Ensemble Learning for Power Systems TTC Prediction With Wind Farms

Abstract

Being aware of the reliable margin of vital tie-lines, acting on the connection of power exporting area and power importing area, is significant to power systems. However, the high penetration of wind power causes fast variation of boundary limit parameters such as the available amount of power that can be transferred on the tie-lines, namely, total transfer capability (TTC), which may result in the inaccurate security assessment. Unfortunately, the traditional optimal power flow-based TTC model has computation burden for online applications. To address this problem, computational efficiency is improved via a datadriven TTC predictor based on an ensemble learning architecture in this paper. In the first stage, a daily profiles-based method including probabilistic sampling is proposed to simulate plenty of operation scenarios as data samples for ensemble training. Then, a hybrid feature selection approach, which is composed of the maximal information coefficient and nonparametric independence screening, is applied to determine the most correlative features to the objective variable. To enable the TTC predictor with high accuracy and generalization ability, a novel ensemble learning scheme for TTC predictor is constituted through clustering few adaptive hierarchical GA-based neural networks (AHGA-NNs predictor). At last, a modified New England test system is used to validate the proposed methodology. The results illustrate that combining with the appropriate feature selection, the presented ensemble learning has high performance on creating the accurate TTC predictor, which enables online secure margin monitoring for the vital tie-lines. INDEX TERMS Artificial neural networks, ensemble learning, feature selection, total transfer capability, wind power.

Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

“…Different methods can be used for data forecasting in a microgrid, such as methods based on artificial neural networks (ANNs), to generate wind forecast and power consumption prediction [27,28], which might be extended via short-term load forecasting (STLF) [29], solutions based on neural networks and evolutionary algorithms [30], and adaptive hierarchical genetic algorithm-based neural networks (AHGA-NNs predictor) [31] for wind farms. Other methods are based on fuzzy logic [32], forecast Weibull, and lognormal probability distribution functions, for forecasting wind and solar photovoltaic power output [33], and the least-squares support vector machine (LS-SVM) [34].…”

Recent Developments in Machine Learning for Energy Systems Reliability Management

“…Finally, to help security assessment, [150] uses neural networks to estimate the transmission reliability margin. On the other hand, [151] proposes a method to predict total transfer capability (available amount of power that can be transferred on the tie-lines), which is an ensemble model with adaptive hierarchical GA-based neural networks. A hybrid feature selection based on Maximal Information Coefficient (MIC) and nonparametric independence screening is used.…”

Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression

“…3) Randomly extract a subset U from the unlabeled training sample set U . For each f ui in U , i is the set of its k nearest neighbor labeled training samples in L 1 , and the most confident unlabeled training sample f u is identified by maximizing the deviation of Mean Squared Error (MSE) over i as in (12):…”

“…However, the historical scenarios cannot cover all possible real-time operating conditions, so the online adaptability of this method is poor. In order to improve this situation, the studies in [12] and [13] establish the dynamic TTC estimation model offline based on the day-ahead wind power prediction, and estimate the real-time TTC online according to the deviation between the real-time operating condition and the day-ahead operating condition. However, the error of the day-ahead wind power prediction is quite big (more than 40% [14]), causing the deviation of the operating condition too large that the accuracy of the online TTC estimation cannot be guaranteed.…”