Application of fuzzy logic to power system stabilizer

Power systems stability is a complex problem which was first recognised in 1920 and has been widely investigated by power system engineers ever since. The first laboratory test on a practical power system was conducted in 1924, followed by the first field test in the following year. The models and method of analysis were relatively simple, with long fault clearing times (0.5 to 2.0 seconds). In 1930, network analysers (which were analogue simulators of the power system) were developed and this led to the improvement of stability analysis. In early 1950’s, they were used to analyse problems which required detailed models of the synchronous machine, excitation system and speed governor. In the mid 1950’s, the first digital computer program for power systems stability was developed. Since the 1960's most of the industry efforts and interests relating to system stability have been concentrated on transient stability. Power systems are designed and operated to criteria concerning transient stability (Kundur, 1994). There have been significant developments in equipment modelling and testing, for synchronous machines, excitation systems and loads. In addition, using high speed fault clearing, fast exciters and special stability aids have been used to improve the transient stability of power systems. The high speed exciters adversely affect the small signal stability associated with local plant mode of oscillations by introducing negative damping of the rotor angle oscillations. Such problems have been solved using power systems stabilisers (PSS). The incorporation of a power systems stabiliser (PSS) into the excitation controller is to improve the system’s performance where the system’s damping is low. At the same time, it can also combat the damping reductions introduced by an AVR (Hughes, 1991). The damping of the rotor angle oscillations can be improved by adding a supplementary signal to the excitation control system to produce a component of the electrical torque on the rotor in phase with speed variations (Larsen and Swann, 1981). Figure 5.1 shows the block diagram of a power system stabiliser added to the excitation control system. The rotor angle oscillations of a generator feeding power to a large inter-connected power system occur in the frequency range of 0.2 to 2 Hz. Different signals have been used as the input to the PSS including: the rotor speed deviation, the bus frequency, the electrical power deviation and the accelerating power (Padiyar, 1996). When a speed signal is employed as an input for the PSS, then a phase lead compensator is required to provide sufficient phase lead (Hughes, 1991). A transient gain or washout is normally used to remove any steady state offset in the speed signal. This filter acts as a high pass filter and is required to ensure that the stabilising signal (PSS output) does not affect the steady state regulation characteristics

Neural Network Based Inferential Model For Ethane Steam Cracking Furnace

The product yield distribution of ethane steam cracking is typically obtained using analysers and lab sampling. Since both methods take time to produce results, primarily depending on them to determine main product yield will hinder immediate control action on the process. In order to resolve this issue, an inferential sensor is required. In this study, a neural network based inferential model is developed. The ethane steam cracking process has been modelled using ASPEN Plus and validated with industrial data taken from literature. The relative error (RE) of the model outputs obtained are less than 10%. The ASPEN Plus model is used for input variable selection, nonlinearity assessment, and data generation for neural network modelling. The input variable selection study found that five variables are significantly influential to the ethane and ethylene yields, namely reactor pressure, coil outlet temperature, steam-hydrocarbon ratio, feed composition, and fuel composition. Nonlinearity assessment of the process shows that the process exhibit asymmetrical response and input multiplicities characteristics, and thus, can be classified as a nonlinear process. Data generated from the ASPEN Plus model is used for training, validation, and testing. Two methods have been used to generate the data which are sequential excitation and simultaneous excitation. Four variables are individually excited and combined to make a sequential excitation profile. Data from sequential excitation is divided into training and validation while data from simultaneous excitation is used solely for testing. Three neural network model, namely the Feedforward Neural Network (FFNN), the Generalized Regression Neural Network (GRNN), and the Extreme Learning Machine Neural Network (ELM-NN) are developed and they are evaluated in terms of prediction accuracy and computational time. The evaluation results show that ELM-NN prediction accuracy is higher than FFNN and GRNN. To train, the best model for ELM-NN, GRNN, and FFNN models require 0.0068 seconds, 0.35 seconds, and 12 seconds respectively. In terms of computation time of new set of input data sample, all three models require less than 0.05 seconds to compute one sample of data. However, computation time of the trained GRNN model increases exponentially with the increasing amount of data samples in a batch while for trained FFNN and trained ELM-NN model, the increment is not significant. Out of the three models, the ELM-NN gives the best performance in terms of prediction accuracy and computational time. The R2 of the ELM-NN model is 91.3% and 82.6% for ethane and ethylene yield respectively. The model requires 0.0068 seconds to train and 0.0001 seconds to compute ethane yield and ethylene yields from a new set of input data. This makes the model suitable for applications in real time inferential control system

