Comparative Study of Energy Consumption and CO2 Emissions of Variable-Speed Electric Drives with Induction and Synchronous Reluctance Motors in Pump Units

Goman, V.; Prakht, Vladimir; Kazakbaev, Vadim; Dmitrievskii, Vladimir

doi:10.3390/math9212679

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…The global energy demand continues to grow year by year and by 2030, is expected to reach 170% of the consumption of 1990 after adjusting estimates following the COVID-19 pandemic according to the IEA [1,2]. Historically, the energy mix has heavily relied on fossil fuels, presenting major sources of CO 2 and greenhouse gases that generate climate change [3]. The lack of profitable fossil-fuel fields combined with increased awareness of environmental concerns led to the need for a common framework endorsed in the Paris agreements, which states a limit in the global temperature increase of 2 • C [4].…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

et al. 2023

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Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained.

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Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

et al. 2023

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“…As a result, even minor increases of motor drive efficiency have a significant positive impact on energy systems, decreasing power loss and fuels burn [2]. Considering the latest tendencies on decreasing carbon emissions, these facts are the key reasons for toughening of standards on device efficiency, which force manufactures to increase efficiency of their devices [3]- [5]. It results in the focusing of the manufactures of power converters on the algorithms, which increase energy efficiency, and on the proper tuning of control systems, which demands precise information on parameters of the objects under control [6].…”

Section: Introductionmentioning

confidence: 99%

Offline Measurement of Stator Resistance and Inverter Voltage Drop Using Least Squares

Dianov¹,

Anuchin

2023

IEEE Access

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Modern industrial and commercial electrical drives are typically designed to control motors of different types, operating under scalar, field-oriented or direct torque control. These drives execute various algorithm such as sensorless control, temperature monitoring, etc., which require the knowledge of motor drive parameters. In order to do this, the inverters allow data to be input manually or execute selfcommissioning routines before running the motor. The parameters necessary for proper operation include motor phase resistance and inverter voltage drop, which are especially important in low-speed range. This paper presents an offline technique for estimation of mentioned parameters, which can be obtained with enough precision for the overwhelming majority of applications. The proposed algorithm consequently injects DC-current of several levels into the stator of a motor, measuring the corresponding voltages. Each pair of current and voltage is a point in the voltage current characteristic of motor drive, thus set of these points maybe approximated with a first order polynomial using least squares method. The parameters of polynomial are desired inverter voltage drop and phase resistance. The experimental section analyzes estimation errors and their dependence on the number of injected levels, their values and filtering capability of measuring algorithm. After that, the authors give suggestions on algorithm parameters selection, depending on the demanded precision. Finally, the authors demonstrate a mass-producing dishwasher motor drive, which adopted this technique.

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“…With the latest trends on decreasing carbon emissions and green energy development [1], [2], [3], the role of electrical systems have rapidly increased. Furthermore, recent advances in battery technologies are making autonomous systems more and more attractive and popular.…”

Section: Introductionmentioning

confidence: 99%

Inverter Temperature Monitoring of Cordless Tool Motor Drives

Dianov¹

2023

IEEE Open J. Ind. Electron. Soc.

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Nowadays, with the recent advances in battery technologies, cordless tools are gaining more popularity and many manufacturers have enhanced their series of autonomous devices with powerful instruments like circular saws and punches. However, the development of battery-operated tools is not an easy task and many challenging problems must be solved. The most significant of these are limited working time without recharging and controllability of the motor drives. As a result, tool manufacturers are substituting conventional universal motor drives with permanent magnet synchronous motor drives, which require more complicated electronics, but can provide the desired characteristics. In order to guarantee normal operation and satisfy safety standards, devices have to properly handle any possible hazardous situations. One of these situations is overheating of inverter switches, thus, the temperature of each transistor has to be monitored. This task is typically implemented using negative temperature coefficient (NTC) resistors, whereby voltage is sensed by analog to digital converter of microcontroller. This article proposes a software algorithm for temperature estimation and monitoring of each inverter switch, which allows excludes NTCs, decreasing overall costs and saving space on the printed circuit board. Proper operation of the developed technology is proven by the experimental results and safety certification according to UL60730.

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Comparative Study of Energy Consumption and CO2 Emissions of Variable-Speed Electric Drives with Induction and Synchronous Reluctance Motors in Pump Units

Cited by 16 publications

References 35 publications

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Offline Measurement of Stator Resistance and Inverter Voltage Drop Using Least Squares

Inverter Temperature Monitoring of Cordless Tool Motor Drives

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