This paper presents a simple method to realize a circular polarizer by inserting rods arranged helically in the cylindrical waveguide. In the proposed polarizer, the rods are installed to excite the electric fields on the perpendicular axis to the input field. In addition, the rod locations create a 90⁰ phase difference between two perpendicular axes to convert the polarization from linear to circular. In the proposed structure, the axial ratio is smaller than 0.4 dB and the phase difference is about 90⁰ at 10 GHz frequency. In addition, the reflection coefficients are better than -14 dB for both E_x and E_y polarizations at the same frequency. Also, this structure is very compact compared to similar structures. Furthermore, this is made only of metal. Therefore, there is no dielectric loss and it has high endurance. The validation results have a good agreement with the simulation.
Fault detection is crucial in smart grid control and monitoring operations. The use of smart meters leads to appearance of a large amount of digital data whose conventional and chronological techniques are not efficient enough for processing and decision-making. In this paper, a novel data analysis model based on deep learning and neuro-fuzzy algorithm is proposed for detection and classification of faults in a smart grid. First, the Long Short Term Memory (LSTM) based deep learning model is applied for training the data samples extracted from the smart meters. Then, the Adaptive Neuro Fuzzy Inference System (ANFIS) is implemented for fault detection and classification from the trained data. With this intelligent method proposed, single-phase, two-phase and three-phase faults can be identified using a restricted amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13-node network is used. The results indicate that the combined ANFIS-LSTM deep learning model outperforms existing machine learning methods in the literature in terms of accuracy for fault detection and classification.
The clearing price in electricity spot market is an important reference that guides marker participants in making energy purchase. Current electricity price forecasting methods consider the numerical accuracy of the forecast result only, ignoring the need to optimize economic benefits, while higher numerical precision sometimes leads to lower electricity-purchase gain. This paper proposes a price forecasting method that considers both economic benefits and numerical accuracy. A function representing the relationship between the predicted electricity prices and the cost reference for making energy purchase decisions is calculated, and then introduced to the loss function of the prosumers' forecasting model as a revenue-optimizing term. A sequence comparison neural network structure is designed and added to consumers' forecasting model, so that the results of numerical prediction and comparison both contribute to predicting better prices. By co-optimizing numerical precision and electricity-purchase gain, the prediction is more conducive to reducing the cost of purchasing power. Actual electricity market price data are used to verify the feasibility of the proposed forecasting method in improving economic benefits.
This paper is concerned with the formation control problem for a class of large-scale mobile sensor networks. The dynamic of mobile sensors are modeled by class of semilinear parabolic system, which is a class of partial differential equation(PDE) and has rich geometric family. In this model, the communication topology of agents is a chain graph and fixed. Leader feedback laws which designed in a manner to the boundary control of semilinear parabolic system allow the mobile sensors stable deployment onto planar curves. By constructing appropriate Lyapunov functional and using linear matrix inequality, several sufficient criteria are derived ensuring the mobile sensor networks to be globally asymptotically stable at the equilibrium. A simulation example is provided to demonstrate the usefulness of the proposed formation control scheme.
This paper studies chatter stability of composite cutter bar milling system in rotating coordinate frame. Based on the structural dynamic equation and regenerative milling force model of composite cutter bar in rotating coordinate frame, the continuous distributed chatter analysis model of composite cutter bar milling system is established. The stability of milling system with a rotary symmetric dynamic cutter bar is predicted by using the semi-discrete time domain method. Influences including internal damping, external damping, symmetrical and asymmetric laminates on the stability of milling system are analyzed, and the results obtained in rotating and fixed coordinate frame are compared. It is shown that the results are consistent for symmetrical cutter bar either in the rotating coordinate frame or in the fixed coordinate frame. A new chatter instability zone appears at high rotating speeds due to material internal damping of the rotating composite cutter bar.
