Load shedding apply Neural Network and Power Sensitivity Theory

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Load shedding apply Neural Network and Power Sensitivity Theory

,

L.T. Nghia1, T.T. Giang1, Q.H. Anp P.T.T. Binp Bui.NguyenXuan. Vu1

1Faculty of Electrical and Electronics Engineering, HCMC University of Technology and Education Hochiminh city, Vietnam

2Faculty of Electrical and Electronics Engineering, HCMC University of Technology Hochiminh city, Vietnam

AbstractThis paper proposed the method load shedding on the basis of the combination of Generalized Regression Neural Network, power sensitivity theory and Phase Electrical Distance theories to recover the frequency to allowable limit in the event loss a generator occurring in the electric system. The phase electrical distance calculation intended to calculate the distance between the generator outage and the load to align the load buses in ascending. The phase electrical distance sequence as compared to the fault generator. From there, the loads on the buses which are near the generator outage will be cut firstly, then the next loads until the system return to stable. The effectiveness of the proposed method are tested on the IEEE 39 Bus New England 10 Generators, which demonstrated the effective the proposed method.

Keywords: Load Shedding, Phase Electrical Distance, Power Sensitivity, Neural Network, Frequency Control.

  1. INTRODUCTION.

    Load shedding is an effective method for restoring electrical system frequencies by reducing the load to suit the power supply. Most of the studies related to load shedding are based on rate change of frequency and voltage of the power system.

    H. Bevrani and et al [1] presented an overview of the key issues in the use of frequency rate change (df/dt) in power system emergency control schemes. The role of df/dt in designing effective under frequency load shedding (UFLS) plan was discussed. The impact of new variable renewable sources (such as wind and solar units) on system frequency gradient is analyzed, and the need for the revising of frequency performance standards, re-tuning of automatic UFLS relays, and use of f/t rather than df/dt are emphasized.

    Urban Rudez and et al [2] studied a novel approach to underfrequency load shedding is using the frequency second derivative as a source of information for a forecast of the frequency trajectory. A Newton method based approximation and the interpolation of the frequency second derivative are continuously performed in order to forecast the minimum frequency value using a numerical integration.

    F. Shokooh and et al [3] introduced the new technology of intelligent load shedding application in a large industrial

    improve the frequency response and to reduce the risk of unstable electric system.

    This paper proposes the method of load shedding under pre- designed load shedding rules based on phase electrical distance method and power sensitivity theory. ANN neural networks are used to identify and classify load shedding control strategies based on the designed rules. The effectiveness of the proposed method has been tested on the IEEE 39 Bus New England.

  2. MATERIALS AND METHODS.

    1. Generalized regression neural network.

      Fig. 1. Generalized regression neural network.

      GRNN (Generalized Regression Neural Network) is a powerful tool in the application for identification problems. GRNN is basically the same as fig. 1 including input layer, radial function hidden layer and linear output layer. Neuron network operation must include the spread parameter whose optimum value is determined by the experimental error.

    2. Phase electrical distance [4]

    The definition of phase electrical distance between the two nodes by using the elements of the matrix [/P] is the inverse matrix from the power sensitivity matrix [P/], as follows:

    DP(i,j)=(i/Pi)+(j/Pj)-(j/Pi)-(i/Pj) (1) The components [/P] are extracted from the Jacobian matrix. There are two interpretations of the phase electrical

    distance:

    facility.

