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With the ever-increasing reliance on data for data-driven applications in power grids, such as event cause analysis, the authenticity of data streams has become crucially important. The data can be prone to adversarial stealthy attacks aiming to manipulate the data such that residual-based bad data detectors cannot detect them, and the perception of system operators or event classifiers changes about the actual event. This paper investigates the impact of adversarial attacks on convolutional neural network-based event cause analysis frameworks. We have successfully verified the ability of adversaries to maliciously misclassify events through stealthy data manipulations. The vulnerability assessment is studied with respect to the number of compromised measurements. Furthermore, a defense mechanism to robustify the performance of the event cause analysis is proposed. The effectiveness of adversarial attacks on changing the output of the framework is studied using the data generated by real-time digital simulator (RTDS) under different scenarios such as type of attacks and level of access to data.

The optimal sizing and placement of distributed generators have recently drawn a considerable attention to itself. This paper proposes an evolutionary cuckoo optimization algorithm (COA) for optimal placement of distributed generation (DG) in a distribution system. The optimal DG placement problem is formulated as a cost function of network losses, voltage profile, and DG expenses. The proposed method is validated on a 13-bus distribution system. The results show that any variation in the parameter’s weight in the objective function leads to a significant change in the prediction of the DG’s location and capacity.