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After edge computing became the key highlight in the Internet of Things (IoT) arena, two new concepts have emerged in the IoT-driven data world: datafication and edge autonomy. These new concepts will be explained in depth at the Boards and Solutions 2020 virtual conference to be held on 13-14 October 2020.

According to Charbel Aoun, EMEA business development director for smart cities at Nvidia, 8,000 new IoT devices are connected every 60 seconds. “IoT could turn the world into data that could be used to make macro decisions on resource utilization,” Aoun said. “Intelligent information is a great way to reduce waste and increase efficiencies, and that’s really what the IoT provides.”

What is datafication

While talking about datafication, Aoun gave the example of smart cities and one billion cameras worldwide that are recording 24/7 and generating massive data. “It is impossible for humans to process such amounts of data.” There are million-plus CCTVs in Shanghai, 500,000 in London, 200,000 in Moscow, 25,000 in Los Angeles, and 20,000 in Berlin.

A 1,080-pixel camera operating at 34 frames per second (fps) generates 47 gigabytes of data in 24 hours and 17 terabytes of data in one year. Now a CCTV operator can focus for 30 minutes while looking at 4-16 video streams simultaneously. In other words, for every 100 screens or 100 streams you want to monitor, you need six operators.

Figure 1 Nvidia’s Charbel Aoun illustrates how AI offers city managers new solutions to 21st century urban challenges with some practical examples. Source: Nvidia/Boards and Solutions 2020

At the event, Aoun’s paper “How AI Can Make Cities Smarter – Powering AI City with IVA” will explain how artificial intelligence (AI) helps make sense of the information overload. It will provide insights on datafication and how real-time decision-making based on this data pile can enhance citizens’ lives.

The pile of data generated by connected devices like cameras—embodied by datafication—will need to be processed and analyzed to provide insights and enable actions. That’s where edge devices enter the scene to help make intelligent decisions.

Future of edge: Autonomy

Jim Liu, CEO and founder of ADLINK Technology, says that the edge is where the actions should be decided because most data is collected at the edge. That’s also going to be the premise of his paper “Insights into Edge Autonomy – the Future of Edge Computing” at the virtual conference.

“We believe the future of edge is autonomy since the value is achieved at the edge,” Liu said. He also segmented edge autonomy into two sub-divisions: core autonomy and swarm autonomy. Core autonomy is focused on making a single machine smart and intelligent; however, it’s not enough to have an individual machine smart.

“If you want to fix the efficiency issue, or you want to get benefits, you have to consider how you can make sure all these machines, and people, and even utilities, connect together and ‘co-work’ together,” he added. That’s what Liu calls swarm autonomy.

It’s about making sure that all the information collected by edge devices can be transparent and shared, referring to the ‘swarm’ of intelligent machines. If edge devices can share information and data between themselves, that creates a shared intelligence and hence more autonomy at the edge.

Autonomous decision making

Autonomous decision making is also going to be a key part of the future in industrial automation projects, as highlighted in the paper presented by John Heinlein, vice president for high-performance IoT, automotive and IoT at Arm. The paper titled “Accelerating Innovation in Industrial Automation” will talk about three distinct segments in smart manufacturing and warehousing: classic automation, industrial IoT, and robotics.

In all these segments, Heinlein sees more and more autonomy and more and more autonomous decision-making, which will be important in the future. “We believe with a couple of key challenges addressed, we can get to the smart manufacturing and warehousing of the future.”

Figure 2 Arm’s John Heinlein sees a trend towards more and more autonomy and more and more autonomous decision making. Source: Arm/Boards and Solutions 2020

“There is no industry that will not favorably benefit from better data capture and better data analytics,” he added. “That’s why datafication and edge autonomy are going to be the vital ingredients in driving the smart cities, smart factories, smart homes, and the smart everything of tomorrow.”

For more details about the Boards and Solutions virtual conference taking place on 13-14 October 2020, and to register for free attendance, click here.

