Sampling Rate and Performance of DC/AC Inverters with Digital PID Control—A Case Study

Błachuta, Marian; Bieda, Robert; Grygiel, Rafal

doi:10.3390/en14165170

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…It is advisable to adopt a high sampling rate (which leads to a less total harmonic distortion, even at rectified loads) in order to achieve a superior level of control. It implies the possibility of using a digital control system with the maximum frequency, alternatively an analogue design reflecting the algorithm of control [43][44][45].…”

Section: Modeling Of Pv Mppt Systemmentioning

confidence: 99%

Artificial Intelligence of Things-Based Optimal Finite-Time Terminal Attractor and Its Application to Maximum Power Point Tracking of Photovoltaic Arrays in Smart Cities

Chang

Cheng

2022

Wireless Communications and Mobile Computing

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The combination of artificial intelligence of things (AIoT) and photovoltaic power generation can save energy and reduce carbon emissions and further promote the development of smart cities. In order to obtain the maximum power output from photovoltaic (PV) arrays, we can use optimal maximum power point tracking (MPPT) technique with AIoT sensing to improve system efficiency. The optimal MPPT technique is the finite-time terminal attractor (FTTA) based on the gradient particle swarm optimization (GPSO), which can be applied to track the maximum power of a PV array system. The FTTA not only provides fast finite-time convergence but also attenuates steady-state errors, making it ideal for nonlinear system applications. The GPSO is used to search the control parameters of the FTTA, which is able to find the global best solution. This avoids unmodeled dynamic behavior of the system excited by the quiver, which slows down the control convergence and prematurely traps the system into a local optimum. The MATLAB computer software is used to simulate the proposed PV maximum power point tracking system. The results show that more accurate and better tracking control of the PV array can be produced under partial shading conditions and then improve the steady-state and transient performance.

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Section: Modeling Of Pv Mppt Systemmentioning

confidence: 99%

Artificial Intelligence of Things-Based Optimal Finite-Time Terminal Attractor and Its Application to Maximum Power Point Tracking of Photovoltaic Arrays in Smart Cities

Chang

Cheng

2022

Wireless Communications and Mobile Computing

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“…As the length of the sampling time increases, ∂u ∂v also needs to be lower bounded by − 2 ∆T X −1 . As the typical sampling frequency of real-world inverters is in KHz scale [43], the left hand side of ( 12) is naturally satisfied in most cases. Therefore, we focus on the right hand side condition, ∂u ∂v ≺ 0.…”

Section: B Voltage Stability Conditionmentioning

confidence: 99%

Stability Constrained Reinforcement Learning for Real-Time Voltage Control in Distribution Systems

Feng¹,

Shi²,

Qu³

et al. 2022

Preprint

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Deep Reinforcement Learning (RL) has been recognized as a promising tool to address the challenges in realtime control of power systems. However, its deployment in realworld power systems has been hindered by a lack of explicit stability and safety guarantees. In this paper, we propose a stability constrained reinforcement learning method for real-time voltage control in both single-phase and three-phase distribution grids and we prove that the proposed approach provides a voltage stability guarantee. The key idea underlying our approach is an explicitly constructed Lyapunov function that certifies stability. We demonstrate the effectiveness of our approach with both single-phase and three-phase IEEE test feeders, where the proposed method can reduce the transient control cost by more than 25% and shorten the response time by 21.5% on average compared to the widely used linear policy, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability.

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“…There are many studies describing applications of PID controllers based on generalpurpose MCUs [4][5][6][7][8][9][10][11]. Such regulators are predominantly used for precise motion control [4][5][6][7]10,11], and control over the operation of voltage converters [8,9]. The sampling periods reported in the reference papers are generally measured in milliseconds.…”

Section: Introduction 1pid Regulators In Contemporary Technologiesmentioning

confidence: 99%

“…The sampling periods reported in the reference papers are generally measured in milliseconds. However, in [8], a PID regulator is described with the reported sampling period below 20 µs. It is implemented on the STM32F407 MCU.…”

Section: Introduction 1pid Regulators In Contemporary Technologiesmentioning

confidence: 99%

A Hardware Implementation of the PID Algorithm Using Floating-Point Arithmetic

Kulisz,

Jokiel

2024

Electronics

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The purpose of the paper is to propose a new implementation of the PID (proportional–integral–derivative) algorithm in digital hardware. The proposed structure is optimized for cost. It follows a serialized, rather than parallel, scheme. It uses only one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequentially cyclic manner. The proposed circuit operates on standard single-precision (32-bit) floating-point numbers. It implements an extended PID formula, containing a non-ideal derivative component, and weighting coefficients, which enable reducing the influence of setpoint changes in the proportional and derivative components. The circuit was implemented in a Cyclone V FPGA (Field-Programmable Gate Array) device from Intel, Santa Clara, CA, USA. The proper operation of the circuit was verified in a simulation. For the specific implementation, which is reported in the paper, the sampling period of 516 ns was obtained, which means that the proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on single-precision floating-point numbers. However, the presented solution is much more efficient in terms of cost. It uses 1173 LUT (Look-up Table) blocks, 1026 registers, and 1 DSP (Digital Signal Processing) block, i.e., about 30% of logic resources required by comparable solutions.

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Sampling Rate and Performance of DC/AC Inverters with Digital PID Control—A Case Study

Cited by 7 publications

References 19 publications

Artificial Intelligence of Things-Based Optimal Finite-Time Terminal Attractor and Its Application to Maximum Power Point Tracking of Photovoltaic Arrays in Smart Cities

Artificial Intelligence of Things-Based Optimal Finite-Time Terminal Attractor and Its Application to Maximum Power Point Tracking of Photovoltaic Arrays in Smart Cities

Stability Constrained Reinforcement Learning for Real-Time Voltage Control in Distribution Systems

A Hardware Implementation of the PID Algorithm Using Floating-Point Arithmetic

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