Voltage Collapse Prediction and Control of a Power System Using Classical Probability Theory

The incessant voltage collapse incidences on the Nigeria National grid is a serious concern. The primary dependability issue of voltage collapse makes it difficult to operate a reliable and secure power system network. This paper provides a comparative analysis of voltage stability approach and proposes a Bayesian approach for voltage collapse prediction. Voltage collapse indices, such as the line voltage stability indices (Lmm), fast voltage stability indices (FVSI), line reactive power indices (LQP), new line voltage stability indices (NLVSI), and modern voltage stability indices (MVSI), were explored. A Bayesian approach is then used to owing to the fact that it provides a more comprehensive assessment of the bus voltage's stability. It also provides a framework for updating presumptions on the stability of the system in light of data that has been observed. The 50-bus, 330kV Nigerian national grid test systems' voltage stability was evaluated using VSI with Bayesian techniques, and the weak and important buses were identified. Bus 50 (Jos) has the most critical value, 0.68 p.u., indicating marginal stability, whereas Bus 35 (Delta) has the lowest MVSI value, 0.61 p.u., and is categorized as critical. In contrast, the 50-bus system's weaker buses, such as Guaan's Bus 28 and Kano's Bus 44, show greater voltage stability index values between 0.75 and 0.80 p.u., indicating mild voltage instability without an immediate risk of collapse. Application of the Bayesian approach improves the critical buses from 0.61 to 0.898pu. When DPFC FACTS controller is being applied to weak and critical buses, the Lmn, FVSI, LQP, and NLSI with Bayesian algorithm were improved from 0.528 to 0.80pu and when IPFC was used it increases the weak bus from 0.66–0.97pu. Owing to the fact that DPFC outperforms IPFC for voltage enhancement, it was proposed for the voltage control.