Applied Mechanics and Materials Vol. 793

Abstract: This paper reviews the performance of detailed and average model for distribution static compensator (DSTATCOM) in order to remove voltage flicker, regulate the voltage at point of common coupling (PCC), power factor correction and harmonics elimination. In these models, d-q theory is used to extract fundamental active and reactive power components of the load currents which will be required to estimate the reference source currents. Both models are evaluated and compared in MATLAB/Simulink based simulation platform.

Abstract: This paper presents a new approach known as Modified Swarm Firefly Algorithm (MSFA) technique in solving Directional Overcurrent Relays (DOCRs) coordination problem by minimizing the operating time of primary relays at near-end fault. DOCRs coordination requires the optimal values of Time Multiplier Setting (TMS) whereas the pickup current (I_p) values are based on load and data fault. The proposed technique is tested on IEEE 14-bus system and is compared with Particle Swarm Optimization (PSO). The simulation results revealed that the MSFA technique outperformed PSO in terms of minimizing fitness values.

Abstract: This paper presents the analysis on load models for cost optimization for distributed generation planning. The Embedded Meta EP – Firefly Algorithm technique is performed in order to identify the optimal distributed generation sizing. The result obtained show that the proposed technique has an acceptable performance to simulate the data and voltage dependent load models have a significant effect on total losses of a distribution system consequently will affect the cost of the system.

Abstract: The computation of security risk index in identifying the system’s condition is one of the major concerns in power system analysis. Traditional method of this assessment is highly time consuming and infeasible for direct on-line implementation. Thus, this paper presents the application of Multi-Layer Feed Forward Network (MLFFN) to perform the prediction of voltage collapse risk index due to the line outage occurrence. The proposed ANN model consider load at the load buses as well as weather condition at the transmission lines as the input. In realizing the effectiveness of the proposed method, the results are compared with Generalized Regression Neural Network (GRNN) method. The results revealed that the MLFFN method shows a significant improvement over GRNN performance in terms of least error produced.

Abstract: The aim of this project is to develop parameter estimation for dynamic model of distribution network cell (DNC) using fuzzy system. The parameter value was updated through adaptive neuro-fuzzy inference system (ANFIS). The active and reactive power responses from the fuzzy model were compared with the response from the full DNC model at various types of disturbances. The response of full DNC model was obtained from the UK 11 kV distribution network model. The model was built in DigSILENT PowerFactory software. The results obtained shown that the fuzzy model was more simple as only a few parameters involved in developing the equivalent model. This simplicity was reflected in the low computational time. In conclusion, the parameter estimation using fuzzy system was successfully developed.

Abstract: This paper describes a simple, reliable and swift load-flow solution method with a wide range of practical application. It is attractive for accurate or approximate off-and on-line calculations for routine and contingency purposes. It is applicable for networks of any size and can be executed effectively on computers. The method is a development on conventional load flow principle and its precise algorithm form has been determined to bring improvement to the conventional techniques. This paper presents a comparative study of the new constant Jacobian matrix load flow method built based on several conventional NR load flow methods. Assumptions are made so as to make the matrix constant, thus eliminating the need of calculating the matrix in every iteration. The proposed method exhibits better computation speed.

Abstract: This paper presents a recent swarm intelligence technique viz. Cuckoo Search Algorithm (CSA) for solving the Optimal Chiller Loading (OCL) problem for energy conservation. Multi-chillers system has been widely used by commercial and industrial facilities to provide cooling energy. The main problem of multi-chillers system is that it conserved huge amount of energy. In this study, Partial Load Ratio (PLR) of the chiller is used as the variables to be optimized while the power consumption in kW is selected as the objective function to be minimized. On the other hand, CSA is a one of well-known swarm intelligence techniques that has been used to solve many optimization problems. In order to show the effectiveness of CSA in solving OCL problem, a case study with six-chiller system is considered. Results obtained are compared with other techniques available in literatures.

Abstract: In this papermulti-layer perceptron (MLP) artificial neural networks (ANN) theory is presented as an efficient controllerfor the high voltage direct current (HVDC) power station systems. The results demonstrated successful performance for single mode control using an MLP-ANN based on-line power controller. The main advantage by using ANN controllers such as optimal control system over a wide operating range, which is a capable of on-line adaptation makes the power systems no a prior knowledge and has a huge database with capacity to learn from previous experience.

Abstract: This study was conducted in order to identify the different types of PQD based on a new approach the Analysis Of Variance (ANOVA). ANOVA is used as feature selection for the Power Quality Disturbances (PQD) parameters. The datum of PQD from the PSCAD/EMTDC^® simulation has been validated before feature extraction analysis can be commenced. The obtained datum is then analyzed by using cycle windowing technique based on Continuous S-Transform (CST) to extract the features and its characteristics. Moreover, the study focuses an important issue concerning the identification of PQD selection and detection. The feature and characteristics of four types of signal such as Sag, Swell, Transient and sinusoidal normal signal are obtained. The outcome of the analysis shows that a new approach ANOVA have a different result in term of identification of PQD.

Abstract: This paper presents an intelligent fault location technique for the radial unbalanced distribution systems, based on the meseurments of Short Circuit Power (S/C.P) values at the primary bus. A Multi-Layer Feed ForwardNeural Network (ML-FFNN) with the tunned parameters is designed to evaluate the measurments. The estimated locations of different fault types are compared with the actual distances and Difference Percentage is calculated for each location. To examine the performance of the proposed technique in presence of DG units, the senario is also repeated including a DG unit in the simulated distribution network and the acuired result are presented. The proposed fault location technique is capable of being implemented with the small scale dataset which is applicable for the real distribution networks.