On the basis of the existing analytic models， the chance-constrained programming is introduced to deal with uncertainties， e.g. the malfunctions of protective relays（PRs） and circuit breakers（CBs） as well as false/missing alarms. Thus， a new fault diagnosis analytic model is first developed in the chance-constrained programming framework， and could well accommodate malfunctions of PRs and CBs as well as false/missing alarms. Then， the well-established genetic algorithm combined with Monte Carlo simulations are employed to solve the developed optimization model. An actual fault scenario is served for demonstrating the feasibility and efficiency of the developed model and method， and consistent fault diagnosis results have been obtained as those actually happened. In addition， the computation speed of the developed method meets the on-line fault diagnosis requirement.