Optimization Control Strategy for Large Doubly-Fed Induction Generator Wind Farm Based on Grouped Wind Turbine

Zhou, Shijia; Rong, Fei; Ning, Xiaojie

doi:10.3390/en14164848

Cited by 5 publications

(3 citation statements)

References 24 publications

(26 reference statements)

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“…Therefore, PSO have been widely used in recent years to solve problems in energy engineering, especially issues with high timeliness requirements. Zhou et al [20] optimized control strategy for large doubly fed induction generator wind farm by PSO. Zahedi Vahid M et al [21] optimized the dispatching scheme of the distributed power generation resources by PSO.…”

Section: Optimization Methodsmentioning

confidence: 99%

Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems

Guo¹,

Zheng²,

Cong³

et al. 2021

Energies

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China aims to peak carbon emissions by 2030. As a result, small-scale coal-fired combined heat and power (CHP) units and self-provided units are gradually shut down, and large-scale coal-fired CHP units are a solution to undertake the industrial heat loads. From the perspective of the industrial heat load allocation during the non-heating season, the problems regarding the coal-saving scheduling strategy of coal-fired CHP units are addressed. The steam-water equations of CHP units are established to analyze the heat-power coupling characteristics. The energy utilization efficiency, exergy efficiency and the coal consumption are analyzed. The optimization model of saving coal consumption is established and the adaptive mutation particle swarm optimization (AMPSO) is introduced to solve the above model. The 330 MW coal-fired CHP unit is taken as an example, and the results show that for the constant main flow rate, each increase of 1 t/h industrial steam extraction will reduce the power output by about 0.321 MW. The energy utilization efficiency and the exergy are mainly influenced by industrial steam supply and the power load, respectively. For the CHP system with two parallel CHP units, the unequal allocation of industrial heat load between two units saves more coal than equal allocation. The coal consumption can be reduced when the unit with lower power load undertakes more industrial heat load. In the typical day, the total coal consumption after optimization is 3203.92 tons, a decrease of 14.66 tons compared to the optimization before. The two CHP units in the case can benefit about 5,612,700 CHY extra in one year.

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Section: Optimization Methodsmentioning

confidence: 99%

Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems

Guo¹,

Zheng²,

Cong³

et al. 2021

Energies

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“…To fully leverage the reactive power regulation capacity brought about by the integration of wind farms into the grid, considerations were also made for the principles of reactive power distribution among the turbines within the wind farm, as well as the reactive power optimization regulation between the stator of each wind turbine generator and the grid-side converter; ref. [3] engages wind farms with DFIGs connected to the network as continuous reactive power sources to participate in reactive power optimization, serving a reactive support role. It established a reactive optimization model with the objective function of minimizing the sum of active power network losses and node voltage deviations, taking into account safety indicators, transforming the reactive power optimization problem of the distribution network into a multi-constrained nonlinear mathematical problem, and solving the established model using the Particle Swarm Optimization (PSO) algorithm; ref.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

Xu,

Wang,

Chen

et al. 2024

Processes

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As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant–grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG reactive power pricing model. The objective functions aimed to minimize the cost of reactive power purchased by generators, the price of active power network losses, the total deviation of node voltages, and the depreciation costs of discrete variable actions, thereby establishing a dynamic reactive power optimization model for power systems including doubly-fed wind farms. By introducing Logistic chaotic mapping, the CSA is improved by using the highly stochastic characteristics of chaotic systems, which is known as the Chaotic Cuckooing Algorithm. Meanwhile, the basic cuckoo search algorithm was improved in terms of adaptive adjustment strategies and global convergence guidance strategies, resulting in an enhanced cuckoo search algorithm to solve the established dynamic reactive power optimization model, improving global search capability and convergence speed. Finally, using the IEEE 30-bus system as an example and applying the improved chaotic cuckoo search algorithm for solution, simulation results show that the proposed reactive power optimization model and method can reduce reactive power costs and the number of discrete device actions, demonstrating effectiveness and adaptability. When the improved chaotic cuckoo algorithm is applied to optimize the objective function, the optimization result is better than 7.26% compared to the standard cuckoo search algorithm, and it is also improved compared to both the PSO algorithm and the GWO algorithm.

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“…The efficiency improvement techniques can be divided into two categories; one is the model-based, and another model is the online power search optimization technique. In the model-based scheme, the loss is expressed in machine parameters depending on load conditions [11,12]. Fast convergence for variable wind speeds is the major advantage of this method.…”

Section: Introductionmentioning

confidence: 99%

Optimal Efficiency Control of Wind Mill Induction Generator Using Loss Minimization Technique

Faizal,

Rajam

2023

J. Sci. Res.

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Improvement of the operating efficiency of generators used in large wind power generation systems becomes important since such a large machine's power loss is not negligible. In the conventional method integral controller is used to reduce the power loss by up to 25 % at low wind speeds. The proposed PID controller method is based on flux level reduction, where the flux level is computed from the machine model for the optimum d-axis current of the generator. For the vector-controlled induction generator, the d-axis current controls the excitation level to minimize generator loss. In contrast, the q-axis current controls the generator torque, by which the speed of the induction generator is controlled according to the variation of the wind speed in order to produce the maximum output power, thereby improve the efficiency of the induction generator.

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Optimization Control Strategy for Large Doubly-Fed Induction Generator Wind Farm Based on Grouped Wind Turbine

Cited by 5 publications

References 24 publications

Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems

Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

Optimal Efficiency Control of Wind Mill Induction Generator Using Loss Minimization Technique

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