Improvement of Power System Small-Signal Stability by Artificial Neural Network Based on Feedback Error Learning (original) (raw)
Related papers
2007
Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design.
European Transactions on Electrical Power, 2009
This paper presents a design procedure for a robust and adaptive fuzzy neural network-based power system stabilizer (RAFNNPSS) and investigates the robustness and adaptive feature of the RAFNNPSS for a single machine connected to an infinite bus system and multi-machine power systems in order to enhance the dynamic stability (small signal stability of the system). The parameters of RAFNNPSS are tuned by adaptive neural network (NN). This RAFNNPSS uses adaptive network-based fuzzy inference system (ANFIS) network, which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFNNPSS. Speed deviation of synchronous generator and its derivative are chosen as the input signals to the RAFNNPSS. The dynamic performance of single-machine infinite bus (SMIB) system, a two-area, five-machine, eight-bus power system and a large power system (10-machine, 39-bus New England system) with the proposed RAFNNPSS under different operating conditions and change in system parameters have been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic-based PSS (FPSS) are compared with the proposed RAFNNPSS. The simulation results demonstrate that the proposed RAFNNPSS performs well in damping and quicker response when compared with the other two PSSs.
this paper presents a new adaptive control approach using output state feedback for improving dynamic stability of power systems. The adaptive control structure is based on hyper stability theory. The eigenvalues of electromechanical mode of the system is shifted to a pre specified vertical strip. The control is constructed so that the closed loop system is hyper stable, guaranteeing the dynamic stability improvement of power systems. The changeable gains generated specifically but not uniquely by a nonlinear time varying function act as adaptive mechanism. An output feedback control is preferred and physically possible to implement the control system is synthesized by the measured feedback signals. Compared with MRAC or STAC, the proposed control structure avoids the difficulty of choosing an appropriate reference model and the burden of implementing an online parameter estimator. The power system under investigation consists of a synchronous machine connected to an infinite bus. It has both voltage regulator and speed governor controls. The effectiveness of the controller for damping machine oscillations caused by power system small disturbances is verified by simulation studies.
Simple adaptive control for a power-system stabiliser
IEE Proceedings - Control Theory and Applications, 2000
The paper discusses the stability problem of a synchronous generator connected to an infinite bus. Owing to the time-consuming tuning of a conventional power-system stabiliser and its non-optimal damping in the entire operating range, simple adaptive control is proposed. The following are presented: the influence of the operating point (loading) on the eigenvalues of a simplified linearised model of a synchronous generator; the model reference adaptive control for almost strictly positive real plants and the simple adaptive power-system stabiliser derived from the presented model reference adaptive control theory. The control-system design was carried out for the simplified linearised model of a synchronous generator, but for the evaluation of the control-system performance in the single machine case under small-signal and large-signal disturbances, the full-order non-linear model of the synchronous generator was used. Simulation and experimental results show the applicability of the presented approach.
International Transactions on Electrical Energy Systems, 2012
In this article, the self-recurrent wavelet neural network (SRWNN) is used as a controller in both direct and indirect adaptive control structures to damp the low-frequency power system oscillations when only the inputs and outputs of synchronous generator are accessible for measurement. The gradient descent method using adaptive learning rates (ALRs) is applied to train all weights of SRWNN. The ALRs are derived from the discrete Lyapunov stability theorem, which was applied to guarantee the convergence of the proposed control schemes. Finally, the proposed control schemes are evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate their effectiveness and robustness.
Robust Control Design of Power System Stabilizer Using Artificial Neural Networks
One of the important issues in power systems dynamics is stabilizing by means of the power system stabilizer (PSS). In this paper, an optimal method for designing an artificial neural network (ANPSS) is presented. The main advantage of this method is the robustness to the system parameters variations such as load and governor parameters. The use of ANNPSS also causes the system dynamical behavior is improve remarkably. This method uses a suitable signal as an input signal for PSS. The used neural network is a feed forward artificial neural network with a learning algorithm which has the error back propagation. The learning network data is obtained using the classic power system stabilizer (CPSS). The proposed method of ANPSS design is simulated on a test system. The results are compared with results of system with CPSS. The results show that the system with ANNPSS is capable of stabilizing the system under different situations of the parameters variations of the power system.
