Scale Test System Research Articles

Resilient Dynamic State Estimation for Multi-Machine Power System With Partial Missing Measurements

Accurate tracking the dynamics of power system plays a significant role in its reliability, resilience and security. To achieve the reliable and precise estimation results, many advanced estimation methods have been developed. However, most of them are aiming at filtering the measurement noise, while the adverse affect of partial measurement missing is rarely taken into account. To deal with this issue, a discrete distribution in the interval [0,1] is introduced to depict mechanism of partial measurement data loss that caused by the sensor failure. Then, a resilient fault tolerant extended Kalman filter (FTEKF) is designed in the recursive filter framework. Eventually, extensive simulations are carried on the different scale test systems. Numerical experimental results illustrate that the resilience and robustness of the proposed fault tolerant EKF method against partial measurement data loss.

Small‐signal stability assessment based on virtual regional eigenvalue

Small-signal stability assessment (SSSA) plays a significant role in large-scale power systems operation. Since the computational burden and unavailability of the detailed time-varying model, it is practically difficult to use model-based approaches to meet the real-time demands of SSSA. Considering the neighbour eigenvalues share similar oscillation characteristics, this paper presents an efficient method for SSSA based on looking at the regions that include the neighbour eigenvalues of interest rather than the exact position of eigenvalues. After dividing the insufficient damping area into several regions, the limitations of existing data-driven SSSA resulting from the uncertainty of critical eigenvalues are overcome by virtual regional eigenvalue (VRE), which is introduced to estimate the prominent eigenvalue of neighbour eigenvalues in each divided region. A composite structure based on Long short-term memory is designed to accommodate the different number of eigenvalues, generate VRE and the boundary of eigenvalues in divided regions. The effectiveness of the proposed method is demonstrated by two different scale test systems.

Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems

The optimal operation of modern power systems aims at achieving the increased power demand requirements regarding economic and technical aspects. Another concern is preserving the emissions within the environmental limitations. In this regard, this paper aims at finding the optimal scheduling of power generation units that are able to meet the load requirements based on a multi-objective optimal power flow framework. In the proposed multi-objective framework, objective functions, technical economical, and emissions are considered. The solution methodology is performed based on a developed turbulent flow of a water-based optimizer (TFWO). Single and multi-objective functions are employed to minimize the cost of fuel, emission level, power losses, enhance voltage deviation, and voltage stability index. The proposed algorithm is tested and investigated on the IEEE 30-bus and 57-bus systems, and 17 cases are studied. Four additional cases studied are applied on four large scale test systems to prove the high scalability of the proposed solution methodology. Evaluation of the effectiveness and robustness of the proposed TFWO is proven through a comparison of the simulation results, convergence rate, and statistical indices to other well-known recent algorithms in the literature. We concluded from the current study that TFWO is efficient, effective, robust, and superior in solving OPF optimization problems. It has better convergence rates compared with other well-known algorithms with significant technical and economical improvements. A reduction in the range of 4.6–33.12% is achieved by the proposed TFWO for the large scale tested system. For the tested system, the proposed solution methodology leads to a more competitive solution with significant improvement in the techno-economic aspects.

Metaheuristics approaches to profit based unit commitment for GENCOs

The optimum scheduling with maximize the profit from those generating systems have to maintain lots of constraints which are uncertain by nature. There are also have stimulating responsibility of delivered power among the power units which are committed which matters to visit restrictions over a planned scheduled time period to accomplish minimal cost and maximum profit of power generations. In the proposed work, authors have effectively given the combination of chaotic maps with Harris Hawks Optimization with Sine Cosine Algorithm and Slime Mould Algorithm advancement strategy and assessed execution of the proposed improved technique, named as hCHHO-SCA and hCSMA-SCA for standard benchmark of PBUCP are checked for small to large scale test systems i.e. 10-, 40- and 100-generating unit systems are considered and profit of each are evaluated as outcomes are compared with the exiting optimization algorithms in this research work.

