| 1. |
- Chen, Haorui, et al.
(author)
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Forecasting the human and climate impacts on groundwater resources in the irrigated agricultural region of North China Plain
- 2023
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In: Hydrological Processes. - : Wiley. - 0885-6087 .- 1099-1085. ; 37:3
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Journal article (peer-reviewed)abstract
- Climate change has caused significant impacts on water resource redistribution around the world and posed a great threat in the last several decades due to intensive human activities. The impacts of human water use and management on regional water resources remain unclear as they are intertwined with the impacts of climate change. In this study, we disentangled the impact of climate-induced human activities on groundwater resources in a typical region of the semi-arid North China Plain based on a process-oriented groundwater modelling approach accounting for climate-human-groundwater interactions. We found that the climate-induced human effect is amplified in water resources management ('amplifying effect') for our study region under future climate scenarios. We specifically derived a tipping point for annual precipitation of 350 mm, below which the climate-induced human activities on groundwater withdrawal will cause significant 'amplifying effect' on groundwater depletion. Furthermore, we explored the different pumping scenarios under various climate conditions and investigated the pumping thresholds, which the pumping amount should not exceed (4 x 10(7) m(3)) in order to control future groundwater level depletion. Our results highlight that it is critical to implement adaptive water use practices, such as water-saving irrigation technologies in the semi-arid regions, in order to mitigate the negative impacts of groundwater overexploitation, particularly when annual precipitation is anomalously low.
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| 2. |
- Chen, Peng, et al.
(author)
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Holocene monsoon dynamics at Kunlun Pass on the northeastern Qinghai-Tibet Plateau
- 2021
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In: Science of the Total Environment. - : Elsevier. - 0048-9697 .- 1879-1026. ; 771
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Journal article (peer-reviewed)abstract
- Various proxy records have been used for the understanding of environmental and climate variations during the Holocene. Here, for the first time, we use meteoric Be-10 isotope measurements performed on sediments from a drill core collected at the Kunlun Pass (KP) on the northeastern Qinghai-Tibet Plateau (NETP) to investigate hydroclimate changes during the Holocene. The Be-10 flux suggests relative low levels in the Early Holocene, followed by a sharp increase to high values at around 4 ka BP (4 ka BP - 4000 years before present). Afterwards, the Be-10 flux remains on a high level during the Late Holocene, but decreases slightly towards today. These Be-10 deposition patterns are compared to moisture changes in regions dominated by the Indian Summer Monsoon (ISM), East Asian Summer Monsoon (EASM), and the Westerlies. Different from the gradual changes in monsoon patterns, the Be-10 data reveal low levels during the Early Holocene until similar to 4 ka BP when an obvious increase is indicated and a relative high level continues to this day, which is relatively more in agreement with patterns of the Westerlies. This finding provides a new evidence fora shift in the dominant pattern of atmospheric circulation the KP region from a more monsoonal one to one dominated by the Westerlies. Our results improve the understanding of non-stationary interactions and spatial relevance of the EASM, the ISM and the Westerlies on the Qinghai-Tibet Plateau.
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| 3. |
- Chen, Peng, et al.
(author)
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Relationship between precipitation and Be-10 and impacts on soil dynamics
- 2020
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In: Catena (Cremlingen. Print). - : Elsevier BV. - 0341-8162 .- 1872-6887. ; 195
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Journal article (peer-reviewed)abstract
- Meteoric beryllium-10 (Be-10) is commonly used as a proxy of landscape dynamics (erosion and sedimentation rates) and soil development. Soil represents the first-stage reservoir of meteoric Be-10, and variability in the concentration of the isotope in soils may be affected by soil properties and atmospheric deposition. Although many investigations have targeted this issue, there are still problems in estimating the atmospheric input of the isotope in different soil environments. Here, we used Be-10 data measured in soils distributed across China to explore the potential influence of meteorological and pedological conditions on the isotope concentration and related applications. In addition, to determine the mechanisms controlling Be-10 concentrations in topsoil on a regional scale, the soil samples were sub-divided into 18 different catchments according to fluvial systems. The results indicated that there were significant negative correlations between precipitation and the soil Be-10 concentration in high-precipitation regions (> 1200 mm.y(-1)) and significant positive correlations for soils in low precipitation regions (< 1200 mm.y(-1)). The data also revealed that precipitation is the most important variable controlling the Be-10 concentration in soils of China when compared with the effects of soil properties such as grain size, mineralogy, pH, and cation exchange capacity. Land use and soil erosion may have limited impacts on the distribution of Be-10 in soils.
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| 4. |
- Yi, Peng, et al.
