Research On The Sampling Methods Of Power Quality Disturbances Detection Apply To Remote Monitoring Of The Wind Farm

As an abundant renewable energy,wind energy is clean and inexhaustible. Theuse of wind power generation is an effective measure which most of countries take tosolve the energy shortage and improve the quality of the environment. With thecontinuous expansion of large-scale wind farm installed capacity,and wind power ofits own structural characteristics, wind farms connected to the grid due to powerquality problems can not be ignored. How effectively to analysis and control thepower quality problems have become an urgent and important work, it causeswidespread concern in the electrical field at home and abroad. In order to enhance andimprove the power quality of wind power connected to the grid,it is necessary toremote monitor them. An important part of the wind farm power quality remotemonitoring is to solve the problem of the power quality disturbance efficient dataacquisition.(1)Through reading a lot of literature, we study the status quo of the remotemonitoring of the wind farm, and the definition, domestic and international standards,classification and data compression of power quality problems of wind power. Simplynarrative the existing power quality data capture methodsand the research status anddevelopment trends of traditional sampling and compression sampling method.Thenwe explain the background and significance of the thesis.(2)The paper detailed discusses the commonly used sampling methods used inthe wind farms based on the traditional sampling theory, including synchronoussampling theory, quasi-synchronous sampling theory and asynchronous samplingtheory.In the basis of studying the structure and data acquisition of the wind farmsremote and central monitoring system, we analyze and compare the realization andsampling effects of each sampling method, and sum up their advantages anddisadvantages.We focused on the study of several commonly used algorithms ofsoftware synchronization sampling,and provide a theoretical basis for the follow-upefficient data acquisition of power quality disturbance signal through thecomparativestudy of the algorithm simulation results.(3)The paper propose a new data acquisition module sampling techniques used inmonitoring system.We study the background, current research, basic idea, theoreticalframework and the implementation of the compressive sampling theory in depth.Wefocuse on the study of sparse representation of the signal, the design of measurement matrix and signal reconstruction.①The sparsity of the signal is the prerequisite ofcompression sampling,the reconstruction of the high-precision original signal dependson the sparse representation of the signal.And we compare and analyze severaltypical sparse decomposition algorithms.②Less observational data can be used toreconstruct the original signal through the design of effective measurement matrix.Comparative Study of several common observation matrix.③The core issue ofcompressive sampling theory is the reconstruction of the original signal ofN-dimensional from the obtained observation vector Y of M (M<N)-dimensional.Some common reconstruction algorithms and their time complexity andreconstruction precision are compared and analyzed.(4)The paper applies compression sampling theory to wind power qualitydisturbance signal data acquisition.We design a sampling method, which use theGabor redundant atoms library as a sparse basis, using Gaussian random measurementmatrix for observation and orthogonal matching pursuit algorithm to reconstruct theoriginal signal. Matlab simulation test the proposed method, the experimental resultsshow that this method can achieve compression and reconstruction of power qualitydisturbance signal.