# US 11,114,893 B2 · Smart outlet system with fast frequency tracking for power system frequency control using distributed appliances

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 11,114,893 B2
- Document ID: US11114893B2
- Status: Granted
- Kind: B2
- Application number: 16/396,546
- Filed: 2019-04-26
- Published: 2019-12-12
- Granted: 2021-09-07
- Earliest claimed: 2018-06-07
- Applicants: Xiao Lu; Di Shi; Yingmeng Xiang; Haifeng Li; Chunlei Xu; Zhe Yu; Jiangpeng Dai; Shiming Xu; Xueming Li; Haiyun Han; Zhiwei Wang
- Inventors: Xiao Lu; Di Shi; Yingmeng Xiang; Haifeng Li; Chunlei Xu; Zhe Yu; Jiangpeng Dai; Shiming Xu; Xueming Li; Haiyun Han; Zhiwei Wang
- Web page: https://patentgrep.com/patent/US11114893B2

## CPC

- H02J 13/38: the equipment being power outlets
- G05B 23/0254: {based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks}
- H02J 13/12: Monitoring network conditions, e.g. electrical magnitudes or operational status
- H02J 3/14: by switching loads on to, or off from, the networks, e.g. progressively balanced loading
- H02J 2103/30: Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
- H02J 2105/51: {according to a condition being electrical}
- Y02B 70/3225: Demand response systems, e.g. load shedding, peak shaving
- Y02B 90/20: Smart grids as enabling technology in buildings sector (smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00)
- Y02E 60/00: Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y04S 10/30: State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
- Y04S 20/00: Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S 20/222: Demand response systems, e.g. load shedding, peak shaving
- Y04S 40/20: Information technology specific aspects, e.g. CAD, simulation, modelling, system security
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS

## Abstract

Systems and methods are disclosed to control the power grid frequency by capturing the frequency change using an extended Kalman filter method with the distributed smart outlet devices at the low-voltage distribution level; and locally control the relay that provides power to the appliance by comparing the captured frequency with the threshold sent from the cloud control center.

## Claims

### Claim 1

A method to control a power grid frequency, comprising:
distributing a plurality of outlet devices each containing a relay over a grid;
continuously measuring the power grid frequency providing power to each of the distributed outlet devices, wherein the each of the distributed outlet devices is plugged into an outlet;
updating a value of the power grid frequency using an extended Kalman filter within the each of the distributed outlet devices at a low-voltage single-phase distribution level by determining the state transition of extended Kalman filter:
xc[t+1]=cos(xω[t]/ƒs)xc[t]−sin(xω[t]/ƒs)xs[t]+ϵc[t],
xs[t+1]=sin(xω[t]/ƒs)xc[t]+cos(xω[t]/ƒs)xs[t]+ϵs[t],
xω[t+1]=xω[t]+ϵω[t],
where ƒs is the sampling frequency; xc, xs and xω consist of the state of the extended Kalman filter; t is the time; and ϵc, ϵs, and ϵω are the noise;
determining and sending a threshold to the each of the distributed outlet devices at and by a cloud control center;
tracking a frequency change within 0.2 second or 10 cycles and enabling a fast frequency response in response to a frequency fluctuation; and
switching off the relay in the each of the distributed outlet devices that provides power to an appliance based on the result of comparing the power grid frequency with the threshold sent from the cloud control center.

### Claim 2 (depends on 1)

The method of claim 1, comprising switching off the relay when the power grid frequency exceeds the threshold for a predetermined number of times.

### Claim 3 (depends on 1)

The method of claim 1, comprising low-pass filtering voltage and/or current sampling.

### Claim 4 (depends on 1)

The method of claim 1, comprising providing a filtered voltage and/or current sampling as input to an extended Kalman filter (EKF) to calculate the power grid frequency.

### Claim 5 (depends on 1)

The method of claim 1, comprising determining the relationship between a sampling and the power grid frequency:
y[t]=A cos(ωt/ƒs+ϕ)+ε[t]
where y[t] is the sampling at time t, A is the voltage and/or current magnitude, ω=2πƒ and ω is the frequency in the unit of rad/s, ƒ is the power grid frequency, ƒs is the sampling frequency, ϕ is the phase angle, and ε is the noise corresponding to potential harmonics, disturbance, and measurement error.

### Claim 6 (depends on 1)

The method of claim 1, comprising modeling a nonlinear system whose states contain frequencies and voltage and/or current magnitude of measurements.

### Claim 7 (depends on 1)

The method of claim 1, comprising determining a relationship between an ideal sinusoidal signal and the power frequency as:
s
⁡
[
t
]
⁢
=
Δ
⁢
⁢
A
⁢
⁢
cos
⁡
(
ω
⁢
⁢
t
/
f
s
+
ϕ
)
=
⁢
A
⁡
[
cos
⁡
(
ω
⁢
⁢
t
/
f
s
)
⁢
cos
⁡
(
ϕ
)
-
sin
⁡
(
ω
⁢
⁢
t
/
f
s
)
⁢
sin
⁡
(
ϕ
)
]
=
⁢
B
c
⁢
cos
⁡
(
ω
⁢
⁢
t
/
f
s
)
+
B
s
⁢
sin
⁡
(
ω
⁢
⁢
t
/
f
s
)
,
where s[t] is the ideal sinusoidal signal without noise, BcA cos(Ø) and BsA sin(Ø), A is the voltage and/or current magnitude, ϕ is the phase angle, ƒs is the sampling frequency, ω is the power grid frequency in the unit of rad/s, and t is the time.