Covering three tracks over two days, the Boards and Solutions virtual event, will look at trends and products in industrial automation, smart cities and edge computing. The topics and speakers include:

Accelerating Innovation in Industrial Automation, John Heinlein, vice president, high performance IoT, automotive and IoT line, Arm

Embedded Processing Solutions at the Edge: not your Father’s MCU, Philippe Magarshack, technology R&D group vice president and general manager of central CAD and design solutions, STMicroelectronics

Fundamentals of Success for Industrial Automation Innovation in the 5G Era, Hitoshi Shirakabe, vice president, marketing, enterprise infrastructure business division, IoT and infrastructure business unit, Renesas

How AI can make cities smarter – Powering AI City with IVA, Charbel Aoun, EMEA Business Development Director, Smart Cities, Nvidia

Evolution of Cybersecurity Legislation Across IoT, Haydn Povey, CEO, Secure Thingz, and general manager embedded security Solutions, IAR Systems

Why Zero Touch Onboarding and Provisioning is Vital for Secure Smart Cities, Bobby Vale, head of IoT platforms and ecosystem, Advantech

Insights into Edge Autonomy – the Future of Edge Computing, Jim Liu, CEO and founder, ADLINK Technology

AI Edge Streamline Solution into your Embedded Platform, Gian Claudio Lolli, sales director Aaeon South Europe, AAEON

Industry 4.0 at the Edge – A Crucial Move for Manufacturing, Jeff Sharpe, director, IoT & 5G embedded solutions, Supermicro

Nitin Dahad is a correspondent for EE Times, EE Times Europe and also Editor-in-Chief of embedded.com. With 35 years in the electronics industry, he’s had many different roles: from engineer to journalist, and from entrepreneur to startup mentor and government advisor. He was part of the startup team that launched 32-bit microprocessor company ARC International in the US in the late 1990s and took it public, and co-founder of The Chilli, which influenced much of the tech startup scene in the early 2000s. He’s also worked with many of the big names – including National Semiconductor, GEC Plessey Semiconductors, Dialog Semiconductor and Marconi Instruments.

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Renewable energy continues to be a massive trend around the world. As methods for capturing wind, solar, and other forms of renewable energy have developed, the cost and efficiency of renewable energy systems have become more appealing to both companies and consumers. In fact, in 2016, the global monetary investment in renewable energy fell to the lowest number in years, while simultaneously the record was broken for the largest number of renewable energy installations in a given year.

Amongst the components used for renewable energy resources, the inverter stands out as a uniquely critical system component. As most renewable energy is generated in DC, an inverter plays a key role in converting DC into AC for effective integration into existing power grids. In hybrid power systems—that combine different renewable resources—and micro-grid systems, the use of inverters is essential.

Renewable energy inverters also play a critical role in industrial applications where single-phase and three-phase motors and other rotary machines are used. Variable frequency and voltage—obtained from an inverter—embodies the principle of autonomous control in this type of equipment.

To implement the power conversion, DC-AC inverters usually apply the pulse width modulation (PWM) technique. It’s a useful technique wherein switches like power MOSFETs are controlled with pulses of variable widths. In order to obtain an automatic control and regulation, the PWM technique is used to maintain the AC voltage output of the inverter and its frequency at the nominal value independent of the output load.

Many studies and technologies have been developed to obtain a better response from the inverter control system. Conventional inverters change the output voltage according to the changes in the load. To reduce the sensitivity of the output voltage to load changes, PWM-based inverters regulate the output voltage by changing the width of the pulses generated at a comparatively high frequency. As a result, the output voltage depends on the switching frequency and pulse width, which varies according to the value of the load connected at the output. With this type of regulation, the inverters provide a constant nominal voltage and a frequency independent of the output power.

Several methods of generating PWM have been studied. The efficiency parameters of an inverter, such as switching losses and harmonic reduction, are the main factors considered in any modulation technique evaluation. So, sinusoidal PWM (SPWM) is widely used in power electronics as the modulation method for inverter designs.

SPWM inverter concept

A three-phase wave bridge inverter is the most commonly-used inverter topology in industrial applications. To simplify the concept, a single-phase version is analyzed. The single-phase design includes switching transistors or IGBTs on each arm of the H-bridge with antiparallel freewheeling diodes to discharge when the switch is turned off. Its schematic is shown in Figure 1.

