Development of Fast Simulation Model for Doubly-Fed Wind Generation Systems

This paper investigates the problem of remote state estimation in cyber-physical systems subject to stochastic stealthy attacks. Unlike existing studies that assume persistent intrusion, the attack success is modeled as a stochastic process, thereby providing a more realistic characterization of adversarial capabilities. A comprehensive analysis is conducted from both short-term and long-term perspectives. In the short-term analysis, the evolution of the estimation error covariance is examined, and optimal attack strategies are derived under explicit stealthiness constraints, which limit the detection probability of the attacker. In the long-term analysis, the conditions under which the expected estimation error covariance diverges or converges are explored as a function of the attack success rate and strategy. Rigorous necessary, sufficient, and equivalent conditions for error covariance divergence are established. Moreover, the convergence behavior of the estimation process is characterized under various attack designs, revealing critical thresholds and trade-offs between attack frequency and intensity. Simulation results are provided to validate the theoretical findings and to illustrate the quantitative impact of attack parameters on estimation performance degradation.

Multiple linear regression (MLR) models are used to assess the contributions of meteorology/climate and anthropogenic emission control to linear trends of PM2.5 concentration during the period 2013–2018 in three regions in eastern China, namely Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). We find that quantitative contributions to the linear trend of PM2.5 derived based on MLR results alone are not credible because a good correlation in the MLR analysis does not imply any causal relationship, let alone a quantitative relationship. As an alternative, we propose that the correlation coefficient should be interpreted as the maximum possible contribution of the independent variable to the dependent variable, and the residual should be interpreted as the minimum contribution of all other independent variables. Under the new interpretation, the MLR results become self-consistent. We also find that the results of a short-term (2013–2018) analysis are significantly different from those of a long-term (1985–2018) analysis for the period 2013–2018 they overlap, indicating that MLR results depend critically on the length of time analyzed. The long-term analysis renders a more precise assessment, because of additional constraints provided by the long-term data. We therefore suggest that the best estimates of the contributions of emissions and non-emission (including meteorology/climate) to the linear trend in PM2.5 during 2013–2018 are those from the long-term analyses: i.e., emission <51 % and non-emission >49 % for BTH, emission <44 % and non-emission >56 % for YRD, emission <88 % and non-emission >12 % for PRD.

Multiple linear regression (MLR) models are used to assess the contributions of meteorology/climate and anthropogenic emission control to linear trends of PM2.5 concentration during the period 2013–2018 in three regions in eastern China, namely Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). We find that quantitative contributions to the linear trend of PM2.5 derived based on MLR results alone are not credible because a good correlation in the MLR analysis does not imply any causal relationship, let alone a quantitative relationship. As an alternative, we propose that the correlation coefficient should be interpreted as the maximum possible contribution of the independent variable to the dependent variable, and the residual should be interpreted as the minimum contribution of all other independent variables. Under the new interpretation, the MLR results become self-consistent. We also find that the results of a short-term (2013–2018) analysis are significantly different from those of a long-term (1985–2018) analysis for the period 2013–2018 they overlap, indicating that MLR results depend critically on the length of time analyzed. The long-term analysis renders a more precise assessment, because of additional constraints provided by the long-term data. We therefore suggest that the best estimates of the contributions of emissions and non-emission (including meteorology/climate) to the linear trend in PM2.5 during 2013–2018 are those from the long-term analyses: i.e., emission <51 % and non-emission >49 % for BTH, emission <44 % and non-emission >56 % for YRD, emission <88 % and non-emission >12 % for PRD.

Implantation failure after transferring morphologically "good-quality" embryos in invitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) may be explained by impaired endometrial receptivity. Analyzing the endometrial transcriptome analysis may reveal the underlying processes and could help in guiding prognosis and using targeted interventions for infertility. This exploratory study investigated whether the endometrial transcriptome profile was associated with short-term or long-term implantation outcomes (ie success or failure). Mid-luteal phase endometrial biopsies of 107 infertile women with one full failed IVF/ICSI cycle, obtained within an endometrial scratching trial, were subjected to RNA-sequencing and differentially expressed genes analysis with covariate adjustment (age, body mass index, luteinizing hormone [LH]-day). Endometrial transcriptomes were compared between implantation failure and success groups in the short term (after the second fresh IVF/ICSI cycle) and long term (including all fresh and frozen cycles within 12 months). The short-term analysis included 85/107 women (33 ongoing pregnancy vs 52 no pregnancy), excluding 22/107 women. The long-term analysis included 46/107 women (23 'fertile' group, ie infertile women with a live birth after ≤3 embryos transferred vs 23 recurrent implantation failure group, ie no live birth after ≥3 good quality embryos transferred), excluding 61/107 women not fitting these categories. As both analyses drew from the same pool of 107 samples, there was some sample overlap. Additionally, cell type enrichment scores and endometrial receptivity were analyzed, and an endometrial development pseudo-timeline was constructed to estimate transcriptomic deviations from the optimum receptivity day (LH + 7), denoted as ΔWOI (window of implantation). There were no significantly differentially expressed genes between implantation failure and success groups in either the short-term or long-term analyses. Principal component analysis initially showed two clusters in the long-term analysis, unrelated to clinical phenotype and no longer distinct following covariate adjustment. Cell type enrichment scores did not differ significantly between groups in both analyses. However, endometrial receptivity analysis demonstrated a potentially significant displacement of the WOI in the non-pregnant group compared with the ongoing pregnant group in the short-term analysis. No distinct endometrial transcriptome profile was associated with either implantation failure or success in infertile women. However, there may be differences in the extent to which the WOI is displaced.

