This paper proposes a load survey system to determine the load characteristics of various customer classes in an electric utility company. The questionnaires are adopted to find the power consumption of key electric appliances. The actual power consumption of hundreds of customers are collected by intelligent meters. Sampling theory has been applied to find the proper sample size of both questionnaires and field tests so that the customer load characteristics will be derived with sufficient confidence level. Statistical analysis is then performed to find the power consumption model of each customer class based on the power measurement of field tests. The temperature effect on the power consumption of each customer class is also investigated by differentiating the proposed power consumption model with respect to the ambient temperature. The proposed method has been adopted by the Taipower company to better understand the customer load characteristics to support various functions of power system planning and operation in a more effective manner.

The substation loading is highly correlated with the customers served. The substations in a distribution system can be categorized as residential, commercial and industrial. Each type has a different power consumption pattern. The substation loading will be varied according to the combination of the above three types of customers. In this paper, a supervisory functional artificial neural network (ANN) technique is applied to solve the load forecasting of three Taipower substations which serve the different customer types. The load forecasting accuracy is enhanced by considering the temperature effect on the substation load demand. With the converged ANN models derived by a training procedure, the temperature sensitivity of the substation load demand is easily obtained by the recall process. It is suggested that the substation load forecasting can be performed efficiently by the proposed method to support distribution operation effectively.

This paper develops the coordination of a load shedding scheme for a large integrated steelmaking cogeneration facility. A detailed description of each design procedure is given. The loads are tripped by the underfrequency relays to prevent the power system from collapsing when the plant becomes isolated due to utility service outage. Suitable models of generators, exciters, governors and loads are included in the transient stability program to derive the proper formulation of the load shedding scheme. The key factors such as underfrequency settings, number of load shedding steps, size and location of the loads to be tripped, relay time delay and the coordination with the generator protection scheme were examined carefully during the computer simulation. The case of an actual system fault has been selected for the transient stability study to prove the accuracy of the load shedding scheme. The load shedding hardware has been installed in the plant according to the proposed strategy to prevent the system from blackout when a disturbance occurs.

This paper proposes systematic procedures to derive the load pattern of various customer classes in a utility company. The questionnaires are adopted to find the power consumption of key electrical appliances. The customer load information is obtained through intelligent equipment which records customers' electricity demand on a 15-minute interval basis throughout the year. Five hundred meters are installed on statistically selected samples from the various customer classes. By the proposed sampling theory, the customer load characteristics will be derived with a sufficient confidence level. Statistical analysis is then performed to find the typical load pattern of each customer class based on the power measurements of field tests. The temperature effect on the power consumption of each customer class is then solved by investigating the relationship between customer power consumption and the ambient temperature. The proposed procedure has been adopted by Taipower Company to determine the customer load pattern to provide valuable information for better distribution planning and to design better load management programs to enhance system operating efficiency.

This paper proposes an artificial neural network (ANN) based feeder loss analysis for distribution system analysis. The functional-link network model is examined to form the artificial neural network architecture to derive various loss calculation models for distribution feeders with different configurations. The ANN is a feedforward network that uses a standard back-propagation algorithm to adjust the weights on the connection path between any two processing elements. The typical daily load curve of the study feeder for each season is derived to field test data. A three-phase load flow program is then executed to create the ANN training sets to solve the exact feeder loss. A sensitivity analysis is performed to determine the key factors of feeder loss, which are feeder loading and power factor, primary and secondary conductor length, and transformer capacity. The above key factors form the variables of the ANN input layer. By applying the artificial neural network with pattern recognition capability, this study has developed the seasonal loss calculation models for both an overhead and an underground distribution feeder. Two practical feeders in the Taiwan Power Company (Taipower) distribution system have been selected for computer simulation to demonstrate the effectiveness and accuracy of the proposed ANN loss models. By comparing the loss models derived by the conventional regression technique, it is found that the proposed loss models can estimate feeder loss in a very effective manner and provide a better tool for distribution engineers to enhance system operation efficiency.