intelligent wind farm technologies - medpower...

Intelligent Wind Farm TechnologiesDr. Liu Yongqian

Professor, School of Renewable EnergyState Key Laboratory of Alternate Electrical Power System

with Renewable Energy SourcesNorth China Electric Power University, Beijing, China

Email: [email protected]

mailto:[email protected]

North China Electric Power University (NCEPU)

Key university that featured by energy and electricity in ChinaMOE（Ministry of Education） and top 16 electric companies in China are council membersEstablished in 195830,000 students, 2 campuses

• Beijing (Headquarter) • Baoding city of Hebei Province ( about 150 km from Beijing)

School of Renewable Energy, NCEPU 1st School of Renewable Energy in China（2007），1st undergraduate program on

Wind Energy and Power Engineering (2006). 1463 students, 100 teachers, 26 professors, 29 associate professors.

7 Research Centers

Solar Energy Research and Engineering CenterWind Power Research Center

Hydroelectric Energy & Engineering Research CenterNew Energy Materials and Photoelectric Technology Center

Biomass Energy Research CenterNew Energy Resources and Urban Environment Research Center

Hydropower Resettlement Research Center

Renewable Energy Science and EngineeringNew Energy Materials and Devices

Hydrology and Water ResourcesHydraulic and Hydro-Power Engineering

4 Undergraduate Majors

Outline

Part 1 Wind Power in China

Part 2 Intelligent wind farm technologies

Part 3 Our research

Part 4 Conclusions

Part 1: Wind power in China

Coal62.24%

hydro19.20%

wind9.21%

solar 7.33%

nuclear2.02%

Coal71.79%hydro

18.32%

wind4.54%

Solar1.49%

nuclear3.82%

Installed capacity mix in China, Dec 2017

Electric energy mix in China in 2017

Electric energy in China in 2017: 64,951 TWh 2, 950 TWh from wind, accounting for 4.5% ,

increased by 24.4%, the third largest electric power sources in China.

New installed capacity in 2017

Cumulative capacity Dec 2017 Increased by

Offshore wind power 1, 160 MW 2, 790 MW 97%

Wind turbine Export 641 MW 3, 205 MW 21%

Wind power in China

Wind power in China

China

AmericaGermanyIndia

SpainEngland

FranceBrazilCanada

Italy

Rest of the world

Top 10 cumulative capacity Dec 2017

China

AmericaGermany

IndiaSpain

England

FranceBrazilCanada Italy Rest of

the world

Top 10 newly installed capacity Jan-Dec 2017

Energy revolution to clean and low-carbon power system

Newly installed capacity in 2017 Cumulative capacity Dec 2017 Increase by

World 52.6GW 539.6GW 10.8%China 19.5GW 188.2GW 11.6%

Proportion (China/world) 37.1% 34.9%

Climate changeEnvironment pollutionFossil fuel resources

Drivers for wind

Challenge and OpportunityNo subsidies after 2020

Part 2: Intelligent wind farm technologies

Site-specific wind farm design

Customized wind turbine design

Precise wind turbine control

Integrated wind farm control

Optimized operational and maintenance strategy

Grid friendly support service control

Data-driven management of wind farm

2 Intelligent wind farm technologies2.1 Why intelligent wind farm technologies are prevailing?

Intelligent sensing device （smart sensors）

Advanced communication （IPv6 based AMI ）

AI algorithms（Deep Learning）

Robotics

1. Decrease the Levelized Cost of Energy (LCOE) of wind power

Increase the wind farm production

Decrease the maintenance costs

Improve the grid support ability

2. IT and other enabling technologies Intelligent control

Big Data

Advanced visualization（VR, AR）

Computing power （GPU,TPU, Cloud Computing ）

LCOE of wind energy

2 Intelligent wind farm technologies2.2 What is intelligence?

We consider the constrained and well-defined AI which could perceives its

environment and takes actions to maximize its chances of success.

We not consider the General intelligence or strong AI . It is a long-term

goal of AI research.

Definition by AI researches[1,2] ：

interact with the environment

achieve a particular goal

adapt to different goals and environments

Intelligent Wind Farm

Functions？

goals？

low-level ability High-level ability ?

