Method for assessing voltage sag sensitivity of sensitive equipment

Abstract

The invention provides a method for assessing voltage sag sensitivity of sensitive equipment. The method includes steps of determining an uncertain region of voltage tolerance curves of the sensitive equipment to be assessed; creating an entropy model and a maximum entropy model for the uncertain region by the aid of a probability density function; importing historical monitoring data and solving the maximum entropy model by an optimization algorithm to obtain analytic expressions of the probability density function; integrating the analytic expressions of the probability density function to obtain the voltage sag sensitivity of the sensitive equipment to be assessed. Artificial assumption that voltage sag amplitudes and duration are independent from one another is omitted in the entropy model and the maximum entropy model. The method has the advantages that the type of the probability density function is not set in advance, the artificial assumption that variables, namely the voltage sag amplitudes and the duration, are independent from one another is omitted, and the accuracy of the voltage sag sensitivity, which is assessed on the basis, of the sensitive equipment can be improved.

Description

technical field [0001] The invention relates to the field of electric variable monitoring, in particular to an evaluation method for voltage sag sensitivity of sensitive equipment. Background technique [0002] Power quality issues have been widely concerned in recent years. On the one hand, the power load of the power system has undergone major changes, and the wide application of non-linear, asymmetrical or impulsive charges has caused a large number of power quality problems; on the other hand, with the progress and development of science and technology, the widely used Electronic devices and electronic technology have higher and higher requirements for power quality. [0003] Especially in industrial production, electronic power equipment is widely used, such as sensitive equipment such as computers, programmable logic controllers, and speed-adjustable drives. They are very sensitive to power quality problems, and one of the most serious power quality problems is voltag...

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Problems solved by technology

[0005] 1. Measured statistical method. The ITIC curve drawn by IEEE (Institute of Electrical and Electronics Engineers) based on large-scale computer experiments and historical data (proposed in its standard IEEE Std446) is the representative of measured statistical method. Its limitation lies in the inherent and Influenced by external factors, the sensitivity of different equipment and the same equipment in different environments has different characteristics, so a large amount of historical monitoring data (that is, measured data) is required to evaluate a certain equipment, which is more troublesome to use in practical applications

[0006] 2. The fuzzy evaluation method. The main feature is that the voltage sag is quantified and evaluated based on the membership function, and the voltage sag index needs to be classified in advance. Its disadvantage is that the membership function must be determined for each factor, which is cumbersome, and The determination of the weight of each factor has a certain degree of subjectivity

[0007] 3. The random evaluation method takes the sag amplitude and duration as random variables, first assumes the type of its probability density function subjectively, and the common assumption is normal distribution or uniform distribution, and then calculates the parameters of the function from samples. This method The artificial assumption of its probability density function type may lead to inaccurate evaluation results

[0008] 4. The existing evaluation method of voltage sag sensitivity based on the principle of maximum entropy is characterized by making full use of existing information, requiring only small data samples, and not relying on the probability density function of human subjective assumptions, which can effectively improve However, in its probability density function and established maximum entropy model, the two variables of voltage sag magnitude and duration are assumed to be independent of each other. The duration is short, and the duration is longer when the voltage sag is small. It can be seen that the voltage sag and duration are not necessarily two independent variables. Subjective assumptions that the two are independent variables will reduce the evaluation results the accuracy of

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