Based on the maximum entropy principle, a probability density function (PDF) is derived for the distribution of wave heights in a random wave field, without any more hypothesis. The present PDF, being a non-Rayleigh f...Based on the maximum entropy principle, a probability density function (PDF) is derived for the distribution of wave heights in a random wave field, without any more hypothesis. The present PDF, being a non-Rayleigh form, involves two parameters: the average wave height H— and the state parameter γ. The role of γ in the distribution of wave heights is examined. It is found that γ may be a certain measure of sea state. A least square method for determining γ from measured data is proposed. In virtue of the method, the values of γ are determined for three sea states from the data measured in the East China Sea. The present PDF is compared with the well known Rayleigh PDF of wave height and it is shown that it much better fits the data than the Rayleigh PDF. It is expected that the present PDF would fit some other wave variables, since its derivation is not restricted only to the wave height.展开更多
Distributed generation including wind turbine(WT) and photovoltaic panel increases very fast in recent years around the world, challenging the conventional way of probabilistic load flow(PLF) calculation. Reliable and...Distributed generation including wind turbine(WT) and photovoltaic panel increases very fast in recent years around the world, challenging the conventional way of probabilistic load flow(PLF) calculation. Reliable and efficient PLF method is required to take this chage into account.This paper studies the maximum entropy probabilistic density function reconstruction method based on cumulant arithmetic of linearized load flow formulation,and then develops a maximum entropy based PLF(MEPLF) calculation algorithm for power system integrated with wind power generation(WPG). Compared with traditional Gram–Charlier expansion based PLF(GC-PLF)calculation method, the proposed ME-PLF calculation algorithm can obtain more reliable and accurate probabilistic density functions(PDFs) of bus voltages and branch flows in various WT parameter scenarios. It can solve thelimitation of GC-PLF calculation method that mistakenly gains negative values in tail regions of PDFs. Linear dependence between active and reactive power injections of WPG can also be effectively considered by the modified cumulant calculation framework. Accuracy and efficiency of the proposed approach are validated with some test systems. Uncertainties yielded by the wind speed variations, WT locations, power factor fluctuations are considered.展开更多
This paper presents a distribution free method for predicting the extreme wind velocity from wind monitoring data at the site of the Runyang Suspension Bridge (RSB), China using the maximum entropy theory. The maximum...This paper presents a distribution free method for predicting the extreme wind velocity from wind monitoring data at the site of the Runyang Suspension Bridge (RSB), China using the maximum entropy theory. The maximum entropy theory is a rational approach for choosing the most unbiased probability distribution from a small sample, which is consistent with available data and contains a minimum of spurious information. In this paper, the theory is used for estimating a joint probability density function considering the combined action of wind speed and direction based on statistical analysis of wind monitoring data at the site of the RSB. The joint probability distribution model is further used to estimate the extreme wind velocity at the deck level of the RSB. The results of the analysis reveal that the probability density function of the maximum entropy method achieves a result that fits well with the monitoring data. Hypothesis testing shows that the distributions of the wind velocity data collected during the past three years do not obey the Gumbel distribution. Finally, our comparison shows that the wind predictions of the maximum entropy method are higher than that of the Gumbel distribution, but much lower than the design wind speed.展开更多
This paper proposes a generation dispatch model based on the maximum entropy principle. The objective is to find an optimal generation dispatch strategy that minimizes the generation cost and satisfies the security co...This paper proposes a generation dispatch model based on the maximum entropy principle. The objective is to find an optimal generation dispatch strategy that minimizes the generation cost and satisfies the security constraints of power systems, while taking into account the uncertainty of wind power. Since in many situations, only partial information of the probabilistic variables can be obtained, the maximum entropy principle is introduced to find the most likely realized probability distributions of the power flow, thus providing an accurate probabilistic circumstance to solve the generation dispatch model. The proposed method is tested on the IEEE 39-bus system, and is compared with the methodologies based on Monte Carlo simulation and Gram-Charlier expansions.展开更多
文摘Based on the maximum entropy principle, a probability density function (PDF) is derived for the distribution of wave heights in a random wave field, without any more hypothesis. The present PDF, being a non-Rayleigh form, involves two parameters: the average wave height H— and the state parameter γ. The role of γ in the distribution of wave heights is examined. It is found that γ may be a certain measure of sea state. A least square method for determining γ from measured data is proposed. In virtue of the method, the values of γ are determined for three sea states from the data measured in the East China Sea. The present PDF is compared with the well known Rayleigh PDF of wave height and it is shown that it much better fits the data than the Rayleigh PDF. It is expected that the present PDF would fit some other wave variables, since its derivation is not restricted only to the wave height.
基金supported by National Natural Science Foundation of China(No.51625702,No.51377117,No.51677124)National High-tech R&D Program of China(863Program)(No.2015AA050403)
文摘Distributed generation including wind turbine(WT) and photovoltaic panel increases very fast in recent years around the world, challenging the conventional way of probabilistic load flow(PLF) calculation. Reliable and efficient PLF method is required to take this chage into account.This paper studies the maximum entropy probabilistic density function reconstruction method based on cumulant arithmetic of linearized load flow formulation,and then develops a maximum entropy based PLF(MEPLF) calculation algorithm for power system integrated with wind power generation(WPG). Compared with traditional Gram–Charlier expansion based PLF(GC-PLF)calculation method, the proposed ME-PLF calculation algorithm can obtain more reliable and accurate probabilistic density functions(PDFs) of bus voltages and branch flows in various WT parameter scenarios. It can solve thelimitation of GC-PLF calculation method that mistakenly gains negative values in tail regions of PDFs. Linear dependence between active and reactive power injections of WPG can also be effectively considered by the modified cumulant calculation framework. Accuracy and efficiency of the proposed approach are validated with some test systems. Uncertainties yielded by the wind speed variations, WT locations, power factor fluctuations are considered.
基金Project supported by the National Natural Science Foundation of China (Nos. 50725828 and 50808041)Scientific Research Foundation of Graduate School of Southeast University (No. YBJJ0923)the Teaching and Research Foundation for Excellent Young Teacher of Southeast University,China
文摘This paper presents a distribution free method for predicting the extreme wind velocity from wind monitoring data at the site of the Runyang Suspension Bridge (RSB), China using the maximum entropy theory. The maximum entropy theory is a rational approach for choosing the most unbiased probability distribution from a small sample, which is consistent with available data and contains a minimum of spurious information. In this paper, the theory is used for estimating a joint probability density function considering the combined action of wind speed and direction based on statistical analysis of wind monitoring data at the site of the RSB. The joint probability distribution model is further used to estimate the extreme wind velocity at the deck level of the RSB. The results of the analysis reveal that the probability density function of the maximum entropy method achieves a result that fits well with the monitoring data. Hypothesis testing shows that the distributions of the wind velocity data collected during the past three years do not obey the Gumbel distribution. Finally, our comparison shows that the wind predictions of the maximum entropy method are higher than that of the Gumbel distribution, but much lower than the design wind speed.
文摘This paper proposes a generation dispatch model based on the maximum entropy principle. The objective is to find an optimal generation dispatch strategy that minimizes the generation cost and satisfies the security constraints of power systems, while taking into account the uncertainty of wind power. Since in many situations, only partial information of the probabilistic variables can be obtained, the maximum entropy principle is introduced to find the most likely realized probability distributions of the power flow, thus providing an accurate probabilistic circumstance to solve the generation dispatch model. The proposed method is tested on the IEEE 39-bus system, and is compared with the methodologies based on Monte Carlo simulation and Gram-Charlier expansions.
