Research on Blockchain-Enabled Smart Grid for Anti-Theft Electricity Securing Peer-to-Peer Transactions in Modern Grids.

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain's core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower-upper decomposition (LUD), lower-upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users' "honesty coefficients," providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods.

A stochastic model of preventive maintenance strategies for wind turbine gearboxes considering the incomplete maintenance.

In contemporary large wind farms, the combination of condition-based maintenance (CBM) and time-based maintenance (TBM) has become a prevalent approach in preventive maintenance, which is an indispensable part to ensure the safe, stable and environmental operation of equipment. However, the utilization of an inappropriate maintenance strategy may result in over-maintenance or under-maintenance, leading to unstable equipment operation. Furthermore, the majority of preventive maintenance involves replacement maintenance, which may have adverse effects on the performance of wind turbines with excessive maintenance time. Therefore, this paper takes the gearbox as a case study to introduce the incomplete maintenance parameters into the failure rate function to establish a state model based on the stochastic differential equation (SDE) and describing the state change of incomplete maintenance. And then simulating the state model of the gearbox and the joint preventive maintenance strategy of TBM and CBM through examples, resulting the time-based incomplete maintenance (TBIM) is proposed based on the TBM and the incomplete maintenance, and a new joint preventive maintenance strategy incorporating TBIM and CBM is proposed. Through developing the decision-making process of the maintenance strategy to optimize the inappropriate maintenance which including over-maintenance and under-maintenance and simulating the optimized preventive maintenance strategy to compare with that of TBM and CBM and verify the superiority and effectiveness of the proposed maintenance method.

Condition-based opportunistic maintenance strategy for multi-component wind turbines by using stochastic differential equations.

The components of wind turbines are complex in structure and the working environment is harsh, which makes wind turbines face problems such as high failure rates and high maintenance costs. In this paper, the stochastic differential equation model has been established for the harsh operating environment of wind turbines, and used Brownian motion to simulate random disturbances; aiming at the problem of high failure rate of wind turbines, based on Weibull distribution, a new model has been established by combining operating time and equipment state to calculate the failure rate; in the analysis of monitoring data, the Higher-Order Moment method and Bayesian method were used to solve the parameters. The opportunity maintenance threshold curve and preventive maintenance threshold curve were obtained by analyzing Time-Based Maintenance and Condition-Based Maintenance. Therefore, the Condition-Based Opportunistic Maintenance strategy was obtained. The effectiveness of the proposed method was finally verified by arithmetic examples.

Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm.

An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

Clinical grade production and characterization of a fusion protein comprised of the chemokine CCL2-ligand genetically fused to a mutated and truncated form of the Shiga A1 subunit.

First generation chemokine ligand-Shiga A1 (SA1) fusion proteins (leukocyte population modulators, LPMs) were previously only obtained in small quantities due to the ribosomal inactivating protein properties of the SA1 moiety which inhibits protein synthesis in host cells. We therefore employed 4-aminopyrazolo[3,4-d]-pyrimidine, an inhibitor of Shiga A1, to allow the growth of these cells prior to induction and during the expression phase post-induction with IPTG. Scale-up allowed the production of gram quantities of clinical grade material of the lead candidate, OPL-CCL2-LPM. A manufacturing cell bank was established and used to produce OPL-CCL2-LPM in a fed-batch fermentation process. Induction of the expression of OPL-CCL2-LPM led to the production of 22.47 mg/L per OD(600) unit. The LPM was purified from inclusion bodies using solubilization, renaturation, refolding and chromatography steps. The identity and purity of the OPL-CCL2-LPM was determined using several analytical techniques. The product retained the ability of the SA1 moiety to inhibit protein synthesis as measured in a rabbit reticulocyte lysate cell-free protein synthesis assay and was cytotoxic to target cells. Binding studies established that the protein exerts its effects via CCR2, the cognate receptor for CCL2. Clinical trials in inflammatory nephropathies are planned.

Expression system for high levels of GAG lyase gene expression and study of the hepA upstream region in Flavobacterium heparinum.

A system for high-level expression of heparinase I, heparinase II, heparinase III, chondroitinase AC, and chondroitinase B in Flavobacterium heparinum is described. hepA, along with its regulatory region, as well as hepB, hepC, cslA, and cslB, cloned downstream of the hepA regulatory region, was integrated in the chromosome to yield stable transconjugant strains. The level of heparinase I and II expression from the transconjugant strains was approximately fivefold higher, while heparinase III expression was 10-fold higher than in wild-type F. heparinum grown in heparin-only medium. The chondroitinase AC and B transconjugant strains, grown in heparin-only medium, yielded 20- and 13-fold increases, respectively, in chondroitinase AC and B expression, compared to wild-type F. heparinum grown in chondroitin sulfate A-only medium. The hepA upstream region was also studied using cslA as a reporter gene, and the transcriptional start site was determined to be 26 bp upstream of the start codon in the chondroitinase AC transconjugant strain. The transcriptional start sites were determined for hepA in both the wild-type F. heparinum and heparinase I transconjugant strains and were shown to be the same as in the chondroitinase AC transconjugant strain. The five GAG lyases were purified from these transconjugant strains and shown to be identical to their wild-type counterparts.

Development of a genetic system for the transfer of DNA into Flavobacterium heparinum.

Flavobacterium heparinum (now Pedobacter heparinus) is a Gram-negative soil bacterium which can produce yellow pigments. It synthesizes five enzymes that degrade glycosoaminoglycan molecules. The study of this unique bacterium has been limited by the absence of a genetic manipulation system. In this paper, the construction of a conjugation/integration plasmid system and a broad-host-range plasmid, both of which contain a F. heparinum functional selective marker created by placing the trimethoprim resistance gene, dhfrII, under the control of the hepA regulatory region is described. Both plasmids were introduced into F. heparinum by conjugation and/or electroporation, and trimethoprim resistant colonies were obtained. Fifty electroporants were obtained per microgram covalently closed circular plasmid DNA. The existence of integrated plasmid DNA was confirmed by Southern hybridization and PCR. The existence of a derivative of the broad-host-range plasmid pBBR1 in F. heparinum was demonstrated by plasmid digestion and Southern hybridization, and by transformation of Escherichia coli.