In this dissertation, the transformer management system has been developed to monitor transformer over loading and generate warning message in conduit mapping management system (CMMS) of Taipower company. The transformer over loading prediction is performed by both offline and online modes. Performs the transformer loading estimation by using the customer monthly energy consumption in customer information system (CIS) and the connectivity attributes of transformer and customers served in CMMS system of Taipower company. The daily load curve of distribution transformer is derived considering the typical daily load patterns which have been developed in load survey study. The warning message will be generated when the peak loading estimated is lager then the transformer rated capacity. To enhance the accuracy of transformer attributes in CMMS system, the transformer phasing measurement system (TPMS) and the connectivity identification system to identify all of the customers served by each transformer are developed. It is difficult to receive the 1 pulse per second signal form global positioning system for timing synchronization of TPMS measuring units for phasing measurement of transformers located in basement, the temperature compensated crystal oscillation with Fuzzy calibration algorithm is used to maintain the timing synchronization within 10o deviation for measurement period of 2 hours. To solve the incorrect problem of transformer and customer connectivity in CMMS, the power line carrier technology is applied in the design of connectivity measurement system for the identification of customers served by the transformer. The peak loading of transformer is estimated by including the temperature effect and the overloading flag of transformer is displayed on the CMMS automatic mapping system. For the online TLM system, the embedded transformer terminal unit is developed for the real time measurement of transformer loading and insulation oil temperature. For the transformer with abnormal operation condition, the alarm signals will be generated and transmitted to the TLM master station via hybrid communication system for the activation of demand response function to execute the load shedding control of customer loads.

Power industry restructuring has created many opportunities for customers to reduce their electricity bills. In order to facilitate the retail choice in a competitive power market, the knowledge of hourly load shape by customer class is necessary. Requiring a meter as a prerequisite for lower voltage customers to choose a power supplier is not considered practical at the present time. In order to be used by Energy Service Provider (ESP) to assign customers to specific load profiles with certainty factors, a technique which bases on load research and customers¡¦ monthly energy usage data for a preliminary screening of customer load profiles is required. Distribution systems supply electricity to different mixtures of customers, due to lack of field measurements, load point data used in distribution network studies have various degrees of uncertainties. In order to take the expected uncertainties in the demand into account, many previous methods have used fuzzy load models in their studies. However, the issue of deriving these models has not been discussed. To address this issue, an approach for building these fuzzy load models is needed. Load aggregation allows customers to purchase electricity at a lower price. In some contracts, load factor is considered as one critical aspect of aggregation. To facilitate a better load aggregation in distribution networks, feeder reconfiguration could be used to improve the load factor in a distribution subsystem. To solve the aforementioned problems, two data mining techniques, namely, the fuzzy c-means (FCM) method and an Artificial Neural Network (ANN) based pattern recognition technique, are proposed for load profiling and customer class assignment. A variant to the previous load profiling technique, customer hourly load distributions obtained from load research can be converted to fuzzy membership functions based on a possibility¡Vprobability consistency principle. With the customer class fuzzy load profiles, customer monthly power consumption and feeder load measurements, hourly loads of each distribution transformer on the feeder can be estimated and used in distribution network analysis. After feeder models are established, feeder reconfiguration based on binary particle swarm optimization (BPSO) technique is used to improve feeder load factors. Test results based on several simple sample networks have shown that the proposed feeder reconfiguration method could improve customers¡¦ position for a good bargain in electricity service.