False measurements pose a major challenge to the stable operation of the smart grid. This paper aims to develop a real-time detection method for false measurements in the smart grid, especially when the error is in the function of Jacobean matrix. Considering its high robustness and excellent short-term prediction effect, the fuzzy time series was selected as the basis for our model. The detection is realized in three steps: fuzzification, fuzzy relationship determination and defuzzification. The established model was tested on a 30-node network using the PSSE. The results show that our model can accurately detect the false measurement that are inputted in the Jacobian matrix, which are not detectable by conventional systems.
The false data injection in the power grid is a major risk for a good and safety functioning of the smart grid. The false data detection with conventional methods are incapable to detect some false measurements, to remedy this, we have opted to use machine learning which we used five classifiers to conceive an effective detection [k-nearest neighbour (KNN) algorithm, random trees, random forest decision trees, multilayer perceptron and support vector machine]. Our analysis is validated by experiments on a physical bus feeding system performed on PSS/in which we have developed a dataset for real measurement. Afterward we worked with MATLAB software to construct false measurements according to the Jacobean matrix of the state estimation. We tested the collected data with different classification algorithms, which gives good and satisfactory results.
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