Makmur Saini

New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke's transformation

This paper presents a new algorithm for fault detection and classification using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarkes transformation on parallel transmission. Alpha and beta (mode) currents generated by Clarkes transformation were used to convert the signal of discrete wavelet transform (DWT) to get the wavelet transform coefficients (WTC) and the wavelet energy coefficient (WEC). Daubechies4 (Db4) was used as a mother wavelet to decompose the high frequency components of the signal error. The simulation was performed using PSCAD/EMTDC for transmission system modeling. Simulation was performed at different locations along the transmission line with different types of fault and fault resistance, fault location and fault initial angle on a given power system model. Four statistic methods utilized are in the present study to determine the accuracy of detection and classification faults. The results show that the best Clarke transformation occurred on the configuration of 12-24-48-4, respectively. For instance, the errors using mean square error method, the errors of BPNN, Pattern Recognition Network and Fit Network are 0.03721, 0.13115 and 0.03728, respectively. This indicates that the BPNN results are the lowest error.

The influence of throat length and vacuum pressure on air pollutant filtration using ejector

Environmental pollution can be caused by natural events or human treatment through uncontrolled industrial and technological activities. In fact, such pollution can threaten living creatures including on humans. It is caused by the inclusion of particles or other chemical compounds that are not present in the natural component so that it exceeds the required amount. In this paper, a construction design with pressure pollution control system was done to find appropriate pressure to eliminate the exhaust gas using sawdust and pipes as a funnel simulation in the industry. This research used the ejector system to filter air contaminated with sawdust. The results showed that the highest vacuum pressure value was 87.5kPa with the mass of the adsorbed sawdust up to 32.2 grams while for the lowest vacuum pressure of 90.5 kPa, the adsorbed sawdust was equal to 15.6 grams.

Single-Line to Ground-Fault Detection for Unit Generator-Transformer Based on Wavelet Transform and Neural Networks

This paper proposes an approach for the detection of the single line to ground fault on a unit generator-transformer, based on the extraction of statistical parameters from wavelet transform based neural network. In the simulation, the current and voltage signals were found decomposed over wavelet analysis into several approximations and details. The simulation of the unit generator-transformer was carried out using the Sim-PowerSystem Blockset of MATLAB. The statistical parameters analysis involved measurement of the dispersion factors (range and standard deviation) of wavelet coefficients. Regarding the pattern recognition of neural networks performance, the accuracy of SLG-fault detection of neural networks was 97.45 %. The results indicated that dispersion factor feature of wavelet transforms was accurate enough in distinguishing a single line to ground-fault and normal condition for a unit generator-transformer.

Transmission Line Using Discrete Wavelet Transform and Back-Propagation Neural Network Based on Clarke’s Transformation

This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

Ground fault currents in unit generator-transformer at various NGR and transformer configurations

Single line to ground faults are the most frequent faults likely to occur in the electric power system. The effect of ground fault is determined by generating station arrangements and transformer connections. In this paper, the performance of the generator within the single line to ground fault at various Neutral Grounding Resistors (NGR) and transformer configurations is studied. Simulations were conducted in MATLAB/Simulink and the results are analyzed. A comparison with the impact of faults at various transformer connections is presented. The impact of faults for generator with NGR is also analyzed. On the unit generator-transformer, fault current in the generator neutral is greatest at Yg-Yg transformer connection followed by Yg-Y and Yg-Δ, In addition, for the transformer winding Y-Y, Y-Yg, Y-Δ, Δ-Y, Δ-Yg and Δ-Δ no current flows through the NGR of generator.

Fault analysis using PSCAD/EMTDC for 150 kV South Sulawesi transmission system

Short circuit fault as one of the characteristics of transient disturbances in electric power systems that must be addressed by the safety equipment. The increase at occurrence of short circuit generates large electrical currents and at a very low voltage. This research will address the simulation of short circuit interruption in the 150 kV transmission line system. The method is used to perform the simulation with the help of PSCAD / EMTDC and PWS (Power World Simulator) softwares to obtain the characteristic of current and voltage on the 150 kV transmission network system in South Sulawesi. This discussion aims to examine the changes in current and voltage during short circuit fault with or without fault impedance and fault location distance. In this case, it will be taken case of short circuit between the air ducts with Bus PKEP to PPARE, For a distance fault of 22.5 km obtained by the fault current from the bus PKEP biggest, while at a distance of 66.5 km is acquired the fault current of the largest from the bus PPARE, of four types of errors are analyzed on the Z = 0 ohm is the largest short circuit interruption occurs in three phase fault (LLL), while at Z = 10 ohm = 15 ohm Z of the biggest mistakes of the line shortcircuit line to ground faults (LLG).