With the rapid growth of load demand, the distribution system is becoming very complicated such that the operation efficiency and service quality are deteriorated during recent years. Engineers have to solve the problems by applying new technologies to enhance the efficiency of distribution system. In this thesis, an immune algorithm(IA) based on weighting selection as a decision maker is proposed to reach the desired switching operations such that transformer and feeder loading balance can be achieved. The IA antigen and antibody are equivalent to the objective and the feasible solution for a conventional optimization method. The concept of the information entropy is also introduced as a measure of diversity for the population to avoid falling into a local optimal solution. This algorithm prevents the possibility of stagnation in the iteration process and achieves the fast convergence for the global optimization. With the object-orient programming(OOP), this research project is to create the relationship of distribution element objects and encapsulation of data with all 22KV underground systems in Taichung district. The OOP does provide an effective tool for the management of distribution system database and the fault detection, isolation, and service restoration(FDIR) function of feeders and main transformers. According to the attributes of line switches, we can create the 22KV distribution system configuration with the topology processor. In order to calculate the current flows of line switches, this project will also execute the three phase load flow program with the customer information system(CIS), load survey, outage management information system(OMIS), and the data of all feeders and main transformers. In this thesis, the IA is used to solve the optimal switching problem by considering the customer load characteristics for the normal operation and the overload contingency of the distribution system. The efficiency of immune algorithm to solve the problem is verified by comparing to the computing time of the conventional binary integer programming for decision making of switching operation. A Taichung district distribution system is selected for computer simulation to demonstrate the effectiveness of the proposed methodology for solving the optimal switching operation of distribution system. The result of this thesis will be an important reference for distribution automation in Taiwan.

In this dissertation, the artificial intelligent Petri nets is applied to find the optimal switching operation for service restoration and feeder loading balance for 18-feeders distribution systems that containing the whole 24-hours load profiles of service zones. After the fault location has been identified and isolated for a system fault contingency, the Petri nets model with inference mechanism is derived and applied to solve the optimal load transfer among distribution feeders. For system normal operation condition, the load balancing among distribution feeders is obtained by the Petri nets model to enhance the operation efficiency of distribution systems. The switching operation, which will result in the loading balance among distribution feeders, is derived by the Petri nets model according to the loading cost of distribution systems. To determine the effectiveness of the proposed methodology, a Taipower (Taiwan Power Company) distribution system which serves a mixed types of customers is selected to perform the computer simulation. It is found that the Petri nets approach can enhance the solution process of fault restoration with proper load transfer and improve feeder load balance for distribution systems by considering the load characteristics of the service customers.

When a permanent fault occurs in distribution systems, the fault location should be detected, isolated and the un-faulted but out of service areas have to be restored. The outage areas can be minimized by the switching operation based on the system load characteristics. By integrating the Outage Management Information System (OMIS), the connectivity of customers and feeder/transformer, the Customer Information System (CIS), the Automated mapping /Facility Management (AM/FM) with the customer load patterns, the hourly load demand and the service priority index of each distribution feeder and each service zone are calculated. By this way, the service restoration of the most power demand and customers can be obtained for the fault contingency of distribution system. To enhance the effectiveness of switching operation for fault contingency of distribution system, the Expert System with CLIPS has been developed by considering the operation rules in the application software program. A underground distribution system with 26 feeders in Kaohsiung District of Taiwan Power Company has been selected for computer simulation to solve the proper switching operation by taking into account the service priority of customers. It has been verified that the proposed methodology can restore the customer power service effectively by Expert System with distribution operation rules.

Based on the load survey study, a stratified sampling method is proposed to select the proper size of customers so that the load patterns derived can represent the load behavior of whole customer population. In this study there are 1315 customers out of Taipower customers over various service classes are selected for the installation of intelligent meters in the field to measure the power consumption within every 15 minutes. The bad data detection is performed to identify the abnormal power consumption by executing the Chi-square test. The standardized daily load pattern of each customer class has been derived with the mean per-unit method of customer load. The billing data are retrieved from the customer information system and applied to derive the customer daily load pattern by considering the customer load patterns. According to the total power consumption by all customers within the same class and considering the corresponding daily load pattern, the daily load profile of the customer class is then determined. By aggregating the load profiles of all customer classes, the daily load composition and load model of each service district can therefore be solved. By the same manner, the daily load pattern of whole Taipower system can be derived and it can be used to support the proper design of tariff structure according to the respective contribution of system power demand by each customer class. To investigate the overloading of distribution main transformers during the summer season, the correlations analysis of customer power consumption and temperature is performed. The effect of temperature change to the power consumption of each customer class is solved by multiple regression analysis with 95% confidential level. Based on the temperature sensitivity and the corresponding load composition, the load change due to temperature rise for various customer classes can be estimated. To demonstrate the impact of temperature change to distribution system operation, considering the temperature sensitivity of power consumption and load composition solves the power demand at each load bus. By updating the bus load demand due to temperature change, the feeder loading and power loss is therefore derived. To resolve the over loading problem of distribution feeders and main transformers during the summer season, a temperature adaptive switching operation has been proposed to perform the proper load transfer among the feeders/main transformers. In this dissertation, the effect of temperature change to the time varying characteristics of load buses and power transmission in Taipower is investigated. The dc circuit model of Taipower system and the temperature effect of customer power consumption are considered in the stochastic load flow analysis. With the temperature rise, the power demand of northern buses is increased dramatically and more power has to be transmitted from the southern region. The large voltage angle difference is significantly various between system buses during the summer peak period. It is suggested that the safety margin assessment of system operation has to be executed by considering the temperature effect to the bus loading of power systems.