Sensoring Leakage Current to Predict Pollution Levels to Improve Transmission Line Model via ANN

Pollution insulator is a serious threat to the safety operations of electric power systems. Leakage current detection is widely employed in transmission line insulators to assess pollution levels. This paper presents the prediction of pollution levels on insulators based on simulated leakage current and voltage in a transmission tower.The simulation parameters are based on improved transmission line model with leakage current resistance insertion between buses. Artificial neural network (ANN) is employed to predict the level of pollution with different locations of simulated leakage current and voltage between two buses. With a sufficient number of training, the test results showed a significant potential for pollution level prediction with more than 95% Correct Classification Rate (CCR) and output of the ANN showed high agreement with Simulink results

Agarwood grading estimation using artificial neural network technique and carving automation

Agarwood is a fragrant dark resinous wood formed when Aquilaria tress infected with a certain type of mold and appears like wood defects. It is the most valuable non-timber product has been traded in international markets because of its distinctive aroma, and can be processed into incense and perfumes. Agarwood grade is determined by several characteristics, such as black colour intensity, smell, texture and weight through visual inspection. However, this could lead to several problems such as false grading results. Traditionally, the carving process of separation of the uninfected Aquilaria wood that lacks of the dark resinous accomplished by using simple tools like knife and chisel. Hence, an expert worker is required to complete the task. In this paper, the Artificial Neural Network (ANN) technique is used to classify the Agarwood based on the features extraction from Gabor Filter and percentage of black colour estimation. At first, the images of seven groups of wood defects or knots are identified: dry, decayed, edge, encased, horn, leaf, and sound defect with total sample of 410 knots. Then, these images of knots are matched into three grade groups of Agarwood. Next, the experimental results show that the Agarwood can be classified into three grades groups based on knot and black intensity. A set of selected images of knots were used as trace patterns and carved on pieces of wood blocks by using a Computer Numerical Control (CNC) machine where the total time taken for each carving process was calculated. For each image, two Gabor Filter features and percentage of black colour were used as ANN inputs. In conclusion, the total accuracy of the experiments is 98% and the total time of carving is increased with the increased of grade group number

Static security assessment on power system using artificial neural network

In modern industrialized society, a supply of electric energy is expected to be reliable and continuous since a high availability of secure power system is essential for its’ progress. A secure power system is expected to be free from risk or danger and to have the ability to withstand without exception to any one of the pre-selected list of credible contingencies. The objective of this research is to investigate the reliability of the Static Security Assessment (SSA) in determining the security level of power system from serious interference during operation. Therefore, back propagation Artificial Neural Network (ANN) is implemented to classify the security status in the test power system. Offline Newton-Raphson load flow is employed to gather the input data for the ANN. The large dimensionality of input data is scaled down by screening process to reduce the computational time during ANN training process. This method has been tested with 4 bus test system and IEEE 24 bus test system. Bus voltage and thermal line variables are set as a limit to the developed method. It has been discovered that error of trained ANN are within the acceptable range if compared to similar results from published works. The ANN has been found to be faster than the conventional method in predicting the security level of the tested system. It is concluded that the ANN works well in providing status of the current operating point for specific contingency of power system

Study of leakage current distribution in wooden pole using ladder network model

A wooden pole is the most popular choice as the physical support structure for an electrical distribution network. A recent increase in the failures of wooden poles that lead to pole fires warrant further investigation into the performance of wooden poles and pole design. This paper examines the leakage current distributions on the radial, heartwood and sapwood section of the wood pole and the effect of the metal insertion in wooden structures using an electrical ladder network model. This paper presents the findings from two wooden pole models: a basic wooden pole and a complete wooden pole with cross-arm and supporting bars attached. The results show that the bulk of the leakage current flows through the internal section under wet weather conditions and the metal insertion along the radial of the wood increase the magnitude of the leakage current. The model takes into consideration the pole dimension, rain parameter, moisture content, air resistance, and preservative effect (chromated copper arsenate) on the wooden pole

Feasibility study of leakage current shunting method based on the ladder network model

This paper investigates the nonuniformed distribution of leakage current in a wooden pole with the cross arm attached by using the network ladder model and evaluates the effectiveness of leakage current shunting arrangements that could minimize the occurrence of pole fire. The mitigation method that is proposed in this paper diverts excessive leakage current from a fire-prone hotspot along the wooden structure by using a special shunting method. A comparison between the existing shunting methods and the new cost-effective shunting method is presented. The findings in this paper will be beneficial toward the understanding of the current flow in the internal wooden power pole and, thus, help us to find new methods that can effectively mitigate the pole fire

Control techniques for three-phase four-leg voltage source inverters in autonomous microgrids: a review

The application of the four-leg inverter as an effective interface for renewable and sustainable distributed energy resources (DERs) is gaining more attention with the advances in power electronics technology. One of the key technologies in inverter-based distributed generation (DG) systems is the four-leg voltage source inverter (VSI) that is utilized to operate in autonomous four-wire microgrids. Four-leg VSIs are becoming increasingly popular in four-wire microgrid, because they can not only achieve a proper control scheme in autonomous mode but also cope with the prescribed power quality requirements. The aim of this paper is to provide an overview of the main characteristics of recently used control strategies for four-leg VSIs operating in autonomous microgrids. First, two commonly-used four-wire inverter configurations are discussed, and their advantages and disadvantages are compared. Afterwards, the most up to date control techniques for three-phase four-leg VSIs operating in islanded microgrid from the reference frame point of view are described. Lastly, a comparative analysis is carried out where the benefits and drawbacks of each strategy are assessed, and then some suggestions are put forward for the future research

Power quality improvement in autonomous microgrids using multi-functional voltage source inverters: a comprehensive review

Multi-functional voltage source inverters (VSIs) have attracted increasing attention in recent years for their advantageous auxiliary services for power quality enhancement in autonomous microgrids. These types of VSIs can not only achieve a proper control scheme in autonomous mode but also cope with the prescribed power quality and stability requirements. These functionalities are integrated within the same device, thereby significantly improving the cost-effectiveness of microgrids while decreasing the investment and bulk compared with those of multiple devices with independent functionalities. Control strategies for power quality enhancement in autonomous microgrids using multi-functional VSIs are comprehensively reviewed in this paper. In addition, such VSIs are discussed in detail, and comparisons of which are also provided. Lastly, a number of future research directions for multi-functional VSIs are recommended