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

An investigation on trade openness, fiscal policy and economic growth in Malaysia: Using an ARDL bounds testing approach

This study examines the impacts of trade openness and fiscal policy on economic growth in Malaysia between 1970 and 2003 using the autoregressive distributed lag (ARDL) approach and bounds test as proposed by Pesaran et al. (2001). Based on a structure consistent with the endogenous growth theory, the ARDL results show that, overall, trade openness and fiscal policy have strong positive impacts on economic growth in Malaysia over this period. This paper also develops a system instrumental variable method to estimate the structural speed of adjustment coefficient in an error correction model

Flood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems

Flood forecasting is crucial for early warning system and disaster risk reduction. Yet the flood river water levels are difficult and challenging task that it cannot be easily captured with classical time-series approaches. This study proposed a novel intelligence system utilised various machine learning techniques as individual models, including radial basis function neural network (RBF-NN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and long short-term memory network (LSTM) to establish intelligent committee machine learning flood forecasting (ICML-FF) framework. The combination of these individual models achieved through simple averaging method, and further optimised using weighted averaging by K-nearest neighbour (K-NN) and genetic algorithm (GA). The effectiveness of the proposed model was evaluated using real case study for Malaysia's Kelantan River. The results show that ANFIS outperforms as individual model, while ICML-FF-based model produced better accuracy and lowest error than any one of the individuals. In general, it is found that the proposed ICML-FF is capable of robust forecasting model for flood early warning systems

Effects of fiscal policy and institutions on the economic growth of Asian economies: Evidence from dynamic panel data analysis

This paper investigates the effects of fiscal policy and institutions on the economic growth of Asian economies through the application of the GMM-SYS approach to dynamic panel estimator as a preference analysis. It examines two different channels through which fiscal policy can affect long-run economic growth in Asia. The first channel is when aggregate government expenditure, aggregate of other fiscal variables, and institution affect the real per capita GDP, and the second channel is to determine the role of institutions on the real per capita GDP. The dynamic panel data result, especially GMM-SYS, established a longrun relationship between fiscal policy, institution, and economic growth. We found positive and statistically significant impact of aggregate of government expenditure and aggregate of other fiscal variables and institution on real per capita GDP. Furthermore, we found that there is a role of institutions on the real per capita GDP

Artificial neural networks in surrogate modeling

Offline optimization of controller parameters for complex non-linear processes can be time consuming, even with high performance computers. This chapter demonstrates how a Radial Basis Function ANN can be utilized to tune the controller parameters for a non-linear process quickly. The ANN strategy used is basically to approximate the relationship between the controller parameters and the values of the objective function used. This strategy is called metamodeling or surrogate modeling (Gorissen et. al., 2006). The process used in this chapter is the mixing process, which is a multivariable and intrinsically non-linear plant. The Radial Basis Function Neural Network surrogate model used was able to give a good approximation to the optimum controller parameters in this case. In the design of control systems, one often has a complicated mathematical model of a system that has been obtained from fundamental physics and chemistry. The system will usually consist of inputs and outputs and in practice; it is normally desired to find the optimum controller parameter values that would give optimal outputs of the system. The simulations needed when applying optimization algorithms might be very expensive computationally owing to the complexity of the actual model. In spite of the advances in computer technology, the computational time to simulate the actual model might still be long and thus it becomes impractical to rely exclusively on simulation for the purpose of design optimization. Thus there is a need for metamodeling, that is, for the determination of simpler models that involve less computation but are good approximations to the complicated model

A novel laser diode wavelength stabilisation technique for use in high resolution spectroscopy

Tuneable diode laser absorption spectroscopy (TDLAS) based gas sensors are widely used for trace gas detection for their high selectivity and sensitivity. The laser source used in TDLAS requires a narrow line width in the order of 10s of MHz, with a wavelength stability multiple orders lower than the molecular absorption line width, which is, for example, 4.1GHz (38pm) for an air broadened methane line. TDLAS requires the use of a laser diode with a long term wavelength stability of better than 10% of the absorption line width of the target gas species. The wavelength stability of the laser is highly temperature dependent as the wavelength increases with increasing temperature. Therefore, control of the temperature of the laser diode is vital for stabilising the laser emission wavelength. In this thesis, a novel method has been proposed to measure and stabilise the temperature of a laser diode. The laser diode emission wavelength was stabilised by using its measured junction voltage in a control feedback loop. In order to determine the junction voltage, a series resistance correction term was identified, which was the novel part of this wavelength stabilisation technique. The laser diode junction and forward voltages were calculated from the forward voltage drop of the laser diode at measured at various operating temperatures. The laser diode series resistance was measured dynamically and was subtracted from the forward voltage to calculate the junction voltage. Both the forward voltage and series resistances were found to be temperature dependent. This method was investigated for its short term (~ 5minute) and long term (~ 1 hour) wavelength stability and was compared with other available methods. The laser diode wavelength stability attained using this method has been also investigated at various ambient temperatures (10-40 °C). ...[cont.

Application of particle swarm optimization for solving optimal generation plant location problem

The global demand for energy especially-in-developing-countries,-has-been witnessing a tremendous growth due to rapid population growth, economic growth and developing industrial sectors. Therefore, it is necessary to forecast the future energy needs and expand generation capacity to meet the increasing peak demand.-This-paper-presents-an-optimization approach to determine the optimal location for installing a new generator in which the technical, economic and environmental aspects are all taken into consideration. The location that yields the minimum fuel costs, total emission and system loss is considered as the optimal generation plant location. The- formulated- objective- function- and- its constraints compose an optimization problem is solved using particle swarm optimization (PSO). The proposed PSO based optimization approach is tested on IEEE 14-bus system and IEEE 30-bus system to illustrate the potential of the new approach. The simulation results have shown that the proposed approach is indeed capable of determining the optimal generation location that can save much overall fuel cost as well as reduce the total emissions of generators and losses in the network

Low bit rate speech coders

A great number ofspeech coding has been developed in recent years and many others are in their advanced level of development. Most of these encoding algorithms use hybrid methodology of wavefonn coding and voice coding commonly knov.m as (vocoding). The speech quality ofthese coders is comparable with Pulse Code Modulation (PCM); a popular encoding technique used in digital telephon

Multiplexing and operational scenarios

The design of multiplexer operation can be based upon certain simulation parameters. In this chapter a design of STDM is based upon some parameters considering a 64 Kbps satellite channel. Allowing each user a capacity of 6.4kbps, 10 users can be multiplexed in a normal situation. Using VAD and packet discarding for each of the users allmv more users on the same link. In this chapter design architecture is proposed and explained in detail