In this paper, an adaptive sliding mode observer (ASMO) associated with a phase locked loop (PLL) is assessed for the sensor-less control of a rotor-tied doubly-fed induction generator (RDFIG). In the proposed PLL-ASMO estimator, the ASMO utilizes the stator current, the stator voltage and the back electromotive force (EMF) as state variables. The proposed ASMO is used in order to estimate the back-EMF from which the slip position/speed is extracted using a PLL. The design of the ASMO gains is based on the Lyapunov stability criteria to ensure the convergence of the proposed observer in a finite time. Therefore, the main contribution of this paper is to propose a PLL-based ASMO estimator that aims to improve the estimation by reducing the chattering effect. A comparative study between the standard PLL-SMO estimator and the ASMO-PLL estimator is presented. Also, For the first time, an adaptive sliding mode observer is used for the sensor-less control of a RDFIG. The performance of the proposed sensor-less control strategy is validated through simulation and experimental measurements under various operating conditions. Furthermore, the estimator is shown to be robust to machine parameter variation.
Due to its stochastic nature, wind energy imposes unprecedented challenges on the power grid, and a properly scheduled reserve is essential to accommodate wind power’s intermittency and volatility. Many power reserve scheduling studies have considered the uncertainties of the renewable energy integration but few address how different wind speed forecast techniques influence the scheduling of reserves in the congested transmission networks. In this paper, three forecasting techniques: artificial neural network, autoregressive integrated moving average, and probability distribution function-based model are adopted to forecast one day of wind speed at Taylor, TX in 2012. To evaluate the impacts of the forecast techniques on power reserve scheduling, a stochastic reserve optimization model was developed to ensure the delivery of reserve in the event of transmission congestion and ramping constraints. A modified RTS-96 test system was employed and the results claim that different forecast models significantly affect the amount of scheduled up and down reserves in a stochastic reserve optimization problem. The level of operating reserve that is induced by wind is not constant during all hours of the day. Dynamic up and down reserves will be needed with a large scale of wind farm integration.
This paper highlights the impact of curved and flat vehicular plastic parts on the radiation characteristics of two dual-band antennas for C-V2X applications. The radiation patterns of the antennas are measured in SATIMO near field measurement system and are compared during the following setups: (a) antennas alone in the near field system, without the presence of a plastic part; (b) antennas mounted on the inside curved surface of a driver’s side mirror cover; (c) antennas mounted on the outside curved surface of the driver’s side mirror cover; (d) antennas mounted on a flat trunk lid; (e) antennas mounted on a curved plastic retrieved from the A-pillar of a vehicle. Comparison among the antennas radiation pattern measurements during these different setups, results in the conclusion that the inside surface of the side mirror cover is the most suitable position to mount the presented dual-band antennas. The curvature of the inside surface at the point where the antenna was mounted is less steep than the placement point at the outside surface, allowing the antenna to keep its polarization axis mostly unaffected. Moreover, the curve of the inside surface makes the antenna radiation more directional, creating an increase in the antenna gain. The side mirror cover, compared to trunk lid, is further from the ground protecting the antenna radiation from additional reflections.
The paper assesses the feasibility of forming a composite excitation pulse with a high potential to combat the noise and onset ambiguity when estimating the target resonance behaviour in a radar target signal. The assessment investigates four composite pulse configurations of unified or adaptive setups for the fractional bandwidth and peak weight to find the best setup in enhancing the resonance signature robustness. The assessment uses the method pencil function to extract the resonance parameters of the composite time data (coherent) and then determine the degree of robustness over-extraction onset and range of noise level. Determining the robustness rate requires finding the error between the original excitation frequencies and the extractable resonant frequencies and, second, the similarity between the original and reconstructed pulse waveforms. The qualitative assessments of the robustness merit concluded that the adaptive configuration of peak weight and small adaptive fractional bandwidth outperforms the other configurations in enhancing the resonance signature robustness.
Liben Building, constructed in 1822 AD, is located in Xinnan Village, Hukeng Town, Yongding District, Longyan City, Fujian Province, China. This square earthen building covers an area of about 2,100 square meters. In 1931 AD, the building was sacked and burned down by bandits during a war, leaving behind only the remnants of the walls of the main building. In the meta-universe environment, firstly, this study adopted blockchain DAO technology to conduct a study on the digital collection, storage, processing, display and dissemination of Liben Building, revealing the problems with digital regeneration of cultural heritage. Then, the questionnaire with 20 questions was designed, and 158 valid completed copies of the questionnaire were collected. Combining influence relationship with sample clustering analysis methods, this paper explored the findings of a study of the historical sites under blockchain digital heritage preservation and protection in the metaverse.