StatCom's B-Spline Neural Network Control (original) (raw)

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.

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.

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.

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.

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.

Adaptive PI Controllers for Doubly Fed Induction Generator using B-spline Artificial Neural Networks

International Journal of Computer Applications, 2013

This paper presents the design and simulation of adaptive PI controllers for doubly fed induction generators using b-spline neural networks. The control structure is based on a back-toback arrangement where the interest variables are regulated by PI linear controllers. Also, to deal with the nonlinear and uncertain system conditions, we proposed that the control parameters are updated online. The main task is that the power converters operation adapt by itself during the grid changes. Then, the basic problem consists of tuning the PI controllers simultaneously when the system and load are subjected to disturbances. The applicability of the proposal is demonstrated by simulation in a three-node grid, where one end is an infinite bus and the other connects the wind system, between them there are two transmission lines. The results show that the proposed controllers' tuning is comparable to that obtained by a conventional design, without requiring of a detailed model, which enable optimal speed tracking for maximum energy capture from the wind.

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.

Voltage Regulation Using STATCOM with PI and Adaptive PI Controls

International Journal of Engineering & Technology

In power systems, voltage instability problems occur due to its continuous demand in heavily loaded networks. So it is essential to stabilize the voltage levels in power systems. The stabilization of power systems can be improved by Flexible Alternating Current Transmission System (FACTS) devices. One of the FACTS devices named Static Synchronous Compensator (STATCOM) injects the compensating current in phase quadrature with line voltage and replicate as inductive reactance to produce capacitive power for the AC grid or as capacitive reactance to draw inductive power from the AC grid for controlling power flow in the line. This paper proposes Adaptive PI control over conventional PI that normally self-adjusts the controller gains under disturbances and helps in improving the performance and attaining a preferred response, irrespective of the change of working conditions. The work is implemented under MATLAB/SIMULINK environment. This method performs more efficient than the original ...

Adaptive PI Control of STATCOM for Voltage Regulation

—STATCOM can provide fast and efficient reactive power support to maintain power system voltage stability. In the literature, various STATCOM control methods have been discussed including many applications of proportional-integral (PI) controllers. However, these previous works obtain the PI gains via a trial-and-error approach or extensive studies with a tradeoff of performance and applicability. Hence, control parameters for the optimal performance at a given operating point may not be effective at a different operating point. This paper proposes a new control model based on adaptive PI control, which can self-adjust the control gains during a disturbance such that the performance always matches a desired response, regardless of the change of operating condition. Since the adjustment is autonomous, this gives the plug-and-play capability for STATCOM operation. In the simulation test, the adaptive PI control shows consistent excellence under various operating conditions, such as different initial control gains, different load levels, change of transmission network, consecutive disturbances, and a severe disturbance. In contrast, the conventional STATCOM control with tuned, fixed PI gains usually perform fine in the original system, but may not perform as efficient as the proposed control method when there is a change of system conditions.