    Conventional methods of system load shedding often have to wait until the frequency drops below the allowable threshold

    D (i, j) i

    P P

    i

    j

    Pj

    j

    P i

    • i

    Pj

    (2)

    to make the load shedding decision. However, due to the complexity of the power system, it is imperative to treat it

    Write this distance in another way:

    j j

    (3)

    promptly after a fault in order to minimize the damage, to

    D (i, j)

    i i

    P Pj

    Pj

    P P

    i i

    j i

    is phase difference between j and i caused by a

    PowerWorld simulation software to shed for each generator in trouble at different load levels. (including 41 load levels from

    P

    j

    P

    j

    60% to 100%). Dismissed until the rotor angle, frequency and

    active power injection in j.

    voltage of the buses are within the allowable range of stopping,

    i

    P

    j is phase difference between i and j caused by a

    P

    so that for each case the incident will have the number of load shedding corresponding to that case. The incident data collected

    would correspond to a number of load shedding from the trained

    i i

    active power injection in i. Then else:

    j j

    (4)

    neuron function.

    Location of load shedding: Use phase electrical distance for calculating distance between nodes. The load shedding position

    D (i, j)

    i i

    will be based on the distances from the generator outage

    j

    P P P P i i

    Pj

    (generator bus) to the remaining load buses to the load shedding

    j j

    is variation in phase of j caused by a active power

    order, or in other words the priority of the nodes closer to the

    generator will be first off, because these load nodes directly

    P

    j

    P

    i

    affect the generator is the most trouble.

    The flowchart load shedding process is shown in Fig. 3 and

    transit from j to i. 4.

    is variation in phase of i caused by a active power

    i i

    P P

    i j

    transit from i to j.

    Two nodes electrically very close will always have a very small phase difference corresponding to the small distance D. Therefore, in the event of a loss of generator, it should be cut the load at the buses which near the generator in advance.

    Fig. 2. The block diagram of the relationship between the generator j and

    the loads

    Fig. 3. Flowchart load shedding online

    With: D

    P

    ( j,1) D

    P

    ( j,2) D

    P

    ( j,3)…. D

    P

    ( j, n)

    Prioritized load shedding: Load 1 Load 2 Load 3

    Load n

  3. PROPOSED METHODS.

    1. Set up load shedding program.

      Load shedding are based on three main factors: the timing, the amount of load to be shed and the location of load shedding The timing: The system data is sent to the control center for continuous measurement, when the system frequency is within the allowable range of 59.7Hz<f<60.3Hz, the system frequency is within the allowable value, whereas if the system frequency is outside of the allowable range, the load shedding program will start, the neuron function will be activated to identify the generator with the fault and the load shedding sequence. The time is calculated after the time of the incient is 300ms. This interval includes time intervals: measurement of data acquisition, data transfer, data processing, and load circuit

      breaker operation. [5]

      The amount of load to be shed: After obtaining the load shedding sequence list for each generator failure, use the offline

      Fig. 4. Flowchart simulator sampling and neural network training

      Fig. 5. The process of developing a load shedding strategy

      The process of developing a load shedding strategy, including steps:

      From: Dp(i,j) = (i/Pi) + (j/Pj) – (j/Pi) – (i/Pj), with (i/Pi), (j/Pj), (j/Pi) and (i/Pj) are the corresponding elements in row i colum j in matrix [J1].

      The Jacobian matrix has the following general form:

      Fig. 6. General Jacobian matrix

      Step 1: Extract the Jacobian matrix J1 Step 2: Inverse J1

      Step 3: Apply phase electrical distance formula

      Step 4: Building the relationship between the nodes containing the generator and the nodes containing the load.

      Step 5: Arranges the order of the load shedding with the corresponding generator failure.

    2. Application ANN to identify the failure.

    Identification of failure in electrical systems is often difficult due to its complexity. Traditional failure identification methods are often unsuitable, which can cause problems not to be resolved in time to endanger the system. Thus, the ANN method with advantages of computational speed as well as efficiency will best solve these difficult problems that other traditional methods cannot solve.

    First, need to build data sets for ANN learning. This database is based on parameters at different load levels of the system (rate change of generator power, load power, voltage at buses, power variation on line, rate change of frequency at buses). The database must meet the following requirements:

    The database must fully display the status of the operation and must show all the various failure scenarios.

    The database created should ensure objectivity of the parameters of the test power system.