A robust dispatch model for integrated electricity and heat networks considering price-based integrated demand response

Wind power integration may be restricted due to the asynchrony of electricity and heat loads and the strong coupling between the power generation and heat production of combined heat and power (CHP) units. The price-based integrated demand response (PBIDR) can adjust electricity and heat loads to accommodate wind power. But, it is tricky to formulate appropriate electricity and heat prices to make the system having enough flexibility and achieves the best economic benefits. This paper proposes a robust dispatch model for integrated electricity and heat networks (IEHNs) based on PBIDR. The linear model of the district heating network (DHN) is first derived based on the equivalent infinitesimal replacement theorem and the assumption that the supply temperatures at load nodes are approximately equal. Then, a computationally tractable PBIDR model is formulated in terms of the theory of price elasticity of demand and the concept of thermal sensation vote (TSV). Finally, the dispatch model is constructed as a two-stage robust optimization problem to hedge the uncertainty of the wind power output, the ambient temperature, and the PBIDR. The effectiveness of the proposed model is verified by two different scale test systems.

Solving power systems optimization problems in the presence of renewable energy sources using modified exchange market algorithm

This paper introduces a novel evolutionary algorithm namely modified exchange market algorithm (MEMA) to solve the various problems in the power system operation topics such as combined heat and power economic emission dispatch (CHPEED), and multi-area economic dispatch (MAED) in the presence of renewable energy sources (RESs). The MEMA is a new version of the EMA that has been modified to improve its performance and to find a better solution with high robustness. To scrutiny the applicability of the MEMA, it is employed to solve the different problems with considering RESs. Also, to prove the scalability and evaluate the performance of the proposed method, several large and small scale test systems are used in each case. Also, three different approaches are considered to investigate the impacts of RESs. In the first approach, RESs are assumed to be a negative load, while in the latter case, their cost impacts on the problem are also considered. Finally, in the last approach, the uncertainty of RES and its stochastic nature are considered. In all test systems, the results obtained by the proposed method are compared with the results of other suggested techniques in this era. Also, the robustness of the proposed method is investigated using parametric and non-parametric statistical tests. The comparative and statistical analysis confirm the superiority and robustness of the proposed method over the other methods in all test systems.

An Efficient Hybrid Approach to Solve Bi-objective Multi-area Dynamic Economic Emission Dispatch Problem

Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature.

Hybrid Optimization Algorithm to Solve the Nonconvex Multiarea Economic Dispatch Problem

In this paper, multiarea economic dispatch (MAED) problems are solved by a novel straightforward process. The solved MAED problems include transmission losses, tie-line constraints, multiple fuels, valve-point effects, and prohibited operating zones in which small, medium, and large scale test systems are involved. The methodology of tackling the problems consists in a new hybrid combination of JAYA and TLBO algorithms simultaneously to take the advantages of both to solve even nonsmooth and nonconvex MAED problems. In addition, a new and simple process is used to tackle with the interaction between areas. The objective is to economically supply demanded loads in all areas while satisfying all of the constraints. Indeed, by combining JAYA and TLBO algorithms, the convergence speed and the robustness have been improved. The computational results on small, medium, and large-scale test systems indicate the effectiveness of our proposed algorithm in terms of accuracy, robustness, and convergence speed. The obtained results of the proposed JAYA–TLBO algorithm are compared with those obtained from ten well-known algorithms. The results depict the capability of the proposed JAYA–TLBO based approach to provide a better solution.

Research on multi-time scale doubly-fed wind turbine test system based on FPGA + CPU heterogeneous calculation

As the proportion of renewable energy increases, the interaction between renewable energy devices and the grid continues to enhance. Therefore, the renewable energy dynamic test in a power system has become more and more important. Traditional dynamic simulation systems and digital-analog hybrid simulation systems are difficult to compromise on the economy, flexibility and accuracy. A multi-time scale test system of doubly fed induction generator based on FPGA+ CPU heterogeneous calculation is proposed in this paper. The proposed test system is based on the ADPSS simulation platform. The power circuit part of the test system is setup up using the EMT (electromagnetic transient simulation) simulation, and the control part uses the actual physical devices. In order to realize the close-loop testing for the physical devices, the power circuit must be simulated in real-time. This paper proposes a multi-time scale simulation algorithm, in which the decoupling component divides the power circuit into a large time scale system and a small time scale system in order to reduce computing effort. This paper also proposes the FPGA+CPU heterogeneous computing architecture for implementing this multitime scale simulation. In FPGA, there is a complete small time-scale EMT engine, which support the flexibly circuit modeling with any topology. Finally, the test system is connected to an DFIG controller based on Labview to verify the feasibility of the test system.