[1] Russell Stuart J and P. Norvig(2002). Artificial Intelligence: A Modern Approach. [2] Nilsson, Nils (2009). The Quest for Artificial Intelligence: A History of Ideas and Achievements

Environments？

2 Intelligent wind farm technologies2.3 What is intelligent wind farm?

Definition：

Through intelligent Control-maintenance-

management platform, intelligent wind

farm can continuously improves itself to

maximize wind power generation,

minimize O&M costs and meet

requirements of the power system.Functions

goals

Environments Atmosphere (onshore and offshore)

Power system

Maximize the wind power generation

Minimize the O&M costs

Efficiently meet the power system requirements

Wind turbine generation systems

Sensing and communication

Accurate prediction and diagnoses

Integrated control-maintenance-management system

…

2 Intelligent wind farm technologies2.4 Philosophy of Intelligent wind farm

Life cycle Optimization

Integration of control, maintenance and management

Interaction and fusion of multi-source informationComprehensive sensing and monitoring of atmosphere information, wind farm information and power

system information, etc.

Real-time informational interaction between different devices, systems, and platforms.

Deep data fusion for multi-source unstructured data by big data analysis and machine learning techniques.

Wind farm design Operation and maintenance Retrofitting Decommissioning

Unified management of wind farm energy flow and information flowCoordinated organization of equipment, data and personnel in a wind farm

2 Intelligent wind farm technologies2.5 Characteristics of Intelligent wind farm

EvolutionAdaption

IntegrationDiagnosis

Self-learn form the history data

Optimization by humans

Responding to atmospheric environments

Responding to the power systems

Diagnosis and Health Management

Localization mechanism

remaining component life estimation+

+

System objectData monitoring

Maintenance

Operation-maintenance-

management integration platform

Management

Operation

Automatic decision

2.6 Framework of intelligent wind farm

Wind Farm Production ProcessInformation Flow

Energy Flow

Big Data Framework

Intelligence Applications

SCADA system DHM system Smart sensor WPF server

Energy management platform Intelligent robot Sparepart system Substation server

Storage framework Access framework Scheduling framework Database

Server, operating system Data backup Safety management Data management

Operation control application Maintenance strategy application Production Management Application

2 Intelligent wind farm technologies2.7 Intelligence applications

Intelligence applications for control

Intelligence applications for maintenance

Wind farm energy efficiency assessment based on big data analysis Self-organized wind farm management system Wind farm data quality control

Control strategy optimization based on operational data Combination of reinforcement learning and wind farm control Wind farm maximum wind power potential estimation based

on advance sensing and data analysis.

Multi-objective optimal control of wind plant Fault-tolerant control combined with fault diagnosis High precision wind power forecasting based on large scale

sensor network

Wind turbine Diagnosis and Health Management using Deep learning (supervised learning and transfer learning) Wind turbine component life estimation Preventive maintenance and opportunity maintenance strategy optimization combined with fault diagnosis system and

component life estimation

Intelligence applications for management

2 Intelligent wind farm technologies

• 1、 SMART strategy - National Renewable Energy Laboratory SMART strategy： With the support of The U.S. Department of Energy’s (DOE’s) Wind

Energy Technologies Office Atmosphere to Electrons (A2e) applied research program, the

National Renewable Energy Laboratory (NREL) proposed a strategy named "System

Management of Atmospheric Resource through Technology”.

Strategic objectives：SMART wind power plants will be designed and operated to achieve

enhanced power production, more efficient material use, lower operation and maintenance

and servicing costs, lower risks for investors, extended plant life, and an array of grid control

and reliability features. (A reduction of 50% or more from current cost levels )

Focus：The ability to truly understand, control, and predict the performance of the

future wind plant relies on understanding and tying together a range of physical

phenomena from regional weather systems to the wind flow that passes over

individual wind turbine rotor blades.

Major technical research areas：

（ 1 ） Performance, risk, uncertainty, and financing. （ 2 ） High-fidelity modeling,

verification, and validation.（3）Wind power plant controls.（4）Integrated system

design and analysis.（5）Wind power plant reliability.

2.8 Current status and practice of intelligent wind farm

2 Intelligent wind farm technologies• 2、Long-term research challenges in wind energy a research agenda by the EAWE

The European Academy of Wind Energy (EAWE), united the important wind power research universities and institutions from 14 European countries,

discussed the long-term research challenges in the field of wind power, explained the current technology frontiers and technical limitations from 11

different research fields, and raised the issues that should be resolved first in the future development of wind power.[1]

11 research areas：1. Materials and structures 2. Wind and turbulence 3. Aerodynamics4. Control and system identification 5. Electricity conversion6. Reliability and uncertainty modelling 7. Design methods8. Hydrodynamics, soil characteristics and floating turbines9. Offshore environmental aspects10. Wind energy in the electric power system11. Societal and economic aspects of wind energy

[1] Kuik G. V, Peinke J. etc. (2016). Long-term Research Challenges in Wind Energy - A Research Agenda by the European Academy of Wind Energy. Wind Energ. Sci., 1, 1–39, 2016

Some point of views：

（1）The definition of CoE of future wind farms requires a multi-disciplinary approach.