### Claim 8 (depends on 1)

The method of claim 1, comprising determining the evolution of an ideal sinusoidal signal as:
s
⁡
[
t
+
1
]
=
⁢
B
c
⁢
cos
⁡
(
ω
⁡
(
t
+
1
)
/
f
s
)
+
B
s
⁢
sin
⁡
(
ω
⁡
(
t
+
1
)
/
f
s
)
=
⁢
B
c
⁡
[
cos
⁡
(
ω
⁢
⁢
t
/
f
s
)
⁢
cos
⁡
(
ω
/
f
s
)
-
sin
⁡
(
ω
⁢
⁢
t
/
f
s
)
⁢
sin
⁡
(
ω
/
f
s
)
]
+
⁢
B
s
⁡
[
sin
⁡
(
ω
⁢
⁢
t
/
f
s
)
⁢
cos
⁡
(
ω
/
f
s
)
+
cos
⁡
(
ω
⁢
⁢
t
/
f
s
)
⁢
sin
⁡
(
ω
/
f
s
)
]
.
where s[t+1] is the ideal sinusoidal signal of the next time step t+1, BcA cos(Ø) and Bs−A sin(Ø), A is the voltage and/or current magnitude, ϕ is the phase angle, ƒs is the sampling frequency, ω is the power grid frequency in the unit of rad/s, and t is the time.

### Claim 9 (depends on 1)

The method of claim 1, comprising defining extended Kalman filter system states as instantaneous magnitudes and frequencies as:
x
⁡
[
t
]
=
[
x
c
⁡
[
t
]
x
s
⁡
[
t
]
x
ω
⁡
[
t
]
]
=
[
B
c
⁢
cos
⁡
(
ω
⁢
⁢
t
/
f
s
)
B
s
⁢
sin
⁡
(
ω
⁢
⁢
t
/
f
s
)
ω
]
where xc, xs and xω consist of the state x[t] of the extended Kalman filter, BcA cos(Ø) and Bs−A sin(Ø), A is the voltage and/or current magnitude, ϕ is the phase angle, ƒs is the sampling frequency, ω is the power grid frequency in the unit of rad/s, and t is the time.

### Claim 10 (depends on 1)

The method of claim 1, comprising considering a white noise in the extended Kalman filter state transition as:
x[t+1]=ƒ(x[t])+ϵ[t]
where the transition function ƒ(•) is a nonlinear function indicating the extended Kalman filter system state transition relationship between the current state x[t] and the state x[t+1] of next time step, and ϵ[t] is a white Gaussian noise with zero mean.

### Claim 11 (depends on 1)

The method of claim 1, comprising determining a relationship between the observation and state of the extended Kalman filter as:
y[t]=Hx[t]+ϵ[t]
where y[t] is the observation at time t, H is a vector [1 1 0], x[t] is the state of extended Kalman filter at time t, ϵ[t] is the noise at time t.

### Claim 12 (depends on 11)

The method of claim 11, comprising approximating a nonlinear extended Kalman filter system with a first-order linearization and applying a linear extended Kalman filter to find an optimal Kalman gain.

### Claim 13 (depends on 12)

The method of claim 12, wherein the nonlinear system and previously presented measurements are used to calculate new state predictions.

### Claim 14 (depends on 13)

The method of claim 13, wherein a state space model is re-linearized around updated state estimations.

### Claim 15 (depends on 13)

The method of claim 13, where {circumflex over (x)}[t|j] denotes a minimum mean squared error estimate of x[t] given measurements up to and including time j, and P[t|j] is a co-variance matrix of estimation errors, comprising determining:
starting from the initial, estimating {circumflex over (x)}[0|−1] and P[0|−1],
where
F
t
=
∂
f
⁡
(
x
)
∂
x
⁢

x
=
x
^
⁡
[
t
|
t
]
is the linearized system, and Nis the number of measurements; and predicting ƒ({circumflex over (x)}[t|t]) as:
{circumflex over (x)}c[t+1|t]={circumflex over (x)}c[t|t] cos({circumflex over (x)}ω[t|t]/ƒs)−{circumflex over (x)}s[t|t] sin({circumflex over (x)}ω[t|t]/ƒs),
{circumflex over (x)}s[t+1|t]={circumflex over (x)}c[t|t] sin({circumflex over (x)}ω[t|t]ƒs)+{circumflex over (x)}s[t|t] cos({circumflex over (x)}ω[t|t]/ƒs),
{circumflex over (x)}ω[t+1|t]={circumflex over (x)}ω[t|t]
where {circumflex over (x)}c, {circumflex over (x)}s, and {circumflex over (x)}ω are the predicted values of the state of extended Kalman filter, t is the time, ƒs is the sampling frequency.