本文選題：疲勞損傷 + 相空間重構(gòu)��； 參考：《華北電力大學》2014年博士論文

【摘要】：隨著電力系統(tǒng)中風電并網(wǎng)比例的增加,風能的隨機波動性對傳統(tǒng)電力系統(tǒng)經(jīng)濟調(diào)度和安全運行帶來挑戰(zhàn)。研究在風電功率預測與電力系統(tǒng)的負荷約束條件下,風電場內(nèi)機組優(yōu)化調(diào)度問題,不僅能減少風力發(fā)電機組的冗余運行和磨損浪費,避免機組的頻繁啟停,還可以降低運行成本,提高風電場輸出功率的電能質(zhì)量,有效減輕風電波動性對電網(wǎng)的影響,從而在保證電力系統(tǒng)安全性的前提下,提高電力系統(tǒng)的消納風電能力和經(jīng)濟效益。 以風電場功率預測數(shù)據(jù)為基礎(chǔ),重點研究了以降低風力發(fā)電機組疲勞載荷損傷相對量和降低集電系統(tǒng)損耗為目標的風電場內(nèi)機組優(yōu)化調(diào)度的算法,完成了以下研究工作。 (1)提出了不同運行工況下風力發(fā)電機組關(guān)鍵部件相對疲勞損傷量的計算方法。根據(jù)華北某風電場的風資源數(shù)據(jù),利用瑞利分布的風速累積分布函數(shù),基于GH-Bladed模擬了1.5MW風力發(fā)電機組的疲勞載荷,利用雨流循環(huán)計數(shù)法,得到風力發(fā)電機組各個部件的疲勞載荷譜,然后根據(jù)仿真計算和Miner法則得到的風力發(fā)電機組關(guān)鍵部件的相對疲勞損傷量,可為風電場內(nèi)機組優(yōu)化運行提供評價準則。 (2)基于相空間重構(gòu)的神經(jīng)網(wǎng)絡風電功率預測算法的應用。風電場內(nèi)機組優(yōu)化調(diào)度是以風力發(fā)電機組的短期和超短期功率預測值為研究基礎(chǔ),根據(jù)混沌-相空間重構(gòu)的原理可知風力發(fā)電機組的風速和風電功率時間序列數(shù)據(jù)具有混沌的屬性的基礎(chǔ)上,將相空間重構(gòu)與神經(jīng)網(wǎng)絡相結(jié)合,建立混沌-Elman、混沌-BP和混沌-Volterra級數(shù)的風電功率預測模型,經(jīng)實例驗證,分析比較得出混沌-Elman模型的預測效果相對較好,能夠提高預測的精度和穩(wěn)定性。 (3)建立以風電場集電系統(tǒng)網(wǎng)損最小為目標的機組優(yōu)化調(diào)度模型。以風電場內(nèi)集電系統(tǒng)網(wǎng)損最小為目標函數(shù),電網(wǎng)調(diào)度要求、風力發(fā)電機組有功輸出的功率上下限、風力發(fā)電機組無功輸出的功率上下限、風力發(fā)電機組端電壓上下限、變壓器變比上下限等為約束條件,建立機組優(yōu)化調(diào)度的數(shù)學模型,分別采用粒子群優(yōu)化算法和粒子群-遺傳優(yōu)化算法進行尋優(yōu)。結(jié)果表明,粒子群-遺傳算法在優(yōu)化效果和運算效率方面均優(yōu)于單一粒子群算法。 (4)建立了以風電場內(nèi)機械損傷量最小為目標的機組組合優(yōu)化模型。基于前述的相對疲勞損傷量模型,建立機組組合模型,合理配置機組啟停方案,以期在調(diào)度期內(nèi)風電場整體機械損傷最小,延長機組運行效率和使用壽命。然后利用改進二進制粒子群優(yōu)化算法(BPSO)、遺傳優(yōu)化算法(GA)、粒子群-遺傳混合優(yōu)化算法(BPSO-GA),進行優(yōu)化求解。結(jié)果表明,BPSO-GA比單一GA和BPSO提高了優(yōu)化性能,運行期間總疲勞損傷量最��；引入粒子群優(yōu)化參數(shù)的BPSO-GA算法的計算時長相對BPSO算法略長,但比GA算法計算時長要短；三種模型的計算時長從大到小依次為：GA, BPSO-GA, BPSO。 (5)建立了基于機組優(yōu)先級分類的風電場內(nèi)功率分配模型。以風力發(fā)電機組發(fā)電功率、風速平均值和均方根差值作為特征值,分析機組發(fā)電性能,并分別采用SOFM神經(jīng)網(wǎng)絡算法與基于模擬退火遺傳算法的模糊C均值聚類算法建立機組優(yōu)先級分類模型。將發(fā)電性能較好的一類作為優(yōu)先執(zhí)行發(fā)電計劃的機組,計及線路損耗后的發(fā)電計劃,對風電場內(nèi)其余機組進行兩層優(yōu)化,外層是以風力發(fā)電機組相對疲勞損傷量最小為目標的機組優(yōu)化出力,內(nèi)層是確定機組間負荷滿足電網(wǎng)調(diào)度要求的最優(yōu)功率分配。通過兩層分配,得到既滿足電網(wǎng)調(diào)度需求,又降低風電場運行損耗的功率分配結(jié)果。結(jié)果表明,基于遺傳模擬退火算法的模糊聚類算法分類方法的疲勞損傷量比自組織特征映射神經(jīng)網(wǎng)絡分類方法的疲勞損傷量較小,SAGA-FCM分類方法下停機的機組臺數(shù)較多。風力發(fā)電機組分類后優(yōu)化調(diào)度,能夠使風電場機組運行優(yōu)化,提高風電場輸出電能質(zhì)量。
[Abstract]:With the increase of the proportion of apoplexy in the power system, the stochastic volatility of wind energy brings challenges to the economic dispatch and safe operation of the traditional power system. Under the condition of wind power forecasting and the load constraint of the power system, the optimal scheduling problem of the unit in the wind farm can not only reduce the redundant operation and the wear wave of the wind turbine. To avoid the frequent start and stop of the unit, it can also reduce the operating cost, improve the power quality of the output power of the wind farm, reduce the influence of the light wind wave on the power grid, and improve the power system's ability to eliminate wind and electricity and the economic benefit on the premise of ensuring the safety of the power system.
Based on the prediction data of wind power, this paper focuses on the optimization scheduling algorithm of wind farms in wind farms aiming at reducing the relative amount of fatigue load damage and reducing the loss of the collecting system. The following research work has been completed.
(1) the calculation method of the relative fatigue damage of the key components of the wind turbine under different operating conditions is proposed. According to the wind resource data of a wind farm in North China, the cumulative distribution function of the wind velocity distribution is used to simulate the fatigue load of the 1.5MW wind turbine based on GH-Bladed, and the wind power generation is obtained by the rain flow cycle counting method. The fatigue load spectrum of each component of the unit, and the relative fatigue damage of the key components of the wind turbine based on the simulation calculation and the Miner rule, can provide the evaluation criteria for the optimal operation of the unit in the wind farm.