    The process of creating a database based on simulation PowerWorld and is done through the following stages:

    Fig. 7. Simulation steps for input, output sampling

    Fig. 8. Sampling process

    Fig. 9. Neural network training model with inputs and outputs

  4. TESTING ON THE IEEE 39 BUS NEW ENGLAND

    Fig. 10. The IEEE 39 bus New England

    The proposed method is tested on The IEEE 39 bus New England, using Powerworld software to collect samples and Matlab software for neural network training.

    1. Calculate the phase electrical distance.

      The phase electrical distance calculation is done in the following steps:

      Step 1: Take the Jacobian matrix J1 from Powerworld

      Bus

      Order

      Bus 30

      Bus 32

      Bus 33

      Bus 34

      Bus 35

      Bus 36

      Bus 37

      Bus 38

      Bus 39

      1

      Load 25

      Load 4

      Load 20

      Load 20

      Load 23

      Load 23

      Load 25

      Load 29

      Load 39

      2

      Load 3

      Load 7

      Load 16

      Load 16

      Load 21

      Load 21

      Load 3

      Load 28

      Load 8

      3

      Load

      18

      Load

      8

      Load

      24

      Load

      24

      Load

      16

      Load

      24

      Load

      26

      Load

      26

      Load

      7

      4

      Load 4

      Load 3

      Load 15

      Load 15

      Load 24

      Load 16

      Load 18

      Load 27

      Load 3

      5

      Load 16

      Load 12

      Load 21

      Load 21

      Load 15

      Load 15

      Load 27

      Load 25

      Load 4

      6

      Load 26

      Load 15

      Load 18

      Load 18

      Load 18

      Load 18

      Load 4

      Load 18

      Load 18

      7

      Load

      27

      Load

      16

      Load

      3

      Load

      3

      Load

      3

      Load

      3

      Load

      16

      Load

      3

      Load

      25

      8

      Load 15

      Load 18

      Load 23

      Load 23

      Load 4

      Load 4

      Load 15

      Load 16

      Load 16

      9

      Load 24

      Load 24

      Load 4

      Load 4

      Load 27

      Load 27

      Load 24

      Load 15

      Load 15

      10

      Load 8

      Load 25

      Load 27

      Load 27

      Load 26

      Load 26

      Load 8

      Load 24

      Load 27

      11

      Load

      7

      Load

      27

      Load

      26

      Load

      26

      Load

      25

      Load

      25

      Load

      7

      Load

      4

      Load

      24

      12

      Load 21

      Load 21

      Load 25

      Load 25

      Load 8

      Load 8

      Load 21

      Load 21

      Load 26

      13

      Load 39

      Load 26

      Load 8

      Load 8

      Load 7

      Load 7

      Load 39

      Load 8

      Load 21

      14

      Load 23

      Load 23

      Load 7

      Load 7

      Load 20

      Load 20

      Load 23

      Load 7

      Load 12

      15

      Load 12

      Load 39

      Load 12

      Load 12

      Load 12

      Load 12

      Load 28

      Load 23

      Load 23

      16

      Load 28

      Load 20

      Load 39

      Load 39

      Load 39

      Load 39

      Load 29

      Load 39

      Load 20

      17

      Load 29

      Load 28

      Load 28

      Load 28

      Load 28

      Load 28

      Load 12

      Load 12

      Load 28

      18

      Load 20

      Load 29

      Load 29

      Load 29

      Load 29

      Load 29

      Load 20

      Load 20

      Load 29

      TABLE 2. Proposed load shedding strategy

      J1.

      Fig. 11. The Jacobian matrix

      Step 2: Use the inv function of matlab to invert the matrix Step 3: Apply phase electrical distance formula: Dp(j,i) =

      Dp(i,j) = (i/Pi) + (j/Pj) – (j/Pi) – (i/Pj)

      From the above formula, use the matlab software to set the matrix related to the phase electrical distance between the nodes in the network together, called the matrix Dp.