（2）Wind power stations have to fulfil the “L3 conditions”: low cost, long-lasting, and low service requirement.

（3）Specific technical solutions should be proposed because of the multi-scale characteristics of wind farms.

（4） Tremendous data can help the design and operation of a wind power station, data filtering and quality control is very important.

（5）The entire wind energy conversion process should be calculated by well-validated computer programs covering the lifetime.

2 Intelligent wind farm technology• 3、GE's digital wind farm

(1) Wind farm design service based on GE's Predix platform

(2) Digital wind farm optimization service during the operation phase of the wind farm

(3) Operation and maintenance decision service based on operational mode and abnormal state analysis

• 4、Gold wind's smart Wind Farm Solution (1) Centralized monitoring for wind farm cluster and regional service sharing

(2) Preventive maintenance with intelligent fault diagnosis and big data analysis

(3) End-to-end wind farm cluster performance management

(4) Centralized power prediction and energy management in the wind farm cluster level

• 5、Vestas (1) Aerodynamics Upgrades, Extended Cut Out, Power Performance Optimization and Power

Uprate.

(2) Asset optimization management services including monitoring and preventive diagnostics for wind turbines, weather stations and substations

(3) Power prediction, weather prediction and icing prediction services.

3 Our research

3.1 Condition Diagnosis and Health Management of Wind Turbine

Definition• Condition diagnosis and health

management is a technology that provide intelligent health monitoring of equipment to avoid serious failures in the future. This technology is the core technology of predictive maintenance, which can determine the maintenance schedule by sensor data and analysis algorithm.

• Mainly includes：• Early fault diagnosis• Remaining useful life prediction

Observed data RUL

Early Fault Diagnosis

Remaining Useful Life Prediction

Failure Threshold

Con

ditio

n In

dica

tors

Source: NASA Ames Research Center

Condition diagnosis and health management technique bring the following advantages to wind turbine maintenance tasks：

• By identifying the early failures of key components of wind turbine and predicting the remaining useful life, maintenance tasks and spare parts can be prepared in advance. The efficiency of maintenance work is improved, and the downtime of wind turbine is reduced.

• It can assist in determining the root cause of the failure, enabling maintenance staff to perform appropriate operations without consuming too much resources to determine the cause of the failure.

• Avoid unnecessary maintenance work and expenses.

Advantages

Procedure

1. Data Acquisition• Real-time operational data of the wind

turbine under normal condition.• Real-time operational data of the wind

turbine under failure condition.• Maintenance data（condition data）

2. Data Preprocess• Mark operational data based on

condition data• Eliminate the outliers in the data

Acquire DataMaintenance

Data

Sensor Data

Develop Diagnosis Model or Prediction Model

Condition Feature

Extraction

TrainModel

Preprocess Data

Deploy & Integrate

3. Diagnosis or prediction model• Fault diagnosis model• Remaining useful life prediction model

4. Deployment • Cloud deployment• Embedded deployment• Hybrid deployment

Condition Diagnosis and Health Management

Fault models and dynamics model of wind turbine drive system

Condition diagnosis of wind turbine drive train based on vibration analysis

Fault diagnosis of wind turbine based on SCADA data

Fault diagnosis and health management research and system development ofwind turbine

Main research fields：

Research achievement： 4 dissertations (2 PhD dissertations) 11 papers, including 6 SCI and EI 1 invention patent Fault diagnosis and health management system of wind

turbine Second Award of the National Science and Technology

Progress Award in China

Example analysis： Fault diagnosis of wind turbine bearing

Gaussian mixture model for wind turbine

drivetrain performance degradation prediction

Fault Diagnosis of Wind Turbine Bearing

Normal

Ball Fault

Inner Fault

Outer Race Fault@3

Outer Race Fault@6

Outer Race Fault@12

Motivation： The vibration data of

different conditions have very different time domain and frequency domain waveforms.

Combining image recognition methods, using convolutional neural network to achieve fault diagnosis.

