Transient stability enhancement of modern power grid using predictive wide-area monitoring and control

1 online resource (217 pages) : PDF

2016

University of North Carolina at Charlotte

This dissertation presents a real-time Wide-Area Control (WAC) designed based on artificial intelligence for large scale modern power systems transient stability enhancement. The WAC using the measurements available from Phasor Measurement Units (PMUs) at generator buses, monitors the global oscillations in the system and optimally augments the local excitation system of the synchronous generators. The complexity of the power system stability problem along with uncertainties and nonlinearities makes the conventional modeling non-practical or inaccurate. In this work Reinforcement Learning (RL) algorithm on the benchmark of Neural Networks (NNs) is used to map the nonlinearities of the system in real-time. This method different from both the centralized and the decentralized control schemes, employs a number of semi-autonomous agents to collaborate with each other to perform optimal control theory well-suited for WAC applications. Also, to handle the delays in Wide-Area Monitoring (WAM) and adapt the RL toward the robust control design, Temporal Difference (TD) is proposed as a solver for RL problem or optimal cost function. However, the main drawback of such WAC design is that it is challenging to determine if an offline trained network is valid to assess the stability of the power system once the system is evolved to a different operating state or network topology. In order to address the generality issue of NNs, a value priority scheme is proposed in this work to design a hybrid linear and nonlinear controllers. The algorithm so-called supervised RL is based on mixture of experts, where it is initialized by linear controller and as the performance and identification of the RL controller improves in real-time switches to the other controller. This work also focuses on transient stability and develops Lyapunov energy functions for synchronous generators to monitor the stability stress of the system. Using such energies as a cost function guarantees the convergence toward optimal post-fault solutions. This energy energy functions are developed on inter-area oscillations of the system identified online with Prony analysis. Finally, this work investigates the impacts of renewable energy resources, in specific Doubly Fed Induction Generator (DFIG)-based wind turbines, on power system transient stability and control. As the penetration of such resources is increased in transmission power system, neglecting the impacts of them will make the WAC design non-realistic. An energy function is proposed for DFIGs based on their dynamic performance in transient disturbances. Further, this energy is augmented to synchronous generators' energy as a global cost function, which is minimized by the WAC signals. We discuss the relative advantages and bottlenecks of each architecture and methodology using dynamic simulations of several test systems including a 2-area 8 bus system, IEEE 39 bus system, and IEEE 68 bus system in EMTP and real-time simulators. Being nonlinear-based, fast, accurate, and non-model based design, the proposed WAC system shows better transient and damping response when compared to conventional control schemes and local PSSs.

doctoral dissertations

Electrical engineering
Power resources
Artificial intelligence

Ph.D.

Artificial Intelligence
Energy
Power Systems
Stability

Electrical Engineering

Kamalasadan, Sukumar

Kakad, Yogendra
Cecchi, Valentina
Tempest, Brett

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2016.

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