A scenario-based robust energy management method accounting for the worst-case amount of renewable generation (RG) and load is developed in this paper. The economic and robust model is formulated to ...maximize the total exchange cost while getting the minimum social benefits cost at the same time. Uncertainty of RG and load is described as an uncertain set produced by interval prediction. Then, the Taguchi's orthogonal array (OA) testing method is used to provide possible testing scenarios. A simple, but practical, search strategy based on OA is designed for solving the optimization problem. By optimizing the worst-case scenario, the energy management solution of the proposed model is robust against most of the possible realizations of the modeled uncertain set by Monte Carlo verification. Numerical cases on the typical microgrid system show the effectiveness of the model and solution strategy. In addition, the influence of exchange electricity price and other parameters are also discussed in the cases.
In this paper, an online power system transient stability assessment (TSA) problem is mapped as a two-class classification problem and a novel data mining algorithm the core vector machine (CVM) is ...proposed to solve the problem based on phasor measurement units (PMUs) big data. First of all, an offline training, online application framework is proposed, which contained four sub-steps, namely features selection, offline training, online application, and assessment evaluation. First, 24 features are selected to present the system status. Then in the offline training procedure, the PMU big data is generated by time domain simulation, and a CVM model is trained and tested. In the online application procedure, an interface between PMU data center and feature calculation program is set up to collect real time specific PMU big data and the CVM trained is applied to the TSA problem. Last but not least, the evaluation indices are calculated. Compared with other support vector machines, the proposed CVM based assessment algorithm has the higher precision, meanwhile, it has the least time consumption and space complexity. As long as online PMU big data are received, TSA can be done simultaneously. Case studies on the IEEE New England 39-bus system, and real systems in China and the U.S., exhibit the speed and effectiveness of the proposed algorithm.
Despite its wide applications in power grid monitoring, the classic discrete Fourier transform (DFT)-based synchrophasor estimation algorithms suffer from significant errors when the power system ...operates under off-nominal frequency conditions. This phenomenon is caused by spectral leakage of DFT and becomes even more severe for single-phase synchrophasor estimation. To address this issue, a theory to eliminate the spectral leakage-caused errors is proposed and a Clarke transformation-based DFT synchrophasor estimation algorithm is proposed to implement the theory in this paper. The Clarke transformation constructs a second signal that has exactly 90° phase angle difference from the original single-phase input signal and helps eliminate the estimation errors for a wide frequency range. The proposed algorithm is tested under the conditions required in the phasor measurement unit standard C37.118.1-2011 and C37.118.1a-2014, as well as the harmonic and noise conditions not required in the standard to verify its performance. More importantly, the idea of using Clarke transformation can be used for other DFT-based synchrophasor algorithms in order to achieve higher synchrophasor measurement accuracy under dynamic conditions. An example is presented at last to demonstrate the expandability of the proposed idea.
Low damping ratios of interarea modes in the power system result in peaks at their natural frequencies in the discrete Fourier transform (DFT) representation of ringdown data. This has been a ...motivation to develop a nonparametric DFT-based modal identification method. However, this method has difficulty estimating damping ratios. This paper proposes a parametric DFT-based method to solve such a problem in the non-parametric one. To effectively remove the decaying dc component in ringdown data, the proposed method uses a difference sequence between two sets of ringdown frequency data with oscillation against each other. This method estimates coefficients of the numerator and denominator in the Laplace transform of the frequency difference sequence by curve-fitting of DFT values around peak frequencies. Then, modal natural frequencies and damping ratios are calculated from estimated coefficients of the denominator. This method is compared to a non-parametric DFT-based method and a Prony-based method through simulations on the signal-to-noise ratio and is applied to identify real power systems from ringdown frequency data in the Frequency Monitoring Network system.
Wind speed prediction with spatio-temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites ...simultaneously is investigated by using spatio-temporal correlation. This paper proposes a model for wind speed prediction with spatio-temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio-temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio-temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.