Figure 1 This basic H-bridge circuit shows the key building blocks of an inverter. Source: Dialog Semiconductor

The transistors—usually power MOSFETs—are identified as S1, S2, S3, and S4. The switches are alternated such that both transistors of the same arm are not conducting or opened simultaneously, thereby preventing a short circuit.

To generate the alternating current in the load, transistors operate in a pair: S1 and S2 conducting and S3 and S4 opened or vice versa. Table 1 shows the different switching stages and the applied voltage to the load.

Table 1 This outline shows the logic of switches. Source: Dialog Semiconductor

A square wave inverter, also known as basic inverter, is operated by two square waves in the opposite phase and with a frequency equal to the desired frequency at the output. One of the waves is applied to S1 and S2 and the other waveform to S3 and S4. Figure 2 shows the PWM control signals and the obtained voltage in the load if this type of inverter is used.

Figure 2 PWM control signals and output voltage look like this if a basic inverter is used. Source: Dialog Semiconductor

The PWM technique is based on the generation of constant amplitude pulses and modulation of the pulse duration by varying the duty cycle. The reference or modulated signal is the desired signal output—sinusoidal in the case of voltage waveforms at the output of an inverter—and the carrier signal must have a frequency much greater than the modulated one. This is the main reason for the usage of sinusoidal PWM or SPWM as the modulation method for PWM inverters.

SPWM characteristics

SPWM is based on constant amplitude pulses with different duty cycles for each period. The width of pulses is obtained by modulation of a carrier to obtain the desired output voltage and to reduce its harmonic content.

The carrier signal of SPWM is usually a triangular wave with a high frequency, generally in several kHz. The modulation signal of SPWM is a sinusoidal waveform with a frequency equal to the desired output voltage frequency, generally 50 Hz or 60 Hz.

In Figure 3, a simplified schematic of sinusoidal modulation is shown. The switching signal is generated by comparing the sinusoidal waveform and the triangular carrier waveform. The comparator output is high when the sinusoidal voltage is greater than the triangular voltage. The output pulses of the comparator are used as the gate pulses of the H-bridge presented in the previous section.

Figure 3 A simplified schematic shows how SPWM generates the switching signal. Source: Dialog Semiconductor

To obtain better results, the frequency ratio between the triangular and the sinusoidal waveforms must be an integer N = fC/fS, where fC is the carrier frequency or the triangular waveform and fS is the modulation frequency or the sine waveform. With this condition, the number of voltage pulses per half-cycle results in N/2. This effect can be seen in Figure 4, where the triangular, sine, and the PWM output waveforms are shown.

Figure 4 The frequency ratio between the triangular and the sinusoidal waveforms must be an integer. Source: Dialog Semiconductor

The modulation process of the duty cycle is designed for modulation index m equal to or less than one. If m is higher, there will be periods of the triangle signal in which there is no intersection of the carrier and the modulation signal will exist. The effect on the output signal is shown in Figure 5. However, it’s important to note that a certain amount of over-modulation is sometimes used to obtain a higher AC voltage amplitude.

Figure 5 A certain amount of over-modulation is sometimes required to raise AC voltage amplitude. Source: Dialog Semiconductor

When SPWM is analyzed in terms of waveform quality, the harmonics must be considered. The SPWM generates different harmonics of several orders in the voltage waveform. However, the dominant ones are of order N and N±2 whereas N is defined as fC/fS. If over-modulation is considered, the output voltage has a higher harmonics content as a trade-off in generating a higher voltage. By varying the sinusoidal voltage, the output voltage can be regulated.

These concepts of triangular waveforms with fixed amplitude and frequency and sinusoidal waveforms with a fixed frequency equal to the output frequency and variable amplitude are the basis of the SPWM generator implemented in this article. The SPWM generator is shown in the block diagram in Figure 6.

