Power system security improvement using an OPA model and IPSO algorithm

Abstract

The growing demand for power generation makes power systems increasingly more complex to operate and less secure against outages, so the risk of extensive blackouts is growing and needs to be addressed. Cascading failures are the main reason for extensive blackouts, so to investigate this effect a new method including a standard blackout model, named ORNL-PSerc-Alaska (OPA), and static synchronous series compensator (SSSC) placement using an improved particle swarm optimization (IPSO) algorithm is proposed. This study provides two optimization approaches and presents optimal corrective actions. The results will help operators to implement corrective actions like optimal generation and load redispatching to return the system to a stable operating condition. The effectiveness of the proposed model is demonstrated using a realistic transmission network. Simulation results show a significant effect of SSSC on decreasing the risk of cascading failures and the ability of the proposed method to prevent blackout.

Transmission Security Enhancement under (N−1) Contingency Conditions with Optimal Unified Power Flow Controller and Renewable Energy Sources Generation

“…By having NR power flow solution, the voltage stability indices are computed to update the overall objective function expressed in Eq. (11). The solution that minimizes the overall objective function is considered the best solution.…”

“…12. Repeat steps from ( 9) to (11) until it n = it max and stop and store the results as base case. 13.…”

“…In [10], Moth flame optimization (MFO) and JAYA blended MFO (J-MFO) algorithms have been used for optimizing the location and parameters of SVC and TCSC devices towards loss minimization and installation cost of SVC and TCSC. In [11], power system security enhancement using optimal controls of static synchronous series compensator (SSSC) and standard blackout model (OPA) has been proposed for minimizing the risk of cascaded tripping to avoid blackout. The problem of optimal location and sizing of SCCC is solved aimed using an improved particle swarm optimization (IPSO) by aiming the minimization of generators' redispatch cost and load shedding costs.…”

Localisation of facts compensator using reptile search algorithm for enhancing power system security under multi‐contingency conditions

“…Heuristic optimisation methods are widely used in FACTS localisation issues. Three heuristic methods, for example, Tab Search (TS) and Gene Algorithm (GA) are used, and to improve system security, simulated annealing is consumed for optimal placement [13]. The best position with the variable of UPFCs is found using a differential search method to boost the stability of the transmission system under contingency conditions [14] However, solution for FACTS allotment issues is carried out by heuristic optimisation methods that enable large computational problems because those methods give more reliable and easier operations than existing optimisation methods.…”

Probabilistic Vulnerability Assessment of Transmission Lines Considering Cascading Failures

“…A few power grid vulnerability assessment methods have been previously proposed for studying cascading failure, namely, structural analysis methods based on complex network theory [5][6][7], fault simulation methods based on self-organizing criticality theory [8][9][10], fault evolution methods based on cellular automata theory [11,12], and fault chain extraction methods based on Heinrich causal chain theory [13][14][15].…”