Accurate event analysis in real time is of paramount importance for high-fidelity situational awareness such that proper actions can take place before any isolated faults escalate to cascading ...blackouts. Existing approaches are limited to detect only single or double events or a specified event type. Although some previous works can well distinguish multiple events in small-scale systems, the performance tends to degrade dramatically in large-scale systems. In this paper, we focus on multiple event detection, recognition, and temporal localization in large-scale power systems. We discover that there always exist "regions" where the reaction of all buses to certain event within each region demonstrates high degree similarity, and that the boundary of the "regions" generally remains the same regardless of the type of event(s). We further verify that, within each region, this reaction to multiple events can be approximated as a linear combination of reactions to each constituent event. Based on these findings, we propose a novel method, referred to as cluster-based sparse coding (CSC), to extract all the underlying single events involved in a multievent scenario. Multiple events of three typical disturbances (e.g., generator trip, line trip, and load shedding) can be detected and recognized. Specifically, the CSC algorithm can effectively distinguish line trip events from oscillation, which has been a very challenging task for event analysis. Experimental results based on simulated large-scale system model (i.e., NPCC) show that the proposed CSC algorithm presents high detection and recognition rate with low false alarms.
With the growing integration of renewable power generation, plug-in electric vehicles, and other sources of uncertainty, increasing stochastic continuous disturbances are brought to power systems. ...The impact of stochastic continuous disturbances on power system transient stability attracts significant attention. To address this problem, this paper proposes an analytical assessment method for transient stability of multimachine power systems under stochastic continuous disturbances. In the proposed method, a probability measure of transient stability is presented and analytically solved by stochastic averaging. Compared with the conventional method (Monte Carlo simulation), the proposed method is many orders of magnitude faster, which makes it very attractive in practice when many plans for transient stability must be compared or when transient stability must be analyzed quickly. Also, it is found that the evolution of system energy over time is almost a simple diffusion process by the proposed method, which explains the impact mechanism of stochastic continuous disturbances on transient stability in theory.
This letter proposed an approach to identify the change of inertia distribution in high renewable power systems. Using the footprints of electromechanical wave propagation at the distribution level, ...this approach provides a new and non-invasive way to aware the system inertia distribution for primary frequency response. Actual measurements and high renewable dynamic models validated effectiveness of the approach.
Phasor measurement units (PMUs) are taking an increasingly important role in power system dynamic security monitoring and control. However, traditional discrete Fourier transforms (DFTs) used by PMUs ...cannot obtain accurate phasor measurements during frequency excursion and transient events, being limited by its static phasor model. In this paper, a synchrophasor estimation algorithm for M-class PMUs based on the dynamic phasor model is proposed. In this algorithm, the dynamic phasor within an observation data window is approximated by the second-order Taylor expansion. The linear relationship between the second-order coefficients of the Taylor expansion and the errors caused by the DFT averaging effect is explored. Then, the linear relationship is used to compensate the raw measurement errors under a dynamic condition. Two digital filters are applied not only to eliminate the spectrum leakage caused by the dynamic inputs, but also to make the PMU immune to the out-of-band signals. The algorithm is investigated through simulation, laboratory experiments, and real data from the power system according to IEEE C37.118.1. The test results demonstrate that the proposed algorithm can meet the requirements of the standards.
Wavelet networks (WNs) are an efficient model of nonlinear signal processing developed in recent years. This paper presents a comparative study of WN efficiency for the detection of incipient faults ...of power transformers. After 700 groups of training and testing gases-in-oil samples are processed by fuzzy technology, we compare and analyze the network training process and simulation results of five WNs which include two types of WNs with two different activation functions and evolving WN. A lot of diagnostic examples show that the diagnostic accuracy and efficiency of the proposed five WN approaches prevail those of the conventional back-propagation neural-network method and are suitable for faults diagnosis of power transformers, especially with the evolving WN achieving superior performance.
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