A Reliability Assessment Approach for Integrated Transportation and Electrical Power Systems Incorporating Electric Vehicles

With the increasing utilization of electric vehicles (EVs), transportation systems and electrical power systems are becoming increasingly coupled. However, the interaction between these two kinds of systems are not well captured, especially from the perspective of transportation systems. This paper studies the reliability of integrated transportation and electrical power system (ITES). A bidirectional EV charging control strategy is first demonstrated to model the interaction between the two systems. Thereafter, a simplified transportation system model is developed, whose high efficiency makes the reliability assessment of the ITES realizable with an acceptable accuracy. Novel transportation system reliability indices are then defined from the view point of EV’s driver. Based on the charging control model and the transportation simulation method, a daily periodic quasi sequential reliability assessment method is proposed for the ITES system. Case studies based on RBTS system demonstrate that bidirectional charging controls of EVs will benefit the reliability of power systems, while decreasing the reliability of EVs travelling. Also, the optimal control strategy can be obtained based on the proposed method. Finally, case studies are performed based on a large scale test system to verify the practicability of the proposed method.

Liquidus Tracking: Large scale preservation of encapsulated 3-D cell cultures using a vitrification machine

Currently, cryo-banking of multicellular structures such as organoids, especially in large volumes at clinical scale >1 L, remains elusive for reasons such as insufficient dehydration and cryoprotectant additive (CPA1) penetration, slow cooling and warming rates and devitrification processes. Here we introduce the concept of Liquidus Tracking (LT) using a semi-automated process for liquid volumes of up to 450 ml including 130 ml of alginate encapsulated liver cells (AELC) that archived controlled and reversible vitrification with minimized toxicity.First a CPA solution with optimal properties for LT was developed by employing different small scale test systems. Combining sugars such as glucose and raffinose with Me2SO improved post-exposure (at +0.5 °C) viabilities from 6% ±3.6 for Me2SO alone up to 58% ±6.1 and 65% ±14.2 respectively (p < 0.01). Other permeating CPAs (e.g. ethylene glycol, propylene glycol, methanol) were investigated as partial replacements for Me2SO. A mixture of Me2SO, ethylene glycol and glucose (ratio 4:2:1– termed LTdeg) supported glass-forming tendencies with appropriate low viscosities and toxicities required for LT. When running the full LT process, using Me2SO alone, no viable cells were recovered; using LTdeg, viable recoveries were improved to 40% ±8 (p<0.001%). Further refinements of improved mixing technique further improved recovery after LT. Recoveries of specific liver cell functions such as synthesis of albumin and alpha-fetoprotein (AFP) were retained in post thaw cultures.In summary: By developing a low-toxicity CPA solution of low viscosity (LTdeg) suitable for LT and by improving the stirring system, post-warming viability of AELC of up to 90% and a AFP secretion of 89% were reached. Results show that it may be possible to develop LT as a suitable cryogenic preservation process for different cell therapy products at large scale.

Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system

Abstract This article proposes an efficient meta-heuristic approach, namely, oppositional grey wolf optimization (OGWO) algorithm for resolving the optimal operating strategy of economic load dispatch (ELD) problem. The proposed algorithm combines two basic concepts. Firstly, the hunting behavior and social hierarchy of grey wolves are used to search optimal solutions and secondly, oppositional concept is integrated with the grey wolf optimization (GWO) algorithm to accelerate the convergence rate of the conventional GWO algorithm. To show the performance of the proposed algorithm, it is applied on small, medium and large scale test systems for solving ELD problems of 13-unit, 40-unit and 160-unit systems. Comparative studies are carried out to scrutinize the efficiency of the proposed OGWO approach over the conventional GWO and other approaches available in the literature. The simulation results clearly suggest that the proposed OGWO approach is capable of finding better solutions in terms of computational time and fuel cost than the other techniques.

