PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications

In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be largely employed in Wide Area Monitoring, Protection and Control (WAMPAC) applications. However, a standard approach towards ROCOF measurements is still missing. In this paper, we investigate the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS). For this analysis, we select three state-of-the-art window-based synchrophasor estimation algorithms and compare different signal models, ROCOF estimation techniques and window lengths in datasets inspired by real-world acquisitions. In this sense, we are able to carry out a sensitivity analysis of the behavior of a PMU-based UFLS control scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters, as long as the harmonic and inter-harmonic distortion within the measurement pass-bandwidth is scarce. In the presence of transient events, the synchrophasor model looses its appropriateness as the signal energy spreads over the entire spectrum and cannot be approximated as a sequence of narrow-band components. Finally, we validate the actual feasibility of PMU-based UFLS in a real-time simulated scenario where we compare two different ROCOF estimation techniques with a frequency-based control scheme and we show their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE Transactions on Instrumentation and Measurement (acceptance date: 9 March 2019

Associations of [18F]-APN-1607 Tau PET Binding in the Brain of Alzheimer's Disease Patients With Cognition and Glucose Metabolism.

Molecular imaging of tauopathies is complicated by the differing specificities and off-target binding properties of available radioligands for positron emission tomography (PET). [18F]-APN-1607 ([18F]-PM-PBB3) is a newly developed PET tracer with promising properties for tau imaging. We aimed to characterize the cerebral binding of [18F]-APN-1607 in Alzheimer's disease (AD) patients compared to normal control (NC) subjects. Therefore, we obtained static late frame PET recordings with [18F]-APN-1607 and [18F]-FDG in patients with a clinical diagnosis of AD group, along with an age-matched NC group ([18F]-APN-1607 only). Using statistical parametric mapping (SPM) and volume of interest (VOI) analyses of the reference region normalized standardized uptake value ratio maps, we then tested for group differences and relationships between both PET biomarkers, as well as their associations with clinical general cognition. In the AD group, [18F]-APN-1607 binding was elevated in widespread cortical regions (P < 0.001 for VOI analysis, familywise error-corrected P < 0.01 for SPM analysis). The regional uptake in AD patients correlated negatively with Mini-Mental State Examination score (frontal lobe: R = -0.632, P = 0.004; temporal lobe: R = -0.593, P = 0.008; parietal lobe: R = -0.552, P = 0.014; insula: R = -0.650, P = 0.003; cingulum: R = -0.665, P = 0.002) except occipital lobe (R = -0.417, P = 0.076). The hypometabolism to [18F]-FDG PET in AD patients also showed negative correlations with regional [18F]-APN-1607 binding in some signature areas of AD (temporal lobe: R = -0.530, P = 0.020; parietal lobe: R = -0.637, P = 0.003; occipital lobe: R = -0.567, P = 0.011). In conclusion, our results suggested that [18F]-APN-1607 PET sensitively detected tau deposition in AD and that individual tauopathy correlated with impaired cerebral glucose metabolism and cognitive function

Increased Vesicular Monoamine Transporter 2 (VMAT2) and Dopamine Transporter (DAT) Expression in Adolescent Brain Development: A Longitudinal Micro-PET/CT Study in Rodent

Background: Brain development and maturation in adolescence is a complex process with active changes of metabolic and neurotransmission pathways. Positron emission tomography (PET) is a useful imaging modality for tracking metabolic and functional changes in adolescent brain. In this study, changes of glucose metabolism, expression of vesicular monoamine transporter 2 and dopamine transporter during adolescent brain development in rats were investigated with PET/CT.Methods: A longitudinal PET/CT study of age-dependent changes of VMAT2, DAT and glucose metabolism in adolescent brain was conducted in a group of Wistar rats (n = 6) post sequential intravenous injection of 18F-PF-(+)-DTBZ, 11C-CFT, and 18F-FDG, respectively. PET acquisition was performed at 2, 4, 9, and 12 months of age. Radiotracer uptake in different brain regions, including the striatum, cerebellum, and hippocampus, were quantified and recorded as Standardized uptake value (SUV) and striatal specific uptake ratio (SUVR: SUV in brain regions/SUV in cerebellum).Results: Variable uptake of 18F-PF-(+)-DTBZ and 11C-CFT were detected, with highest level uptake in the striatum and accumbens. There was significant age-dependent increase of 18F-PF-(+)-DTBZ and 11C-CFT uptake in the striatum from 2 months of age (SUV: 1.36 ± 0.22, 1.37 ± 0.39, respectively), to 4 months (SUV: 2.22 ± 0.29, 2.04 ± 0.33), 9 months (1.98 ± 0.34, 2.09 ± 0.18), 12 months (SUV: 1.93 ± 0.19, 2.00 ± 0.17) of age, SUV of 18F-FDG also increased from 2 months of age to older ages (SUV in the striatum: 3.71 ± 0.78 at 2 month, 5.28 ± 0.81, 5.14 ± 0.73, 4.94 ± 0.50 at 4, 9, 12 month, respectively).Conclusion: Age-dependent increases of striatal of 18F-FDG, 18F-PF-(+)-DTBZ, and 11C-CFT uptake were detected in rats from 2 to 4 month of age, demonstrating striatal development presents over the first 4 months of age. Four months of age can be considered a safe threshold to launch brain disease studies for exclusion of confusion of continuing tissue development. These findings support further investigation of age-dependent changes in expression of DAT, VMAT2, and glucose metabolism for their potential use as a new imaging biomarker for study of brain development and functional maturation

