Neurocontroller for Power Electronics-Based Devices (original) (raw)
Related papers
A B-spline network based neural controller for power electronic applications
Neurocomputing, 2010
Conventional multi-layer feedforward ANN controllers with back-propagation training are too complex to be implemented in fast-dynamic power electric systems. This paper proposes a controller for power electric systems based on a type of on-line trained neural network called the B-spline network (BSN). Due to its linear nature and local weight updating, the BSN controller is more suitable for real-time implementation than conventional multi-layer feedforward neural controllers. Based on a frequency domain stability analysis, a design methodology for determining the two main parameters of the BSN is presented. The design procedure of the proposed BSN controller is straightforward and simple. Experimental results of UPS inverters with the proposed controller under various conditions show that the proposed controller can achieve excellent performance.
StatCom's B-Spline Neural Network Control
… and Exposition: Latin America, 2006
Abstract-The B-spline neural net is a convenient tool to execute the power system voltage control, with the possibility of carrying out such task on-line. The controller's design simplicity and its performance compared with the conventional PI controller are shown, specially ...
Improvement of the performance of STATCOM in terms of voltage profile using ANN controller
International Journal of Power Electronics and Drive System (IJPEDS), 2020
The electronic equipmentsare extremely sensitive to variation in electric supply. The increasing of a nonlinear system with several interconnected unpredicted and non-linear loads are causing some problems to the power system. The major problem facing the power system is power quality, controlling of reactive power and voltage drop. A static synchronous compensator (STATCOM) is an important device commonly used for compensation purposes, it can provide reactive support to a bus to compensate voltage level. In this paper, the Artificial Neural Network (ANN) controlled STATCOM has been designed to replace the conventional PI controller to enhance the STATCOM performance. The ANN controller is proposed due to its simple structure, adaptability, robustness, considering the power grid non-linearities. The ANN is trained offline using data from the PI controller. The performance of STATCOM with case of Load increasing and three-phase faults case was analyzed using MATLAB/Simulink software on the IEEE 14-bus system. The comprehensive result of the PI and ANN controllers has demonstrated the effectiveness of the proposed ANN controller in enhancing the STATCOM performance for Voltage profile at different operating conditions. Furthermore,it has produced better results than the conventional PI controller.
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.
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.
International journal of electrical & electronics research, 2024
The stability of the electrical network is considered a major challenge in the development of energy systems based on various sources. This research provides a comparison of the dynamic performance of FACTS devices such as STATCOM and SVC. These techniques, which are integrated stability devices with a multi-source power system, are used. The neural network technology unit is used to control FACTS devices to enhance the performance of power sources under abnormal and different conditions. Testing is conducted under conditions of three-phase short circuit to ground at bus (3) in the system. MATLAB/Simulink is used for modeling and simulation. The obtained results demonstrate the impact of the control unit based on SVC and STATCOM in reducing system oscillations and improving dynamic system performance during the post-fault period. The comparison confirms the superior dynamic performance and quick fault recovery of the control unit.
Neural Controller for UPS Inverters Based on B-Spline Network
IEEE Transactions on Industrial Electronics, 2000
This paper proposes a controller for uninterruptible power supply inverters based on a particular type of onlinetrained neural network, which is called the B-spline network (BSN). Due to its linear nature and local weight updating, the BSN controller is more suitable for real-time implementation than conventional multilayer feedforward neural controllers. Based on a frequency-domain stability analysis, a design methodology for determining the two main parameters of the BSN are presented. The model is found to be similar to that of an iterative learning control (ILC) scheme. However, unlike ILC, which requires a complex digital filter design that involves both causal and noncausal parts, the design procedure of the proposed BSN controller is straightforward and simple. Experimental results under various conditions show that the proposed controller can achieve excellent performance, comparable to that of a high-performance ILC scheme developed earlier. The proposed controller is an attractive alternative to both the multilayer feedforward neural controller and iterative learning controller in this and similar applications.
Fast Transient Hybrid Neuro Fuzzy Controller for STATCOM During Unbalanced Voltage Sags
2018
A static synchronous compensator (STATCOM) is generally used to regulate voltage and improve transient stability in transmission and distribution networks. This is achieved by controlling reactive power exchange between STATCOM and the grid. Unbalanced sags are the most common type of voltage sags in distribution networks. A static synchronous compensator (STATCOM) is generally used to maintain voltage and improve transient stability. This is achieved by regulating reactive power exchange between compensator device and grid. In this paper, A hybrid neuro-fuzzy current controller for STATCOM control is proposed. The controller has minimum mass of calculations. Learning process is carried out by an improved supervisory error-back propagation (SEBP) method instead of usual EBP algorithm. This results in better performance and efficiency and leads to a robust model with fast transient capability. The model is developed in MATLAB/SIMULINK environment. STATCOM operation during scenarios of balanced and unbalanced voltage sags is studied. Performance is compared with the operation of a conventional proportional-resonant controller. The results show faster dynamic and better capability of neuro-fuzzy controller in responding to voltage sag occurrences.
Adaptive critic design based neurocontroller for a STATCOM connected to a power system
38th IAS Annual Meeting on Conference Record of the Industry Applications Conference, 2003., 2003
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), a member of the adaptive critic designs (ACDs) family, is used for the design of the STATCOM neurocontroller. This neurocontroller provides nonlinear optimal control with better performance compared to the conventional PI controllers.
Modeling of SVC Controller based on Adaptive PID Controller using Neural Networks
International Journal of Computer Applications, 2012
The Flexible AC Transmission System (FACTS) technology is a promising technology to achieve complete deregulation of power system based on power electronic devices, used to enhance the existing transmission capabilities in order to make the system flexible and independent in operation then the system will be kept within limits without affecting the stability. Complete closed-loop smooth control of voltage can be achieved using shunt connected FACTS devices. Static VAR Compensator (SVC) is one of the shunt connected devices, which can be utilized for the purpose of voltage and reactive power control in power systems. In this paper the considered structure of SVC consists of (TCR-FC) which is applied at SMIB system model, the dynamic equations for the (SMIB-SVC) model will be presented, the system equations expressed in terms of state space equations then by using MATLAB the plant of the system model will be presented under various loading conditions. A Neuro-PID controller model has been developed to improve on the response and performance of a conventional Proportional plus Integral plus Derivative (PID) controller which control the response of the plant model by developing a self-tuning/adaptive Neural-PID controller. The proposed Neuro-controller was developed using the back propagation algorithm. The ANN based PID (ANN-PID) controller compared with ANN controller through MATLAB simulation results. Comparison of performance responses of ANN controller and ANN-PID controller show that ANN-PID controller has quite satisfactory generalization capability, feasibility and reliability, as well as the accuracy in the system; the superiority of the performance of ANN over PID controller is highlighted under various loading conditions.