Abstract-The dynamics of the onset of oscillations in a wave guide cavity based Gunn Oscillator (GO) has been critically examined through numerical simulations and experimental studies.The transition of the GO from a non-oscillatory to an oscillatory state and the same in the reverse direction occurs at different critical values of the dc bias voltage applied to the GO. In presence of a weak RF field in GO cavity, oscillations with broad band continuous spectrum and multiple discrete line spectrum are observed at the GO output for different values of dc bias below the above mentioned critical values. Analysing the numerically obtained time series data, chaos quantifiers have been obtained to prove the occurrence of the chaotic oscillations in the GO. Experimental results and observations of numerical simulation show good qualitative agreement.
Due to the restructuring of the power system, customers always try to obtain low-cost power efficiently and reliably. As a result, there is a chance to violate the system security limit, or the system may run in risk conditions. In this paper, an economic risk analysis of a power system considering wind and pumped hydroelectric storage (WPHS) hybrid system is presented with the help of meta-heuristic algorithms. The value-at-risk (VaR) and conditional value-at-risk (CVaR) are used as the economic risk analysis tool with two different confidence levels (i.e., 95% and 99%). The VaR and CVaR with higher negative values represent the system in a higher-risk condition. The value of VaR and CVaR on the lower negative side or towards a positive value side indicates a less risky system. The main objective of this work is to minimize the system risk as well as minimize the system generation cost by optimal placement of wind farm and pumped hydro storage systems in the power system. Sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO) are used to solve optimal power flow problems. The novelty of this paper is that the MFO algorithm is used for the first time in this type of power risk curtailment problem. The IEEE 30 bus system is considered to analyze the system risk with the different confidence levels. The MVA flow of all transmission lines is considered here to calculate the value of VaR and CVaR. The hourly VaR and CVaR values of the hybrid system considering the WPHS system are reported here and the numerical case studies of the hybrid WPHS system demonstrate the effectiveness of the proposed approach. To validate the presented approach, the results obtained by using the MFO algorithm are compared with the SQP and ABC algorithms’ results.
Two X-band microwave Gunn oscillators have been separately operated with "below threshold" dc bias voltages under the influence of an injected weak RF field in their respective cavities to generate chaotic oscillations. The output of one such chaotic Gunn oscillator is injected into the other through a controllable coupling network to explore the possibility of synchronization between two oscillators. We establish through numerical simulation that (i) two oscillators with identical design parameters attain a state of complete synchronization and (ii) two oscillators with slightly different design parameters attain a state of generalized synchronization for reasonable value of coupling strengths. The occurrence of generalized synchronization has been proved through the "auxiliary" slave system approach of nonlinear analysis. Results of hardware experiments are incorporated to qualitatively support the observations made through numerical simulation.
PoolCo electricity trading is one of the most capable bidding practices for executing a centralized energy market model. In the PoolCo market model, each seller and buyer submit their bid price and bid quantity to the independent market operator, which they are ready to sell and buy from the market respectively. The market operator regulates the equilibrium market price and volume by considering the acquiesced bid price and bid quantity to settle the market. To maximize the social welfare of market participants, the optimal bidding strategy of a wind farm integrated system is represented as a centralized power market model. Initially, the bid price and bid quantity for consumers and suppliers have been calculated using the Monte-Carlo simulation (MCS) approach. Secondly, a wind farm is incorporated into the system with the help of locational marginal price (LMP). The market operator determines market clearing price (MCP) and market clearing volume (MCV) based on the submitted bid price and bid quantity of suppliers and buyers in order to find the eligible buyers and suppliers. After obtaining MCP and MCV, the market operator reschedules the supplier's bid quantity with the help of an artificial gorilla troops optimizer (AGTO) algorithm to maximize social welfare by pleasing the system constraints. The AGTO algorithm is used here for the first time to solve the market-clearing power simulation (MCPS) problem with the integration of wind farm. To show the feasibility and effectiveness of the proposed bidding strategy, modified IEEE 14-bus and modified IEEE 30-bus test systems are used here along with a wind farm of 5 MW and 30 MW rated capacity, respectively. Results obtained by using the AGTO algorithm have been compared with those obtained by other optimization algorithms like honey badger algorithm (HBA), artificial bee colony (ABC), particle swarm optimizer (PSO), and slime mould optimizer (SMO) algorithms. INDEX TERMSWind farm, locational marginal price, Monte-Carlo simulation, artificial gorilla troops optimizer algorithm, slime mould optimizer algorithms.
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