Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network

During the operation and maintenance of the power system, power outages and supply-demand imbalances can disrupt the normal power supply process. This issue must be mitigated or even resolved through the implementation of an appropriate power system risk warning. The article proposes a self-assessment and early warning strategy for power system hazards based on an enhanced ant colony optimization algorithm (IACO) and a BP neural network. First, a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM) is used to assign weights comprehensively to indicators that have a significant impact on the stability and safety of power system operation, thereby avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. Training and testing samples for the BP neural network were calculated based on the weight allocation procedure described previously. Then, IACO is employed to global optimize the weights and thresholds of the BP neural network, and an enhanced BP neural network model for independent power system risk assessment is developed. The designed risk assessment and warning strategy was finally evaluated. The results indicate that the proposed power system risk assessment and early warning method can precisely predict the actual operating status of the power system based on weight values, thereby enhancing power supply quality by providing technical personnel with a data reference. In the operation and maintenance process of the power system, factors such as power failures and supply-demand imbalances can have adverse effects on the normal power supply process. It is necessary to reduce or even solve this problem through corresponding power system risk warning. Based on this, the article proposes a self-assessment and early warning strategy for power system risks based on improved ant colony optimization algorithm (IACO) and BP neural network. Firstly, a combination of Analytic Hierarchy Process (AHP) and Entropy Weighting Method (EWM) is used to comprehensively assign weights to indicators that have a significant impact on the stability and safety of power system operation, avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. According to the above weight allocation process, training and testing samples for the BP neural network were calculated and obtained. Then, IACO is used to global optimization the weights and thresholds of the BP neural network, and an improved BP neural network model for power system risk independent assessment is established. Finally, the designed risk assessment and warning strategy was tested. The results indicate that the proposed power system risk assessment and early warning method can accurately predict the actual working status of the power system based on weight values, providing data reference for technical personnel, and thereby improving power supply quality.image