(author)
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Evaluation of groundwater discharge into surface water by using Radon-222 in the Source Area of the Yellow River, Qinghai-Tibet Plateau
- 2018
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In: Journal of Environmental Radioactivity. - : Elsevier BV. - 0265-931X. ; 192, s. 257-266
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Journal article (peer-reviewed)abstract
- Understanding hydrological processes in the Source Area of the Yellow River (SAYR), Qinghai-Tibet Plateau, is vital for protection and management of groundwater and surface water resources in the region. In situ water measurements of exchange rates between surface water and groundwater are, however, hard to conduct because of the harsh natural conditions of the SAYR. We here present an indirect method using in situ 222Rn measurements to estimate groundwater discharge into rivers and lakes in the SAYR. 222Rn was measured in rivers, lakes, groundwater and springs during three sampling periods (2014–2016), and the results indicate large variability in the concentration of the isotope. The data also indicate decreasing 222Rn trends in groundwater in the cold season (the Feb-2015 sampling period) which may be linked to frequency of capturing 222Rn in the frozen ground caused by geocryogenic processes. In addition, permafrost spatial extent and freeze-thaw processes have strongly affected the hydrological conditions in the region.
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| 5. |
- Chen, Peng, et al.
(author)
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Tendency of soil erosion dynamics by coupling radioisotopes and RUSLE model on the Southeastern Tibetan Plateau in response to climate warming and human activity
- 2023
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In: Catena. - : Elsevier BV. - 0341-8162. ; 223
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Journal article (peer-reviewed)abstract
- Soil erosion has created landscape problems in many parts of the world and in particular in cold regions where the sensitive permafrost conditions have changed due to climate warming. Such a case occurred in the Tibetan Plateau (TP), which has been strongly affected by global warming and human activities. Monitoring technologies, like remote sensing and field surveys were used to explore soil erosion rates in the TP, but they were limited by the resolution and meteorological disturbance factors or the spatial and time scales. Here, we present for the first time 210Pbex (excess lead-210) and 137Cs (caesium-137) data of soils from the southeastern TP (SETP) covering an area of 640,000 km2. In the permafrost-dominant areas, the results show mean soil-erosion rates in the last 56–100 years that were relatively higher (1891 t·km−2·a-1) based on 210Pbex than those based on 137Cs (1623 t·km−2·a-1). Modelling results from the Revised Universal Soil Loss Equation (RUSLE) indicate relatively high mean soil erosion rates of 4363 and 4394 t·km−2·a-1 using a period covering the last 40 or 10 years respectively. Our data suggest accelerating erosion rates on the SETP that are linked to permafrost degradation, and glacier and snow melting due to accelerating global climate warming. The increase in ground surface temperature of ∼2 °C in the last four decades has further shifted the regional hydrology, affecting the degeneration of vegetation cover and a further increase in soil-erosion rates. However, our radionuclides data also expose low erosion rates in the seasonally frozen ground at some sampling sites which indicates the complex nature of erosion trends in cold regions that require careful adaptation of soil management.
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| 6. |
- Chen, Peng, et al.
(author)
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Use of Be-10 isotope to predict landscape development in the source area of the Yellow River (SAYR), northeastern Qinghai-Tibet Plateau
- 2019
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In: Journal of Environmental Radioactivity. - : ELSEVIER SCI LTD. - 0265-931X .- 1879-1700. ; 203, s. 187-199
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Journal article (peer-reviewed)abstract
- The magnitude of soil and sediment erosion and accumulation processes can profoundly affect landscape development and hamper efficient management of natural resources. Consequently, estimating the rates and causes of these processes is essential, particularly in remote regions, for prediction of changes in landform and river evolution and protection of local ecosystem. We here present the results of a soil and sediment erosion investigation in the Source Area of the Yellow River (SAYR), northeast Qinghai-Tibet Plateau based on a combined analysis of Be-10 cosmogenic isotope and Soil and Water Assessment Tool (SWAT) simulation modelling. The data reveal variable soil erosion trends that range between 103 and 830 t km(-2) a(-1). The low values occur in the western part of the basin that are associated with low sediment yield, while the high values appear in the dominant sediment export part of the basin along the main stream of the Yellow River in the east. Generally, soil and sediment accumulation is characterized by high Be-10 concentration in the western part and the northwest of Ngoring Lake. The style of landform development by the erosion/accumulation processes is closely linked to the distribution and degradation extent of the permafrost in the study region. Soil surface erosion increases with more permafrost degradation from the western to the eastern part of the basin, and surface soil particles are dominantly removed from the surface rather than deeper layers.
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| 7. |
- Cheng, Li, et al.