Time Domain Frequency Domain

Source: Case Western Reserve University Bearing Experiment Data


• One-dimensional convolutional neural network diagnosis model

• Extract fault characteristics directly from time domain signals

• Two-dimensional convolutional neural network diagnosis model

• Time spectrum of the vibration signal obtained by short-time Fourier transform

• Extract fault characteristics from time spectrum

Proposed two diagnosis models with different structures

5 Conv1D, 16

5 Maxpooling1DRelu

5 Conv1D, 32

5 Maxpooling1D

Relu

5 Conv1D, 64

5 Maxpooling1DRelu

5 Conv1D, 128

5 Maxpooling1D

Dense, 256

Dropout, 0.5Relu

Dense, 64

Relu

Relu

Dense, 6SoftMax

Time domain vibration sample

2×2 Conv2D, 16

2×2 Maxpooling2DRelu

2×2 Conv2D, 32

2×2 Maxpooling2D

Relu

2×2 Conv2D, 64

2×2 Maxpooling2DRelu

2×2 Conv2D, 128

2×2 Maxpooling2D

Dense, 256

Dropout, 0.5Relu

Dense, 64

Relu

Relu

Dense, 6SoftMax

Frequency domain spectrum

Yuanchi Ma, Yongqian Liu, Zhiling Yang, et al. Multi-channel deep convolutional neural network for wind turbine fault diagnosis method: China, 201710662249.2.[P]. 2017.8

Comparison of results:


2D CNN1D CNN The darker the color

on the diagonal, the better the diagnostics.

The effect of 2D convolutional neural network on outer race fault identification is better than that of 1D convolutional neural network.


Compared with the classic model(feature extraction + SVM )

Classic model is less accurate in identifying specific fault locations.

Procedure:• The prediction procedure is divided

into offline part and online part.• The offline part extracts the condition

characteristics(mean, kurtosis) from the health wind turbine, and uses the maximum likelihood method to estimate the parameters of the Gaussian mixture model.

• The online part deploys a well-trained model, using the negative log-likelihood function value as the decline index to predict the decline trend of the drivetrain.

Drivetrain Performance Degradation Prediction

Vibration signal of wind turbine (healthy）

Extracting features

Training GMCM

Real-time vibration data

Extracting features

GMCM

Negative log-likelihood probability

Decline index

Offline part Online part

Xu Qiang. Research on state diagnosis method of wind turbine drive chain. PhD thesis, North China Electric Power University, Beijing, 2015.

Characteristic:

• Extracting fault characteristics of key components of wind turbine based on fault mechanism.

• Suitable for real-time prediction of health condition of high ratio drivetrain.

• Compared with the traditional early warming methods for monitoring the variation of kurtosis distribution, the prediction of degradation trend is over 60% faster.

• Improve the safety and reliability of wind turbines in complex environments.

Performance Degradation(Our method Vs Classic method)

Drivetrain Performance Degradation Prediction

Xu Qiang. Research on state diagnosis method of wind turbine drive chain. PhD thesis, North China Electric Power University, Beijing, 2015.

3 Our research

3.2 Wind Power Forecasting

Research fieldsa. NWP wind speed correctionb. Statistic WPF methodsDeterministic WPF• Data driven• Similar days• WTs groupingProbabilistic WPF

c. Physical WPF methods• CFD pre-calculated flow fields

Research achievements 10 academic dissertations, including 3 PhD

dissertations 55 papers, including 49 SCI or EI 7 invention patents 1 intelligent wind power forecasting system

3.2 Wind Power Forecasting

3.2 a. NWP wind speed correction

3 models (multiple linear regression, radial basis function neural network and Elman neural network) are established for correcting the NWP wind speed error.

Frequency distribution for NWP wind speed error

MonthMean value of wind

speed (m/s)Error of NWP wind

speed Correlation coefficient

NWP Measured RMSE MAEJan. 6.41 5.61 3.33 2.75 0.52Feb. 8.32 6.77 3.64 2.78 0.68Mar. 7.58 6.17 2.95 2.29 0.76Apr. 9.08 7.59 3.27 2.48 0.73May. 7.68 6.01 3.55 2.74 0.62Jun. 8.22 6.97 2.89 2.27 0.68Jul. 6.69 5.53 3.35 2.58 0.44Aug. 7.08 5.43 3.20 2.50 0.59Sep. 6.39 5.10 2.93 2.25 0.58Oct. 6.37 6.04 2.40 1.70 0.71Nov. 5.48 5.69 1.24 0.94 0.92Dec. 11.21 9.1 3.79 2.97 0.77

Comparison of NWP and measured wind speed over the sample year

NWP error characteristics

Results

Annual RMSE variation of corrected and original NWP wind speed (based on last 10 days of each month)

Little NWP error may incur huge error in wind power forecasting because of the cubic relationships between wind speed and wind power.

