particle filter and its potential applications in smart grid
DESCRIPTION
Particle filter and its potential applications in smart grid. Zhiguo Shi. Outline. Introduction to Zhejiang University Fundamental concept Particle filter algorithm Application to SOC/SOH of battery charge Discussion. Outline. Introduction to Zhejiang University Fundamental concept - PowerPoint PPT PresentationTRANSCRIPT
Particle filter and its potential applications in smart grid
Zhiguo Shi
Outline
• Introduction to Zhejiang University
• Fundamental concept
• Particle filter algorithm
• Application to SOC/SOH of battery charge
• Discussion
Outline
• Introduction to Zhejiang University
• Fundamental concept
• Particle filter algorithm
• Application to SOC/SOH of battery charge
• Discussion
23/4/19
Goal: Estimate a stochastic process given some noisy observations
Concepts:– Bayesian filtering– Monte Carlo sampling
sensort
Observed signal 1
t
Observed signal 2
ParticleFilter
t
Estimation
Big picture
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Problem formulations
• Estimate a stochastic process given some noisy observations
• How?
Step 1: Build system dynamic model
State equation: xk=fx(xk-1, uk)
xk state vector at time instant k
fx state transition functionuk process noise with known
distribution
Step 1: Build system dynamic model
State equation: xk=fx(xk-1, uk)
xk state vector at time instant k
fx state transition functionuk process noise with known
distribution
23/4/19
Problem formulations
• Estimate a stochastic process given some noisy observations
• How?
Step 2: Build observation model
Observation equation: zk=fz(xk, vk)
zk observations at time instant kfx observation functionvk observation noise with known
distribution
Step 2: Build observation model
Observation equation: zk=fz(xk, vk)
zk observations at time instant kfx observation functionvk observation noise with known
distribution
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Problem formulations
• Estimate a stochastic process given some noisy observations
• How?
Step 3: Use particle filterStep 3: Use particle filter
x
Posterior
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Motivations
• The trend of addressing complex problems continues
• Large number of applications require evaluation of integrals
• Non-linear models• Non-Gaussian noise
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Applications
• Signal processing– Image processing and
segmentation– Model selection– Tracking and navigation
• Communications– Channel estimation– Blind equalization– Positioning in wireless
networks
• Other applications1)
– Biology & Biochemistry– Chemistry– Economics & Business– Geosciences– Immunology– Materials Science– Pharmacology &
Toxicology
– Psychiatry/Psychology– Social Sciences
An Example
x
y
T rajec to ry
xk xk + 1
ykyk + 1
zkzk + 1
States: position and velocity xk=[xk, Vxk, yk, Vyk]T
Observations: angle zk
Observation equation: zk=atan(yk/ xk)+vk
State equation:xk=Fxk-1+ Guk
Blue – True trajectory
Red – Estimates
Outline
• Introduction to Zhejiang University
• Fundamental concept
• Particle filter algorithm
• Application to SOC/SOH of battery charge
• Discussion
23/4/19 ISEE, ZJU
Basic Idea
• Representing belief by sets of samples or particles
• are nonnegative weights called importance factors
• Updating procedure is sequential importance sampling with re-sampling
( ) ~ { , | 1,..., }i it t t tBel x S x w i n
itw
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Particle filter illustration
Step 0: initialization
Each particle has the same weight
Step 1: updating weights. Weights are proportional to p(z|x)
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Particle filter illustration (Continued)
Particles are more concentrated in the region where the person is more likely to be
Step 3: updating weights. Weights are proportional to p(z|x)
Step 4: predicting.
Predict the new locations of particles.
Step 2: predicting.
Predict the new locations of particles.
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Particle filtering algorithm
Initialize particles
Output
Output estimates
1 2 M. . .
Particlegeneration
New observation
Exit
Normalize weights
1 2 M. . .
Weigthcomputation
Resampling
More observations?
yes
no
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Resampling
M
m
mk M
x1
)(1
1,
x
M
mm
km
k wx 1)()( ,
M
m
m
kM
x1
)(~ 1,
M
m
mk M
x1
)(1
1,
M
mm
km
k wx 1)(1
)(1 ,
M
m
m
kM
x1
)(
1
~ 1,
M
m
mk M
x1
)(2
1,
Outline
• Introduction to Zhejiang University
• Fundamental concept
• Particle filter algorithm
• Application to SOC/SOH of battery charge
• Discussion
23/4/19
Battery management in Electrical Vehicle[1]
• The cost of the power system can reach up to 1/3 of the total cost of the electric vehicle.
• The consistency of batteries is essential to the life and safety of the whole vehicle system
[1] Gao, M., et al., Battery State of Charge online Estimation based on Particle Filter, Proceeding of the 4th International Congress on Image and Signal Processing, pp. 2233-2236, 2011.
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Battery capacity under different discharging rates
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System model
• State Transition function
• Observation function
Proportion coefficientt related to discharge rate
Nominal capacity of batteryInstantaniously discharge current
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Simulation results
Outline
• Introduction to Zhejiang University
• Fundamental concept
• Particle filter algorithm
• Application to SOC/SOH of battery charge
• Discussion
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Hope: my crude remarks may draw forth by abler people
• Fundamentally, the particle filter can be applied to systems described by state equation representation with state transition function and observation function.
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Battery Charge Management
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Smart Grid Network Status Control
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Short Term Electricity Price Prediction for Home Appliance Control
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