: The M&V Toolkit is a set of spreadheets and macros that can be used to estimate Measurement and Verification (M&V) costs. It allows a user to determine what method of monitoring will provide the necessary data at an acceptable cost. The toolkit estimates costs for four M&V methods (organized by methods of gathering data), each compatible with IPMVP 2001 and AHRAE Guide-line 14 standards. The methods included are: long-term analysis, short-term analysis, monthly utility bill analysis, and a calibrated simulation model. Summaries of costs for various methods can be easily compared using built in report summaries.

Because of energy and environmental policies in the last decade, a lot of photovoltaic generations will be installed into power systems in Japan in the future. In this situation, it is of great concern that disconnection of PV due to voltage drop caused by a system fault has an impact on power system stability. To avoid its disconnection, Fault Ride Through (FRT) requirement is applied for PVs in Japan. In this paper, transient stability and voltage stability of a multi-machine power system with a large penetration of PV considering several fault ride through requirements and dynamic load characteristics are discussed. The simulation results show that t he fault ride through requirements for PVs don't necessarily improve the power system stability.

In this study, the relationships between Uzbekistan's economic growth, renewable energy use, rural electricity use, and greenhouse gas emissions were investigated using an ARDL model based on data from 2000 to 2022. The long-term results showed that renewable energy production had a significant positive impact on economic growth (coefficient: 0.608, P&lt;0.01), while greenhouse gas emissions had no significant impact (coefficient: -0.00004, P&gt;0.05). However, rural electricity use had a negative impact on economic growth (coefficient: -6.815, P&lt;0.01). The short-term analysis shows that renewable energy production has a rapid and significant positive impact on economic growth (coefficient: 0.0001, P&lt;0.05). In addition, the previous period's economic growth rate (coefficient: 0.643, P&lt;0.05) and greenhouse gas emissions (coefficient: 0.0001, P&lt;0.05) have a positive impact on economic growth in the short term.

In this study, the relationship between economic and political uncertainties in Greece and price movements of the Athens Stock Exchange and its volatility are examined through 19 econometric models calculated by using the Chowdhury (1993) volatility calculation method. Panel data analysis is conducted. Kapetanios (2005) structural break unit root test is performed, and it is observed that series are stationary at different levels. The bounds testing approach developed by Pesaran et al. (2001) is applied to detect cointegration between the series. To find out structural breaking dates that are critical and affect the performance of stock exchange, Bai-Perron (2003) multiple structural change method is performed by using dummy variables. In the long-term analysis, a negative relationship between the political and economic uncertainty indicators in Greece and the Athens Stock Exchange Index is determined. Uncertainties in tax policies (EPUT), economic uncertainties (EU) and uncertainties in banking policies (EPUB) are determined factors as having the most impact on the stock exchange. In the short-term analysis, it is observed that the uncertainties in question are also effective on the Athens Stock Exchange Index, but they did not have a significant effect on the price volatility of the stock exchange.

The following paper analyzes relationships between CO2 emissions, economic growth, total energy consumption, and employment across five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) over the period between 1992 and 2022, and uses panel econometric methods to do so. Annual World Bank data has been log-transformed to improve statistical properties and interpret coefficients as elasticities. The empirical strategy includes panel unit root tests (LLC, IPS, ADF-Fisher, PP-Fisher), Pedroni cointegration test, Pooled Mean Group-Autoregressive Distributed Lag (PMG-ARDL) estimation, Dumitrescu-Hurlin panel Granger causality tests, and Forecast Error Variance Decomposition (FEVD). The results have shown the variables to be first-order integrated and have a long-run equilibrium relationship. The short-term analysis has revealed an economic growth’s unidirectional effect on CO2 emissions, indicating a direct deterioration of the environmental situation against economic activity expansion. The long-term analysis has shown how economic growth, energy use, and employment jointly determine emission dynamics while adjustments occur based on the emissions indicator. The variance decomposition has revealed GDP and energy use to be the key long-term determinants and insignificance of employment’s effect. Recommendations include reducing energy intensity, developing low-carbon energy, diversifying the economy, creating green jobs, and strengthening regional cooperation.