Uncovering distinct progression patterns of tau deposition in progressive supranuclear palsy using [18F]Florzolotau PET imaging and subtype/stage inference algorithm.

BACKGROUND Progressive supranuclear palsy (PSP) is a primary 4-repeat tauopathy with diverse clinical phenotypes. Previous post-mortem studies examined tau deposition sequences in PSP, but in vivo scrutiny is lacking. METHODS We conducted [18F]Florzolotau tau positron emission tomography (PET) scans on 148 patients who were clinically diagnosed with PSP and 20 healthy controls. We employed the Subtype and Stage Inference (SuStaIn) algorithm to identify PSP subtype/stage and related tau patterns, comparing clinical features across subtypes and assessing PSP stage-clinical severity association. We also evaluated functional connectivity differences among subtypes through resting-state functional magnetic resonance imaging. FINDINGS We identified two distinct subtypes of PSP: Subtype1 and Subtype2. Subtype1 typically exhibits a sequential progression of the disease, starting from subcortical and gradually moving to cortical regions. Conversely, Subtype2 is characterized by an early, simultaneous onset in both regions. Interestingly, once the disease is initiated, Subtype1 tends to spread more rapidly within each region compared to Subtype2. Individuals categorized as Subtype2 are generally older and exhibit less severe dysfunctions in areas such as cognition, bulbar, limb motor, and general motor functions compared to those with Subtype1. Moreover, they have a more favorable prognosis in terms of limb motor function. We found significant correlations between several clinical variables and the identified PSP SuStaIn stages. Furthermore, Subtype2 displayed a remarkable reduction in functional connectivity compared to Subtype1. INTERPRETATION We present the evidence of distinct in vivo spatiotemporal tau trajectories in PSP. Our findings can contribute to precision medicine advancements for PSP. FUNDING This work was supported by grants from the National Natural Science Foundation of China (number 82272039, 81971641, 82021002, and 92249302); Swiss National Science Foundation (number 188350); the STI2030-Major Project of China (number 2022ZD0211600); the Clinical Research Plan of Shanghai Hospital Development Center of China (number SHDC2020CR1038B); and the National Key R&D Program of China (number 2022YFC2009902, 2022YFC2009900), the China Scholarship Council (number 202006100181); the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198)

Frequency-Dependent Relationship Between Resting-State fMRI and Glucose Metabolism in the Elderly

Both glucose metabolism and resting-state fMRI (RS-fMRI) signal reflect hemodynamic features. The objective of this study was to investigate their relationship in the resting-state in healthy elderly participants (n = 18). For RS-fMRI signal, regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), and degree of centrality (DC) maps were generated in multiple frequency bands. Glucose uptake was acquired with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). Linear correlation of each pair of the FDG-PET and RS-fMRI metrics was explored both in across-voxel way and in across-subject way. We found a significant across-voxel correlation between the FDG-PET and BOLD-fMRI metrics. However, only a small portion of voxels showed significant across-subject correlation between FDG-PET and BOLD-fMRI metrics. All these results were similar across all frequency bands of RS-fMRI data. The current findings indicate that FDG-PET and RS-fMRI metrics share similar spatial pattern (significant across-voxel correlation) but have different underlying physiological importance (non-significant across-subject correlation). Specifically, FDG-PET measures the mean glucose metabolism over tens of minutes, while RS-fMRI measures the dynamic characteristics. The combination of FDG-PET and RS-fMRI provides complementary information to reveal the underlying mechanisms of the brain activity and may enable more comprehensive interpretation of clinical PET-fMRI studies. Future studies would attempt to reduce the artifacts of RS-fMRI and to analyze the dynamic feature of PET signal

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS This study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning. RESULTS The proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP. CONCLUSION This study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis

Differential diagnosis of parkinsonism based on deep metabolic imaging indices.

