Abstract
Asset management of power assets is a decision-making process that manages risk by considering asset condition and the consequences of failure while ensuring supply reliability and cost-effective maintenance within a limited budget. However, when maintenance plans are developed and budgets are allocated separately for each asset class, it is difficult to compare the investment effects across different types of assets and to establish an investment plan that reflects organization-wide asset management objectives. To address this limitation, the maintenance effects of heterogeneous assets must be evaluated on a common economic basis to prioritize investments, and the assets to be maintained and the timing of maintenance must be determined jointly within the available budget. This paper proposes a Deep Q-network-based integrated maintenance optimization method for heterogeneous assets, namely power transformers and gas-insulated switchgear, using risk and investment value assessments. The proposed method reflects condition changes caused by asset deterioration and maintenance, and it learns a maintenance policy that accounts for long-term investment effects under a shared budget constraint. In the simulation, the Benefit–Cost ratio is calculated from the present values of the benefits and costs estimated using synthetic data for power transformers and GIS, and it is incorporated into the Deep Q-Network algorithm as the investment value to derive a maintenance strategy. The proposed method is then compared with independent maintenance strategies for each asset class under the same total budget and evaluation period, and its applicability is verified in terms of maintenance cost, expected loss reduction benefit, and investment efficiency.