Study On Key Technologies Of Optimal Maintenance For Transmission&Distribution Equipments

Transmission&Distribution Equipments are the main elements which constitute power system. Under the environment that electricity demand is increasing and power utilities have been operated commercially, the availability and maintenance cost of the equipments play important roles on the system operation reliability and the benefit and competitiveness of the utilities. As a result, utilities are required to obtain the health condition of equipments in time. Moreover, they are needed to detect and diagnose failures for the equipments and then prevent or eliminate the failures according to some appropriate inspection and maintenance mechanism. Based on full summarization of the present maintenance mechanisms in power grid and research achievements obtained in other industrial fields, a more reasonable mechanism aimed to optimal maintenance is put forward as below. Utilities should reduce the frequency of periodic maintenance, and avoid applying the breakdown maintenance method on critical elements. The condition based maintenance has a promising future which should be carried out gradually. All of these methods must be coordinated by reliability-centered maintenance scheduling in order to ensure the reliability of the system and the reduction of the maintenance cost. The condition based maintenance is the core of the optimal maintenance. Realization of the optimal maintenance mechanism for Transmission&Distribution Equipments is based on the development of several technologies, such as condition monitoring, condition assessment, fault diagonosis, optimization of maintenance scheduling, and enterprise informationization. Some key problems of these technologies are studied in the dissertation, and mainly applied to the maintenance of power transformers.In Chapter 2. the fault detection technology of condition mornitoring is mainly analyzed. Fault detection based on nonlinear system identification demands high identification accuracy, so a novel radial basis function neural network (RBFNN) model based on a hybrid learning algorithm differential evolution and particle swarm optimization (DEPSO) is proposed in this paper to predict the shell temperature for SF6-insulated transformers. The DEPSO automatically adjusts the number and positions of hidden layer RBF centers. The weights of output layer are decided by the recursive least squares algorithm. The proposed DEPSO-RBFNN model is trained and tested based on the field data collected from a SF₆-insulated transformer. The test results indicate that the DEPSO-RBFNN possesses far superior identification precision than BP neural network (BPNN), EP-RBFNN and PSO-RBFNN.Transmission&Distribution Equipment condition assessment is a multiple-attribute decision-making (MADM) problem. On the basis of rational partition of condition, a synthetic evaluation index system is needed, which includes condition mornitoring records, working surroundings, operation and maintenance history et al. Considering the fuzziness and uncertainty of condition assessment indices, an improved evidential reasoning (ER) approach to the transformer condition assessment is presented. The initial basic probability assignment of evidence is accessed by means of analytic hierarchy process (AHP) and fuzzy evaluation method. The newly approved method is applied to the case in which high conflict occurs. The results of an example analysis present its effectiveness. The method to build the evaluation index system and the improved evidential reasoning are also suitable when applied to the condition assessment of other Transmission&Distribution Equipments, such as breakers and transmission lines.In this thesis, Chapter 4 develops a novel support vector machine (SVM), i.e. multiple kernels learning multicategory support vector machine (MKLM-SVM), for the faults diagnosis in transformers. Unlike traditional SVM that may fail under some various circumstances, the MKLM-SVM method has some good theoretical properties, the MKLM-SVM method is only based on a simple objective function, and the classification results can be directly calculated on the basis of a simple decision function; the MKLM-SVM method can calculate an optimal kernel function on the basis of linear combinations of basic kernels, further boosting the overall performance; the solutions for the MKLM-method can be efficiently calculated by iteratively solving two convex optimization functions with a low computation cost. The test results show that the proposed method has high classification accuracy, which proves its effectiveness and usefulness.A new model for Transmission&Distribution Equipments maintenance scheduling which intends to find the most reliable maintenance schedule without violating any restrictions is proposed in this paper. The scheduling problem is solved considering the mapping between the equipment condition and equipment failure rate. A functional relationship between failure rate and maintenance measures has also been developed, which assess the impact of maintenance. An improved immune algorithm is proposed to power system maintenance scheduling optimization problem. In order to realize the parallel global and local search capabilities, this algorithm generates the next population under the guidance of the previous superior antibodies in a small and a large neighborhood respectively. Through analysis of Markov chain, the proposed method is proved to be convergent on whole solution space. The test results on IEEE RTS96 single area system demonstrate that the proposed model and optimization method are effective.In order to facilitate information integration and improve flexibility of Transmission&Distribution Equipment Optimal Maintenance Information System (TDE-OMIS), a Service-Oriented Architecture (SOA) based framwork is introduced. The SOA based TDE-OMIS include several kinds of service,such as application services, business services and business process services, which have been functional described and modeled in detail. The Common Information Model (CIM) and the Model-Driven (MD) methodology are introduced into the realization of the SOA based on the Web Services technology. SOA is a dynamic demand-oriented enterprise architecture model, which provides a new kind of service-driven and distributed collaborative operation mode. Service-Oriented optimal maintenance information integration framework can adapt the continuous change of business and technology, so it can reuse the software and reduce the complexity and cost of information integration greatly.