ABSTRACT The goal of this paper is to coordinate directional overcurrent relays using the Evolutionary Particle Swarm Optimization (EPSO) Algorithm. EPSO Algorithm has gained a lot of interest for its simplicity, robustness and easy... more
ABSTRACT The goal of this paper is to coordinate directional overcurrent relays using the Evolutionary Particle Swarm Optimization (EPSO) Algorithm. EPSO Algorithm has gained a lot of interest for its simplicity, robustness and easy implementation. Coordinate directional overcurrent relays on a meshed network deals with a large volume of data, with many calculations and constraints. So that, this work shows the viability of how EPSO algorithm can solve a non-linear coordination problem.
A tuning process of the PI (proportional-integral) controller gains of a doubly-fed induction generator's (DFIG) rotor side converter is described in this work. The purpose is to tune PI controllers to help the DFIG to survive to... more
A tuning process of the PI (proportional-integral) controller gains of a doubly-fed induction generator's (DFIG) rotor side converter is described in this work. The purpose is to tune PI controllers to help the DFIG to survive to network faults, avoiding being tripped-off by under-voltage relays. The ride-through-fault capability of DFIGs improves system reliability and allows them to participate in the
This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices... more
This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a
ABSTRACT In this paper, we analyze how demand dispatch combined with the use of probabilistic wind power forecasting can help accommodate large shares of wind power in electricity market operations. We model the operation of day-ahead and... more
ABSTRACT In this paper, we analyze how demand dispatch combined with the use of probabilistic wind power forecasting can help accommodate large shares of wind power in electricity market operations. We model the operation of day-ahead and real-time electricity markets, which the system operator clears by centralized unit commitment and economic dispatch. We use probabilistic wind power forecasting to estimate dynamic operating reserve requirements, based on the level of uncertainty in the forecast. At the same time, we represent price responsive demand as a dispatchable resource, which adds flexibility in the system operation. In a case study of the power system in Illinois, we find that both demand dispatch and probabilistic wind power forecasting can contribute to efficient operation of electricity markets with large shares of wind power.
ABSTRACT This paper analyzes the application of clustering techniques for wind power scenario reduction. The results have shown the unimodal structure of the scenario generated under a Monte Carlo process. The unimodal structure has been... more
ABSTRACT This paper analyzes the application of clustering techniques for wind power scenario reduction. The results have shown the unimodal structure of the scenario generated under a Monte Carlo process. The unimodal structure has been confirmed by the modes found by the information theoretic learning mean shift algorithm. The paper also presents a new technique able to represent the wind power forecasting uncertainty by a set of representative scenarios capable of characterizing the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative scenarios associated with a probability of occurrence can be created finding the areas of high probability density. This will allow the reduction of the computational burden in stochastic models that require scenario representation.
The inevitable wind power forecast errors result in differences between forecasted and observed wind power. To mitigate their economic impact, combining the wind power with pumped hydro energy storage may be used. In order to deliver a... more
The inevitable wind power forecast errors result in differences between forecasted and observed wind power. To mitigate their economic impact, combining the wind power with pumped hydro energy storage may be used. In order to deliver a joint operational strategy for a wind power plant combined with storage, one requires reliable wind power forecasts. The forecasts commonly only consist of
This paper reports new contributions to the advancement of wind power uncertainty forecasting be-yond the current state-of-the-art. A new kernel density forecast (KDF) method applied to the wind power problem is described. The method is... more
This paper reports new contributions to the advancement of wind power uncertainty forecasting be-yond the current state-of-the-art. A new kernel density forecast (KDF) method applied to the wind power problem is described. The method is based on the Nadaraya-Watson estimator, and a time-adaptive version of the algorithm is also proposed. Results are presented for different case-studies and compared with linear and splines quantile regression.
Page 1. AbstractThe impact of wind power forecasting on unit commitment and dispatch is investigated in this paper. We present two unit commitment methods to address the variability and intermittency of wind power. The ...
This paper presents a method used to validate a spatial load forecasting model based on fuzzy systems implemented in a Geographical Information System. The validation process confirms the adequacy of the rule base, and also it is strictly... more
This paper presents a method used to validate a spatial load forecasting model based on fuzzy systems implemented in a Geographical Information System. The validation process confirms the adequacy of the rule base, and also it is strictly necessary to define the confidence ...
Page 1. IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 20, NO. 4, OCTOBER 2005 2509 Improving the IEC Table for Transformer Failure Diagnosis With Knowledge Extraction From Neural Networks Vladimiro Miranda ...
ABSTRACT This paper presents a new algorithm to estimate the optimal importance sampling (IS) probability distribution in generating capacity reliability (GCR) problems. The proposed approach results from a combination of the... more
ABSTRACT This paper presents a new algorithm to estimate the optimal importance sampling (IS) probability distribution in generating capacity reliability (GCR) problems. The proposed approach results from a combination of the cross-entropy (CE) concepts with the standard analytical GCR assessment. A mathematical analysis of the CE equations is carried out to demonstrate that the optimal change of measure or distortion can be obtained by simply dividing the annualized GCR indices for two different configurations of the generating system. Under these hypotheses, a straightforward algorithm based on fast Fourier transform is proposed to systematically obtain the optimal distorted unavailabilities for all generating units in the system. The accuracy and computational performance of the proposed approach are compared with the standard CE optimization process using different generating systems. The IEEE-RTS 79, IEEE-RTS 96, and two configurations of the Brazilian South-Southeastern system are all used for this purpose.
... REFERENCES D. E. Goldberg, “Genetic algorithms in Search, Optimization and Machine Learning” (book), 1989 Addison-Wesley V. Miranda, JV Ranito, LM Proenqa, “Genetic Algorithms in Optimal Multistage Distribution Network Planning”,... more
... REFERENCES D. E. Goldberg, “Genetic algorithms in Search, Optimization and Machine Learning” (book), 1989 Addison-Wesley V. Miranda, JV Ranito, LM Proenqa, “Genetic Algorithms in Optimal Multistage Distribution Network Planning”, IEEERES 1994 Winter Meeting ...
AbstractThis paper reports new results in adopting entropy concepts to the training of neural networks to perform wind power prediction as a function of wind characteristics (speed and direc-tion) in wind parks connected to a power grid.... more
AbstractThis paper reports new results in adopting entropy concepts to the training of neural networks to perform wind power prediction as a function of wind characteristics (speed and direc-tion) in wind parks connected to a power grid. Renyi's entropy is combined with a ...
