Oscillation Damping Neuro-Based Controllers Augmented Solar Energy Penetration Management of Power System Stability

The appropriate design of the power oscillation damping controllers guarantees that distributed energy resources and sustainable smart grids deliver excellent service subjected to big data for planned maintenance of renewable energy. Therefore, the main target of this study is to suppress the low-frequency oscillations due to disruptive faults and heavy load disturbance conditions. The considered power system comprises two interconnected hydroelectric areas with heavy solar energy penetrations, severely impacting the power system stabilizers. When associated with appropriate controllers, FACTs technology such as the static synchronous series compensator provides efficient dampening of the adverse power frequency oscillations. First, a two-area power system with heavy solar energy penetration is implemented. Second, two neuro-based controllers are developed. The first controller is constructed with an optimized particle swarm optimization (PSO) based neural network, while the second is created with the adaptive neuro-fuzzy. An energy management approach is developed to lessen the risky impact of the injected solar energy upon the rotor speed deviations of the synchronous generator. The obtained results are impartially compared with a lead-lag compensator. The obtained results demonstrate that the developed PSO-based neural network controller outperforms the other controllers in terms of execution time and the system performance indices. Solar energy penetrations temporarily influence the electrical power produced by the synchronous generators, which slow down for uncomfortably lengthy intervals for solar energy injection greater than 0.5 pu. © 2023 by the authors.Ministry of Science, ICT and Future Planning, MSIP: 2019M3F2A1073164; National Research Foundation of Korea, NRFThis research was supported by the Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M3F2A1073164)

Coupled binary linear programming–differential evolution algorithm approach for water distribution system optimization

A coupled binary linear programming-differential evolution (BLP-DE) approach is proposed in this paper to optimize the design of water distribution systems (WDSs). Three stages are involved in the proposed BLP-DE optimization method. In the first stage, the WDS that is being optimized is decomposed into trees and the core using a graph algorithm. Binary linear programming (BLP) is then used to optimize the design of the trees during the second stage. In the third stage, a differential evolution (DE) algorithm is utilized to deal with the core design while incorporating the optimal solutions for the trees obtained in the second stage, thereby yielding near-optimal solutions for the original whole WDS. The proposed method takes advantage of both BLP and DE algorithms: BLP is capable of providing global optimal solution for the trees (no loops involved) with great efficiency, while a DE is able to efficiently generate good quality solutions for the core (loops involved) with a reduced search space compared to the original full network. Two benchmark WDS case studies and one real-world case study (with multiple demand loading cases) with a number of decision variables ranging from 21 to 96 are used to verify the effectiveness of the proposed BLP-DE optimization approach. Results show that the proposed BLP-DE algorithm significantly outperforms other optimization algorithms in terms of both solution quality and efficiency.Feifei Zheng, Angus R. Simpson and Aaron C. Zecchi

Self-adaptive differential evolution algorithm applied to water distribution system optimization

Differential evolution (DE) is a relatively new technique that has recently been used to optimize the design for water distribution systems (WDSs). Several parameters need to be determined in the use of DE, including population size, N; mutation weighting factor, F; crossover rate, CR, and a particular mutation strategy. It has been demonstrated that the search behavior of DE is especially sensitive to the F and CR values. These parameters need to be fine-tuned for different optimization problems because they are generally problem-dependent. A self-adaptive differential evolution (SADE) algorithm is proposed to optimize the design of WDSs. Three new contributions are included in the proposed SADE algorithm: (1) instead of pre-specification, the control parameters of F and CR are encoded into the chromosome of the SADE algorithm, and hence are adapted by means of evolution; (2) F and CR values of the SADE algorithm apply at the individual level rather than the generational level normally used by the traditional DE algorithm; and (3) a new convergence criterion is proposed for the SADE algorithm as the termination condition, thereby avoiding pre-specifying a fixed number of generations or computational budget to terminate the evolution. Four WDS case studies have been used to demonstrate the effectiveness of the proposed SADE algorithm. The results show that the proposed algorithm exhibits good performance in terms of solution quality and efficiency. The advantage of the proposed SADE algorithm is that it reduces the effort required to fine-tune algorithm parameter values.Feifei Zheng, Aaron C. Zecchin and Angus R. Simpso

