Mechanism Analysis of Intelligent Diagnosis of Generator Electrical Faults
Analysis of Intelligent Diagnosis Mechanism of Electrical Faults in Generators YAN Bu-yi1, LIN Ji-qun1, LIN Li-qing2, JIANG Chuan-wen3, DU Song-huai3 (1. Department of Electrical Engineering, Fuzhou University, Fuzhou 350002, China; 2. Fujian Shuikou Hydropower Plant, Fuzhou, Fujian 350800, China; 3. Electrical Engineering, Shanghai Jiaotong University Department, Shanghai 200240) paste relationship, established fuzzy relation equation, realized intelligent diagnosis, and realized simulation operation by software, the diagnosis result was accurate and reliable. : Generator; electrical fault; intelligent diagnosis of the operating state of the generator directly affects the safety and economy of the operation of the power system, timely and accurate determination of faults in operation is very important. However, it is quite difficult to monitor the operating conditions and perform online diagnosis under operating conditions. The research on intelligent diagnosis of power system faults is very active at home and abroad, but there is less overall research on electrical fault diagnosis of generators. In this paper, combined with the operating experience of power plants and power grids, the artificial intelligence principle is used to realize the electrical fault diagnosis of generators.
Since the generator failure phenomenon is closely related to the cause of the failure, each failure phenomenon may be caused by one or more failure causes, and each failure will cause certain phenomena correspondingly, plus a large generator. There are many kinds of faults, so the complexity and ambiguity of the fault phenomenon, cause and mechanism are difficult to describe with accurate mathematical model, and it is difficult to completely rely on the deterministic criterion to determine the nature of the fault. This paper will be the experience of generator operation and expert system. The principle and fuzzy theory are combined to find out the fuzzy relationship between the cause and phenomenon of electrical faults in generator operation. The fuzzy diagnosis is used to realize the intelligent diagnosis of electrical faults of generators. The introduction of fuzzy theory can solve some factors in the production process. The uncertain relationship between each other, in the field of electrical fault diagnosis of generators, the fuzzy relationship matrix can be used to reflect the relationship between some fault phenomena and the cause of the fault, as described below.
Let XY denote the input and output respectively, and represent the quantized set of the fault cause and the fault phenomenon respectively, then: 1, 2, ..., n; then the fuzzy relation equation is: where R = (ri /) mKn, ri / belongs, The relationship strength between Xi and y, r/=0 means irrelevant, the larger ri/, the closer the relationship, the smaller n/ is, the smaller the relationship. , Yongtai, Fujian, lecturer at Fuzhou University.
Analysis of common causes and phenomena of electrical faults in the line. First determine the input, that is, the electrical fault cause matrix X, such as the stator A phase ground fault, see Table 1 and then determine the output, that is, the electrical fault phenomenon matrix Y, such as UaI represents the phase A voltage drop of the machine, denoted by y4, detailed See Table 2 Table 1 Input Fuzzy Set (Fault Cause) Fault Cause Input Fault Cause Input Stator A Phase Ground Rotor One Point Grounding Stator B Phase Ground Rotor Two Points Grounding Stator C Phase Grounding Running Loss of Magnetic Stator Three Phase Grounding Accident Overload stator A phase inter-turn short circuit external short circuit fault stator B phase interturn short circuit stator C phase interturn short circuit table 2 output fuzzy set (fault phenomenon) fault phenomenon output fault phenomenon output fault phenomenon output An U Ua / A /b Uf Ub / c / f C Table 2, A. is the machine zero sequence current; is the machine zero sequence voltage; Q <0 is the reactive meter indicates a negative value; U is the stator Insulation monitoring voltage; (/a-/a) is the differential current of the primary and secondary sides of the differential protection relay; Ilhn is the neutral unbalance current; U is the positive voltage of the rotor winding to ground; Uf is the excitation voltage; /f is The rotor current is combined with the operating experience of a thermal power unit in a power plant and consulted relevant experts. The fuzzy relationship matrix R between the fault cause and the fault phenomenon is formed. See Table 3 in Table 3 for the relationship between 23 electrical fault phenomena and 12 electrical fault causes to indicate the relationship strength between x and y/. , 0.9 indicates a close relationship, 0.5 indicates a general relationship, and 0 2 indicates a small relationship. Representing how to determine the fuzzy relation matrix elements and thresholds is the key to determining the correctness of fuzzy reasoning. Their value is a reflection of expert knowledge and generator operation practice, which is derived from long-term analysis and refinement. For the evaluation of the fuzzy relation matrix element ri/, the "fuzzy statistical method" can be used.
The value should not be greater than the largest element in the output matrix. If Xi>w, then y/ is probably caused by xi; if xi
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