A Proportional-Integrator Type Adaptive Critic Design-Based Neurocontroller for a Static Compensator in a Multimachine Power System

A novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks, is presented in this paper. The action dependent heuristic dynamic programming, a member of the adaptive critic designs family is used for the design of the STATCOM neurocontroller. This neurocontroller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach, the proposed neurocontroller is capable of dealing with actual rather than deviation signals. Simulation results are provided to show that the proposed controller outperforms a conventional PI controller for a STATCOM in a small and large multimachine power system during large-scale faults, as well as small disturbances

Fully Evolvable Optimal Neurofuzzy Controller Using Adaptive Critic Designs

A near-optimal neurofuzzy external controller is designed in this paper for a static compensator (STATCOM) in a multimachine power system. The controller provides an auxiliary reference signal for the STATCOM in such a way that it improves the damping of the rotor speed deviations of its neighboring generators. A zero-order Takagi-Sugeno fuzzy rule base constitutes the core of the controller. A heuristic dynamic programming (HDP) based approach is used to further train the controller and enable it to provide nonlinear near-optimal control at different operating conditions of the power system. Based on the connectionist systems theory, the parameters of the neurofuzzy controller, including the membership functions, undergo training. Simulation results are provided that compare the performance of the neurofuzzy controller with and without updating the fuzzy set parameters. Simulation results indicate that updating the membership functions can noticeably improve the performance of the controller and reduce the size of the STATCOM, which leads to lower capital investment

Optimal Wide Area Controller and State Predictor for a Power System

An optimal wide area controller is designed in this paper for a 12-bus power system together with a Static Compensator (STATCOM). The controller provides auxiliary reference signals for the automatic voltage regulators (AVR) of the generators as well as the line voltage controller of the STATCOM in such a way that it improves the damping of the rotor speed deviations of the synchronous machines. Adaptive critic designs theory is used to implement the controller and enable it to provide nonlinear optimal control over the infinite horizon time of the problem and at different operating conditions of the power system. Simulation results are provided to indicate that the proposed wide area controller improves the damping of the rotor speed deviations of the generators during large scale disturbances. Moreover, a robust radial basis function network based identifier is presented in this paper to predict the states of a multimachine power system in real-time. This wide area state predictor (WASP) compensates for transport lags associated with the present communication technology for wide area monitoring of the electric power grid. The WASP is also robust to partial loss of information caused by larger than expected transport lags or even failed sensors throughout the network

Toward Fault Adaptive Power Systems in Electric Ships

Shipboard Power Systems (SPS) play a significant role in next-generation Navy fleets. With the increasing power demand from propulsion loads, ship service loads, weaponry systems and mission systems, a stable and reliable SPS is critical to support different aspects of ship operation. It also becomes the technology-enabler to improve ship economy, efficiency, reliability, and survivability. Moreover, it is important to improve the reliability and robustness of the SPS while working under different operating conditions to ensure safe and satisfactory operation of the system. This dissertation aims to introduce novel and effective approaches to respond to different types of possible faults in the SPS. According to the type and duration, the possible faults in the Medium Voltage DC (MVDC) SPS have been divided into two main categories: transient and permanent faults. First, in order to manage permanent faults in MVDC SPS, a novel real-time reconfiguration strategy has been proposed. Onboard postault reconfiguration aims to ensure the maximum power/service delivery to the system loads following a fault. This study aims to implement an intelligent real-time reconfiguration algorithm in the RTDS platform through an optimization technique implemented inside the Real-Time Digital Simulator (RTDS). The simulation results demonstrate the effectiveness of the proposed real-time approach to reconfigure the system under different fault situations. Second, a novel approach to mitigate the effect of the unsymmetrical transient AC faults in the MVDC SPS has been proposed. In this dissertation, the application of combined Static Synchronous Compensator (STATCOM)-Super Conducting Fault Current Limiter (SFCL) to improve the stability of the MVDC SPS during transient faults has been investigated. A Fluid Genetic Algorithm (FGA) optimization algorithm is introduced to design the STATCOM\u27s controller. Moreover, a multi-objective optimization problem has been formulated to find the optimal size of SFCL\u27s impedance. In the proposed scheme, STATCOM can assist the SFCL to keep the vital load terminal voltage close to the normal state in an economic sense. The proposed technique provides an acceptable post-disturbance and postault performance to recover the system to its normal situation over the other alternatives