No abstract

The three-phase DSTATCOM is prone to higher dynamics due to grid disturbances. The proportionate affine projection algorithm (PAPA) is an adaptive filter that can be used for DSTATCOM control. In order to overcome the sluggish convergence speed of adaptive filters, PAPA is proposed in this paper. The convergence rate versus the steady-state error is a trade-off in conventional adaptive filters. However, the utilization of two adaptive filters in CSS-PAPA increases the convergence and decreases the steady-state error. The suggested filter has the advantage of having a lower computational cost than a standard adaptive filter. The proposed filter helps the inverter to work as a shunt compensator. The goal of the suggested controller is to adjust for reactive power and unity power factor during faulty conditions. The proposed DSTATCOM controller has undergone experimental validation in the laboratory.

One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.