Investigation of the Performance of of Synchronous Generators Equipped with Nonlinear Excitation Controller

Investigation of the dynamic performance of a synchronous generator connected to an infinite bus (SMIB) system is carried out in this paper. The generator is equipped with a nonlinear excitation control law based on the concepts of geometric homogeneity and feedback linearization. A new positive parameter, called the dilation gain, is introduced in the control law for improved damping of oscillations and better dynamic performance. Two models of the system are employed for the study, and a disturbance in form of a network fault with varied durations is applied to test the performance of the system. Simulation results as well as MATLAB® code for testing for exact linearization of an affine nonlinear system are provided

Simulating Cortical Feedback Modulation as Changes in Excitation and Inhibition in a Cortical Circuit Model.

Cortical feedback pathways are hypothesized to distribute context-dependent signals during flexible behavior. Recent experimental work has attempted to understand the mechanisms by which cortical feedback inputs modulate their target regions. Within the mouse whisker sensorimotor system, cortical feedback stimulation modulates spontaneous activity and sensory responsiveness, leading to enhanced sensory representations. However, the cellular mechanisms underlying these effects are currently unknown. In this study we use a simplified neural circuit model, which includes two recurrent excitatory populations and global inhibition, to simulate cortical modulation. First, we demonstrate how changes in the strengths of excitation and inhibition alter the input-output processing responses of our model. Second, we compare these responses with experimental findings from cortical feedback stimulation. Our analyses predict that enhanced inhibition underlies the changes in spontaneous and sensory evoked activity observed experimentally. More generally, these analyses provide a framework for relating cellular and synaptic properties to emergent circuit function and dynamic modulation

Gang Confrontation: The case of Medellin (Colombia)

Protracted conflict is one of the largest human challenges that have persistently undermined economic and social progress. In recent years, there has been increased emphasis on using statistical and physical science models to better understand both the universal patterns and the underlying mechanics of conflict. Whilst macroscopic power-law fractal patterns have been shown for death-toll in wars and self-excitation models have been shown for roadside ambush attacks, very few works deal with the challenge of complex dynamics between gangs at the intra-city scale. Here, based on contributions to the historical memory of the conflict in Colombia, Medellin's gang-confrontation-network is presented. It is shown that socio-economic and violence indexes are moderate to highly correlated to the structure of the network. Specifically, the death-toll of conflict is strongly influenced by the leading eigenvalues of the gangs' conflict adjacency matrix, which serves a proxy for unstable self-excitation from revenge attacks. The distribution of links based on the geographic distance between gangs in confrontation leads to the confirmation that territorial control is a main catalyst of violence and retaliation among gangs. Additionally, the Boltzmann-Lotka-Volterra (BLV) dynamic interaction network analysis is applied to quantify the spatial embeddedness of the dynamic relationship between conflicting gangs in Medellin, results suggest that more involved and comprehensive models are needed to described the dynamics of Medellin's armed conflict.Comment: 18 pages, 9 figures. Statistical analysis was largely improved. arXiv admin note: text overlap with arXiv:1107.0539 by other author

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models

Data Acquisition and Control System of Hydroelectric Power Plant Using Internet Techniques