Liu Y, Wang Y, Li L, et al. Numerical weather prediction wind correction methods and its impact on

computational fluid dynamics based wind power forecasting[J]. Journal of Renewable & Sustainable

Energy, 2016, 8(3):770-778.

3.2 a. NWP wind speed correction

Proposed a multi-to-multi NWP correction method based on stacked denoising auto-encoder (SDAE).

NWP patterns

0 25 50 75 100 125 150 175 200 225 250 275 288

Time/10min

0

5

10

15

20

win

d sp

eed

（m

/s）

NWP wind speed

measured wind speed

mean measured wind speed

mean NWP wind speed

Time series of NWPs and wind speed measured at multi-sites

Multi-to-multi network of SDAE for NWP correction

Comparison and improvement of the proposed SDAE-m-m method and benchmarks

Correction network

Results

Advantages Captured more features and labels of wind speed Ability to learn spatial correlation Correction accuracy improved by 15% and 18%

compared to NN and SVM (RMSE)

Yan J, Zhang H, Liu Y, et al. Forecasting the High Penetration of Wind Power on Multiple Scales Using

Multi-to-Multi Mapping[J]. IEEE Transactions on Power Systems, 2017, PP(99):1-1.

One-site NWP wind speedcorrection methods can not considerthe micro-scale effects, such asterrain, wake, roughness andobstacles, the correction accuracy islimited.

3.2 b. Statistic WPF methods

Framework and architecture of the ensemble SDAEs

WPF framework WPF results

Advantages Applicable for the high penetrated and correlated wind

power in a region Captured the variable correlation patterns of wind and

power output with respect to a range of local factors Higher wind power forecasting and greater improvement

when the correlation of wind power outputs is strong

Comparisons of different regional WPF methods

Proposed a multi-to-multi wind power forecasting method based on ensemble SDAEs.

Yan J, Zhang H, Liu Y, et al. Forecasting the High Penetration of Wind Power on Multiple Scales Using Multi-to-Multi Mapping[J]. IEEE Transactions on Power Systems, 2017, PP(99):1-1.

Wind power forecasting methodswhich only consider the in-siteinformation, can not utilize thetemporal - spatial dependencybetween wind farm cluster, theforecasting accuracy is limited.

NRMSE WF1 WF2 WF3 WF4 WF5 WF6 WF7 Avg

SDAE-m-m 0.15 0.15 0.16 0.15 0.17 0.14 0.15 0.15

NN-1-1 0.19 0.17 0.18 0.17 0.18 0.16 0.16 0.17

SVM-1-1 0.18 0.17 0.18 0.17 0.17 0.16 0.16 0.17

RF-1-1 0.19 0.17 0.19 0.18 0.18 0.17 0.16 0.18

—Data driven

Proposed a dynamic wind power forecasting method based on wind process recognition.

Theoretical power curve and empirical power generation scatters

Comparisons of different WPF methods

Width of power curve Slope of power curve

Wind speed Wind direction Wind characteristics

NWP error characteristics

Clustering method

Running condition of wind turbine generator

K-means/Spectral cluster

Process 1# Process 2# Process N#…

Results

Structure of clustering model

Advantages Considered external changing conditions Forecasting models can be recognized and mapped in real time Improved forecasting accuracy


Most traditional wind power forecasting methods are static without considering external changing conditions.

—Similar days

11.8%13.5%13.7% 14.9%14.6% 15.1%

0%

4%

8%

12%

16%

RVM ANN

RM

SE

K-means Spectral cluster Direct forecastingYan J, Liu Y, Zhang H, et al. Dynamic Wind Power Probabilistic Forecasting Based on Wind Scenario Recognition[J]. Modern Electric Power, 2016.

Proposed a method to directionally select training samples with similar wind speed features in a given day based on the wind speed cloud model.

