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Frequency is one of the most important characteristics in power system monitoring, control and protection. Frequency variations can be observed with significant changes in operating conditions. High penetration levels of renewable energy pose variability and uncertainty challenges for grid operation. It is essential to have innovative methodologies to take necessary actions to overcome these challenges...
Recent studies on Power Electronic Interfaces (PEI) demonstrate that enhanced performance of a PEI can be achieved through optimal tuning of its controller parameters. As an example nowadays, voltage source converters (VSC) are extensively utilized as the interface between DC energy sources and the power system. Photovoltaic (PV) inverters have emerged as one of the most indispensable and widely utilized...
To achieve a high penetration level of intermittent renewable energy, the operation and control of power systems need to account for the associated high variability and uncertainty. Power system stability and security need to be ensured dynamically as the system operating condition continuously changes. A wide-area measurement based dynamic stochastic optimal power flow (DSOPF) control algorithm using...
The demand of power and the size and complexity of the power system is increasing. Wide area monitoring and control is an integral part in transitioning from the traditional power system to a Smart Grid. However, wide area monitoring becomes challenging as the size of the electric power grid, and consequently the number of components to be monitored, grows. Wide area monitor (WAM) designed using feed-forward...
An adaptive critic design (ACD) based dynamic optimal power flow control (DOPFC) is proposed in this paper as a solution to the smart grid operation in a high short-term uncertainty and variability environment. With the increasing penetration of intermittent renewable generation, power system stability and security need to be ensured dynamically as the system operating condition continuously changes...
For Artificial Neural Networks (ANN) to become more widely used in power systems and the future smart grids, ANN based algorithms must be capable of scaling up as they try to identify and control larger and larger parts of a power system. This paper goes through the process of scaling up an ANN based identifier as it is driven to identify increasingly larger portions of a power system. Distributed...
A large nonlinear dynamic system usually has complex dynamic modes corresponding to the system's eigenvalues. These eigenvalues govern the system's local behavior and thus are critical information for designing system operation and control strategies. Without the availability of the system's analytical model, which is often the case for large nonlinear systems, the system's eigenvalues need to be...
The electric power grid is a complex adaptive system under semi-autonomous distributed control. It is spatially and temporally complex, non-convex, nonlinear and non-stationary with a lot of uncertainties. The integration of renewable energy such as wind farms, and plug-in hybrid and electric vehicles further adds complexity and challenges to the various controllers at all levels of the power grid...
A novel nonlinear optimal neurocontroller for a static compensator (STATCOM) connected to a power system, using artificial neural networks, is presented in this paper. The heuristic dynamic programming (HDP) method, a member of the adaptive critic designs (ACD) family, is used for the design of the STATCOM neurocontroller. The proposed controller is a nonlinear optimal controller that provides coupled...
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