Study On Distributed Generation System Islanding Detection Based On Artificial Immune Algorithm

As the contribution of distributed generation to the electric power production increases, the effects on the power system grow more important. There will be a series of issues when the distributed generations are grid-connected. The one of the most important is the islanding detection. There are two kinds of islanding detection method: active islanding detection and passive islanding detection. Although the active islanding detection methods have high sensitive, the power quality degrading and system unstability will have to be added to the system by them. And there are non-detection zones in all current passive islanding methods. The distributed generation passive islanding detection methods based on the harmonic power pattern recognizing using artificial immune system are presented in the paper for solving issues of the harmonic pattern changes as the distributed generators and loads turn on or off.The harmonic patterns in distributed generation system are studied firstly. The algorithm of the current harmonic powers and the mapping of the state space of the harmonic power are analysized.The mapping of the harmonic powers state space using the integral of odd harmonic frequency spectrums is presented. An islanding detection method using clustering analysis is studied.The negative selecton is the basic mechanism to recognize intrusive foreign antigens for the nature immune system. The binary matching rules of the negative selection are analysized in this paper. The performance of the r-contiguous matching, r-chunk matching and Hamming distance maching rules is compared. A negative selection algorithm with detection rules is presented. Using genetic algorithm to evolve rules to cover the non-self space, the number of rules is very small as well as a good covering of the non-self space is gotten.According to the mechanisms of B celll and T cell recognizing the foreign antigen, the gene rearrangement and somatic mutation of B cell, T cell and B cell interaction as T cell recognizing antigen, the T module detectors and B module detectors are presented. The T-module is initialized by choosing points at random and can learn new patterns and tune the detection position when it is used to detect islanding. Using mutation and clone mechanism the B-module reacts to all frequently occurring state vector values and presents its results to the T-module. The B-module also plays a role in updating the T-module. A death mechanism is also introduced for the clone vectors of B-module for controlling the population of B-modules. Thereafter, the algorithms of islanding detection can almostly detection all the current harmonic power pattern. And the complexity of the algorithm is accessible for practical application.In practical application, it is difficult to get the enough pattern samples of distributed generaton islanding state. An approach to get the islanding harmonic pattern using the real negative selection algorithm is presented. The input of the algorithm is the harmonic patterns of the grid-connected distributed generation system. The volume of the self state space is caculated using the numerical analysis. Then, the distribution of antibody sets in the self sated space is optimized using simulated annealing algorithm. The islanding patterns is detected using artificial neural nentwork classifier.By using PSCAD and Matlab, various islanding operation conditions and normal load variation are simulated. And all the algorithms proposed in this paper are verified.