Figure 6 This block diagram shows how the SPWM generator is designed and implemented. Source: Dialog Semiconductor

A high-frequency triangular waveform is necessary to generate the SPWM signals. This task is implemented with finite state machines (FSM), counters, and D-type flip flops, and is referenced as HF triangle generator in the above diagram. The generator is based on the AN-CM-265 programmable limits PWM, generating a PWM with a triangular variation of the duty cycle. As a triangular waveform is required, a low-pass filter is applied to eliminate the very high frequency of the square wave.

This triangular waveform is compared with an external low-voltage 50 or 60 Hz sine waveform with GreenPAK analog comparators. With this comparison, the sinusoidal modulation of the PWM is implemented as described in the previous section. Finally, an inverter is used to generate the complementary signals for the SPWM outputs.

Figure 7 The output of the SPWM generator is connected to an H-bridge. Source: Dialog Semiconductor

As seen in Figure 7, the SPWM output and its complementary signal are connected to each transistor of the same leg. The output of the H-bridge contains an LC-filter, so the high-frequency component of the SPWM is filtered, and finally, the sinusoidal waveform of 50 or 60 Hz is applied to the load.

Implementing SPWM generator

The implementation of the SPWM generator is based on the SLG46826V, a configurable mixed-signal IC (CMIC) that contains FSM digital counters, high-speed analog comparators, and high-frequency oscillators. That allows the CMIC to be used for generating the required triangular waveform and the sinusoidal modulation.

As mentioned earlier, the high-frequency triangular waveform generator is based on the AN-CM-265 programmable limits PWM. The implementation of the generator in the GreenPAK Designer software can be seen in Figure 8.

Figure 8. The implementation of the triangle waveform generator is shown here in the GreenPAK Designer software. Source: Dialog Semiconductor

The generator uses the internal 25 MHz oscillator, configured for an output frequency of 12.5 MHz. This clock, combined with macrocells CNT1 and CNT2, generates the corresponding square waveform with the desired duty cycle.

With this configuration, the triangular waveform has a period of 1 ms, so a 1-kHz triangular waveform is obtained. In this design, a 50-Hz SPWM inverter is implemented but can be modified for 60 Hz or other frequencies by changing the period of the triangular waveform.

The high-frequency PWM with triangular variation is connected to PIN 16, where the corresponding, external low-pass filter is connected. This filter is based on a first-order RC filter, with a 1.5 kΩ resistor and a 10 nF capacitor, so the cut-off frequency of the filter is 10.6 kHz.

The output of the filter, shown in Figure 6, is connected to the high-speed analog comparator ACMP0H. The configuration of ACMP0H is shown in Figure 9.

Figure 9 This configuration of the high-speed analog comparator shows odd output control. Source: Dialog Semiconductor

This block is used to compare the voltage between PIN 20 and PIN 3. For best performance, the hysteresis and the bandwidth limit options must be disabled. Thus, a low-voltage sinusoidal waveform generator must be connected to PIN 3, so the sinusoidal PWM modulation is obtained (Figure 10).

Figure 10 This is the block diagram of the modulator that facilitates the sinusoidal PWM modulation. Source: Dialog Semiconductor

To generate the complementary signals for PWM output, the 3-L1 look-up table is configured as a logical inverter. Finally, PWM outputs are connected to PIN 5 and PIN 6. As PIN 8 and PIN 9 are connected to the I2C module of the GreenPAK chip, it’s necessary to connect them to VDD using a pull-up resistor. The entire SPWM implementation diagram is shown in Figure 11.

Figure 11 Here is a complete view of the SPWM generator implementation. Source: Dialog Semiconductor

Testing SPWM implementation

To test the implementation, the entire system was assembled and analyzed with an oscilloscope. The 50-Hz sine wave signal was generated with a function signal generator, with a corresponding amplitude and offset such that it can be connected directly to the SLG46826V CMIC. The entire system can be seen in Figure 12.

Figure 12 A view of the entire setup used to test the SPWM generator implementation. Source: Dialog Semiconductor

This article has demonstrated the implementation of an SPWM generator, one of the most widely used methods for implementing power inverters commonly used in applications such as motor controls and renewable energy, including each step of SPWM generation and how it can be connected and filtered at the output.

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