Pdf Small Size Converter Free Download

This is helpful when you plan to either upload your images online or send it via e-mail. Even sending files via WhatsApp, there are file size restrictions that might prevent you from sending larger images.

CRF (Constant Rate Factor): CRF method attempts to keep a constant perceived video quality. To do that, it uses different compression levels on different frames. For the H264 encoder, possible CRF values range from 0 to 51. Higher values mean more compression (reduced file size), lower values mean better quality (but bigger file size). The default is set at 23.

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Preset: Presets refers to the video compression speed. Choosing a slower preset allows better optimization (lower bitrate/file size) for a given video quality level. If you prefer a lower file size over encoding time, choose a slower preset.

This method allows you to set a target file size for your video as a percentage of the original size. For example, if you set it to 60% for a 1Gb file, we will attempt to make your compressed file size 600Mb or less.

When you save DGN file with Batch Convert the file will not directly

open in MicroStation and will not create a thumbnail preview in the DWG.

When you open the DWG file which is converted with Batch Convert,

do a save setting (thumbnail preview will be added) , and close the DWG.

You will now see that the file size is the same as the other DWG.

Note: When doing a File > Export DWG , the DWG file size will be the

same as using Batch Convert to DWG

For decentralized electrification in remote areas, small-sized wind energy systems (WESs) are considered sustainable and affordable solution when employing an efficient, small-sized component converter integrated with a less-sophisticated, cost-effective MPPT controller. Unfortunately, using a conventional buck DC/DC converter as a MPP tracker suffer from input current discontinuity. The latter results in high ripples in the tracked rectified wind power which reduces the captured power and affects system operation especially in standalone applications which are self-sufficient and independent of grid support. Furthermore, these ripples propagate to the machine side causing vibration and torque stress which impacts turbine performance and safety. To solve this issue, a large electrolytic capacitor is placed at the buck converter input to buffer these ripples, yet at the cost of larger size, losses and reduced reliability. Oppositely, the developed C1, D4 and D6 buck converters have the merit of continuous input current at small component-size. In this paper, dynamic modelling of these three converters is developed to select the one with the least input current ripples to replace the traditional buck converter in the considered WES system. Consequently, fluctuations in the tracked power are minimized and the large buffer capacitor is eliminated. This enhances system lifetime, reduces its cost and increases tracking efficiency. Moreover, mechanical power and torque fluctuations are minimized, thus maintaining machine protection. Furthermore, a sensorless MPPT algorithm, based on converter averaged state-space model, is proposed. Being dependent on variable-step P&O algorithm, the proposed approach features simple structure, ease of control and a compromise between tracking time and accuracy besides reduced cost due to the eliminated current sensor. Simulation results verified the effectiveness of the selected converter applying the proposed MPPT approach to efficiently track the wind power under wind variations with cost-effective realization.

However, with its intermittent nature due to wind speed variations, capturing the most energy possible from wind energy conversion systems (WECSs) must be ensured3. This implies continuous tracking to the maximum power via an efficient maximum power point tracking (MPPT) algorithm realized by a reliable converter configuration4. To this end, a number of MPPT techniques have been developed and effectively implemented5,6,7,8,9,10,11. These methods can be classified into four main categories; direct power control (DPC), indirect power control (IPC), smart and hybrid techniques, yet each has its own advantages and limitations. Since MPPT techniques differ in many aspects (implementation complexity, accuracy, tracking speed, number of sensors, parameters dependence and prior knowledge requirement, etc.), selecting the most convenient MPPT scheme is application and system-size dependent12.

For standalone electrification applications in remote areas where grid access is expensive or unavailable, off-grid small-size WESs are considered one of the cost-effective solutions in locations where wind energy is abundant12,13. Yet, some challenging aspects should be considered to maintain high reliability, minimal complexity and reduced cost of the considered decentralized WES.