Bacterial foraging optimization and adaptive version for economically optimum sitting, sizing and harmonic tuning orders setting of LC harmonic passive power filters in radial distribution systems with linear and nonlinear loads

This paper presents bacterial foraging optimization (BFO) algorithm and its adaptive version to optimize the planning of passive harmonic filters (PHFs).The important problem of using PHFs is determining location, size and harmonic tuning orders of them, which is reach standard levels of harmonic distortion with applying minimum cost of passive filters.In this study to optimize the PHFs location, size and setting the harmonic tuning orders in the distribution system, considered objective function includes the reduction of power loss and investment cost of PHFs. At the same time, constraints include voltage limits, number/size of installed PHFs, limit candidate buses for PHFs installation and the voltage total harmonic distortion (THDv) in all buses. The harmonic levels of system are obtained by current injections method and the load flow is solved by the iterative method of power sum, which is suitable for the accuracy requirements of this type of study. It is shown that through an economical placement and sizing of PHFs the total voltage harmonic distortion and active power loss could be minimized simultaneously.The considered objective function is of highly non-convex manner, and also has several constraints. On the other hand due to significant computational time reduction and faster convergence of BFO in comparison with other intelligent optimization approach such as genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC) the simple version of BFO has been implemented. Of course other versions of BFO such as Adaptive BFO and combination of BFO with other method due to complexity of harmonic optimization problem have not considered in this research.The simulation results for small scale test system with 10 buses, showed the significant computational time reduction and faster convergence of BFO in comparison with GA, PSO and ABC. Therefore in large scale radial system with 34 buses, the proposed method is solved using BFO.The simulation results for a 10-bus system as a small scale and 34-bus radial system as a large scale show that the proposed method is efficient for solving the presented problem.

Design and implementation of weaving‐type pad for contactless EV inductive charging system

The purpose of this study is to develop a new weaving-type pad for electric vehicles (EV) contactless inductive charging system. This proposed charging pad which consists of a weaving-type coil is fabricated to obtain a uniform mutual inductance profile over the charging surface and to solve the problem of present inductive coupled structure in which the EV has to be placed exactly on a specific place. Apart from weaving-type pad, type E core and two overlapping coils are utilised as a secondary pickup to guide and to pick more magnetic flux. The analysis and simulation of magnetic characteristic for comparing the proposed weaving-type pad to simple shape pad are performed by finite-element-analysis (FEA) software. Phase locked loop circuit is utilised to match the operating frequency and resonant frequency in order to solve the loading effect problem caused by impedance variation during the battery charging procedure. To validate the concept and system design, a laboratory scale test system with weaving-type pad for EV contactless inductive charging is built and tested. The coupling efficiency is found to be 66% for an air gap of 9 cm between the weaving-type charging pad and pickup when a 2 Ah lithium iron phosphate battery is charged.

Loading margin calculation with line switching: A decomposition method

This paper presents a model for loading margin calculation integrated with transmission line switching. A mixed-integer nonlinear optimization model is developed which maximizes the loading margin of the system subject to various operational system constraints. An iterative algorithm, based on the Benders decomposition method, is used to solve the problem. The proposed method is tested on medium and large scale test systems and optimization results are compared with that of a non-decomposed method to show the efficacy of the proposed approach.

Evolutionary Multi-Objective Day-Ahead Thermal Generation Scheduling in Uncertain Environment

This paper addresses day-ahead thermal generation scheduling as a realistic multi-objective optimization problem in an uncertain environment considering system operation cost, emission cost and reliability as the multiple objectives. The uncertainties occurring due to unit outage and load forecast error are incorporated using loss of load probability (LOLP) and expected unserved energy (EUE) reliability indices. For solving the above-mentioned scheduling problem, a multi-objective generation scheduling algorithm (MOGSA) is proposed in this paper. Three case studies are performed on large scale test systems considering two different bi-objective optimization models and a three-objective optimization model that may be chosen by the system operator according to his/her own preference. The simulation results demonstrate the advantages of solving the thermal generation scheduling problem as a realistic multi-objective optimization problem in an uncertain environment. Finally the authors suggest a systematic procedure for the system operators to choose a single solution for the thermal generation scheduling problem.