Vodní energie se nyní stala nejlepším zdrojem elektrické energie na zemi. Vyrábí se pomocí energie poskytované pohybem nebo pádem vody. Historie dokazuje, že náklady na tuto elektrickou energii zůstávají konstantní v průběhu celého roku. Vzhledem k mnoha výhodám, většina zemí nyní využívá vodní energie jako hlavní zdroj pro výrobu elektrické energie.Nejdůležitější výhodou je, že vodní energie je zelená energie, což znamená, že žádné vzdušné nebo vodní znečišťující látky nejsou vyráběny, také žádné skleníkové plyny jako oxid uhličitý nejsou vyráběny, což činí tento zdroj energie šetrný k životnímu prostředí. A tak brání nebezpečí globálního oteplování. Použití internetové techniky k ovladání několika vodních elektráren má velmi významné výhody, jako snížení provozních nákladů a flexibilitu uspokojení změny poptávky po energii na straně spotřeby. Také velmi efektivně čelí velkým narušením elektrické sítě, jako je například přidání nebo odebrání velké zátěže, a poruch. Na druhou stranu, systém získávání dat poskytuje velmi užitečné informace pro typické i vědecké analýzy, jako jsou ekonomické náklady, predikce poruchy systémů, predikce poptávky, plány údržby, systémů pro podporu rozhodování a mnoho dalších výhod. Tato práce popisuje všeobecný model, který může být použit k simulaci pro sběr dat a kontrolní systémy pro vodní elektrárny v prostředí Matlab / Simulink a TrueTime Simulink knihovnu. Uvažovaná elektrárna sestává z vodní turbíny připojené k synchronnímu generátoru s budicí soustavou, generátor je připojen k veřejné elektrické síti. Simulací vodní turbíny a synchronního generátoru lze provést pomocí různých simulačních nástrojů. V této práci je upřednostňován SIMULINK / MATLAB před jinými nástroji k modelování dynamik vodní turbíny a synchronního stroje. Program s prostředím MATLAB SIMULINK využívá k řešení schematický model vodní elektrárny sestavený ze základních funkčních bloků. Tento přístup je pedagogicky lepší než komplikované kódy jiných softwarových programů. Knihovna programu Simulink obsahuje funkční bloky, které mohou být spojovány, upravovány a modelovány. K vytvoření a simulování internetových a Real Time systémů je možné použít bud‘ knihovnu simulinku Real-Time nebo TRUETIME, v práci byla použita knihovna TRUETIME.Hydropower has now become the best source of electricity on earth. It is produced due to the energy provided by moving or falling water. History proves that the cost of this electricity remains constant over the year. Because of the many advantages, most of the countries now have hydropower as the source of major electricity producer. The most important advantage of hydropower is that it is green energy, which mean that no air or water pollutants are produced, also no greenhouse gases like carbon dioxide are produced which makes this source of energy environment-friendly. It prevents us from the danger of global warming. Using internet techniques to control several hydroelectric plants has very important advantages, as reducing operating costs and the flexibility of meeting changes of energy demand occurred in consumption side. Also it is very effective to confront large disturbances of electrical grid, such as adding or removing large loads, and faults. In the other hand, data acquisition systems provides very useful information for both typical and scientific analysis, such as economical costs reducing, fault prediction systems, demand prediction, maintenance schedules, decision support systems and many other benefits. This thesis describes a generalized model which can be used to simulate a data acquisition and control system of hydroelectric power plant using MATLAB/SIMULINK and TrueTime simulink library. The plant considered consists of hydro turbine connected to synchronous generator with excitation system, and the generator is connected to public grid. Simulation of hydro turbine and synchronous generator can be done using various simulation tools, In this work, SIMULINK/MATLAB is favored over other tools in modeling the dynamics of a hydro turbine and synchronous machine. The SIMULINK program in MATLAB is used to obtain a schematic model of the hydro plant by means of basic function blocks. This approach is pedagogically better than using a compilation of program code as in other software programs .The library of SIMULINK software programs includes function blocks which can be linked and edited to model. Either Simulink Real-Time library or TrueTime library can be used to build and simulate internet and real time systems, in this thesis the TrueTime library was used.