cane power plant, with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as biomass. The energy plant is bound to produce steam in different pressures, cold water and, imperiously, has to produce and maintain an amount of electric power throughout each month, defined by contract rules with a local distribution network operator (DNO). The proposed predictive control structure uses feedforward compensation of estimated future disturbances, obtained by the Double Exponential Smoothing (DES) method. The control algorithm has the task of performing the management of which energy system to use, maximize the use of the renewable energy sources, manage the use of energy storage units and optimize energy generation due to contract rules, while aiming to maximize economic profits. Through simulation, the proposed system is compared to a MPC structure, with standard techniques, and shows improved behavior.Ministerio de Economía y Competitividad CNPq401126/2014-5Ministerio de Economía y Competitividad CNPq303702/2011-7Ministerio de Economía y Competitividad DPI2016-78338-