Adaptive critic design based neurocontroller for a STATCOM connected to a power system (original) (raw)
Adaptive critic designs based coupled neurocontrollers for a static compensator
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 control for the line voltage and the dc link voltage regulation loops of the STATCOM. An action dependent approach is used, in which the controller is independent of a model of the network. Moreover, a proportional-integrator approach allows the neurocontroller to deal with the actual signals rather than the deviations. Simulation results are provided to show that the proposed ACD based neurocontroller is more effective in controlling the STATCOM compared to finely tuned conventional PI controllers.
IEEE Transactions on Industrial Electronics, 2007
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.
IEEE Transactions on Power Systems, 2006
This paper presents a novel nonlinear optimal controller for a Static Compensator (STATCOM) connected to a power system, using artificial neural networks and fuzzy logic. The Action Dependent Heuristic Dynamic Programming (ADHDP), a member of the Adaptive Critic Designs (ACD) family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system. Two multimachine systems are considered in this study: a 10-bus system and a 45-bus network (a section of the Brazilian power system). Simulation results are provided to show that the proposed controller outperforms a conventional PI controller in large scale faults as well as small disturbances. respectively. He started his graduate studies at Georgia Institute of Technology, Atlanta GA in 2001, where he is currently a research assistant working towards his PhD degree. Salman's research focuses on the applications of computational intelligence based techniques on wide area (supervisory level) monitoring and control of interconnected power systems. He is also active in design and hardware implementation of fuzzy and neural network based controllers for FACTS devices in a power system. His main areas of interest include power system operation and dynamics, state estimation, nonlinear systems and control, fuzzy and neural systems. In addition to his work related activities, Salman is an active member of the Georgia Tech family. He was the President of the Iranian Student Association in 2003-05 and the Vice-President of the IEEE Power Engineering Society
IEEE Transactions on Industry Applications, 2003
This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC), which consists of the automatic voltage regulator and turbine governor, to control a synchronous generator in a power system using a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN). The heuristic dynamic programming (HDP) based on the adaptive critic design technique is used for the design of the neurocontroller. The performance of the MLPN-based HDP neurocontroller (MHDPC) is compared with the RBFN-based HDP neurocontroller (RHDPC) for small as well as large disturbances to a power system, and they are in turn compared with the CONVC. Simulation results are presented to show that the proposed neurocontrollers provide stable convergence with robustness, and the RHDPC outperforms the MHDPC and CONVC in terms of system damping and transient improvement.
Fully Evolvable Optimal Neurofuzzy Controller Using Adaptive Critic Designs
IEEE Transactions on Fuzzy Systems, 2008
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.
Neural Network Control of the StatCom in Multimachine Power Systems
2007
This paper presents the application of neural networks for controlling the static synchronous compensator (StatCom) device. The primary duty of the StatCom is the regulation of the AC bus bar voltage where the device is connected. Additionally, a secondary task may be added to such device for obtaining a positive interaction with other controllers in order to mitigate low frequency oscillations. For this task, a neural network is proposed due to its simple structure, adaptability, robustness, considering the power grid nonlinearities. The applicability of the proposition is studied by digital simulation exhibiting satisfactory performance. Results of simulation for different disturbances and operating conditions demonstrate the effectiveness of the feedback variables selected in the control scheme.
Adaptive Critic Designs for Optimal Control of Power Systems
Proceedings of the 13th International Conference on, Intelligent Systems Application to 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.
Design of artificial neuron controller for STATCOM in dSPACE environment
Applied Soft Computing, 2013
Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt-ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with PI and Artificial Neural Network Controller (ANNC). The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network requires less computation time. The ANNC has the ability to generalize and can interpolate in between the training data. The ANNC designed was tested on a 75 V, ±3KVAR STATCOM in real time environment via stateof-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that it was producing better results than the PI controller.
Neurocontroller for Power Electronics-Based Devices
Lecture Notes in Computer Science, 2009
This paper presents the Static Synchronous Compensator's (Stat-Com) voltage regulation by a B-Spline neural network. The fact that the electric grid is a non-stationary system, with varying parameters and configurations, adaptive control schemes may be advisable. Thereby the control technique must guarantee its performance on the actual operating environment where the Stat-Com is embedded. An artificial neural network (ANN) is trained to foresee the device's behavior and to tune the corresponding controllers. Proportional-Integral (PI) and B-Spline controllers are assembled for the StatCom's control, where the tuning of parameters is based on the neural network model. Results of the lab prototype are exhibited under different conditions.
Intelligent control schemes for a static compensator connected to a power network
Second IEE International Conference on Power Electronics, Machines and Drives, 2004
Two intelligent controllers are designed for a static compensator (STATCOM) connected to a single machine infinite bus power system (SMIB): a novel nonlinear adaptive controller using artificial neural networks based on the indirect adaptive control technique and a Takagi-Sugeno type fuzzy controller. Both schemes provide nonlinear adaptive control with better performance compared to the conventional PI controllers. Simulation results are presented to compare the performances of these controllers with that of the conventional PI controllers.