| Nowadays, risk assessment for electric power system becomes an important issue. However, most risk analysis methods for electric power grid are based on historical statistics, and those methods are lack of considerating equipments’health status, operation situation and environment. In an operating power system, different equipment has its own failure probability and the probability changes with the equipment status. Therefore, finding the relationship between system risk and equipments’reliability, and then building a new power system risk assessment framework based on equipments’condition are very important for condition maintenance(CM), dispatching decision-making and etc. Achieving those targets, some works and innovations have been made as follows:(1) Transformer health status is evaluated based on evidential reasoning (ER) approach. An evidential reasoning (ER) approach to the transformer condition assessment is presented. And Analytic Hierarchy Process (AHP) is also used to analyse the transformer’s attributes. Then, the transformer assessment results are converted to reliability data for power system risk assessment. Thus, the system risk can be better evaluated with the consideration of transformers’on-line condition.(2) High Voltage Breaker health status is evaluated based on real-time monitoring information. Electrical endurance of high voltage circuit breaker is calculated in this paper. Considering the machinery status, an on-line health condition evaluation approach to high voltage circuit breaker is presented. The evaluation results are converted to reliability date, and used to support the power system risk assessment. A real-time monitoring system for High Voltage Breaker is proposed in this paper. This monitoring system can be used to evaluate the high voltage circuit breaker’s electrical endurance, and machinery conditions are also monitored. With these monitoring data, high voltage circuit breaker health status evaluation can be achieved in practice.(3) Support vector data description is used for equipments’status classification and fault giagnosis. A new machine learning approach called’Support Vector Data Description (SVDD)’ which is based on sample data training is employed for electric equipment classification. The SVDD approach can classify equipment status easily with strong extendibility, high accuracy rate and less training sample data. Several simulation processes are presented to confirm the advantages of the SVDD.(4) A new framework for power transmission risk assessment is proposed, which considers not only historical failure statistics of transmission systems but also operation failure risks of system components. Different from traditional risk assessment, under the new framework, both up-to-date component failure risks and traditional system risk indices can be processed with the proposed outage model. Thus, the system risk can reflect the real situation better than the traditional way. The influence degrees to system risk, which made by different equipments and different system operation modes, can be indicated in this new framework. Thus, the weak links of power system can be pointed out. Meanwhile, the advantages for developing the power system reliability, which caused by equipment condition maintenance can be embodied. This new risk assessment framework can be used in power grid planning, and also can provide good supports for operation and maintenance.(5) A three dimension information monitoring scheme for electric equipments is proposed and practised. A3D information model based on object-oriented technique is designed for the equipment monitoring. With this model, electric power equipment monitoring can be achieved in a new way, which is much comprehensive, real and direct than the traditional way. Based on the proposed method,3D information transformer model is built. |
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