Abstract:Based on a Double Deep-Q Network with deep ResNet (DDQN-ResNet), this paper proposes a novel method for transmission network expansion planning (TNEP). Since TNEP is a large scale and mixedinteger linear programming (MILP) problem, as the transmission network scale and the optimal constraints increase, the numerical calculation and heuristic learning-based methods suffer from heavy computational complexities in calculation and training. Besides, due to the black box characteristic, the solution processes of th… Show more
“…The planning and operation modeling in the power grids with maximizing reliability is studied in [13]. The article in [14] presents an economic approach to power flow analysis, taking into account factors such as fuel costs in power plants and the operation of units during peak demand. Meanwhile, [15] models power flow with a focus on the cost of transmission lines and incorporates the use of Flexible AC Transmission Systems (FACTS) to enhance voltage index.…”
The proliferation of renewable energy sources, particularly wind farms, is rapidly gaining momentum owing to their numerous benefits. Consequently, it is imperative to account for the impact of wind farms on transmission expansion planning (TEP), which is a crucial aspect of power system planning. This article presents a multi-objective optimization model that utilizes DC load flow to address the TEP challenge while also incorporating wind farm uncertainties into the model. The present study aims to optimize the expansion and planning of the TEP in the power system by considering investment and maintenance costs as objective functions. To achieve this, a multiobjective approach utilizing the shuffled frog leaping algorithm (SFLA) is proposed and implemented. The proposed objectives are simulated on the RTS-IEEE 24-bus test network. The results obtained from the proposed algorithm are compared with those of the Genetic Algorithm (GA) to assess and validate the proposed approach.
“…The planning and operation modeling in the power grids with maximizing reliability is studied in [13]. The article in [14] presents an economic approach to power flow analysis, taking into account factors such as fuel costs in power plants and the operation of units during peak demand. Meanwhile, [15] models power flow with a focus on the cost of transmission lines and incorporates the use of Flexible AC Transmission Systems (FACTS) to enhance voltage index.…”
The proliferation of renewable energy sources, particularly wind farms, is rapidly gaining momentum owing to their numerous benefits. Consequently, it is imperative to account for the impact of wind farms on transmission expansion planning (TEP), which is a crucial aspect of power system planning. This article presents a multi-objective optimization model that utilizes DC load flow to address the TEP challenge while also incorporating wind farm uncertainties into the model. The present study aims to optimize the expansion and planning of the TEP in the power system by considering investment and maintenance costs as objective functions. To achieve this, a multiobjective approach utilizing the shuffled frog leaping algorithm (SFLA) is proposed and implemented. The proposed objectives are simulated on the RTS-IEEE 24-bus test network. The results obtained from the proposed algorithm are compared with those of the Genetic Algorithm (GA) to assess and validate the proposed approach.
“…e new residual module Swish Gated Block proposed in this paper improves the residual module in ResNet [12][13][14]. e Swish Gated Block is composed of Swish module and residual, Swish module contains a convolutional layer and Swish activation function.…”
Section: Swish Module and Loss Functionmentioning
confidence: 99%
“…Scientific Programming [13]. e perceptual loss is calculated based on the feature map and expressed as follows:…”
An automatic intelligent coloring model of animation sketch based on enhanced deep learning is proposed. In the proposed model, generative adversarial networks (GANS) are adopted. The U-net network based on the Swish function residual enhancement is used in the generative model, and the ResNet network is used in the discriminant model. The U-net embedded with the Swish Gate module is adopted to transmit feature map information. The perceptual network on the discriminator is used to perceive the perceptual features of the generated image and the actual image and calculate the perceptual loss. Experiment results show that perceptual loss can better capture the difference between black-and-white images and color images, so as to better train the network end-to-end. After comparative analysis, it can be concluded that compared with the existing methods, the proposed model has greater advantages in processing animation sketches. The color images it generates have higher visual quality and richer color diversity and matching.
“…The premise of directional seeding is directional seeding. In order to achieve directional seeding, scholars oriented seeds by seed belt [5], adsorption [6], vibration [7], image recognition and mechanism combination [8], but there are certain requirements on the working conditions in the process of seed orientation. In this paper, based on the research of corn seed mechanized directional seeding device, a directional seeding device based on centrifugal filling, differential directional deflection and diversion ring seeding is proposed, which provides a new thinking for the design of corn seed directional seeding device.…”
Directional seeding of maize seeds is the premise of directional seeding of maize. In order to improve the directional seeding performance of maize seeds, a directional seeding device of maize seeds based on centrifugal seed filling and differential directional principle is proposed. In this paper, the principle of centrifugal filling and differential speed orientation is expounded. The EDEM discrete element simulation software is used to complete the single factor simulation test of the influence of the diameter of the conical disc, the rotation speed of the conical disc, the difference between the conical disc and the conical disc, and the cone angle on the directional seeding performance. The test results are consistent with the principle.
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