CD4 T lymphocyte autophagy is upregulated in the salivary glands of primary Sjögren’s syndrome patients and correlates with focus score and disease activity

Background: Primary Sjögren’s syndrome (pSS) is a common chronic autoimmune disease characterized by lymphocytic infiltration of exocrine glands and peripheral lymphocyte perturbation. In the current study, we aimed to investigate the possible pathogenic implication of autophagy in T lymphocytes in patients with pSS. Methods: Thirty consecutive pSS patients were recruited together with 20 patients affected by sicca syndrome a nd/or chronic sialoadenitis and 30 healthy controls. Disease activity and damage were evaluated according to SS disease activity index, EULAR SS disease activity index, and SS disease damage index. T lymphocytes were analyzed for the expression of autophagy-specific markers by biochemical, molecular, and histological assays in peripheral blood and labial gland biopsies. Serum interleukin (IL)-23 and IL-21 levels were quantified by enzyme-linked immunosorbent assay. Results: Our study provides evidence for the first time that autophagy is upregulated in CD4+ T lymphocyte salivary glands from pSS patients. Furthermore, a statistically significant correlation was detected between lymphocyte autophagy levels, disease activity, and damage indexes. We also found a positive correlation between autophagy enhancement and the increased salivary gland expression of IL-21 and IL-23, providing a further link between innate and adaptive immune responses in pSS. Conclusions: These findings suggest that CD4+ T lymphocyte autophagy could play a key role in pSS pathogenesis. Additionally, our data highlight the potential exploitation of T cell autophagy as a biomarker of disease activity and provide new ground to verify the therapeutic implications of autophagy as an innovative drug target in pSS

A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals

Properly determining the discriminative features which characterize the inherent behaviors of electroencephalography (EEG) signals remains a great challenge for epileptic seizure detection. In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. The normal as well as epileptic EEG recordings were frst decomposed into various frequency bands by means of wavelet packet decomposition, and subsequently, statistical features at all developed nodes in the wavelet packet decomposition tree were derived. Instead of using the complete set of the extracted features to construct a wavelet neural networks-based classifer, an optimal feature subset that maximizes the predictive competence of the classifer was selected by using the CSA. Experimental results on the publicly available benchmarks demonstrated that the proposed feature subset selection scheme achieved promising recognition accuracies of 98.43–100%, and the results were statistically signifcant using z-test with p value <0.0001

Research trends in combinatorial optimization

Acknowledgments This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities through the project COGDRIVE (DPI2017-86915-C3-3-R). In this context, we would also like to thank the Karlsruhe Institute of Technology. Open access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

Metaheuristic optimization of reinforced concrete footings

The primary goal of an engineer is to find the best possible economical design and this goal can be achieved by considering multiple trials. A methodology with fast computing ability must be proposed for the optimum design. Optimum design of Reinforced Concrete (RC) structural members is the one of the complex engineering problems since two different materials which have extremely different prices and behaviors in tension are involved. Structural state limits are considered in the optimum design and differently from the superstructure members, RC footings contain geotechnical limit states. This study proposes a metaheuristic based methodology for the cost optimization of RC footings by employing several classical and newly developed algorithms which are powerful to deal with non-linear optimization problems. The methodology covers the optimization of dimensions of the footing, the orientation of the supported columns and applicable reinforcement design. The employed relatively new metaheuristic algorithms are Harmony Search (HS), Teaching-Learning Based Optimization algorithm (TLBO) and Flower Pollination Algorithm (FPA) are competitive for the optimum design of RC footings