Parthenin hormesis in plants depends on growth conditions

The phenomenon that toxins can be stimulatory at low concentrations to plants has been known now for more than 50 years under the designation of hormesis. Although up to now still largely unexplained, the plant growth enhancing effects of chemicals is gaining increased attention in relation to increasing crop production. However, the use of chemical hormesis has been hampered by insufficient predictability of the effects. The reasons for this lack of adequate predictability may be manifold, and knowledge of the possible causes of variations is needed if chemical hormesis is to have practical implications in the field. With the objective of determining the causes of variability of hormesis, experiments were conducted under controlled conditions to investigate the impact of environmental factors (temperature, light and nutrient availability) on plant growth stimulation by the natural phytotoxin parthenin. A small scale test system using Lactuca sativa as test species was used to study the expression and progression of parthenin hormesis under various conditions in whole-plant concentration–response experiments. Results showed that parthenin hormesis is constrained by manipulations of experimental conditions that affect the general plant growth patterns. Parthenin induced hormesis only took place at below maximal but still at good growth conditions for root elongation in L. sativa. If growth conditions were changed to promote root elongation, the hormetic response diminished or simply disappeared. The same applied for conditions that repressed root elongation. Variation was mainly in the amplitude of hormesis, rather than in the concentration range of hormetic effects. Studying the progression of hormesis for a longer period showed that hormesis remained impaired only at conditions promoting root elongation, while hormesis was merely delayed at poor growth conditions. Hence, the effect of experimental conditions on parthenin hormesis was principally explainable by the effect of experimental factors on growth rate and development of L. sativa. Based on this, it is questionable if parthenin or other phytotoxin induced hormesis can ultimately be used to predictably increase crop yield under the conditions encountered in the field. The potential might rather be for greenhouse crops, where growth conditions can be more carefully regulated.

A kernel‐based clustering approach to finding communities in multi‐machine power systems

AbstractWhen a power system is subjected to a large disturbance, effective identification of vulnerable bus and generator groupings in the complex power grid contributes to the analysis of power system dynamic behavior. Also, computationally simple and reliable identification of clusters is of primary importance in the successful application of hybrid transient stability assessment methods. In the paper, a kernel‐based clustering approach is proposed to finding communities of power grids and generators, using data clustering and multi‐machine equivalent concept. The calculation of the cluster analysis is simple and fast, as only the network topology and generator dynamic parameters along the post‐fault trajectory are required. Case studies using different scale test systems are given to illustrate the validity of the proposed method. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Testing of photomultiplier tubes for use in the surface detector of the Pierre Auger observatory

In the array of water Cherenkov detectors of the Pierre Auger Observatory, 4800 large photomultiplier tubes (PMTs) will be used. Before being deployed, each PMT is evaluated to check that various parameters, such as the linearity, dark noise, and gain, fall within a specified range. The large scale test system, designed and constructed for this purpose, is capable of testing multiple large PMTs simultaneously. The test system and the results of the tests for the first 3964 PMTs are presented in this paper.

Bacterial lipopolysaccharides induce genes involved in the innate immune response in embryos of the zebrafish ( Danio rerio)

The innate immune response in fish represents an early, rapid defence against pathogens. Environmental contaminants could disturb this defence and negatively influence the ability to protect against infection. However, analysis of immune-modulation has not yet been included in testing strategies for environmental risk assessment of chemicals. In order to establish an efficient, small scale test system, the ability to induce the innate immune response by bacterial lipopolysaccharides in zebrafish embryos was investigated. The level of expression of various genes involved in inflammation was used as the endpoint. We could show that immersion of embryos in LPS induced the gene expression of two key pro-inflammatory cytokines, tumor necrosis factor α and interleukin 1β in 32 h old zebrafish embryos. The gene induction required the removal of the chorion prior to lipopolysaccharide exposure.