(author)
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Neural-Network-Based Impedance Estimation for Transmission Cables Considering Aging Effect
- 2023
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In: 2023 8th IEEE Workshop on the Electronic Grid, eGRID 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
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Conference paper (peer-reviewed)abstract
- In power-electronic-based power systems like wind farms, conducting stability analysis necessitates a comprehensive understanding of the system impedance across a wide frequency range, from sub-harmonic frequencies up to the Nyquist frequency of control systems of power converters. The cable aging effect can significantly impact the cable impedance, while accurately estimating the degree of aging proves challenging. To avoid the requirement for precise aging prognostic, this paper proposes an approach based on Artificial Neural Networks (ANN) that enables the estimation of AC cable impedance in a wind farm solely through fundamental frequency measurements. The data used for training the ANN is obtained from the cable model in PSCAD, incorporating physical and geometrical parameters, which accurately approximates real cables within power systems. The training results of the ANN validate the accuracy of the proposed identification approach. As a result, the proposed approach effectively eliminates the potential misjudgment of system stability caused by the aging effect of power cables.
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| 8. |
- Cheng, Li, et al.
(author)
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Online Identification of Wind Farm Wide Frequency Admittance with Power Cables Using the Artificial Neural Network
- 2023
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In: 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1530-1535
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Conference paper (peer-reviewed)abstract
- In power-electronic-based power systems like wind farms, stability analysis requires knowledge of system impedance across a wide frequency range, from sub-harmonic frequencies to the Nyquist frequency. Although it is feasible to take the fundamental frequency measurement during power system operation, obtaining a wide-frequency impedance curve in real time is very challenging. This paper proposed an ANN-based approach to estimate wide-frequency system admittance of wind farms with power cables, through fundamental frequency measurements. Real-life uncertainties are considered, including shunt capacitor injection, filter inductance variance, cable aging, errors in voltage and current measurements, and the variance of other system parameters. The generalization ability of the ANN is validated on a new dataset with different uncertainty distributions, and the error sensitivity to the potential system parameter variance is evaluated. These results can be referenced in the data acquisition step in future neural-network-based applications.
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| 9. |
- Li, Yufei, et al.
(author)
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Machine Learning at the Grid Edge : Data-Driven Impedance Models for Model-Free Inverters
- 2024
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In: IEEE transactions on power electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8993 .- 1941-0107. ; 39:8, s. 10465-10481
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Journal article (peer-reviewed)abstract
- It is envisioned that the future electric grid will be underpinned by a vast number of smart inverters linking renewables at the grid edge. These inverters' dynamics are typically characterized as impedances, which are crucial for ensuring grid stability and resiliency. However, the physical implementation of these inverters may vary widely and may be kept confidential. Existing analytical impedance models require a complete and precise understanding of system parameters. They can hardly capture the complete electrical behavior when the inverters are performing complex functions. Online impedance measurements for many inverters across multiple operating points are impractical. To address these issues, we present the InvNet, a machine learning framework capable of characterizing inverter impedance patterns across a wide operation range, even with limited impedance data. Leveraging transfer learning, the InvNet can extrapolate from physics-based models to real-world ones and from one inverter to another with the same control framework but different control parameters with very limited data. This framework demonstrates machine learning as a powerful tool for modeling and analyzing black-box characteristics of grid-tied inverter systems that cannot be accurately described by traditional analytical methods, such as inverters under model-predictive control. Comprehensive evaluations were conducted to verify the effectiveness of the InvNet.
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| 10. |
- Li, Yufei, et al.
(author)
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Neural Network Models and Transfer Learning for Impedance Modeling of Grid-Tied Inverters
- 2022
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In: 2022 IEEE 13TH INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS FOR DISTRIBUTED GENERATION SYSTEMS (PEDG). - : Institute of Electrical and Electronics Engineers (IEEE).
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Conference paper (peer-reviewed)abstract
- The future power grid will be supported by a large number of grid-tied inverters whose dynamics are critical for grid stability and power flow control. The operating conditions of these inverters vary across a wide range, leading to different small-signal impedances and different grid-interface behaviors. Analytical impedance models derived at specific operating points can hardly capture nonlinearities and nonidealities of the physical systems. The applicability of electromagnetic transient (EMT) simulations is often limited by the system complexity and the available computational resources. This paper applies neural network and transfer learning to impedance modeling of gridtied inverters. It is shown that a neural network (NN) trained by data automatically acquired from EMT simulations outperforms the one trained by traditional analytical models when unknown information exist in simulations. Pre-training the NN with analytically calculated data can greatly reduce the amount of simulation data needed to achieve good modeling results.
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