Majid Jamil

Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

Abstract To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate.

Generalized Neural Network Approach for Global Solar Energy Estimation in India

In India, large areas of land are barren and sparsely populated, making these areas suitable as locations for large central power stations based on solar energy. India is located in the equatorial sun belt of the earth, thereby receiving abundant radiant energy from the sun. Estimating solar energy accurately is a big task to exploit the solar potential for power generation. A number of conventional and intelligent models are available; how- ever, the results are not satisfactory due to extreme simplicity of their parameterization. In this paper, application of generalized neural network (GNN), a modified approach of artificial neural network (ANN), is proposed to estimate solar energy to overcome the problems of ANN such as a large number of neurons and layers required for complex function approximation, which do not affect the training time only but also the fault tolerant capabilities of the ANN. The mean relative error in the estimation of global solar energy is found around 4% whereas the same using fuzzy logic is 6% approximately. Therefore, it is concluded that the GNN technique is found more accurate for the estimation of global solar energy.

Fault detection and classification in electrical power transmission system using artificial neural network

AbstractThis paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB® environment.

Security issues in IPv6

The current generation of IP, version 4 (IPv4), has been in use for more than 20 years, since its inception in 1980 and has supported the Internets rapid growth during that time. IPv4 has proven to be robust, easily implemented and interoperable. This is a tribute to its initial design. However, the current Internet has grown much bigger than was anticipated. There are several problems such as impending exhaustion of the IPv4 address space, configuration complexities, and poor security at the IP level and inadequate QoS support for real-time delivery of data. To address these and other concerns, the Internet engineering task force (IETF) has developed a suite of protocols and standards known as IP version 6 (IPv6). The new features introduced such as auto-address configuration, end to end connectivity, mandatory support for security and mobility pose a great challenge on security for future networks based on IPv6. This paper identifies and examines security threats relating to the new features introduced in IPv6.

Empirical correlation of estimating global solar radiation using meteorological parameters

An empirical model for the estimation of solar energy on the basis of Angstroms model is proposed in this work. Seven regression equations are developed by using different meteorological parameters such as mean sunshine duration per hour, temperature, relative humidity, wind speed, and rainfall. The performance of the model is determined on the basis of statistical indicators like correlation coefficient(r), coefficient of determination (R2), root mean square error (RMSE), mean percentage error (MPE), and mean bias error (MBE). The results show that the equation with the highest value of r, R2 and the least value of RMSE, MPE, and MBE provides better results.

Optimal placement of SPV based DG system for loss reduction in radial distribution network using heuristic search strategies

The appropriate location of distributed generation (DG) is very important for loss reduction in a radial distribution system. In this paper a methodology for determining the optimum size and location for installing the solar photovoltaic (SPV) based DG system for supplying the active power at the node in a radial distribution system for loss reduction is presented. The objective of this paper is to apply heuristic search strategies to determine the node for the appropriate placement of DG. In heuristic approach, a critical node, called sensitive node is selected based upon maximum power losses caused for installing DG system. This method ensures that voltage constraints are met. This heuristic approach is useful for large distribution system and can be useful for online implementation. Test results have been tested on IEEE-33 bus system and presented.

Wavelet based ANN approach for fault location on a transmission line

In this paper an approach based on Neural Network sing Discrete wavelet transform is proposed to locate the fault of the three phase transmission line. The different test cases are carried out on the basis of extensive simulation studies using MATLAB toolbox for different fault operating conditions. The proposed method requires the three phase post fault currents to be measured (fault signals) at one end (sending end).In order to accomplish the task, features of the post fault currents are extracted in terms of Wavelet Energy using Discrete wavelet transform(DWT) first and then fed to train with feed forward Neural Network (FNN) for each case. The FNN estimates the location of the fault with a very high degree of accuracy which results in, an improvement over the existing iterative method of fault location, for a wide variation of fault operating conditions hence the method is reliable and robust.

A comparison of photovoltaic maximum power point techniques

The growing demand for electricity & depletion of fossil fuel makes increasing use of Renewable sources of energy day by day. Solar energy has gained much importance as it is an inexhaustible and easily available energy source. But the harnessing of solar energy using PV modules comes with its own problems that arise from the change in insolation conditions. These changes in insolation conditions severely affect the efficiency and output power of the PV modules. So in order to maximize the output power of PV Panels, several MPPT techniques have been developed and these techniques are well available in the literature. In this paper different MPPT techniques are reviewed and compared with their pros & cons. A Fuzzy Logic based MPPT controller is also implemented using MATLAB FUZZY Logic toolbox. This paper may be considered as a reference for the future work of PV generation.

Techno-economic viability of three different energy-supplying options for remote area electrification in India

Distributed power generation based on renewable energy sources brings new life and hope to electrify remote villagers. The proposed work suggests the techno-economic feasibility of three different energy-supplying alternatives namely the solar photovoltaic (SPV) system, diesel generator system and extending the grid connection for energy supply to a remote village located around 15 km away from the place where grid supply is available. Design of all the above three systems along with their economic analysis is carried out in this paper. A comparison on the basis of economic indicators like unit cost of electricity, net present value and payback period among the three systems is also discussed. The results show that the SPV system is economically more viable among the three options apart from environment benefits. This study may provide a new direction for energy planners to supply energy in remote non-electrified areas of India through SPV systems.

Sensitivity analysis of fault quantities

In this paper, a method for the sensitivity analysis of sequence fault currents and voltages in an unloaded overhead transmission line for perturbation in line parameters is described. The sensitivity of these quantities with respect to conductor radius, spacing between conductors, earth resistivity, height of conductor and frequency are evaluated for an overhead transmission line. The sensitivity predicts the effect of each parameter on fault currents and voltages, which help in tap setting of the relays for protecting the transmission line against faults. To demonstrate the potential of the proposed scheme, the sensitivity of fault currents and voltages for a three phase, 220 kV line has been studied and the results are presented.