There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

On robust estimation of the location parameter

In the world, wind power is rapidly becoming a generation technology of significance. Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting tools are urgent needed under the relevant issues associated with the integration of wind energy into the power system. This paper gives a bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting. Based on the assessment of wind power forecasting models, further direction for additional research and application is proposed.

A review on the forecasting of wind speed and generated power

http://pierrepinson.com/31761/Literature/foley2012.pdf

Current methods and advances in forecasting of wind power generation

In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given disturbance signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using multiresolution signal decomposition techniques. We demonstrate and test our proposed technique to detect and localize disturbances with actual power line disturbances. In order to enhance the detection outcomes, we utilize the squared wavelet transform coefficients of the analyzed power line signal. Based on the results of the detection and localization, we carry out an initial investigation of the ability to uniquely characterize various types of power quality disturbances. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each power quality disturbance.

Power quality assessment via wavelet transform analysis

Wind power is a fast growing source of renewable energy. In this chapter, the process of conversion of the kinetic energy inherent in the wind to electrical energy is described. Numerous technologies exist and compete in order to achieve this objective, but in general, wind turbines and wind power plants (WPPs) rely on electrical machinery and power converters working together to achieve this purpose in an optimal and stable fashion. In this chapter, prevailing generator and converter types are discussed, as are some designs which may be seen in future turbines. Turbine and plant level control is also discussed.

Wind Power Generation

In spectral analysis of two or more signals, it is desirable to quantify the statistical relationships among them. The linear coherence spectra based on Fourier analysis have been widely used to quantify the linear relationships between two stationary fluctuating signals. However, in the case of nonstationary signals, the Fourier-based linear coherence spectrum has a limited utility, since temporal information is now required. It is the purpose of this paper to extend the notion of the classical linear coherence spectra to the time-scale coherence spectrum. it will be shown that not only is the new time-scale coherence spectrum capable of measuring linear relationships between two nonstationary signals in the time-scale domain but also of detecting their phase differences as well. Using the Cauchy-Schwarz inequality, it can be shown that the time-scale coherence spectrum is bounded by zero and one. The latter number indicates a perfect linear relation between the two nonstationary signals at a particular time and scale. The efficacy of the new method is demonstrated and compared with that of the classical coherence spectrum through numerical examples.

Development of a linear time-scale coherence spectrum

—The increasing uncertainty of distributed energy resources promotes the risks of transient events for power systems. To capture event dynamics, Phasor Measurement Unit (PMU) data is widely utilized due to its high resolutions. Notably, Machine Learning (ML) methods can process PMU data with feature learning techniques to identify events. However, existing ML-based methods face the following challenges due to salient characteristics from both the measurement and the label sides: ( 1 ) PMU streams have a large size with redundancy and correlations across temporal, spatial, and measurement type dimensions. Nevertheless, existing work cannot effectively uncover the structural correlations to remove redundancy and learn useful features. ( 2 ) The number of event labels is limited, but most models focus on learning with labeled data, suffering risks of non-robustness to different system conditions. To overcome the above issues, we propose an approach called Kernelized Tensor Decomposition and Classification with Semi-supervision (KTDC-Se). Firstly, we show that the key is to tensorize data storage, information filtering via decomposition, and discriminative feature learning via classification. This leads to an efficient exploration of structural correlations via high-dimensional tensors. Secondly, the proposed KTDC-Se can incorporate rich unlabeled data to seek decomposed tensors invariant to varying operational conditions. Thirdly, we make KTDC-Se a joint model of decomposition and classification so that there are no biased selections of the two steps. Finally, to boost the model accuracy, we add kernels for non-linear feature learning. We demonstrate the KTDC-Se superiority over the state-of-the-art methods for event identification using PMU data.

/pdf/structural-tensor-learning-for-event-identification-with-1edr2tre.pdf

Structural Tensor Learning for Event Identification with Limited Labels

This paper describes a time-tested, efficient procedure for using synchrophasor data to detect grid oscillations higher than the common grid-wide 0.2 Hz to 0.8 Hz range. Higher frequency oscillations in the 1.4 Hz to 10 Hz range are becoming more common and are often tied to remote PV generation facilities, wind farms, and even to conventional generators whose control systems intermittently react with the grid to produce oscillations. The detection procedure is described in sufficient detail so that anyone with conventional 30 points per second synchrophasor data that includes voltage phase angle readings can implement and use it.

Surya Santoso

Papers

Wind Power Generation

Development of a linear time-scale coherence spectrum

Structural Tensor Learning for Event Identification with Limited Labels

Synchrophasor Detection and Observations of Grid Oscillations in the 1.4 Hz to 10 Hz Range