Predicted day Similar day 1 Similar day 2 Similar day 3 Similar day 4 Similar day 5

Win

d sp

eed

(m/s

)

4

6

8

10

12

14

Box-plot of wind speed among predicted day and similar days

Forecasting error comparison of the proposed method and traditional method in random days of each month

Time series (15min)

0 50 100 150

Pow

er (M

W)

0

0.4

0.8

1.2

1.6

2.0Predictive power based on similar daysTraditional predictive powerReal power

Forecasting results of two methods based on measured data

MonthRMSE(%) MAE(%)

Similar Random Similar Random

Jan. 18.1% 20.8% 17.7% 21.1%

Feb. 12.5% 15.5% 18.4% 21.9%

Mar. 14.4% 17.2% 14.9% 17.3%

Apr. 13.3% 16.6% 16.0% 18.3%

May. 9.9% 11.6% 15.6% 18.2%

Jun. 6.2% 10.4% 8.3% 11.8%

Jul. 9.8% 13.0% 10.9% 13.9%

Aug. 7.9% 9.6% 9.9% 11.9%

Sep. 7.6% 9.4% 10.9% 13. %

Oct. 9.4% 11.3% 10.4% 12.8%

Nov. 13.7% 16.9% 13.9% 17.0%

Dec. 14.9% 17.8% 19.6% 21.7%

Year 11.48% 14.18% 13.88% 16.58%

Two situations of similar day selection based on cloud model

Advantages Considered the complex diversity and ambiguity of the actual weather system Improved the learning ability in capturing the randomness Improved the fuzziness of wind speed in designated time period Improved forecasting accuracy

Results

Training sample selected


YAN Jie, XU Chengzhi, LIU Yongqian, et al. Short-term wind power prediction based on daily similar wind speed cloud model[J]. Automation of Electric Power System, 2018, 42(06): 53-59.

—Similar days

Proposed a grouping method for wind turbines by considering the flow correlation.

Flow chart of self-organizing feature mapping wind turbine grouping

Wind speed

Elevation

Prevailing wind direction coordinate system

Wind speed correlation

Grouping model of wind turbine generations

Grouping results of wind turbine generations

Reference points for wind power forecasting

Prevailing wind direction of wind farm

Boundary of wind farm

Location coordinates of wind turbine generations

Structure of wind turbine grouping

Results

Technical route RMSETesttime

Train time

Wind tower as reference point

16.73% 1.4s 288.2s

Representative WTs as reference points

15.67% 9.4s 2304.4s

Each WT as a reference point

15.09% 46.3s 9504.6s

Results of WPF for different technical routes

Conversion diagram of prevailing wind coordinate

Advantages Reflected the flow characteristics of a particular wind farm Facilitated the combination of flow characteristic with the wind

turbines grouping method and wind power forecasting technique Improved the forecasting accuracy and reduced the operation time at

the same time

3.2 b. Statistic WPF methods —WTs grouping

Liu Y, Gao X, Yan J, et al. Clustering methods of wind turbines and its application in short-term wind power forecasts[J]. Journal of Renewable &

Sustainable Energy, 2014, 6(5):474-482.

Only one representative WT→ reduce forecasting accuracyEach WT is forecasted individually→increases the computational cost

Probabilistic forecasting of VVRVM on a day in December Intervals of the wind power for different confidence levels

Proposed a probabilistic wind power forecasting method based on varying variance relevant vector machine.

Advantages Ability to predict deterministic future wind power as well as its

fluctuation range under given confidence level Could well simulate the power generation process of wind under

variable meteorological conditions Just required small amount of relevance samples

Modeling steps

Modeling steps of probabilistic wind power forecasting model

Probabilistic wind power forecasting

Operation data of wind farm

Numerical Weather Prediction Confidence level

Created training samples for each month

Grouped according to NWP error grade

Screened training samples

Normalization processing

Initialized model parameters

Calculated the posterior distribution of weight coefficients

Calculated the mean and variance of the posterior distribution

Stopped the iteration, outputted parameters

Periodically updated

Corrected variances

YES

NO

Yan J, Liu Y, Han S, et al. Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine[J]. Renewable & Sustainable Energy Reviews, 2013, 27(6):613-621.

3.2 b. Statistic WPF methods —Probabilistic WPF

ResultsThe probability of forecasting error occurrence in deterministic WPF is almost 100%, which has strong uncertainty.

3.2 c. Physical WPF methods

Terrain model Roughness model

Inflow wind condition discretization

Establishment of CFD simulation method

CFD simulation for flow fields in discrete inflow wind conditions

Wake model

Power curve Database establishment of wind speed and direction

NWP input data

Database establishment of wind power in different inflow conditions

Power prediction of wind farm

Power prediction of single wind turbine

Statistics of the operating wind turbines

Proposed a physical approach of the wind power prediction based on the CFD pre-calculated flow fields.

Structure diagram of wind power prediction

Prediction result when power output decreases from installed capacity to zero

Frequency distribution histogram of forecasting wind power error

Prediction result when power output changes drastically

Advantages Independent of the historical data Applied to newly-built wind farms Short computation time Captures the local air flows more precisely Great performance: 15.2% (RMSE), 10.8% (MAE)

Modeling steps

Results

LI Li, LIU Yong-qian, YANG Yong-ping, et al. A physical approach of the short-term wind power prediction based on CFD pre-calculated flow fields[J]. Journal of Hydrodynamics, 2013, 25(1):56-61.

