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The negative impact of measurement time skew on the static state estimation of the power grid has been exacerbated by increasing variation of system operating conditions. To mitigate the time skew problem, this paper proposes a regression model forecasting (RMF) method to forecast the time-skewed measurements, along with a confidence interval estimation (CIE) method to determine the weights associated with the forecasted measurements. The proposed RMF-CIE method is compared against several benchmark methods through Monte-Carlo simulation on the IEEE 16-machine, 68-bus model. It was observed that the proposed RMF-CIE consistently achieved more accurate state estimation on average. In addition, it was found that its estimation accuracy increases with the decrease of the skew time and variation levels. 

To guide the selection of probabilistic solar power forecasting methods for day-ahead power grid operations, the performance of four methods, i.e., Bayesian model averaging (BMA), Analog ensemble (AnEn), ensemble learning method (ELM), and persistence ensemble (PerEn) is compared in this paper. A real-world hourly solar generation dataset from a rooftop solar plant is used to train and validate the methods under clear, partially cloudy, and overcast weather conditions. Comparisons have been made on a one-year testing set using popular performance metrics for probabilistic forecasts. It is found that the ELM method outperforms other methods by offering better reliability, higher resolution, and narrower prediction interval width under all weather conditions with a slight compromise in accuracy. The BMA method performs well under overcast and partially cloudy weather conditions, although it is outperformed by the ELM method under clear conditions. 

This paper reports two extensions to the authors’ recent work on the design of an optimally robust topology detector for a power transmission circuit with uncertain loads. Such a detector was implemented as a linear discriminator for the IEEE 9-bus system to identify, with a sub-millisecond latency, the intact circuit, or any single open-circuited line, using only the phasor measurements at the generators’ terminals. The first extension aims to replace the previously required bounded uncertain load set by a load distribution that permits rarer measurement outliers. This problem is formulated and solved as a support vector classifier. The second extension explores the solvability of a linear discriminator for topology identification for larger power systems under a bounded uncertain load set. A measure of adequacy of the involved measurement network is introduced, under which a sensor placement problem is formulated for the addition of a minimum number of phasor measurement units to meet a prescribed level of topology identifiability. In this case, sensor placement, detector design, and detector performance and robustness are demonstrated on the IEEE 68-bus system. 

Observability and detectability analyses are often used to guide the measurement setup and select the estimation models used in dynamic state estimation (DSE). Yet, marginally observable states of a synchronous machine prevent the direct application of conventional observability and detectability analyses in determining the existence of a DSE observer. To address this issue, the authors propose to identify the marginally observable states and their associate eigenvalues by finding the smallest perturbation matrices that make the system unobservable. The proposed method extends the observability and detectability analyses to marginally observable estimation models, often encountered in the DSE of a synchronous machine. The effectiveness and application of the proposed method are illustrated on the IEEE 10-machine 39-bus system, verified using the unscented Kalman filter and the extended Kalman filter, and compared with conventional methods. The proposed analysis method can be applied to guide the selection of estimation models and measurements to determine the existence of a DSE observer in power-system planning. 

The state estimation (SE) has been widely used in power system control centers to optimally estimate the states of the power grid in real time. Using different objective functions, many SE algorithms have been proposed to filter out measurement noise in different ways. In this paper, three widely-used SE algorithms, i.e., the weighted least squares (WLS), least absolute value (LAV), and projection statistics (PS) based algorithms, are compared in their estimation accuracy and computation time. The comparison was made using the simulation data generated from the IEEE 14-bus system and IEEE 118-bus system through the Monte-Carlo method. It is found that when the measurement noise is reasonably small and follows the independent Gaussian distribution, the WLS algorithm has the best accuracy and shortest computation time. When some measurements at leverage points were compromised by outliers, the PS based algorithm is the most robust among the three methods. The study results can be used to assist control centers in choosing the right SE algorithm based on the features of the measurement noise and setup. 

ABSTRACT Although alcohols are toxic to many microorganisms, they are good carbon and energy sources for some bacteria, including many pseudomonads. However, most studies that have examined chemosensory responses to alcohols have reported that alcohols are sensed as repellents, which is consistent with their toxic properties. In this study, we examined the chemotaxis of Pseudomonas putida strain F1 to n -alcohols with chain lengths of 1 to 12 carbons. P. putida F1 was attracted to all n -alcohols that served as growth substrates (C 2 to C 12 ) for the strain, and the responses were induced when cells were grown in the presence of alcohols. By assaying mutant strains lacking single or multiple methyl-accepting chemotaxis proteins, the receptor mediating the response to C 2 to C 12 alcohols was identified as McfP, the ortholog of the P. putida strain KT2440 receptor for C 2 and C 3 carboxylic acids. Besides being a requirement for the response to n -alcohols, McfP was required for the response of P. putida F1 to pyruvate, l -lactate, acetate, and propionate, which are detected by the KT2440 receptor, and the medium- and long-chain carboxylic acids hexanoic acid and dodecanoic acid. β-Galactosidase assays of P. putida F1 carrying an mcfP-lacZ transcriptional fusion showed that the mcfP gene is not induced in response to alcohols. Together, our results are consistent with the idea that the carboxylic acids generated from the oxidation of alcohols are the actual attractants sensed by McfP in P. putida F1, rather than the alcohols themselves. IMPORTANCE Alcohols, released as fermentation products and produced as intermediates in the catabolism of many organic compounds, including hydrocarbons and fatty acids, are common components of the microbial food web in soil and sediments. Although they serve as good carbon and energy sources for many soil bacteria, alcohols have primarily been reported to be repellents rather than attractants for motile bacteria. Little is known about how alcohols are sensed by microbes in the environment. We report here that catabolizable n -alcohols with linear chains of up to 12 carbons serve as attractants for the soil bacterium Pseudomonas putida , and rather than being detected directly, alcohols appear to be catabolized to acetate, which is then sensed by a specific cell-surface chemoreceptor protein. 

Additive manufacturing promises a major transformation of the production of high economic value metallic materials, enabling innovative, geometrically complex designs with minimal material waste. The overarching challenge is to design alloys that are compatible with the unique additive processing conditions while maintaining material properties sufficient for the challenging environments encountered in energy, space, and nuclear applications. Here we describe a class of high strength, defect-resistant 3D printable superalloys containing approximately equal parts of Co and Ni along with Al, Cr, Ta and W that possess strengths in excess of 1.1 GPa in as-printed and post-processed forms and tensile ductilities of greater than 13% at room temperature. These alloys are amenable to crack-free 3D printing via electron beam melting (EBM) with preheat as well as selective laser melting (SLM) with limited preheat. Alloy design principles are described along with the structure and properties of EBM and SLM CoNi-base materials.