H.K. Temraz

Fault-current limiter with thyristor-controlled impedance

A new fault current limiter with thyristor-controlled impedance (FCL-TCI) is proposed. The FCL-TCI helps in improving the steady-state stability limit, while providing an effective reduction of short circuit current during the fault. Analytical, experimental and computer simulation studies on a working model are conducted. The effects on improvement of stability limits and reduction of the short-circuit current are shown using the experimental model. The economical benefits of the FCL-TCI are due to the extension of the useful life of the existing circuit breakers and power transformers, and to the improvement in the performance of the transmission line. >

Distribution system expansion planning models: An overview

Abstract This paper presents an overview of selected existing power system distribution expansion models. The models have been divided into four distinct approaches according to two different perspectives: time and physical structure. In each approach, model cost function, constraints, mathematical programming technique used for solution, and the notable merits, as well as the deficiencies associated with the model, are discussed in detail.

A planning model for siting, sizing and timing of distribution substations and defining the associated service area

Abstract A new planning model is developed for solving sizing, sitting and timing of distribution substation and defining the associated service area. The model incorporates a non-discrete function that accurately simulates the different cost components of distribution substations. Voltage, capacity, power conservation and radial-flow constraints are included into the developed model. The efficiency of the developed model is illustrated using a detailed numerical example.

Review of electric load forecasting methods

The different available load forecasting techniques can be classified according to the type of load data pattern, into three classes: (1) stationary; (2) nonstationary; and (3) nonstationary, seasonal and cyclical techniques. The criterion is divided into three stages: (1) identification; (2) estimation; and (3) diagnostic checking. The purpose of the identification stage is to ascertain the techniques(s) that appear to hold more promise for adequately describing a given data set. The paper presents an algorithmic procedure for selecting and constructing the most appropriate electric load forecasting model. From this review, it is clear that models for the selection and construction criteria are essential for a proper forecasting model.

Analytic spatial electric load forecasting methods: A survey

In the paper, analytic spatial electric load forecasting methods are classified into two approaches: trending and multivariate. Trending approaches extrapolate load based on past values of load data. Multivariate techniques either extrapolate or simulate load based on annual small area peak load as well as other variables. Each approach is further divided into two classes. The objective of the paper is to present a comprehensive review of some of the existing methods that describe the general concept of an analytic spatial load forecast, as well as the notable merits and deficiencies associated with the various available techniques.

Applications of noniterative least absolute value estimation for forecasting annual peak electric power demand

A noniterative least absolute value (LAV) technique for estimating the parameters of a selected electric load forecasting model is utilized. The selected forecasting model with the estimated parameters is employed in forecasting the demand of a given data set. The main feature of the LAV technique is its capability of rejecting any bad data in the parameters estimation process without any previous knowledge of their location. To illustrate the efficiency of the LAV technique in electric load forecasting, two types of applications are considered. In the first application, the adequacy of the LAV technique for estimating reliable electric load forecasting model parameters is illustrated. Results have shown that models with parameters estimated using the LAV technique generate better forecasting results than those using least-squares-technique-estimated parameters. In the second application, the efficiency of the LAV technique in estimating good forecasting model parameters for given bad data is demonstrated. The results have shown that the model with parameters estimated using the LAV technique produces quite reasonable forecasting results; whereas the model with least-squares-technique-estimated parameters generates completely unacceptable forecasting results due to the effect of bad data.

Application of partitioning techniques for decomposing large-scale electric power networks

Abstract Two efficient heuristic algorithms for solving cluster problems associated with partitioning of power networks are presented in this paper. Both algorithms are divided into two stages. In the first stage, an initial partition is created based on the electrical distance among system buses. The second stage involves interchanging pairs of buses among the various clusters of the initial partition. The first algorithm solves an optimal k -decomposition problem. In the optimal k -decomposition, the partitioning of a network into k clusters of buses is performed by maximizing the number of uncut links among clusters. This type of decomposition is known to be equivalent to a 0–1 quadratic programming problem which is approximated by a linear transportation problem. By defining a link weight as the electrical distance between linking buses, the algorithm clusters strongly-connected buses together while weakly-connected buses are placed in different clusters. The second algorithm solves the placement problem of n -connected buses in a k -dimensional Euclidean space; such a problem is reduced to finding k eigenvectors of a connectivity matrix, defined as the bus admittance matrix. The node interchange is an iterative heuristic method that can be used to improve an initial partition, such as that obtained by either the k -decomposition technique or the eigenvector approach. The method moves one bus at a time, from one cluster of the initial partition to another, in an attempt to maximize the total electric distance among clusters of the final partition. Applications of the proposed algorithms to both small and medium-size power systems, and comparing the results with those obtained in another study, are illustrated in this paper.

Fuzzy linear parameter estimation algorithms: a new formulation

In this paper, we present a new formulation of a fuzzy linear estimation problem as one of the linear programming where the objective is to minimize the spread of the data points, subject to constraints on each measurement point to guarantee that the original membership is included in the estimated membership. Different models are developed based on fuzzy triangular membership as well as fuzzy numbers of LR-type, and applied to different examples in fuzzy linear regression and finally we apply these models for estimating the electrical load on a substation busbar. It is demonstrated that since the nature of the load is characterized by uncertainty, the proposed technique is well suited to apply for electrical load estimation.

A heuristic algorithm for power-network clustering

An efficient heuristic algorithm for solving cluster problems associated with partitioning of power networks is presented. The algorithm is divided into two stages. The first stage creates an initial partition based on the electrical distance between system buses. The second stage involves interchanging pairs of buses among the various clusters of the initial partition. The first stage solves the placement problem of n connected buses in an r-dimensional Euclidean space; such a problem is reduced to finding r eigenvectors of a connectivity matrix, defined as the bus admittance matrix. The second stage is based on a node interchange technique. The node interchange is an iterative heuristic method that can be used to improve an initial partition. The method moves one bus at a time, from one cluster of the initial partition to another, in an attempt to maximize the total electrical distance between clusters of the final partition. Applications of the proposed algorithm to both small and medium-size power systems are illustrated.

A comprehensive long-term power distribution system expansion planning model

A new long-term power distribution system expansion planning model is developed. The model includes accurate representation of the different nonlinear planning cost functions. The cost of expanding an existing substation by adding transformer units is also included in the planning cost formulation. Moreover, power-conservation, voltage-drop and equipment-capacity constraints are explicitly included in the planning problem formulation. The developed model is illustrated using a numerical example and is tested for both efficiency and accuracy.