The CFD method has not been successfully applied to the wind power forecasting system so far because it takes too much time to calculate.

Intelligent wind power forecasting system

Functions

Ultra-short-term WPFShort-term WPFUncertainty analysis of

forecasting resultsAutomatic remote update

of the modelMulti-point NWPRamps forecasting

Advantages

Mathematical theories Adaptabilities Uncertainty analysis

7 ultra-short-term WPF statistic models

4 short-term WPF statistic models

Physical model based on CFD flow field pre-calculation

ARIMABP、RBF

PSVMORVM

GA-PSVMHHT-ANN

Established or new WFPlain or hilly ground WFOnshore or offshore WF

4 uncertainty analysis methods

Power interval or probabilistic distribution under given confidence level

3 Our research

3.3 Optimal operation of wind farm

3.3 Optimal operation of wind farms

3.3 a. Objective 1： Increase power production by considering the wake effects

3.3.b Objective 2: Extend the life of wind farm by considering the fatigue damage

Fast wake distribution calculation

Wind farm optimal dispatch based on GA and PSO

Wind farm optimal dispatch based on multi-agents

Unit commitment optimization in wind farm with the target of reducing fatigue damage

of wind turbines Research achievements ： 7dissertations (2 doctoral

dissertations) 9 papers（SCI and EI） 2 invention patent

Research projects: National High-techDevelopment Plan (863) Project"Research and Application of WindFarm-Photovoltaic Power StationCluster Control System"

3.3 a. Fast wake distribution calculation method

1. The wake superposition model of multiple wind turbines（a）Calculation method for wake superposition area

（b）wind speed calculation of downstream unit

2. Fast calculation of wind speed distribution in wind farm under different wind direction

（a）determine the arrange order of wind turbines （b）coordinate transformation （c）Fast wind speed distribution calculation

Intersection are between wake and wind rotor

Gu Bo. Wake Fast Calculation and Power Optimal Dispatch for Wind Farms. PhD thesis, North China Electric Power University, Beijing, 2017.

3.2 a. Wind farm optimal dispatch based on GA and PSO

For a wind farm with multiple wind turbines, the overall power output of wind farm can be increased by choosing an appropriate set of axial inducing factors or thrust coefficients.

Fig.1 layout of wind farm Horns Rev Fig.2 Wake wind speed distribution of wind turbines at the row 4.

Fig.3 Wake wind speed distribution of wind turbines at the row 10.

Fig.4 Optimization control calculation process of PSO algorithm

wind speed 8.5m/s, wind direction 270°Speed calculated by wake model

Measured wind speed

wind turbine

Before Optimization

After Optimization

wind turbine

wind speed 8.5m/s, wind direction 222°

Iteration times

Win

d sp

eed（

m/s）

Win

d sp

eed（

m/s）

Pow

er o

utpu

t

Advantages Accurate and fast wake distribution

calculation to meet the control requirements Increase the wind farm output with different

wind conditions

Axial induction factor or thrust coefficient

Wind farm speed distribution

Wind farm power output

Improvement rate9.96%


3.3 a. Wind farm optimal dispatch based on multi-agents

Multi-agent grid

Ag1,1 …… Ag1,2 Ag1,col

Ag2,1 Ag2,2 Ag2, col

⁞ ⁞

⁞ ⁞

Agrow,1 Agrow,2 …… Agrow,col

1 2 3 4 5 6 7 8 9 105

5.1

5.2

5.3

5.4

5.5

5.6

5.7x 107

迭代次数

风电

场整

体输

出功

率

(W)

MA 优化过程输

未优化前输出功率

风向270度，风速 8.5 m /s

1 2 3 4 5 6 7 8 9 106.45

6.5

6.55

6.6

6.65

6.7x 107

迭代次数

风电

场整

体输

出功

率

(W)

MA 优化过程输

未优化前输出功率

风向222度，风速 8.5 m /s

Objective： Maximize the wind farm productionAssumption： Each wind turbine is an intelligent agent interaction between wind turbines through

the Wake effectsAgent design Domain competition operator Mutation operator Self-learning operator

Advantages reduce the dimension of the solution space make the optimal dispatch solution by

designed agent and Fast wake distribution calculation method.

