Adaptive Critic Designs for Optimal Control of Power Systems

The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances

Real-Time Dual Heuristic Programming-Based Neurocontroller for a Turbogenerator in a Multimachine Power System

Based on Dual Heuristic Programming (DHP), a real-time implementation of a neurocontroller for excitation and turbine control of a turbogenerator in a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulation and real-time hardware implementation on a three-machine system demonstrate that the DHP neurocontroller is much more effective than conventional PID controllers, the automatic voltage regulator, power system stabilizer and the governor, for improving dynamic performance and stability under small and large disturbances

A Nonlinear Voltage Controller with Derivative Adaptive Critics for Multimachine Power Systems

Based on derivative adaptive critics, a novel nonlinear optimal voltage/excitation control for a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulations on a three-machine system demonstrate that the nonlinear controller is much more effective than the conventional PID controller equipped with a power system stabilizer for improving dynamic performance and stability under small and large disturbances

Making the Power Grid More Intelligent

Summary form only given. This paper focuses on the applications of intelligent techniques for improving the performances of the power system controllers. Intelligent control techniques lay the foundation of the next generation of nonlinear controllers and have the advantage of further improving the controller\u27s performance by incorporating heuristics and expert knowledge into its design. Most of these techniques are independent of any mathematical model of the power system, which proves to be a considerable advantage

Optimal Neuro-Fuzzy External Controller for a STATCOM in the 12-Bus Benchmark Power System

An optimal neuro-fuzzy external controller is designed in this paper for a static compensator (STATCOM) in the 12-bus benchmark 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 Mamdani fuzzy rule base constitutes the core of the controller. A heuristic dynamic programming-based approach is used to further train the controller and enable it to provide nonlinear optimal control at different operating conditions of the power system. Simulation results are provided that indicate the proposed neuro-fuzzy external controller is more effective than a linear external controller for damping out the speed deviations of the generators. In addition, the two controllers are compared in terms of the control effort generated by each one during various disturbances and the proposed neuro-fuzzy controller proves to be more effective with smaller control effort

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

Excitation and Turbine Neurocontrol with Derivative Adaptive Critics of Multiple Generators on the Power Grid

Based on derivative adaptive critics, neurocontrollers for excitation and turbine control of multiple generators on the electric power grid are presented. The feedback variables are completely based on local measurements. Simulations on a three-machine power system demonstrate that the neurocontrollers are much more effective than conventional PID controllers, the automatic voltage regulators and the governors, for improving the dynamic performance and stability under small and large disturbance

Stability and Control of Power Systems using Vector Lyapunov Functions and Sum-of-Squares Methods

Recently sum-of-squares (SOS) based methods have been used for the stability analysis and control synthesis of polynomial dynamical systems. This analysis framework was also extended to non-polynomial dynamical systems, including power systems, using an algebraic reformulation technique that recasts the system's dynamics into a set of polynomial differential algebraic equations. Nevertheless, for large scale dynamical systems this method becomes inapplicable due to its computational complexity. For this reason we develop a subsystem based stability analysis approach using vector Lyapunov functions and introduce a parallel and scalable algorithm to infer the stability of the interconnected system with the help of the subsystem Lyapunov functions. Furthermore, we design adaptive and distributed control laws that guarantee asymptotic stability under a given external disturbance. Finally, we apply this algorithm for the stability analysis and control synthesis of a network preserving power system.Comment: to appear at the 14th annual European Control Conferenc

Hardware Implementation of a Mamdani Fuzzy Logic Controller for a Static Compensator in a Multimachine Power System

A Mamdani-type fuzzy logic controller is designed and implemented in hardware for controlling a static compensator (STATCOM), which is connected to a ten-bus multimachine power system. Such a controller does not need any mathematical model of the plant to be controlled and can efficiently provide control signals for the STATCOM over a wide range of operating conditions of the power system and during different disturbances. The proposed controller is implemented using the M67 digital signal processor board and is interfaced to the multimachine power system simulated on a real-time digital simulator. Experimental results are provided, showing that the proposed Mamdani fuzzy logic controller provides superior damping compared to the conventional proportional-integral (PI) controller for both small and large scale disturbances. In addition, the proposed controller manages to restore the power system to the steady state conditions with less control effort exerted by the STATCOM, which, in turn, leads to smaller megavar rating and, therefore, less cost for the device. The matrix pencil method analysis of the damping provided by the different controllers indicates that the proposed controller provides higher damping than the PI controller and also mitigates the modes present with the conventional PI control

Distributed model predictive control of steam/water loop in large scale ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method