Research on Large-Signal Stability of DC Microgrid Based on Droop Control

Ensuring the large signal stability of the DC microgrid is the premise of the safe operation of the DC microgrid, but the research on the large-signal stability of microgrids with multiple droop control micro-sources is still scarce. In this paper, a DC microgrid system model with multiple droop control micro-sources was established by appropriate simplification. Addressing the problem that most stability research methods cannot be quantitatively analyzed, the mixed potential function method was used to analyze the large signal stability of the system. However, the criterion obtained by the conventional mixed potential function method is complicated and contains multiple time-varying parameters, which is not convenient for analysis. Therefore, the simple form of the criterion was obtained through simplification and the analysis proved the rationality of the simplification. On this basis, a nonlinear droop control method was proposed to improve the anti-interference ability of the system. Finally, the accuracy of the large signal stability criterion and the effectiveness of nonlinear droop control on the system’s large signal stability were verified by simulation.

POTENTIAL FUNCTION METHOD APPROACH TO PATTERN RECOGNITION APPLICATIONS

Potential function method was originally offered to solve the pattern recognition tasks, then it was generalized to a wider range of tasks, which were associated with the function approximation. Potential function method algorithms are based on the hypothesis of the nature of the function that separates sets according to different classes of patterns. Geometrical interpretation of pattern recognition task includes display of patterns in the form of vector in the space of input signal that allows to perceive the learning as approximation task. The paper describes the essence of potential function method and the learning procedure is shown that is based on practical application of potential methods. Pattern recognition applications with the help of examples of potential functions and company bankruptcy data analysis with the help of potential functions are given.

СТВОРЕННЯ ПРОГРАМНОГО ІНСТРУМЕНТУ ДЛЯ ПРОВЕДЕННЯ ДИФЕРЕНЦІЙНОЇ ДІАГНОСТИКИ НЕВРОЛОГІЧНИХ ХВОРОБ МЕТОДОМ ПОТЕНЦІЙНИХ ФУНКЦІЙ

Informatization in medicine offers many opportunities to enhance quality of medical support, accuracy of diagnosis and provides usage of accumulated experience. Modern program systems are represented as additional tools to get appropriate advice.This article offers the way to provide help for neurology department doctor of NCSH "OKHMATDYT" during diagnosis determining. Decision has been made to design the program system for this purpose based on differential diagnostic model.The key problems in differential diagnosis are symptoms similarity between each other in one disease group and the absence of key symptom. Thus, we need the differential diagnostic model. It is built using the potential function method in characteristics space. Such space of characteristics is formed by 26 points - patients with their symptoms.The main feature of this method is decision function, building during recognition step united with learning, which became possible with modern powerful computers.

ДОСЛІДЖЕННЯ МОДЕЛІ ДИФЕРЕНЦІЙНОЇ ДІАГНОСТИКИ МЕТОДОМ ПОТЕНЦІЙНИХ ФУНКЦІЙ ДЛЯ КЛАСИФІКАЦІЇ НЕВРОЛОГІЧНИХ ХВОРОБ

Informatization in medicine offers a lot of opportunities to enhance quality of medical support, accuracy of diagnosis and provides the use of accumulated experience. Modern program systems are utilized now as additional tools to get appropriate advice.This article offers the way to provide help for neurology department doctor of NCSH «OKHMATDYT» during diagnosis determining. It was decided to design the program system for this purpose based on differential diagnostic model.The key problems in differential diagnosis are symptoms similarity between each other in one disease group and the absence of key symptom. Therefore the differential diagnostic model is needed. It is constructed using the potential function method in characteristics space. This characteristics space is formed by 100-200 points - patients with their symptoms.The main feature of this method here is that the decision function is building during recognition step united with learning that became possible with the help of modern powerful computers.