As an important indication and manifestation of insulation failures in high-voltage equipment,the type identification of partial discharge（PD） is important for the assessment of the insulation state of electrical **...
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As an important indication and manifestation of insulation failures in high-voltage equipment,the type identification of partial discharge（PD） is important for the assessment of the insulation state of electrical *** order to identify the type of partial discharge source accurately,this paper presents a method for partial discharge source type identification based on principal component analysis（PCA） and support vector machine（SVM） with grey wolf optimization algorithm（GWO）.The PCA method is used to select the three features out of 10 that best represent the original data,namely the phase,E/I and I/*** this basis,the SVM kernel function g and the non-negative penalty factor c are optimized by GWO to establish a support vector machine classification model based on the grey wolf optimization algorithm（GWO-S VM）.The results show that PCA is able to extract the main features well and that GWO-SVM can identify partial discharge source more accurately than the genetic algorithm-based support vector machine（GA-SVM）,multilayer perceptron（MLP） and k-nearest neighbor（kNN） classifier.
greywolf Optimizer is a kind of artificial intelligence optimizationalgorithm. Aiming at the problem of local optimum and low convergence accuracy of GWO, this paper uses reverse learning strategy to generate initia...
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greywolf Optimizer is a kind of artificial intelligence optimizationalgorithm. Aiming at the problem of local optimum and low convergence accuracy of GWO, this paper uses reverse learning strategy to generate initial population to increase population diversity;and uses simulated annealing algorithm's strong ability to jump out of local optimum solution to make up for the shortcoming that GWO is easy to fall into local optimum;finally, the first three individuals of population fitness are mutated to improve the improvement of the algorithm. The speed and accuracy of the algorithm are improved to avoid falling into local optimum. The superiority of the improved algorithm is verified by simulation experiments.
This paper deals with automatic generation control of multi area power system using a Fuzzy PID controller. The controller parameters are optimized by greywolf Optimizer (GWO) algorithm. Initially, Hydro-Thermal-Gas ...
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This paper deals with automatic generation control of multi area power system using a Fuzzy PID controller. The controller parameters are optimized by greywolf Optimizer (GWO) algorithm. Initially, Hydro-Thermal-Gas two area power systems is considered and superiority of the proposed controller is verified by comparing the results with GWO optimized classical PID controller as well as recently published optimal controller, such as DE-PID and TLBO-PID controllers. The proposed methodology is also verified with a modified power system with a nuclear plant and HVDC link and reveals better performance when compared with sliding mode controller tuned by TLBO algorithm. The proposed controller is designed to stabilize the frequency deviations of nonlinear power system considering FACTS devices and SMES. The results reveal that IPFC seems to be a promising alternative for frequency and tie-line power stabilization. Also the proposed controller is robust and satisfactory towards random step and sinusoidal load patterns.
While the economic is rapidly developing, human beings are facing a serious ecological problems, sustainable-development has got attention from more and more countries and has been treated as an economic and social de...
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While the economic is rapidly developing, human beings are facing a serious ecological problems, sustainable-development has got attention from more and more countries and has been treated as an economic and social development strategy. The development and utilization of renewable energy can realize the transformation of regional economic development mode and promote the long-term sustainable development of regional economy. One of the latest concepts that has attracted a lot of attention in the power systems is the energy hub (EH). In this paper, a combined energy system (CES) known as the EH consisting of electrical, cooling and heating equipment along with the demand response programs (DRPs) as well as renewable energy resources (RERs) optimization study have been proposed. Moreover, the uncertainty modeling and electrical scenario creation of cooling, heating, wind speed, solar irradiation and the energy carriers prices including electricity and natural gas is presented. The objective of the optimization problem is maximizing the profit of the EH with the existence of DRPs and RERs under four scenarios which is solved by enhanced greywolfoptimization (GWO) algorithm. In order to avoid such deficiencies and to realize a stabilized relationship between exploration and exploitation, a new modified greywolfoptimization (MGWO) algorithm is proposed. The implementation results show high accuracy and power levels of this method to solve the aforementioned problem under different uncertain parameters and various scenarios. The simulation results of proposed EH profit maximization model shows a reduction in purchased electricity from the main grid and a reduction of the overall operation costs.
The rotating machinery is composed of rolling bearing connection, so the fault identification of rolling bearing is a very critical task. We propose a bearing fault identification algorithm based on greywolf optimize...
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The rotating machinery is composed of rolling bearing connection, so the fault identification of rolling bearing is a very critical task. We propose a bearing fault identification algorithm based on greywolf optimizer (GWO) to address the common problems of high signal noise, inability of a single indicator to accurately reflect the true state of bearings, and optimization of support vector machine (SVM) prediction model parameters in bearing fault identification. First, the wavelet soft threshold is used to remove the noise of the original signal and then the empirical Fourier decomposition (EFD) is used in the decomposition and reconstruct signals. Second, in the aspect of feature extraction, the time and frequency domain features of the bearing data are selected to form the mixed domain features of the bearing signal. Finally, aiming at improving the bearing fault prediction accuracy, the GWO algorithm is used to optimize the parameters. Achievements: the signal-to-noise ratio can be effectively improved to 77.8 by using the wavelet denoising, and the parameter modeling optimized by the GWO algorithm can significantly improve the prediction accuracy, with an increase of about 3%-5%. It provides theoretical support for the optimization of bearing fault identification with this technology in the industrial field.
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