Power System Risk Assessment Strategy Based on Weighted Comprehensive
Allocation and Improved BP Neural Network
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. Key
words：Power system; Risk assessment; Comprehensive empowerment;
Improved ant colony optimization algorithm; BP neural network.