An artificial neural network based adaptive power system stabilizer

An artificial neural network (ANN)-based power system stabilizer (PSS) and its application to power systems are presented. The ANN-based PSS combines the advantages of self-optimizing pole shifting adaptive control strategy and the quick response of ANN to introduce a new generation PSS. A popular type of ANN, the multilayer perceptron with error backpropagation training method, is used in this PSS. The ANN was trained by the training data group generated by the adaptive power system stabilizer (APSS). During the training, the ANN was required to memorize and simulate the control strategy of APSS until the differences were within the specified criteria. Results show that the proposed ANN-based PSS can provide good damping of the power system over a wide operating range and significantly improve the dynamic performance of the system     

A neural network based power system stabilizer suitable for on-linetraining-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS     

An adaptive power system stabilizer using on-line trained neuralnetworks

This paper presents an approach to the design of an adaptive power system stabilizer (PSS) based on on-line trained neural networks. Only the inputs and outputs of the generator are measured and there is no need to determine the states of the generator. The proposed neural adaptive PSS (NAPSS) consists of an adaptive neuro-identifier (ANI), which tracks the dynamic characteristics of the plant, and an adaptive neuro-controller (ANC) to damp the low frequency oscillations. These two subnetworks are trained in an on-line mode utilizing the backpropagation method. The use of a single-element error vector along with a small network simplifies the learning algorithm in terms of computation time. The improvement of the dynamic performance of the system is demonstrated by simulation studies for a variety of operating conditions and disturbances     

A self-organizing power system stabilizer using fuzzyauto-regressive moving average (FARMA) model

This paper presents a self-organizing power system stabilizer (SOPSS) which use the fuzzy auto-regressive moving average (FARMA) model. The control rules and the membership functions of the proposed fuzzy logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show the effectiveness of the proposed controller, comparison with a conventional controller for a one-machine infinite-bus system is presented     

Real time control of micro-machine system using micro-computerbased fuzzy logic power system stabilizer

A microcomputer based fuzzy logic power system stabilizer is applied to a micro-machine system to investigate its efficiency in real time control. The stabilizing signal is determined by using measured speed or real power signals at every sampling time to damp the system oscillations. The results show the proposed stabilizer improves the system damping effectively subject to various types of disturbances     

A fuzzy logic based power system stabilizer with learning ability

A fuzzy logic-based power system stabilizer (PSS) with learning ability is proposed in this paper. The proposed PSS employs a multilayer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of artificial neural networks (ANNs) and fuzzy logic control (FLC) schemes. Studies show that the proposed adaptive network-based fuzzy logic PSS (ANF PSS) can provide good damping of power systems over a wide range of operating conditions and improve the dynamic performance of the power system     

Advanced PID type fuzzy logic power system stabilizer

An advanced fuzzy logic control scheme has been proposed for a microcomputer based power system stabilizer to enhance the overall stability of power systems. The proposed control scheme utilizes the PID information of the generator speed. The input signal to the stabilizer is the real power output of a study unit. Simulations show the effectiveness of the advanced fuzzy logic control scheme     

Multivariable self-tuning power system stabilizer simulation andimplementation studies

A multi-input multi-output (MIMO) adaptive power system stabilizer which damps dynamic oscillations is presented. The single-input single-output (SISO) pole-shifting adaptive control algorithm is used as the basis. Its extension to the MIMO controller combines and coordinates the stabilizing contributions from both the excitation and governor systems. The advantage of the adaptive feature is that it can track the operating conditions and system structure changes. The proposed stabilizer can be used with hydro and turbogenerators. Computer simulation results show that the adaptive stabilizer improves the damping of power system oscillations     

Improvement in artificial neural network-based estimation of grid connected photovoltaic power output

This paper presents a method to improve the accuracy of artificial neural network (ANN)–based estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky; hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression.     

An adaptive power system stabilizer based on recurrent neuralnetworks

Application of recurrent, neural networks in the design of an adaptive power system stabilizer (PSS) is presented in this paper. The architecture of the proposed adaptive PSS has two recurrent neural networks. One functions as a tracker to learn the dynamic characteristics of the power plant and the second one functions as a controller to damp the oscillations caused by the disturbances. In the proposed approach, the weights of the neural networks are updated on-line. Therefore, any new information available during actual control of the plant is considered. Simulation studies show that the artificial neural network (ANN) based PSS can provide very good damping over a wide range of operating conditions     

加速功率型电力系统稳定器参数整定方法研究

电力系统稳定器（PSS）是抑制系统低频振荡的有效手段,通常认为以发电机转速偏差和电磁功率偏差为输入信号的加速功率型PSS可避免发电机无功反调问题。结合实例和时域仿真分析,指出在参数整定不合理情况下此类PSS仍可能出现无功反调现象,分析了典型PSS参数对无功反调的影响,在此基础上提出一种基于数字仿真的PSS参数整定方法。对实际电网的时域仿真结果验证了该方法的有效性。     