Improvement rate10.74%

Wind turbines

Wind farm controller

wind speed 8.5m/swind direction 270°

wind speed 8.5m/swind direction 222°Improvement rate

2.93%Po

wer

out

put

Pow

er o

utpu

t

Iteration times Iteration times

Before Optimization

After Optimization

Before Optimization

After Optimization


3.3 b. Wind turbine fatigue load and fatigue damage

Based on GH-Blade and IEC61400-1 standards, the fatigue load of wind turbine and fatigue damage of important components of wind turbine are calculated.

3.83E-08, 0< 934.78E-08, 93< 3261.01E-07, 326< 7531.32E-07, 753< 15001.63E-07, 1500

1.18E-09,0< 15001.28E-09, 1500

2.04E-08,0< 15001.01E-08, 1500

2.41E-12

PP

a PP

P

Pb

P

Pc

Pd

≤ ≤= ≤ ≤

>≤

= >≤

= >=

Equivalent fatigue damage expression of a wind turbine

a- Fatigue damage of wind turbines during normal operation condition.

b - Fatigue damage of wind turbine when starting.

c- Fatigue damage of wind turbine when stopping.

d - Fatigue damage of wind turbine when idling.

P - power output of wind turbine.

Determining the fatigue load condition of wind turbine

Wind turbine model and wind model

Rain flow and Miner fatigue cumulative damage theory

Equivalent fatigue load and relative fatigue damage

Equivalent fatigue load and fatigue damage of blade roots Equivalent fatigue load and fatigue damage of hub center Equivalent fatigue load and fatigue damage of tower bottom Equivalent fatigue load and fatigue damage of tower top

Zhang Jinhua. Research on Unit Optimal Dispatch in Wind Farm. PhD thesis, North China Electric Power University, Beijing, 2014.

3.3 b. Unit commitment optimization for reducing the fatiguedamage of wind turbines

optimization objective1 1

1 1 1 1 1 1 1 1min( ( ) (1 ) (1 ) ( (1 ) ))

T N T N T N T Nj j j j j j j j j j

i i i i i i i i i ij i j i j i j i

F a u t b u u c u u d u t− −

= = = = = = = =

= ⋅ + − + − + − ⋅∑∑ ∑∑ ∑∑ ∑∑

Constraints:(1) unit upper and lower limit constraint(2) load dispatch constraint(3) maximum power change rate constraint

During the whole dispatching period, the starting andstopping of the wind turbines can be selected tominimize the fatigue damage of the whole wind farmunder the constraints.

0 100 200 300 400 5001.26

1.28

1.3

1.32

1.34

1.36

1.38

1.4

1.42

1.44x 10-4

遗传代数

解的变化

进化过程

Convergence of the genetic algorithm

Uints 1 2 3 4 Units 1 2 3 4

1 0 1 0 0 18 1 1 0 0 2 1 1 1 0 19 1 0 0 1 3 0 0 0 0 20 0 0 0 1 4 0 1 0 0 21 1 0 0 0 5 0 1 1 1 22 1 0 1 0 6 0 0 0 1 23 1 0 0 1 7 0 1 1 1 24 1 0 1 0 8 0 0 0 1 25 1 1 0 0 9 0 0 1 0 26 1 1 0 1 10 0 1 1 1 27 1 0 1 1 11 0 0 1 1 28 1 0 0 1 12 0 0 1 1 29 1 1 0 0 13 1 0 1 0 30 0 1 0 0 14 0 0 0 1 31 1 1 0 0 15 0 0 1 0 32 1 1 1 0 16 0 0 1 1 33 1 1 0 1 17 0 1 0 0

The optimal unit commitment

According to the wind power prediction and the dispatching instructions ofthe power system, the optimal unit commitment scheme can be solved byreducing redundant operation and avoiding frequent start-up andshutdown of units.

Optimized by GA

Fitn

ess v

alue

Iteration times

Zhang Jinhua. Research on Unit Optimal Dispatch in Wind Farm. PhD thesis,North China Electric Power University, Beijing, 2014.

Part 4: Conclusions• The ultimate goal of intelligent wind farm technologies is to lower LCOE.

• The integrated framework for Intelligent Control, Maintenance and technical Management System for Wind Farm (ICMMS for wind farm) is presented.

• Our reach on intelligent wind farm technologies are introduced, include Optimal operation control, wind power forecast, condition diagnosis and health management.

• There are still a long way to go for the Intelligent wind farm technologies.

Intelligent wind farm technologies: Bright future!

Thank you!

intelligent wind farm technologies - medpower...

Documents