Research And Realization Of Bus Protection Based On ANN Model

As one of the most important devices of substation, bus and its protection play a big role in reliable and secure operation of power system. The main target of bus protection is the pursuit of perfect performance, almighty function, high reliability and intelligence.For improvement of bus protection, it is necessary to introduce new theory and technology. As a mathematic tool with high intelligence, ANN (Artificial Neural Network) is suitable for the research on new protection. For a long while, the application of ANN to relay protection is based on classification ability. ANN is trained by sample data, which can characterize fault. The various faults of power system can be distinguished and judged by the trained ANN. Unfortunately, lacking of sample data restricts the abroad application of ANN protection to power system.In order to overcome above shortcomings, innovative principle for ANN bus protection based on function approximation ability is presented in this paper. As one of the most important capability of ANN, function approximation ability can be used to design ANN model, which can characterize certain physics object. The protected object of bus protection is a function with certain input and output, which can be represented by ANN model, or approximated by ANN mathematical model. By use of ANN model trained under normal circumstance, inner and outer bus fault can be distinguished clearly.For the research on bus protection theory based on function approximation ability of ANN, it is crucial to establish the functional relation between the input and the output of bus protection object. The inputs are synchronous currents on second side of each CT, and the output is current sum on primary side of CT. ANN model with different activation functions, like linear, PWL and Sigmoid one, are built, training algorithm and its convergence of three models are discussed. As the models are realized with DSP, training examples and their comparison are given also. Linear function is chosen as the activation function finally.Inner and outer fault are distinguished by outputs of bus protection ANN model. Whether output of ANN model increases synchronously with fault time is the method of overcoming CT saturation for bus protection. Under outer fault circumstance, proportional halt mechanism based on ANN model is applied. Under transition from outer to inner fault with CT corruption and CT disconnection, fault power flow direction judgment is used to insure correct operation of bus protection. By use of prediction ofadaptive model and instantaneous increment of current amplitude, fault pre-triggering and triggering mechanism for bus protection are realized in this paper, and sensitivity and reliability are improved consequently. Sensitive pre-triggering is the foundation of CT anti-saturation, and reliable fault triggering is the prerequisite of protection exit.Some key problems on the synchronous data sampling, the synchronous data transmitting and signal processing are solved. Especially, SAIS(Sinusoidal Approximation of Instantaneous Signal)is presented for instantaneous signal processing under fault circumstance, whose foundation is instantaneous amplitude and phase. Examples show that in half period after fault, especially first quarter period, instantaneous amplitude and phase, which are gotten with sinusoidal approximation, can characterize fault clearly.Distributed hardware structure is adopted for bus protection based on ANN model, which is constituted with bay unit and central unit. Bay unit is specialized in data collection, data processing and fault triggering. Central unit is specialized in ANN model training and fault judgment. Real-time multi-task OS is chosen for programming, which is core of software structure.Physical simulation of bus protection based on ANN model is conducted under different locations and types of faults, whose results are promising.