An active islanding detection method based on Frequency-Locked Loop (FLL) for constant power controlled inverter in single-phase microgrid is proposed. This method generates a phase shift comparing the instantaneous frequency obtained from FLL unit with the nominal frequency to modify the reference phase angle. An initial low frequency variable triangular disturbance is added to the phase shift in order to reduce NDZ and accelerate the detection process especially in the case of power matching. With the modified phase angle, the frequency at PCC will be drifted away from the nominal frequency until exceeding the threshold because of the frequency positive feedback after islanding. Besides, FLL is introduced to this method in order to lock frequency quickly considering that the frequency is time-varying during the islanding detection process. Simulation and experiment have been done to evaluate this method.
Short-Term Load Forecasting (STLF) for End-User Transformer Level (EUTL) is challenging due to the high penetration of Electric Heating Loads (EHLs), which exhibit significant uncertainty, nonlinearity, and variability. In this paper, a STLF model is proposed based on the Stacked Auto-Encoder Extreme Learning Machine (SAE-ELM) deep learning framework, which can be used to extract hidden features from the time series load data. In order to improve the capability of extracting deep and diverse features from the data and generate a useful knowledge representation structure, a novel specialized feature indices set is proposed to construct the training sample set. The sliding trend, fluctuation rate, grade of change, and smoothness of the time series are considered and quantified as elements of the training sample set. Then, deep nonlinear features are extracted by using the SAE-ELM with no iterative parameter tuning needed. To illustrate the validity of the proposed model, five numerical cases are conducted. Comparison of results shows that the proposed model improves the capability and sensitivity of dealing with load volatility and forecasting accuracy.INDEX TERMS Short-term load forecasting, feature representation, deep learning, stacked auto-encoder, extreme learning machine.
The general mathematical model of the transcritical CO2 compressor was presented to assess the compressor efficiencies including isentropic efficiency and volumetric efficiency based on the thermodynamic theories and compressor structures. Furthermore, the prototype of the transcritical CO2 system was established and relative measurements were carried out to evaluate the precision of the simulation. Results showed that the volumetric efficiency of the compressor kept decreasing while the isentropic efficiency increased first and then kept almost constant and even declined with the increase in the pressure ratio. Besides, the indicated efficiency and volumetric efficiency declined slightly with the decrease in the suction density corresponding to the increase in suction superheating. As for the effects of compressor structures on the performances, the indicated efficiency increased sharply and then decreased gradually, while the volumetric efficiency kept declining with the increase in the cylinder diameter-to-height ratio, respectively.
Aiming at the integrated energy optimization problem of residential buildings including production energy, energy storage and energy use objects, such as photovoltaic power generation, solar heat, electric heating, heat storage, battery, household power load and so on, this paper formulate the energy scheduling policy under the current running state referring to the scheduling policy of similar running state in history from the point of view of data mining. Firstly, a random matrix of basic status of residential buildings energy is constructed. Based on this, a similarity search method based on standardized Euclidean distance measure is proposed. This method forms four standardized Euclidean distances that are the solar irradiance vector, outdoor temperature vector, residential buildings’ electric load vector and heat load vector by the similarity measure calculation based on the standardized Euclidean distance model. The weight analysis method is used to uniformly process a standardized European distance comprehensive index as a measure of similarity. Then, the paper analyses the threshold setting principle of similarity measure. Finally, the optimization process of scheduling strategy of residential buildings energy based on similarity search method is given. A simulation example of an actual residential buildings under a typical scenario of sunny day in winter shows that the proposed similarity search method based on the standardized Euclidean distance measure can make full use of a large amount of historical operating data and speed up the optimization efficiency of optimal scheduling strategy for integrated buildings energy. The scheduling strategy can guide energy storage to play the role of energy transfer, and guide the electric heating equipment and the time-shifting electric load to work in the valley period through the peak and valley electricity prices, thereby the user’s bill is effectively reduced.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.