Power system voltage collapse prediction using a new line stability index (NLSI-1): A case study of the 330-kV nigerian national grid

Abstract

The cumulative number of historical and recent power system outages substantiates the fact that further studies are necessary for an improved solution to the issue of voltage instability on the grid and the subsequent system collapse. Voltage collapse is a serious reliability issue which inhibits the objective of running a reliable and secure power system network. In this study, a new line stability index (NLSI_1) for predicting voltage collapse is presented. The new index considers a switching logic which is derived from the difference of voltage angle between the two load buses. The index is deployed for performance analysis using the 28-bus, 330-kV Nigeria National Grid (NNG). The simulation implemented in MATLAB shows that the index gives the same results as Line stability index (Lmn) and Fast Voltage Stability Index (FVSI) indices. The base case and the contingency scenarios were considered during the simulation. The base case analysis using the NNG values of all the three indices FVSI, Lmn, and NLSI_1 for simulation generates a value less than one for the entire lines which implies that the NNG is stable in this mode. The values of the three indices are almost the same, which confirms the accuracy of the novel index developed. The analysis for the contingency case reveals that the load bus 16 (Gombe) which has the lowest, maximum permissible reactive load of 139.5MVAR is the weakest; also power line 16-19 is identified as the critical line. The result of the simulation confirms that the accuracy was improved by using NLSI_1.

Voltage collapse prediction using artificial neural network

“…The results obtained indicate the ability of the proposed approach to predict voltage collapse in more complex test networks. These results are validated by those obtained in [1,28]. Table 6 shows the ranking of buses in the NNG-28 bus system in ascending order from weakest to strongest.…”

“…From the table, bus 16 was identified as the weakest bus in the system with a reactive power margin of 139.5 MVar and a percentage change of 32.06% in the voltage magnitude as it satisfies the criterion listed in [26]. The results obtained by the proposed approach agrees with those obtained in [1,28]. An R-value of 0.9989 and an MSE value of 1.2527× 10 −7 were obtained using the developed neural network model after cross-validation was done.…”

Integrating localised energy systems into the Nigerian power network

“…Power infrastructure in Nigeria is inadequate, outdated and poorly maintained which lead to low revenues, high transmission and distribution losses, power theft and poor tariffs [12], [13], [14]. In Nigeria, the total installed generation capacity is 12,522 MW [15], while the available generation capacity of 4,500MW.…”

Reactive power planning with the help of multi-objective genetic algorithm and flexible AC transmission systems devices

“…Includes installation of size and location var resources. However, poor reactive power support can lead to voltage drops and even power outages [3].…”