M. S. Chen

Distribution system power flow analysis-a rigid approach

This approach is oriented toward applications in three phase distribution system operational analysis rather than planning analysis. The solution method is the optimally ordered triangular factorization Y/sub BUS/ method (implicit Z/sub BUS/ Gauss method) which not only takes advantage of the sparsity of system equations but also has very good convergence characteristics on distribution problems. Detailed component models are needed for all system components in the simulation. Utilizing the phase frame representation for all network elements, a program called Generalized Distribution Analysis Systems, with a number of features and capabilities not found in existing packages, has been developed for large-scale distribution system simulations. The system being analyzed can be balanced or unbalanced and can be a radial, network, or mixed-type distribution system. Furthermore, because the individual phase representation is employed for both system and component models, the system can comprise single, double, and three-phase systems simultaneously. >

Three-phase cogenerator and transformer models for distribution system analysis

The authors present detailed three-phase cogenerator and transformer models for analyzing a large scale distribution system. The cogenerator model presented can represent the inherent generator phase imbalance due to distribution system imbalance. The cogenerators can be synchronous or induction and can be on either primary or secondary systems. The transformer models consider the copper and core losses, the winding connection, the phase-shifting between primary and secondary windings, and the off-nominal tapping. An individual phase, as opposed to a balanced three-phase, representation is employed. This approach is oriented toward applications in distribution system operational analysis rather than planning analysis. >

Nonparametric regression based short-term load forecasting

This paper presents a novel approach to short-time load forecasting by the application of nonparametric regression. The method is derived from a load model in the form of a probability density function of load and load affecting factors. A load forecast is a conditional expectation of load given the time, weather conditions and other explanatory variables. This forecast can be calculated directly from historical data as a local average of observed past loads with the size of the local neighborhood and the specific weights on the loads defined by a multivariate product kernel. The method accuracy relies on the adequate representation of possible future conditions by historical data, but a measure to detect any unreliable forecast can be easily constructed. The proposed procedure requires few parameters that can be easily calculated from historical data by applying the cross-validation technique.

Distribution system short circuit analysis-A rigid approach

A rigid approach to short circuit analysis for large-scale distribution systems is introduced. The approach uses an individual (a-b-c) phase-based system representation, a nontrivial transformer model, and includes the contribution due to load. The method can, therefore, be applied to balanced or unbalanced, radial, network, or mixed-type distribution systems. This approach is oriented toward applications in distribution system operation analysis rather than the more typical planning-oriented analysis. The solution method is an iterative compensation method, which uses a single optimally ordered factorization of the bus admittance matrix (Y/sub Bus/), commonly used in power flow analysis, to simulate the fault condition. The use of this method in a short circuit analysis program enables a factorized Y/sub Bus/ solution, resulting in many advantages. Using a common factorization, both power flow and short circuit analyses are possible in a single execution. Since the factorization is unchanged, multiple faults of various types can be simulated in one run of the program. >

Demand forecasting in power distribution systems using nonparametric probability density estimation

Customer demand data are required by power flow programs to accurately simulate the behavior of electric distribution systems. At present, economic constraints limit widespread customer monitoring, resulting in a need to forecast these demands for distribution system analysis. This paper presents the application of nonparametric probability density estimation to the problem of customer demand forecasting using information readily available at most utilities. The method utilizes demand survey information, including weather conditions, to build a probabilistic demand model that expresses both the random nature of demand and its temperature dependence. The paper describes a procedure for developing such a model and its application for demand forecasting based on customer energy usage and outside temperature.

Simplified Feeder Modeling for Loadflow Calculations

Three models of a distribution system are developed in this paper. Included are descriptions of simulation models of the diverse components of a typical distribution system. The three system models are illustrated by examples. They are applied to an actual feeder and the results are analyzed to show the advantages and disadvantages of each model. A unique method is developed for combining the discrete distributed load voltage drop solution with the discrete distributed load losses solution. The development of this combined method is in the form of a tutorial , as are the developments of the components of this method. The conclusion is reached that it is possible to reduce many complex feeders to simple models in the study of feeder voltage profiles and losses with negligible error.

Neural network based demand forecasting in a deregulated environment

The traditional approach to load forecasting is based on processing time series of load and weather factors recorded in the past. In the dynamic environment of the deregulated power industry, historical load data may not always be available. This paper explores the possibility of an alternative approach towards load forecasting based on indirect demand estimation from available customer data. This approach requires utilization of demand models for different customer categories. This paper presents a neural network based method of demand modeling. Neural networks are designed and trained based on the aggregate demands of the groups of surveyed customers of different categories. The performance of such models depends on the neural network design and representativeness of the training data. The forecast accuracy is also affected by the forecasted group size, customer characteristics, customer classification system, and the extent of demand survey. This paper discusses the issues of neural network design and illustrates the proposed method by its application to forecasting demand of residential customers.