The clinical presentations of early idiopathic Parkinson's disease (PD) substantially overlap with those of atypical parkinsonian syndromes like multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). This study aimed to develop metabolic imaging indices based on deep learning to support the differential diagnosis of these conditions. Methods: A benchmark Huashan parkinsonian PET imaging (HPPI, China) database including 1275 parkinsonian patients and 863 non-parkinsonian subjects with 18F-FDG PET images was established to support artificial intelligence development. A 3D deep convolutional neural network was developed to extract deep metabolic imaging (DMI) indices, which was blindly evaluated in an independent cohort with longitudinal follow-up from the HPPI, and an external German cohort of 90 parkinsonian patients with different imaging acquisition protocols. Results: The proposed DMI indices had less ambiguity space in the differential diagnosis. They achieved sensitivities of 98.1%, 88.5%, and 84.5%, and specificities of 90.0%, 99.2%, and 97.8% for the diagnosis of PD, MSA, and PSP in the blind test cohort. In the German cohort, They resulted in sensitivities of 94.1%, 82.4%, 82.1%, and specificities of 84.0%, 99.9%, 94.1% respectively. Employing the PET scans independently achieved comparable performance to the integration of demographic and clinical information into the DMI indices. Conclusion: The DMI indices developed on the HPPI database show potential to provide an early and accurate differential diagnosis for parkinsonism and is robust when dealing with discrepancies between populations and imaging acquisitions

Impact of battery energy storage systems on the dynamic behavior of low-inertia power grids

The extensive deployment of non-synchronous generation determines lower level of grid inertia resulting in deteriorated frequency containment performance and abnormal frequency excursions in case of contingency. This calls for identifying assets, controls, and relaying schemes capable to ensure acceptable grid frequency containment and dynamics satisfying the requirements of existing grid codes. A potential way to counterbalance this lack of inertia is to use large-scale battery energy storage systems (BESSs) since they provide large ramping rates and fast power control. As known, there are generally two main approaches to control converter-interfaced BESSs: grid-following and grid-forming controls. As BESSs may provide significant value to system frequency containment, it is of fundamental importance to quantitatively evaluate the dynamics of low-inertia power grids hosting large-scale BESSs. Within this context, it is also of importance to study the behavior of low-inertia power systems subsequent to large contingencies in order to develop appropriate under-frequency load shedding (UFLS) relaying schemes that may take advantage of nowadays distributed sensing technologies enabled by the Phasor Measurement Units (PMUs). Framed within the EU H2020 project "Optimal System-Mix Of flexibility Solutions for European electricity", the Thesis first characterizes the interplay between converter-interfaced BESSs and low-inertia power grids and, then, provides quantitative assessments of system dynamics and quantifies the benefits associated to different control strategies of BESSs. For this purpose, state-of-the-art detailed dynamic simulation models of power grids, BESS, and controls are implemented on a real-time simulator for detailed numerical analyses. At first, contingency tests are conducted. The results verify the substantial influence of inertia reduction on post-contingency dynamics of power systems and quantitatively prove that converter-interfaced BESSs can effectively limit the frequency decreasing and damp the frequency oscillations. Then, the proposed dynamic models are used for one-day-long simulations to assess the impact of converter-interfaced BESSs on the frequency containment of low-inertia power grids in normal operating conditions. For a practical operative context, a day-ahead schedule layer is considered where reserve levels for frequency containment and restoration are allocated considering the current practice required by European transmission system operators. Numerical analyses on suitably defined metrics applied to grid frequency show that the grid-forming control strategy outperforms the grid-following one, achieving better system frequency containment. As large frequency excursions are more likely to occur due to the decrease of kinetic energy stored in rotating synchronous machines, fast adaptive UFLS schemes are necessary to secure low-inertia power systems under contingency. PMUs provide an effective tool to track the network state in any node of interest with reporting rates in tens of frames per second. In this respect, the Thesis proposes and validates two new UFLS schemes suitable for low-inertia power grids. The first scheme is a centralized UFLS that leverages PMU-fed situational awareness systems and is coupled with an Optimal Power Flow (OPF) problem. The OPF problem is formulated to constrain nodal voltages and branch currents in combination with a model capable of predicting the system response