Risk Evaluation Study Of Offshore Wind Power Project At The Operating Period

Wind power is a kind of renewable clean energy source, which has an important meaning for energy conservation. Along with the constraint of remaining land and electricity grid curtailment of onshore wind farms seriously highlighted, large-scale commercialization of offshore wind power development will be a new direction, will increase rapidly in the next years. The offshore wind power project has come into a grow space period, and at the same time, as a high investment, high technology industry, the potential risks of offshore wind power project at the operating period have gained attention from investors and participants.The paper comprehensively summarizes the related theory of risks in operating period of offshore wind power project, introduces the concept of risk and risk characteristics of offshore wind power project in the operating period, and introduces the risk identification, risk estimation and risk evaluation respectively. It analyses the commonly used risk evaluation method:fuzzy comprehensive evaluation method, the Monte Carlo method and the BP neural network, analyzes their advantages and disadvantages, which provides theoretical basis for the selection of risk evaluation method in this paper.Based on analyzing the principles of establishing risk indexes, the paper comprehensively completes risk identification of offshore wind project in the operating period, combining with risk characteristics and sources of offshore wind power project at the operating period, and establishes the risk evaluation index system of offshore power wind project at the operating period. According to the risk evaluation index system, a risk evaluation method based on support vector machine (SVM) has been proposed to prevent and control risks and reduce the losses caused by risks in the operation period. This paper introduces the principle and algorithms of support vector machine and proposes a risk evaluation model for offshore wind power projects using support vector machine, and analyses the forecast accuracy of the model through the specific project data.In case study, using SVM regression model put forward by the article, the paper uses the MATLAB platform to run L1BSVM kit, which completes data calculation and risk assessment, and analyzes the evaluation results, which verified the SVM model is scientific and effective to evaluate offshore wind power projects. The support vector machines (SVM) enriches the method system of the offshore wind power project risk evaluation. Case study shows that the SVM method has higher accuracy and is more convenient than the traditional evaluation method, and provide basis for the risk, evaluation of offshore wind project.