(2) the application of the neural network wind power prediction algorithm based on phase space reconstruction. The optimization scheduling in the wind farm is based on the short-term and ultra short term power forecast of the wind turbine. According to the principle of the chaotic phase space reconstruction, the wind speed and the wind power time series data are chaotic. On the basis of the attribute, the phase space reconstruction and the neural network are combined to establish the wind power prediction model of chaotic -Elman, chaotic -BP and chaotic -Volterra series. The results of the analysis and comparison show that the prediction effect of the chaotic -Elman model is relatively good, and can improve the accuracy and stability of the prediction.
(3) set up an optimal scheduling model for the minimum loss of the wind electric field collection system, with the minimum loss of the net loss in the wind farm as the objective function, the requirements of the power grid dispatching, the power upper and lower limits of the active output of the wind turbine, the power upper and lower limits of the reactive output of the wind turbine, the upper and lower limit of the end voltage of the wind generator set and the variable pressure The mathematical model of the optimal scheduling of the unit is established, and the particle swarm optimization algorithm and particle swarm optimization algorithm are used to optimize the optimal scheduling of the unit. The results show that the particle swarm optimization algorithm is superior to the single particle swarm optimization in the optimization effect and the operation efficiency.
(4) set up a unit combination optimization model which aims at the minimum amount of mechanical damage in the wind farm. Based on the relative fatigue damage quantity model mentioned above, set up the unit combination model and rationally configure the unit start and stop scheme, in order to minimize the overall mechanical damage of the wind farm in the scheduling period, and the operation efficiency and service life of the long unit, and then use the improvement to improve the operation efficiency and service life of the long unit. Binary particle swarm optimization (BPSO), genetic optimization algorithm (GA), particle swarm genetic hybrid optimization algorithm (BPSO-GA) are used to optimize the solution. The results show that BPSO-GA is better than single GA and BPSO to improve the performance, and the total fatigue damage is minimal during operation, and the phase to BPSO calculation when the BPSO-GA algorithm is introduced into the particle swarm optimization parameters The length of the three models is shorter than that of the GA algorithm. The computation time of the three models is from GA to BPSO-GA, BPSO..
(5) the power distribution model in wind farm based on unit priority classification is established. The power generation power of the wind turbine, the average wind speed and the mean square root difference are used as the eigenvalues to analyze the generating performance of the unit, and the SOFM neural network algorithm and the fuzzy C mean clustering algorithm based on simulated annealing genetic algorithm are used to establish the unit priority. A class of class classification model, which takes a generating unit with better power generation performance as a priority to execute the power generation plan, takes into account the power generation plan after the line loss, and optimizes the two layers of the rest of the wind farm. The outer layer is the optimal output of the unit which aims at minimizing the relative fatigue damage of the wind turbine, and the inner layer is to determine the inter unit load to satisfy the power grid. The optimal power allocation required by the scheduling is obtained by two layers of distribution to obtain the power allocation results that meet both the demand of the power grid scheduling and the loss of the wind farm operation. The results show that the fatigue damage amount of the fuzzy clustering algorithm based on the genetic simulated annealing algorithm is better than the fatigue damage amount of the self organizing feature mapping neural network classification method. There are a lot of units in the SAGA-FCM classification method. The optimal scheduling of wind turbines can optimize the operation of the wind farm unit and improve the output power quality of the wind farm.

【學位授予單位】：華北電力大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TM614

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