The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm

Yang, Zhe; Yang, Kun; Su, Lyuwen; Hu, Hu

doi:10.1007/s11269-019-02435-0

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…The optimized parameters are set as follows: the number of neurons in the input layer is 2; the number of neurons in the output layer is 1; the internal parameters of the LSTM unit are trained by Adam's algorithm; and the initialization method is Xavier. The parameters of the improved particle swarm algorithm are set as follows: the number of populations is 5; the number of evolution is 20; the learning factor of the particles is taken as c 1 = c 2 = 2, any particle X i,0 (n 1 , n 2 , ε, h) for each dimension parameter in the range of [1,100], [1,100], [0.0001, 0.01] and [300, 600], and the velocity of the particle in the range of [−5, 5], [−5, 5], [−0.0005, 0.0005] and [−10, 10]. In IPSO, w max = 0.9, w min = 0.1; In PSO, w = 0.5.…”

Section: Parametric Optimizationmentioning

confidence: 99%

“…In-plant economic operation of hydropower plants is an important way to achieve reasonable load distribution and efficient economic operation of hydropower plant units. In the process of unit load allocation, the solution algorithm will obtain the relationship between hydraulic head, flow and output at the corresponding moment according to the flow characteristic curve of the unit, and then carry out the load allocation among units [1,2]. Therefore, the accuracy of the unit flow characteristic curve fitting has an important impact on the load distribution of hydropower plant units and the economic operation of the plant.…”

Section: Introductionmentioning

confidence: 99%

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Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines

et al. 2022

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One of the most important characteristic curves in the economic operation of hydropower plants is the turbine flow characteristic curve, which illustrates the law of fluctuation between the characteristic parameters of the turbine under various operating situations. This article proposes an IPSO-LSTM-based refinement method for fitting the turbine flow characteristic curve using deep learning methods, and verifies the effectiveness of the method by comparison to solve the problem that traditional mathematical fitting methods are difficult to meet the requirements of today’s many complex working conditions. Firstly, a deep LSTM network model is established based on the input and output quantities, and then the IPSO method is used to find the optimum number of neurons, the learning rate and the maximum number of iterations of the LSTM units in the network model and other key parameters to determine the relevant training parameters. The results show that the model can effectively improve the accuracy of fitting and predicting the turbine flow characteristics, which is of great significance to the study of the economic operation of hydropower plants and the non-linear characteristics of the turbine.

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Section: Parametric Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines

et al. 2022

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Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm

Yang

2021

Water Resour Manage

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An improved binary-coded whale optimization algorithm (IBWOA) is proposed to solve the complex nonlinear problem of short-term hydropower generation scheduling (STHGS). The spatial optimal load distribution is combined with the temporal unit commitment combination model, and the binary array is used to represent the start/stop state of the unit. Sigmoid Function (SF) is used to solve the correspondence between binary array and real number. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. The unit commitment (UC) subproblem was solved by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint, and the economic load scheduling (ELD) subproblem was solved by an optimal stable load distribution table (OSLDT). The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. The proposal is applied to solve the STHGS of the Three Gorges hydropower station. The results show that the method has good convergence, stability, fast calculation speed, and high optimization accuracy.

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The stochastic short-term hydropower generation scheduling considering uncertainty in load output forecasts

Yang

Wang

Yang

2022

Energy

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The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm

Cited by 9 publications

References 21 publications

Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines

Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines

Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm

The stochastic short-term hydropower generation scheduling considering uncertainty in load output forecasts

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