Finally, employing an efficient, simple and low-cost MPPT algorithm is a further challenge facing standalone small-size WECSs which are considered in this work26. P&O search scheme is an appealing candidate, especially for low-power applications, since it requires only voltage and current sensors, rather than mechanical sensors, to compute changes in the tracked power and determine the perturbation direction27. This reduces system cost, size and implementation complexity; thus P&O is frequently deployed in commercial freestanding small-size WECSs using inexpensive microprocessors28. Despite its simple implementation and satisfactory performance, conventional fixed step-size P&O algorithm forces the operating point to oscillate about the MPP during rapid wind changes, which leads to high power fluctuations27. Hence, this limitation was addressed by replacing the constant step-size by a variable one to compromise between tracking accuracy and speed7.

In this paper, it is proposed to employ a continuous input current buck converter in a standalone WES, rather than the traditional buck converter, to eliminate the buffer capacitor and yet minimize tracked power ripples. To assess the three CICO buck converters (C1, D4 and D6) introduced in21 and select the one with least input current ripples, detailed average models are derived for each of the three converters. According to derived equations, D6 was witnessed to attain the least input current ripples i.e. highest tracking efficiency, thus will be considered in simulation work. Moreover, a current sensorless MPPT method, featuring variable-step P&O scheme, is proposed to be implemented by the selected converter to add to system simplicity and reduced cost. In summary, this paper proposes a cost-effective standalone PMSG-based WECS with the following merits;

D6 DC/DC buck converter is applied as the MPP tracker with its continuous input current integrated capability and minimal input current ripples, thus minimizing input power ripples and maximizing tracking efficiency at the least possible component count.

Being dependent on variable-step P&O algorithm, the proposed MPPT scheme features the merits of simple realization, absence of any mechanical sensors and enhanced compromise between tracking time and accuracy as well as further reduction in size and cost due to the eliminated current sensor.

The proposed topology functionality was tested and validated using MATLAB/Simulink. The simulation findings confirmed that when utilized with WESs, D6 outperforms the standard buck converter; achieving minimal mechanical and electrical power oscillations while removing the large buffer capacitor. Moreover, the functionality of the proposed current sensorless MPPT controller is also verified during wind variations with a single voltage sensor rather the voltage and current sensors required by the conventional sensored controller.

Hereby, the system under consideration, shown in Fig. 1, is discussed in details14. It is an off-grid WECS that comprises a wind turbine, a gearless Permanent Magnet Synchronous Generator (PMSG), a passive diode rectifier and a DC/DC converter that bucks the generator rectified voltage to the required DC level and meanwhile acts as the MPP tracker. Table 1 shows considered system parameters.

After harvesting the maximum mechanical power, the latter is used to drive a generator to produce the required electrical energy. Due to their high-power density, high efficiency, and direct drive construction, PMSG-based WESs are an excellent candidate providing a reliable, cost-effective solution16,17. For successful control of generator output power in PMSG-based low-power WESs, a passive rectifier stage followed by a DC-DC converter stage is found to be a more affordable solution18,19.

A full-wave bridge rectifier is applied at the generator output to convert its output AC voltage into rectified DC voltage \(({V}_{r})\) which is the input voltage \(({V}_{i})\) to the following buck converter stage. The rectified DC voltage is computed from Eq. (6) as follows32;

For the considered system, a buck DC/DC converter stage is added after the rectifier stage to step-down the rectifier output DC voltage to the required DC bus level. Meanwhile the switching of this DC/DC converter stage is controlled to extract the maximum available power at the rectifier output, thus this converter is considered the MPP tracker in the considered system. Conventionally, a traditional buck converter is applied as the MPPT tracker whose switching is controlled via conventional fixed-step P&O MPPT technique.

Modeling of conventional buck converter is first carried without employing the input buffer capacitor to verify the buck integrated feature of discontinuous input current. Then, it is modelled again when applying a buffer capacitor at the buck converter input to emphasize this capacitor importance to buffer enlarged input current ripples and minimize their propagation to the machine side when using the buck converter as a MPP tracker in RES applications33.

where Vi and Vo are the converter input and output voltages respectively, Ii and Io are the converter input and output currents respectively and D is the converter duty ratio. 17dc91bb1f