2,403 research outputs found
Robust Matrix Completion State Estimation in Distribution Systems
Due to the insufficient measurements in the distribution system state
estimation (DSSE), full observability and redundant measurements are difficult
to achieve without using the pseudo measurements. The matrix completion state
estimation (MCSE) combines the matrix completion and power system model to
estimate voltage by exploring the low-rank characteristics of the matrix. This
paper proposes a robust matrix completion state estimation (RMCSE) to estimate
the voltage in a distribution system under a low-observability condition.
Tradition state estimation weighted least squares (WLS) method requires full
observability to calculate the states and needs redundant measurements to
proceed a bad data detection. The proposed method improves the robustness of
the MCSE to bad data by minimizing the rank of the matrix and measurements
residual with different weights. It can estimate the system state in a
low-observability system and has robust estimates without the bad data
detection process in the face of multiple bad data. The method is numerically
evaluated on the IEEE 33-node radial distribution system. The estimation
performance and robustness of RMCSE are compared with the WLS with the largest
normalized residual bad data identification (WLS-LNR), and the MCSE
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Spatial Pyramid Matching (SPM) and its variants have achieved a lot of
success in image classification. The main difference among them is their
encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of
Vector Quantization (VQ) into the framework of SPM. Although the methods
achieve a higher recognition rate than the traditional SPM, they consume more
time to encode the local descriptors extracted from the image. In this paper,
we propose using Low Rank Representation (LRR) to encode the descriptors under
the framework of SPM. Different from SC, LRR considers the group effect among
data points instead of sparsity. Benefiting from this property, the proposed
method (i.e., LrrSPM) can offer a better performance. To further improve the
generalizability and robustness, we reformulate the rank-minimization problem
as a truncated projection problem. Extensive experimental studies show that
LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving
competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201
A Static Voltage Security Region for Centralized Wind Power Integration-Part I: Concept and Method
When large wind farms are centrally integrated in a power grid, cascading tripping faults induced by voltage issues are becoming a great challenge. This paper therefore proposes a concept of static voltage security region to guarantee that the voltage will remain within operation limits under both base conditions and N-1 contingencies. For large wind farms, significant computational effort is required to calculate the exact boundary of the proposed security region. To reduce this computational burden and facilitate the overall analysis, the characteristics of the security region are first analyzed, and its boundary components are shown to be strictly convex. Approximate security regions are then proposed, which are formed by a set of linear cutting planes based on special operating points known as near points and inner points. The security region encompassed by cutting planes is a good approximation to the actual security region. The proposed procedures are demonstrated on a modified nine-bus system with two wind farms. The simulation confirmed that the cutting plane technique can provide a very good approximation to the actual security region
LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data
Extreme learning machine (ELM) as a neural network algorithm has shown its
good performance, such as fast speed, simple structure etc, but also, weak
robustness is an unavoidable defect in original ELM for blended data. We
present a new machine learning framework called LARSEN-ELM for overcoming this
problem. In our paper, we would like to show two key steps in LARSEN-ELM. In
the first step, preprocessing, we select the input variables highly related to
the output using least angle regression (LARS). In the second step, training,
we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In
the experiments, we apply a sum of two sines and four datasets from UCI
repository to verify the robustness of our approach. The experimental results
show that compared with original ELM and other methods such as OP-ELM,
GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance
while keeping a relatively high speed.Comment: Accepted for publication in Neurocomputing, 01/19/201
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