Preventive Control Strategy of Cascading Fault considering Safety and Economy

Abstract

The operation and structure of the power system are becoming increasingly complex, and the probability of cascading fault increases. To this end, this paper proposes a cascading fault preventive control strategy that considers safety and the economy. First is to give a mathematical form to discriminate the cascading fault according to the action characteristics of the current-type backup protection. Second, the safety and economy of the system are evaluated in terms of power grid safety margin and generation operation cost, respectively, the initial faults are selected based on the power grid vulnerability and safety margin, and a cascading fault preventive control model is constructed for different initial faults’ scenarios. The model is a two-layer optimization mathematical model, with the inner model being solved by particle swarm optimization to minimize the power grid safety margin. The outer model is solved by the multiobjective algorithm to minimize generation cost and maximizing power grid safety margin. Finally, the calculated Pareto set is evaluated using fuzzy set theory to determine the optimal generator output strategy. The feasibility of the proposed method is verified by conducting a simulation study with the IEEE39 node system as an example.

Cascade feedforward neural network and deep neural network controller on photovoltaic system with cascaded multilevel inverters: Comparison on standalone and grid integrated system

“…The performance analysis of a multilayer cascade H-bridge-based active power filter under load change was described by Narasimhulu et al [17]. Hui-Qiong Deng et al [18] discussed the safety and economics of the preventative control technique for cascading failures. State of charging (SoC) balancing techniques for power matching in cascaded H-bridge MLI have been described by J. Yu et al [19].…”

Cascading fault prevention strategy based on economic dispatch of AC-DC systems

“…Literature [8] establishes a coordinated control model based on system risk and control cost, employing a multi-objective particle swarm algorithm combined with scheduler preferences to identify a scheme meeting both system safety and control measure economy requirements. Literature [9] conducts cascading failures simulation and risk assessment using linear quantization to determine risk gradients, adjusting unit output and load cutting to manage subsequent failures.…”