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Due to the demand for eco-friendly energy, distributed energy resources (DERs) using renewable energy have increased. The increase in DER has caused the power system to become more complex and caused problems in the protection system. Typical problems include an increase in fault current and a problem that causes malfunction of the overcurrent relay (OCR). If the fault current increases and exceeds the capacity of the existing protection devices, it may lead to a large blackout. The most effective way to limit the fault current is to install a superconducting current limiter (SFCL). The installation of SFCL and system penetration of DER both affect OCR operating characteristics. In this paper, a simulated power distribution system is constructed and OCR malfunctions caused by DER penetration and SFCL installation are analyzed.

The three-phase H7 inverter topology installs an additional power semiconductor switch to the positive or negative node of the DC-link for reducing the common-mode voltage (CMV) by disconnecting the inverter from the DC source during the zero-voltage vectors. The conventional CMV reduction method for the three-phase H7 inverter uses modified discontinuous pulse width modulation (MDPWM) and generates a switching signal for the additional switch using logical operations. However, the conventional method is unable to eliminate the CMV for the entire dwell time of the zero-voltage vectors. It only has the effect of reducing the CMV in a limited area of the space vector where the V7 zero voltage vector is applied. Therefore, this paper proposes an optimized modulation method that can reduce the CMV during the entire dwell time of zero-voltage vectors. The proposed method moves the switching patterns by adding an offset voltage to guarantee that only one kind of zero-voltage vector, V7, is applied in the system. It then turns off the seventh switch only during the zero-voltage vector to disconnect the inverter from the DC source. As a result, the CMV and the leakage current are attenuated for the entire dwell time of the zero-voltage vector. Simulation and experimental results confirm the validity of the proposed method.

The use of natural gas as an energy source is increasing significantly due to its low greenhouse gas emissions. However, the common methods of natural gas storage and transportation, such as liquefied or compressed natural gas, are limited in their applications because they require extreme conditions. Gas hydrate technology can be a promising alternative to conventional approaches, as artificially synthesized hydrates provide an economical, environmentally friendly, and safe medium to store energy. Nevertheless, the low rate of hydrate formation is a critical problem that hinders the industrial application of this technology. Therefore, chemical promoters are being developed to accelerate the kinetics of gas hydrate formation. In this paper, the effect of new sodium sulfosuccinate compounds, synthesized based on glycerol and pentaerythritol, on methane hydrate formation was studied. Experiments under dynamic conditions using high-pressure autoclaves demonstrated that the conversion of water-to-hydrate forms increased from 62 ± 5% in pure water to 86 ± 4% for the best promoter at concentration 500 ppm. In addition, the rate of hydrate formation increases 2–4 times for different concentrations. Moreover, none of the synthesized reagents formed foam, compared to sodium dodecyl sulfate, in which the foam rate was 3.7 ± 0.2. The obtained reagents showed good promotional properties and did not form foam, which makes them promising promoters for gas hydrate technology.

With the proliferation of large-scale grid-connected wind farms, subsynchronous oscillation (SSO) incidents associated with Type-4 wind turbines (WTs) with a permanent magnet synchronous generator (PMSG) have occurred frequently. These incidents have caused severe reliability risks to the power grid. Conventionally, P-Q capability charts are utilized to ensure the safety operating region of a synchronous generator. However, a PMSG WT exhibits completely different and dynamic P-Q capability characteristics due to the difference in energy conversion technique and several critical factors related to the WT power converters. This paper presents a comprehensive dynamic P-Q capability study of a PMSG WT with sufficient and accurate considerations of the WT control and operation in the dq reference frame, its power converter constraints, and grid dynamics. Models of a PMSG WT are first developed based on its control principle in the dq reference frame. Then, algorithms for obtaining the P-Q capability charts of the WT are developed with the considerations of complete WT constraints in different aspects. The study analyzes the root cause of many abnormal operations of grid-connected PMSG WTs, reported in the literature, from the dynamic P-Q capability perspectives. The proposed study is verified via an electromagnetic transient (EMT) model of a grid-connected Type-4 WT.

Accurately predicting oilfield development indicators (such as oil production, liquid production, current formation pressure, water cut, oil production rate, recovery rate, cost, profit, etc.) is to realize the rational and scientific development of oilfields, which is an important basis to ensure the stable production of the oilfield. Due to existing oilfield development index prediction methods being difficult to accurately reflect the complex nonlinear problem in the oil field development process, using the artificial neural network, which can predict the oilfield development index with the function of infinitely close to any non-linear function, will be the most ideal prediction method at present. This article summarizes four commonly used artificial neural networks: the BP neural network, the radial basis neural network, the generalized regression neural network, and the wavelet neural network, and mainly introduces their network structure, function types, calculation process and prediction results. Four kinds of artificial neural networks are optimized through various intelligent algorithms, and the principle and essence of optimization are analyzed. Furthermore, the advantages and disadvantages of the four artificial neural networks are summarized and compared. Finally, based on the application of artificial neural networks in other fields and on existing problems, a future development direction is proposed which can serve as a reference and guide for the research on accurate prediction of oilfield development indicators.

Turbulent flow mixing is essential in optimizing microalgal cultivation in raceway ponds. Microalgal cells are however highly sensitive to hydrodynamic stresses produced by turbulent mixing because of their small size. The mechanical properties (wall deformation and von Misses stress) of the microalgal cell wall structure under the influence of turbulent mixing are yet to be explored. High turbulence magnitudes damage microalgal cell walls by adversely affecting their mechanical properties which consequently destroy the microalgal cells and reduce the biofuel production. Therefore, such a study is required to improve the biofuel productivity of microalgal cells before their cell wall damage in raceway pond. This study developed a novel fluid–structure interaction (FSI)-based numerical model to investigate the effects of turbulent mixing on the cell wall damage of microalgal cells in raceway ponds. The study investigated microalgal cell wall damage at four different locations in a raceway pond in consideration of the effects of pond’s hydrodynamic and geometric properties. An experiment was conducted with a laboratory-scale raceway pond to compare and validate the numerical results by using time-dependent water velocities. Microalgal cell wall shear stress, cell wall deformation, and von Misses stress in the raceway pond were investigated by considering the effects of aspect ratios, water depths, and paddle wheel rotational speeds. Results showed that the proposed numerical model can be used as a prerequisite method for the selection of appropriate turbulent mixing. Microalgal cell wall damage is high in shallow and narrow raceway ponds with high paddle rotational speeds.

The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.

Most of the current Russian oil production comes from mature fields. The application of enhanced oil recovery methods on oil fields increases recovery efficiency. This article presents an analysis of the increased field development efficiency methods of the Volga-Ural oil and gas region, which allows the full and efficient development of last-stage fields with unconventional reserves and production stabilization. The selection of the optimum method for a given field is a complex procedure consisting of many stages, from collecting data about the field, through more advanced data interpretation, to working out a detailed proposal for the most efficient extraction method. In this article the instantaneous and average annual growth above wells average was taken as a performance criterion for enhanced oil recovery methods. Based on the performed analysis, it follows that candidate wells for enhanced oil recovery method use must meet the I group parameters (high values of the remaining recoverable reserves and improved reservoir properties, low water cut, obtained oil rate increase). In order to assess the possible increase in production rate after enhanced oil recovery methods hydrodynamic modeling of radial drilling, acid treatment and water-alternated-gas injection for two oil fields of the Volga-Ural oil and gas region were performed.