Band 11, Heft 1

Traditional electric trains have a low power factor, high loading variation change and inefficiency, generating both negative sequence current and the imaginary part of positive sequence current. Delta type Load Balancer associated with specially connected transformers is generally adopted to correct power factor and voltage imbalance. However, the modern trains used in High Speed Railway (HSR) have a power factor approximately close to unity, and therefore only need voltage imbalance correction. This study presents novel Load Balancer schemes, which are a modification of the Delta-type Load Balancer, with various connected transformers to correct imbalance. The symmetric component method is also adopted to determine the installation capacity of the proposed Load Balancer schemes.

In the context of deregulation, unbundling of utility services and open access of transmission facilities is becoming order of the day. In order to achieve transparency and fairness from the point of participants involved, it is necessary to get contributions from each generator (source) to loads, flows in the network and losses for the system. This paper attempts to evaluate real and reactive power flow in the network due to individual sources and its contribution to each load using the principle superposition which is fairly simple and straightforward. As a result, in a multilateral transaction environment, it is possible to fairly allocate charges for real and reactive power usage. In addition the extent of network loading by individual generator is computed which shows direction to manage network congestion in an effective manner. Sample system to illustrate the proposed approach and its implementation on a practical 72 bus Indian Southern grid equivalent 400/220kV network are presented.

An accurate and efficient Short-Term Load Forecasting (STLF) plays a vital role for economic operational planning of both regulated and restructured power systems. This paper presents the STLF models by introducing wavelet transforms, in different ways, with feed-forward Neural Networks (NNs). First, a wavelet-based NN is modeled, where the forecasting has been accomplished in three-stages and the wavelet technique is employed to decompose/reconstruct the original signals and non-linearity of the decomposed signals. Second, an Adaptive Wavelet Neural Network (AWNN) is modeled, which is a new class of NN with continuous wavelet function as the hidden layer node's activation function. Unlike the first model, AWNN does not externally decompose/ reconstruct the original signals and, therefore, this model deals with the problem related to loss of high frequency information that might occur in the wavelet-based NN model. AWNN continuously updates the wavelet parameters (translation and dilation) and layer weights through a back-propagation training algorithm as in classical NNs. The performances of these two models are compared with Multi-Layer Perceptron NN (MLPNN) with the application of day-ahead and hour-ahead load forecasting in the California electricity market. The results are also compared with California Independent System Operator (CAISO)'s forecasted system loads. It is found that due to faster and accurate training capability, AWNN outperforms the MLPNN, wavelet-based NN and CAISO load forecasts.

The theory used by economists to support restructuring of the electric power industry has ignored several important technological constraints and public goods that affect the way in which power is delivered. Similarly, engineers, by using security-constrained optimization to incorporate the demand for reliability, have failed to properly define the economic problem. In this two-part paper we attempt to remedy the deficiencies of both the economists’ approach and the security-constrained optimization approach through a collaboration of economists and engineers to examine the theoretical properties of a networked power system that provides optimal resource allocation.

Electric power is traditionally comprised of valued services, including real and reactive power, voltage, frequency and reliability in its most general sense. In this second part of our two-part paper we show mathematically that of these, only real and reactive power are purely private goods, in that power consumed by one customer cannot be used by another and customers can be excluded from receiving any power. The other ancillary services, including voltage, frequency and reliability are shown to be public goods. The first order conditions presented clearly illustrate that the public goods occurring in electric power systems comprise a significant problem for market design.

A novel method is proposed in this paper for the classification of power quality disturbances using a Probabilistic Neural Network with a Parzen kernel. An attempt has been made to solve the problem as a pattern classification problem in which there is a normal class and a series of abnormal classes. The traditional parametric techniques of pattern classification can't be employed due to unknown parameters of the density functions displayed by the extracted features of the signal. Hence non-parametric pattern classification method was to be adopted and Parzen kernel being used. Parzen kernel is one of the most famous non-parametric techniques and has been a good choice for this purpose with its ease of implementation and good accuracy level. The time varying nature of the probability densities are adaptively identified by Parzen windows. Experimental results have been presented for establishing the efficacy of the method as a tool to automate the Power Quality Classification problem. Various kinds of signals such as Sag, Swell, Momentary flicker, Harmonics were generated and subjected to the above classification scheme. A detailed study on the accuracy and performance of the proposed algorithm has been made with variations in parameters such as the number of training samples and the variance of the Gaussian kernel used.

During a short circuit condition, core of a Current Transformer (CT) may become saturated due to large primary current containing a significant DC component. The saturation of the core distorts the secondary current which may cause malfunction of the protective devices connected to the CT. As a result of significant advances in the technology and deployment of digital relays in the last decade, the distorted secondary current can be reconstructed in a very short time. Several methods, which take into account the non-linear behavior of the CT, have been researched in the recent past. Processing time, accuracy, and online implementation are of prime importance while selecting a particular algorithm. In this paper, a comprehensive review of the different algorithms used for the detection of saturation as well as reconstruction of the distorted secondary current is presented.

This paper presents a new and fast algorithm to prevent distance relay mal-operation during power swings. The algorithm blocks the relay tripping signals during power swing and unblocks the signals if a fault occurs during power swing. The proposed blocking scheme for distance protection incorporates an additional criterion into the conventional relay which is based on the derivative of the line reactive power as seen by the relay. This technique overcomes the shortcoming of conventional power swing detector (PSD) by removing the pre-defined R-X diagram. The conventional PSD has the difficulty in obtaining the timer setting at pre-defined R-X diagram due to varying cycle of power swings. To illustrate the effectiveness of the proposed algorithm, simulations were carried out on the IEEE 39 bus test system using the PSS/E software. Test results show the effectiveness of the proposed scheme in blocking the relay’s false trip signals during power swing.

Lagrangian relaxation (LR) is one of the most promising approaches for solving power system unit commitment (UC) problems by means of decomposition. There are several technical challenges in developing an effective LR solution. The first is to devise an efficient dual optimization procedure. The second is to have a quick and reliable procedure for removing residual violations in the relaxed constraints. The third is to make the solution algorithm less dependent of problem domain knowledge to improve solution portability. This paper presents a general algorithm that depends only on the formal structure of the problem formulation and assumes no specific knowledge in the problem domain. The proposed algorithm has been tested on real unit commitment problems with as many as 50,000 integer variables and has consistently produced good solutions with very limited number of iterations. In addition to being very efficient, the proposed algorithm is also seen to be robust under parametric perturbations.

To study the torsional oscillation phenomena of turbine-generators, a fundamental approach is by electromagnetic transient simulation. In this paper, the structure of the damping coefficient matrix of the shaft torsional model for turbine-generators is studied, and a method to determine the values of the elements of the damping coefficient matrix is developed. Through the analysis of different damping components, the most concerned damping component is identified. Based on the complex modal analysis method widely used in the mechanical vibration engineering, the relationship between the damping coefficient matrix and the torsional mode damping is established, furthermore, an iterative method to compute the damping coefficient matrix is developed. A test study on the turbine-generator shaft torsional model of the IEEE first SSR benchmark system indicates that the proposed torsional damping modeling technique in electromagnetic transient simulation is valid and effective.