Abstract
The inclusion of renewable energy sources is transforming the electrical network from a rigid centralized big network into amalgamation of flexible smaller networks. The climatic issues due to fossil fuels have been a thorn in the system, despite adopted everywhere. In the past decades, renewables’ cost have been higher and lacked proper control; currently the cost is declining and controller improvements increases renewable inclusion. Renewables have been flexible in terms of rating and distributed near consumers. Among the renewables solar and wind turbine based power generation have been the two popular options. Hydro-electric power plants have been the major contributor, but its huge investment cost isn’t suitable for smaller players in grid. Solar power generation sees more adoption due to its high flexibility of having a rating as small as 10W to series-parallel combinations of MW (Mega Watt). Power electronic converters are required to control, interface, integrate and process the varying power tapped from nature. These are high speed devices with switching speeds from few kHz(kilo Hertz) to MHz(Mega Hertz) based on the switches used. They are well suited for the fast varying solar or wind power generation. This improves system dynamics, as the conventional generators - dependent on mechanical or electro-mechanical systems are slower while Power electronic converters are faster. The full capability of the power converters can be brought out only by an apt controller and the major challenge is the capability of the current controller working as the core of the control; generally the outer loop is taken as voltage loop and the inner loop is taken as current loop as the load and generator dynamics change current while voltage needs to be at rated value. In literature, the search for an apt controller lead to new controllers constantly proposed against the well-established Proportional Integral control (PI). The predictive control family, despite used in multiple fields for their in-built advantage of faster dynamics, surprisingly wasn’t used for the fast acting power converters till the last decade. This is due to the limitation of the processors used for controllers to compute large number of instructions and with the increasingly faster processors in the market, the predictive control of power converters is now a booming research topic. Model Predictive Control (MPC) has been the industrially preferred controller next to PI control due to its intuitive nature and the power electronic switches being ON or OFF solves the huge computational problem of MPC, letting us to confine the search space. This variation is called Finite Control Set- Model Predictive Control (FCS-MPC) and is very suitable for a discrete operating device-like a switch. The distributed generators bring ‘black start’ feature to the system, giving capability to start the system following a grid-wide trip and for the faults in transmission line or power outage in main grid they’re connected to, they let the users operate in an independent fashion-technically termed as Islanded mode of operation. This is very crucial as the user isn’t affected by external issues and when needed they can break away from grid; the black start feature lead to connection of multiple islanded generators to link and form the network. The islanded mode operation is usually much more important compared to its counterpart- grid connected operation, as the inverters are no longer dependent on the major electric grid and have to cater to the connected loads while maintaining an acceptable range of voltage and frequency. The implementation of the predictive control for the distributed generators’ voltage source converters operating in islanded mode is of great importance, as in future- the increasing renewable energy penetration makes each and every customer to own nano-grid/ a part of micro-grid operating independently from a main grid for short term or long term. The potency of the predictive controller for the islanded mode operation is explained and explored for varying operating scenarios in this chapter for single and parallel operation of distributed generators.
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