A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction

In this paper, an adaptive control scheme for maximum power point tracking of stand-alone PMSG wind turbine systems (WTS) is presented. A novel procedure to estimate the wind speed is derived. To achieve this, a neural network identifier (NNI) is designed in order to approximate the mechanical torque of the WTS. With this information, the wind speed is calculated based on the optimal mechanical torque point. The NNI approximates in real-time the mechanical torque signal and it does not need off-line training to get its optimal parameter values. In this way, it can really approximates any mechanical torque value with good accuracy. In order to regulate the rotor speed to the optimal speed value, a block-backstepping controller is derived. Uniform asymptotic stability of the tracking error origin is proved using Lyapunov arguments. Numerical simulations and comparisons with a standard passivity based controller are made in order to show the good performance of the proposed adaptive scheme.     

Lab and field tests of a self-tuning power system stabilizer

Digital power system stabilizers have opened new avenues for applying innovative control strategies to damp generator rotor oscillations at steam and hydroelectric power plants. This paper describes test results from simulation studies and field tests where a self-tuning control algorithm was tested using a digital stabilizer. The simulation studies indicated a marked improvement in local mode damping and a contribution to damping at the inter-area mode while initial tests at power plants gave promising results     

Design and analysis of an adaptive fuzzy power system stabilizer

Power system stabilizers (PSS) must be capable of providing appropriate stabilization signals over a broad range of operating conditions and disturbances. Traditional PSS rely on robust linear design methods. In an attempt to cover a wider range of operating conditions, expert or rule-based controllers have also been proposed. Fuzzy logic as a novel robust control design method has shown promising results. The emphasis in fuzzy control design centers around uncertainties in system parameters and operating conditions. Such an emphasis is of particular relevance as the difficulty of accurately modelling the connected generation is expected to increase under power industry deregulation. Fuzzy logic controllers are based on empirical control rules. In this paper, a systematic approach to fuzzy logic control design is proposed. Implementation for a specific machine requires specification of performance criteria. This performance criteria translates into three controller parameters which can be calculated off-line or computed in real-time in response to system changes. The robustness of the controller is emphasized. Small signal and transient analysis methods are discussed. This work is directed at developing robust stabilizer design and analysis methods appropriate when fuzzy logic is applied     

Theory and method for selection of power system stabilizer location

An approach for the selection of best PSS (power system stabilizer) locations in multimachine power systems is proposed. Study shows that the right-eigenvector measures the activity of state variables and the left-eigenvector measures the control effect of control signals. Based on the right and left eigenvector, the concept of sensitivity of PSS effect (SPE) is presented and used to identify the best PSS locations. The method is used to identify the best PSS location is a 13-machine system to increase the damping of an interarea mode. The time-domain simulation results confirm that the prediction of the best PSS location by the SPE method is correct and accurate     

A nonlinear variable structure stabilizer for power systemstability

A nonlinear variable structure stabilizer is proposed in this paper. Design of this stabilizer involves the nonlinear transformation technique, the variable structure control technique and linear system theory. Performance of the proposed nonlinear variable structure controller in a single machine connected to an infinite bus power system and a multimachine system with multimode oscillations is simulated. The responses of the system with the proposed stabilizer are compared with those obtained with some other kinds of stabilizers when the system is subjected to a variety of disturbances. Simulation results show that the nonlinear variable structure stabilizer gives satisfactory dynamic performance and good robustness     

Development of a neural network-based fault diagnostic system for solar thermal applications

The objective of this work is to present the development of an automatic solar water heater (SWH) fault diagnosis system (FDS). The FDS system consists of a prediction module, a residual calculator and the diagnosis module. A data acquisition system measures the temperatures at four locations of the SWH system and the mean storage tank temperature. In the prediction module a number of artificial neural networks (ANN) are used, trained with values obtained from a TRNSYS model of a fault-free system operated with the typical meteorological year (TMY) for Nicosia, Cyprus and Paris, France. Thus, the neural networks are able to predict the fault-free temperatures under different environmental conditions. The input data to the ANNs are various weather parameters, the incidence angle, flow condition and one input temperature. The residual calculator receives both the current measurement data from the data acquisition system and the fault-free predictions from the prediction module. The system can predict three types of faults; collector faults and faults in insulation of the pipes connecting the collector with the storage tank and these are indicated with suitable labels. The system was validated by using input values representing various faults of the system.     

An H2 optimal adaptive power system stabilizer

Application of an H₂ optimal adaptive control algorithm as a power system stabilizer is described in this paper. The algorithm deals with disturbance attenuation in the sense of H₂ norm for nonlinear systems. Results of simulation and experimental studies suggest that the H₂ optimal control algorithm could be successfully used for the control of nonlinear systems such as synchronous generators