Real Time Testing and Validation of a Novel Short Circuit Current (SCC) Controller for a Photovoltaic Inverter

About 45% applications from PV solar farm developers seeking connections to the distribution grids in Ontario were denied in 2011-13 as the short circuit current (SCC) capacity of several distribution substations had already been reached. PV solar system inverters typically contribute 1.2 p.u. to 1.8 p.u. fault current which was not considered acceptable by utility companies due to the need for very expensive protective breaker upgrades. Since then, this cause has become a major impediment in the growth of PV based renewable systems in Ontario. A novel predictive technique has been patented in our research group for management of short circuit current contribution from PV inverters to ensure effective deployment of solar farms. This thesis deals with the real time testing and validation of a short circuit current (SCC) controller based on the above technique. With this SCC controller, the PV inverter can be shut off within 1-2 milliseconds from the initiation of any fault in the grid that can cause the short circuit current to exceed the rated current of the inverter. Therefore, the power system does not see any short circuit current contribution from the PV inverter and no expensive upgrades in protective breakers are required in the system. The performance of the PV solar system with the short circuit current controller is simulated and tested using (i) industry grade electromagnetic transients software PSCAD/EMTDC (ii) real time simulation studies on the Real Time Digital Simulator (RTDS) (iii) physical implementation on dSPACE board to generate firing pulses for the inverter. The validation of controller is done on dSPACE board with actual PV inverter short circuit waveforms obtained from Southern California Edison Short Circuit Testing Lab. This novel technology is planned to be showcased on a physical 10 kW PV solar system in Bluewater Power Distribution Corporation, Sarnia, Ontario. This proposed technology is expected to remove the technical hurdles which caused the denials of connectivity to several PV solar farms, and effectively lead to greater connections of PV solar farms in Ontario and in similar jurisdictions, worldwide

A neural-network-based model predictive control of three-phase inverter with an output LC Filter

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output $LC$ filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors