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This paper presents a new fast method to detect the long term voltage instability of power system based on wide area measurement system (WAMS). The objective of this paper is to quickly assess the long term voltage instability, which may be occurred by load disturbance, using the measurements gathered from phasor measurement units (PMUs). The main features of this method is the preprocessing of the...
Power system static security assessment is one of the most important problems which relate power system secure-stable performance. Static security can be rapidly assessed using the artificial intelligence technology. This paper compares the advantages and disadvantages of Artificial Neural Network (ANN) and Support Vector Machines (SVM) and then selects the SVM algorithm. A new multi-classification...
Accurate short-term wind speed forecasting is very important to improve the security and stability of power grid and to reduce the running cost. In this paper, a method based on Least squares support vector machine (LS-SVM) was proposed to the short-term forecasting. In order to avoid the blindness and inaccuracy of Parameter selection, Genetic algorithm is used to select the optimal regularization...
High carbon content in fly ash seriously affects boiler's economical operation. Due to supercritical boiler's high thermal capacity, the inertia of thermal parameter reflecting combustion is bigger. It is especially important to well control carbon content in fly ash during operation. In this article, based on the analyzing influence factors to the carbon content in Fly Ash, the Least Square Support...
The intelligent, robust and fast multi-class classification of power system disturbances is very important to improve control algorithms for ensuring power system security and reliability, an essential function for smart grid infrastructure. Moreover, in a future power system mostly consisting of distributed generators and renewable energy resources on which the disturbance has more impact, the analysis...
A new methodology base on Least Squares Support Vector Machine (LS-SVM) for the electric power system monthly load forecasting is presented. The presented algorithm embodies the the structural risk minimization(SRM) principle is more generalized performance and accurate as compared to artificial neural network. In the time series the trend component and periodical component are considered to make...
Approached a method to identify power quality disturbance (PQD) type based on support vector machine(SVM) and improved wavelet energy distribution. Firstly, using wavelet transform to analyze PQD signals, extracting disturbance lasting time and energy differences of each level between PQD signal and standard signal as feature vectors, forming the training samples and testing samples. Secondly, pre-process...
A new method to estimate the battery state of charge (SOC) in electric vehicles (EV) based on support vector machine is presented. The key of the proposed method is to establish the relationship of the SOC to the battery current, voltage and temperature by using weighted least squares support vector machine (WLS-SVM). With the goal of achieving the optimal robust estimation of the SOC, the extended...
Most utility companies in developing countries are subjected to major financial losses because of non-technical losses (NTL). It is very difficult to detect and control potential causes of NTL in developing countries due to the poor infrastructure. Electricity theft and billing irregularities form the main portion of NTL. These losses affect quality of supply, electrical load on the generating station...
Fast wavelet transformation can decrease the noise and the correlation among the monthly power load information. A new machine learning method-least square support vector machine (LS-SVM), based on the fast wavelet transformation (WT), was used to build the model to forecast monthly power load. Definition and application of the fast WT and the LS-SVM were introduced. The sym4 wavelet basis was selected...
Grey theory is applied to predict the temperature of the superheated steam. Through modeling with SIS data, the future superheated steam temperature. By calculating the auxiliary volume and the measured parameters associated with B-Mode relational degree, selected to meet the requirements of the auxiliary variables. The overheating the system model based on LS-SVM has a accurate simulation results,...
In this paper, a regression technique as the support vector machines (SVM) configured using an optimization technique as the Chu Beasley Genetic Algorithm (CBGA) is proposed to develop a fault location method. As result, a strategy is proposed to relate a set of descriptors obtained from single end measurements of voltage and current (input), to the fault location (output), in a classical regression...
The fault location method proposed in this paper uses a classification technique as the support vector machines (SVM), and an intelligent search based on variable neighborhood techniques to select the configuration parameters of the SVM. As result, a strategy is proposed to relate a set of descriptor obtained from single end measurements of voltage and current (input) to the faulted zone (output),...
This paper proposes a new hybrid technique, Continuous Genetic Algorithm and Least Squares Support Vector Machine to allocate the real power transfer from generators to loads, namely CGA-LSSVM. CGA is used to obtain the optimal value of hyper-parameters of LS-SVM and supervised learning approach is adopted in the training of LS-SVM model. The technique that uses proportional sharing principle (PSP)...
The prediction model of indoor thermal comfort PMV index based on least squares support vector machine (LS-SVM) is established by using the nonlinear relationship between human thermal comfort and its influencing factors and the characteristic that particle swarm has of fast global optimization. Adopting the parameters of least squares support vector machine optimized by Particle Swarm algorithm,...
Seawater flue gas Desulfurization (SFGD) was adopted in many coal-fired power plants of littoral for its low cost and high desulfurization efficiency. Operating Parameters would seriously affect SFGD efficiency, the desulfurization efficiency can be improved by adjusting reasonable parameters. this paper applied Least Square Support Machine (LSSVM) to build the studying model of seawater desulfurization...
In this paper, we propose a method based on support vector machine (SVM) and mathematical morphology to detect cladding ice on the transmission line in surveillance image. Firstly, we construct a sample set for SVM training process and obtain the classification model. Secondly, the original image pixels are classified into two categories by using of the SVM classification model, based on which we...
This study examines the price estimation capability of MAIS (Multi-Agent Intelligent Simulator) when two types of agents with different learning capabilities coexist in a power trading market. This study identifies that the proposed MAIS, considering the coexistence of different types of agents, can improve its estimation accuracy of wholesale electricity price. This study also reexamines the estimation...
This paper introduces the Hilbert-Huang transform method which is composed of Empirical Mode Decomposition (EMD) and Hilbert Transform. Seven kinds of common power system over-voltages are analyzed by HHT, results show that the instantaneous amplitude spectrum, Hilbert marginal spectrum, Hilbert time-frequency spectrum can be used as characteristic parameters for different types of over-voltage classification...
As a renewable and clean energy source, wind power is being widely utilized all over the world. The uncertainty of wind speed, however, makes certain trouble for the development of wind power generation. In order to relieve the disadvantageous impact of wind speed intermittence on the connected power system, the wind power forecasting needs to be carried out. In this paper, a wind speed and power...
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