Rapid prototyping & deployment of control algorithms for power conversion applications

Author : Bijesh Poyil & Martin Murnane | Analog Devices

01 September 2021

Analog Devices_Rapid prototyping & deployment of control algorithms for power conversion applications
Analog Devices_Rapid prototyping & deployment of control algorithms for power conversion applications

Model-driven development has been adopted by industry as a solution for rapid prototyping & to reduce time-to-market. However, a significant amount of time & effort must typically be put into the final implementation stage to match the performance of the product to the performance of the model. Hence, the full potential of model-driven development is not always fully realised in practice.

The full version of this article was originally featured in the September 2021 issue of EPDT magazine [read the digital issue]. And sign up to receive your own copy each month.

Here, Bijesh Poyil, an engineering manager at Analog Devices (ADI) in India, Bangalore and part of ADI’s Industrial Systems Group (ISG), within its Automation Energy (AEG) business unit, and Martin Murnane, system architect for the Energy Storage & Conversion Team at ADI in Limerick, Ireland discuss how we can address this gap by following some guidelines and techniques during model development…

The increasing depth of penetration of distributed energy resources, such as grid-tied solar inverters, has resulted in the power conversion community looking for better, more efficient and cost-effective solutions for these markets. There are many algorithms and topologies that can improve the output quality and efficiency of the power conversion process. Silicon vendors are coming up with new control processors with features and hardware support to implement these algorithms efficiently. But it’s very expensive to build the hardware prototype of a full inverter and to experiment with its performance under varying conditions. Moreover, any malfunctioning of the algorithm during experimentation could damage the entire system. There are also associated safety standards to be met for products in these markets. So the power conversion industry has always been slow in adopting these innovations into the final product.

Model-driven development has been adopted as a solution to this problem. In model-driven development, a full model of the system is built and simulated before a hardware prototype is generated. This verifies the algorithm functionality and reduces risk significantly. Moreover, current modelling tools support code generation directly from the model that simplifies and relaxes safety certification criteria. However, the industry has not embraced full model-driven development, mainly because: 1. the performance of the end product and the model varies significantly; and 2. the generated code is not very efficient for the target control processor and requires manual modification before taking it to the product.

Figure 1. Solar inverter system
Figure 1. Solar inverter system

In this article, we discuss techniques and approaches that can help make the model performance very close to the final product performance to minimise the risk of hardware changes and delays. We also discuss how the code can be generated efficiently from these models to get the product to market faster.

Model-driven development

Consider a simplified diagram of a grid-tied residential solar inverter, as shown in Figure 1. The solar radiation on the solar panel generates DC proportional to the intensity of the radiation. The converter converts this DC to AC, which can be used by home appliances and can also be fed to the grid. Current and voltages from various points in the signal chain are sensed by appropriate sensors and will be fed to the control processor in the inverter. The algorithm running on the control processor analyses these signals and controls the power modules, such that the generated current and voltage are of required frequency, magnitude and phase with the grid. In this case, the solar panel acts as the power source, and the grid and the home appliances act as the sink. In a different power conversion system, the sources and sink would be different, but most of them will fall into the structure shown in Figure 2.

The primary aim of a power conversion system/algorithm designer is to arrive at the right components and algorithms for the block’s control processor and converter hardware (shown in Figure 2), and meet the desired performance for all source and load variations. So it is important to clearly know the environment the system is going to operate in while designing the system. For example, while designing a solar inverter for a system (shown in Figure 1), the designer should know the places the inverter is expected to be installed, variations in intensity of solar radiations, the efficiencies of the solar panel, grid conditions and so on. In model-driven development, the designer first creates the model of the converter, then simulates the expected variation and verifies that the model works as expected. Most often, the modelling tools will provide models and library blocks for modelling sources and sinks. For example, Simscape Power Systems™ from Mathworks has models for grids, photovoltaic (PV) panels and various loads. These can be used to simulate and verify various use cases of the system.

Figure 2. Power conversion components
Figure 2. Power conversion components

System performance depends on all the components of the system. In some cases, the designer has the freedom to start the design from scratch and decide on all the components of the system to meet constraints on source and load. In some other cases, part of the system may already be fixed, due to reasons outside the control of the designers, and their degree of freedom is limited to few components. In this article, we assume the main aim of the designer is to choose and implement the right control algorithm for an existing topology—but most of the guidelines explained can be applied to a generic case as well...

Read the full article in EPDT's September 2021 digital issue...

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