The relationships between hillslope and fluvial processes were studied in a mountainous area of the Northern Apennines (Italy) where intermittent landslide activity has interacted for a long time with river morphodynamics. The aim of the study was to analyse such relationships in two study sites of the Scoltenna catchment. The sites were analysed in detail and monitored through time. A long-term analysis was carried out based on multitemporal photointerpretation of aerial photos. Slope morphological changes and land use modifications since 1954 were detected and compared with the evolution of the channel morphology. A short-term analysis was also performed based on two monitoring campaigns accomplished in 2021 and 2022 in order to detect possible slope displacements and channel-bed-level changes. The techniques used are global navigation satellite systems and drone photogrammetry accompanied by geomorphological surveys and mapping. The multitemporal data collected allowed us to characterise slope surface deformations and quantify morphological changes. The combination of various techniques of remote and proximal sensing proved to be a useful tool for the analysis of the surface deformations and for the investigation of the interaction between slope and fluvial dynamics, showing the important role of fluvial processes in the remobilisation of the landslide toe causing the displacement of a significant volume of sediment into the stream.

A comprehensive analysis of the July 2021 event that occurred on Lake Como (Italy), during which heavy hailstorms and floods affected the surroundings of Lake, is presented. The study provides a detailed analysis of the event using different observation sources currently available. The employed techniques include both conventional (rain gauges, radar, atmospheric sounding) and non-conventional (satellite-based Earth observation products, GNSS, and lightning detection network) observations for hydro-meteorological analysis. The study is split in three main topics: event description by satellite-based observations; long-term analysis by the ERA5 model and ASCAT soil water index; and short-term analysis by lightning data, GNSS delays and radar-VIL. The added value of the work is the near-real-time analysis of some of the datasets used, which opens up the potential for use in alerting systems, showing considerable application possibilities in NWP modeling, where it can also be useful for the implementation of early warning systems. The results highlight the validity of the different techniques and the consistency among the observations. This result, therefore, leads to the conclusion that a joint use of the innovative techniques with the operational ones can bring reliability in the description of events.

This paper provides a comparison of a designed method of a fault ride through (FRT) circuit, i.e., switch-type fault current limiter (STFCL) and bridge-type fault current limiter (BFCL), to optimize the electrical parameters of grid-connected solar systems (PVSs) under asymmetric single line-to-ground fault and symmetric three-phase fault. The main differences between switch- and bridge-type fault current limiters is the electric component devices such as the bridge rectifier, snubber capacitor, energy absorption bypass and current-limiting inductors. In addition, the designed FRT performance with the inverter control are analyzed in-depth, e.g., a well-adjusted proportional integral (PI) and proposed steepest descent (SD) controller are compared in the fault condition. To compare the proposed method with the conventional method, the AC power and voltage on a common coupling point (PCC) and DC link voltage of the PV system are analyzed with a MATLAB/Simulink model of a 100 kW three-phase grid-connected photovoltaic system. The simulation results of the proposed FRT circuit and SD controller verify the stability improvement and vibration-free and fast and robust responses of electrical parameters on both PV grid sides during asymmetric disturbances.

Human-Machine Interface for Artificial Neural Network Based Machine Tool Process Monitoring

Battery energy storage systems (BESSs) need to comply with grid code and fault ride through (FRT) requirements during disturbances whether they are in charging or discharging mode. Previous literature has shown that constant charging current control of BESSs in charging mode can prevent BESSs from complying with emerging grid codes such as the German grid code under stringent unbalanced fault conditions. To address this challenge, this paper proposes a new FRT-activated dual control strategy that consists of switching from constant battery current control to constant DC-link voltage control through a positive droop structure. The results show that the strategy ensures proper DC-link voltage and current management as well as adequate control of the positive- and negative-sequence active and reactive currents according to the grid code priority. It is also shown that the proposed FRT control strategy is tolerant to initial operating conditions of BESS plant, grid code requirements, and fault severity.

This research proposes an empirical method to estimate the impact on the wholesale electricity market of an increase in the price of CO2 emission allowances. The current literature in this field is mainly focused on long-term simulation analyses, while this study carries out a short-term analysis with microdata from the electricity market. A higher price of CO2 implies an increase in the electricity generation costs of polluting units and therefore an increase in the price of the electricity market. When CO2 becomes more expensive, polluting electricity generators are shifted in the hourly electricity supply curve towards less competitive positions (in favour of less polluting/cheaper units). Displaced polluting units could even be taken out of the market, which would imply a reduction in CO2 emissions. These short-term movements can be reproduced with our microdata of the day-ahead electricity market –data Provided by the Spanish Market Operator (OMIE). According to our results, increases in the carbon price of 10, 20, or 30 € per ton, respectively, cause increases of 1.8%, 4.2% and 5.3% in the electricity price (year 2018), while the negative effect on emissions is relatively small. Our analysis concludes with the estimation of an ARIMA-SARIMA model that looks for the main determinants of the variations in the hourly energy prices and the carbon emissions. The estimations show that the marginal supply technology in the electricity market is important in explaining these variations.Graphical abstract