In this paper the use of artificial neural network in power system stability is studied. A predictive controller based on two neural networks is designed and tested on a single machine infinite bus system which is used to replace conventional power system stabilizers. They have been used for decades in power system to dampen small amplitude low frequency oscillation in power systems. The increases in size and complexity of power systems have cast a shadow on efficiency of conventional method. New control strategies have been proposed in many researches. Artificial Neural Networks have been studied in many publications but lack of assurance of their functionality has hindered the practical usage of them in utilities. The proposed control structure is modelled using a novel data exchange established between MATLAB and DIgSILENT power factory. The result of simulation proves the efficiency of the proposed structure.
Power system stabilizer based on artificial neural network
2011 International Conference on Power and Energy Systems, 2011
This paper describes a systematic approach for designing a self-tuning adaptive power system stabilizer (PSS) based on artificial neural network (ANN). An ANN is used for selftuning the parameters of PSS e.g. stabilizing gain K stab and time constant (T1) for Lead PSS in realtime. The inputs to the ANN are generator terminal active power (P) and reactive power (Q). Investigations are carried out to assess the dynamic performance of the system with selftuning PSS based on ANN (ST-ANNPSS) over a wide range of loading conditions. The simulations are performed using Matlab/Simulink's neural network toolbox. The simulation and experimental results demonstrate the effective dynamic performance of the proposed system. Keywords-power system stabilizer, Artificial Neural Network (ANN), Kstab and T 1 .
SMALL SIGNAL STABILITY ANALYSIS USING FUZZY CONTROLLER AND ARTIFICIAL NEURAL NETWORK STABILIZER
Power system Stabilizers are used in order to damp out the low frequency oscillations which are due to disturbances. This paper attempts to investigate the performance of Conventional Power System Stabilizer (CPSS), Fuzzy Logic Power System Stabilizer (FLPSS) and Artificial Neural Network based Power System Stabilizer (ANNPSS) under Prefault and Postfault conditions for different loadings. The parameters of Conventional Power System Stabilizer (CPSS) is designed using Pole-Placement Technique and the parameters of Fuzzy logic Power System Stabilizer (FLPSS) is tuned to their optimal values in order to minimize the overshoot in step response of rotor angle deviation using Particle Swarm Optimization (PSO) technique whereas Artificial Neural network based Power System Stabilizer (ANNPSS) is trained by Linear Optimal Control (LOC) theory. Designed power system stabilizers are applied to a single machine infinite bus system and are tested in four operational conditions; normal load, heavy load, light load and solid 3-phase fault occurrence in a transmission line. The simulation study reveals that the performance of Linear Optimal Control (LOC) based Artificial Neural Network Power System Stabilizer is much improved with Artificial Neural Network based Power System Stabilizer under different operating conditions.
ABSTRACT The Power systems are subjected to low-frequency disturbances that might cause loss of synchronism and an eventual breakdown of the entire systems. The “low-frequency oscillations”is one of the operational constraints which limit bulk power transmission through the power networks, and also cause an eventual breakdown of the entire systems. For this problem power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system to damp the low-frequency power system oscillations and to offer an extra damping for the synchronous generators. The supplementary power system stabilizer must be capable of providing appropriate stabilization signals over a wide range of operating conditions, and disturbances. However, a conventional power system stabilizer (CPSS) provides a positive damping torque in phase with the speed signal to cancel the effect of the system negative damping torque, because of the gains of this controller are determined for a particular operating conditions. The conventional Power System Stabilizer which uses lead-lag compensation, where the gain settings designed for specific operating conditions, is providing poor performance under different loading conditions. The constantly changing nature of power system makes the design of CPSS is a difficult task. Therefore, it is so difficult to design a stabilizer that could present a good performance in all operating points of electric power systems. This thesis is devoted to overcoming the drawback of conventional power system stabilizers (CPSSs), by designing and modeling of Adaptive Neuro-Fuzzy power system stabilizers (ANFPSSs) are proposed. The new design has a capacity to suppress and damp the oscillations when the generators are subjected to different disturbances. The thesis deals with the design procedure for a fuzzy logic based PSS (FLPSS) and a self-learning adaptive neural network based power system stabilizer (ANFPSS) that improves the dynamic stability and provides supplementary signals as consequences of which extending the power stability limits. The speed deviation of a synchronous machine and acceleration are chosen as the input signals to the proposed controller. The proposed technique has the features of a simple structure, fast response, effective and the economical approach for attaining stabilization of the power systems. The simulation results of the comparative study show that low frequencies inter-area power oscillation damping of ANFIS controller based stabilizer is superior to the conventional power system stabilizer. Moreover, the damping time provided by ANFIS controller based power system stabilizer is 73% less in comparison to CPSS under three - Phase fault conditions.