Forced Oscillation Source Location via Multivariate Time Series Classification

Precisely locating low-frequency oscillation sources is the prerequisite of suppressing sustained oscillation, which is an essential guarantee for the secure and stable operation of power grids. Using synchrophasor measurements, a machine learning method is proposed to locate the source of forced oscillation in power systems. Rotor angle and active power of each power plant are utilized to construct multivariate time series (MTS). Applying Mahalanobis distance metric and dynamic time warping, the distance between MTS with different phases or lengths can be appropriately measured. The obtained distance metric, representing characteristics during the transient phase of forced oscillation under different disturbance sources, is used for offline classifier training and online matching to locate the disturbance source. Simulation results using the four-machine two-area system and IEEE 39-bus system indicate that the proposed location method can identify the power system forced oscillation source online with high accuracy.Comment: 5 pages, 3 figures. Accepted by 2018 IEEE/PES Transmission and Distribution Conferenc

Threshold dynamic time warping for spatial activity recognition

Non-invasive spatial activity recognition is a difficult task, complicated by variation in how the same activities are conducted and furthermore by noise introduced by video tracking procedures. In this paper we propose an algorithm based on dynamic time warping (DTW) as a viable method with which to quantify segmented spatial activity sequences from a video tracking system. DTW is a widely used technique for optimally aligning or warping temporal sequences through minimisation of the distance between their components. The proposed algorithm threshold DTW (TDTW) is capable of accurate spatial sequence distance quantification and is shown using a three class spatial data set to be more robust and accurate than DTW and the discrete hidden markov model (HMM). We also evaluate the application of a band dynamic programming (DP) constraint to TDTW in order to reduce extraneous warping between sequences and to reduce the computation complexity of the approach. Results show that application of a band DP constraint to TDTW improves runtime performance significantly, whilst still maintaining a high precision and recall

Sharpness versus robustness of the percolation transition in 2D contact processes

We study versions of the contact process with three states, and with infections occurring at a rate depending on the overall infection density. Motivated by a model described in [17] for vegetation patterns in arid landscapes, we focus on percolation under invariant measures of such processes. We prove that the percolation transition is sharp (for one of our models this requires a reasonable assumption). This is shown to contradict a form of 'robust critical behaviour' with power law cluster size distribution for a range of parameter values, as suggested in [17].Comment: 31 pages, to appear in Stochastic Processes and their Application

Using Choquet integrals for kNN approximation and classification

k-nearest neighbors (kNN) is a popular method for function approximation and classification. One drawback of this method is that the nearest neighbors can be all located on one side of the point in question x. An alternative natural neighbors method is expensive for more than three variables. In this paper we propose the use of the discrete Choquet integral for combining the values of the nearest neighbors so that redundant information is canceled out. We design a fuzzy measure based on location of the nearest neighbors, which favors neighbors located all around x. <br /

Monotonicity in Ant Colony Classification Algorithms

Classification algorithms generally do not use existing domain knowledge during model construction. The creation of models that conflict with existing knowledge can reduce model acceptance, as users have to trust the models they use. Domain knowledge can be integrated into algorithms using semantic constraints to guide model construction. This paper proposes an extension to an existing ACO-based classification rule learner to create lists of monotonic classification rules. The proposed algorithm was compared to a majority classifier and the Ordinal Learning Model (OLM) monotonic learner. Our results show that the proposed algorithm successfully outperformed OLM’s predictive accuracy while still producing monotonic models