      Step 4: From the relational parameters between the buses in

      the system to the phase electrical distance, the relationship between the nodes containing the generator and the nodes containing the load is shown in Table 1.

      TABLE 1. Table summarizes the relatonship between the nodes containing the generator and the nodes containing the load on phase electrical distance.

      Bus 30

      Bus 32

      Bus 33

      Bus 34

      Bus 35

      Bus 36

      Bus 37

      Bus 38

      Bus 39

      Bus 3

      0.027

      0.041

      0.047

      0.064

      0.045

      0.057

      0.040

      0.065

      0.038

      Bus 4

      0.037

      0.031

      0.050

      0.067

      0.047

      0.060

      0.048

      0.072

      0.038

      Bus 7

      0.045

      0.033

      0.058

      0.075

      0.055

      0.068

      0.056

      0.080

      0.037

      Bus 8

      0.044

      0.033

      0.057

      0.075

      0.055

      0.067

      0.055

      0.080

      0.035

      Bus 12

      0.061

      0.041

      0.071

      0.088

      0.069

      0.081

      0.072

      0.096

      0.059

      Bus 15

      0.041

      0.041

      0.039

      0.056

      0.036

      0.048

      0.051

      0.071

      0.047

      Bus 16

      0.039

      0.044

      0.031

      0.048

      0.029

      0.041

      0.048

      0.067

      0.046

      Bus 18

      0.034

      0.045

      0.043

      0.060

      0.041

      0.053

      0.044

      0.064

      0.044

      Bus 20

      0.070

      0.074

      0.028

      0.018

      0.060

      0.072

      0.079

      0.097

      0.077

      Bus 21

      0.049

      0.053

      0.041

      0.058

      0.023

      0.039

      0.058

      0.076

      0.056

      Bus 23

      0.056

      0.061

      0.048

      0.066

      0.021

      0.024

      0.065

      0.084

      0.063

      Bus 24

      0.044

      0.048

      0.036

      0.053

      0.030

      0.041

      0.053

      0.071

      0.051

      Bus 25

      0.027

      0.054

      0.058

      0.075

      0.055

      0.068

      0.022

      0.061

      0.045

      Bus 26

      0.040

      0.058

      0.056

      0.074

      0.054

      0.066

      0.041

      0.042

      0.053

      Bus 27

      0.040

      0.054

      0.050

      0.067

      0.048

      0.060

      0.044

      0.053

      0.051

      Bus 28

      0.065

      0.083

      0.082

      0.099

      0.079

      0.092

      0.067

      0.026

      0.079

      Bus 29

      0.067

      0.085

      0.084

      0.101

      0.081

      0.093

      0.068

      0.015

      0.080

      Bus 39

      0.053

      0.062

      0.078

      0.095

      0.075

      0.087

      0.066

      0.094

      0.000

      Step 5: Arranges the order of the load shedding with the corresponding generators failure.

      Explanation: According to the suggested strategy table above, if there is a fault at generator 30, the load shedding order will be Load 25 Load 3 Load 18 … until the system is stabilized again. Similarly, for the remaining generators.

    2. Test load shedding on the IEEE 39 bus New England by Powerwold.

      Process simulation sampling with the IEEE 10 generators 39 bus are made as follows:

      Assuming the fault generator is 34, at load level 100%, the frequency and deviation of the rotor angle become unstable.

      Fig. 12. Diagram of rotor angle generator and frequency of bus at fault generator 34 load level 100%

      When the bus frequency and rotor angle exceeded the allowable range, load shedding are made according to the strategic proposed. In this case, just shed the Load 20, the rotor frequency and angle have stabilized.

      Fig. 13. Diagram of rotor angle generator and frequency of bus after shed

      Load 20

      Simulation proceeds from 60% to 100% load level at each load level causing the outage generators from generator 30 to

      39. After simulating and load shedding, the record is aggregated into sample. The result of the number of samples after the simulation of the case must load shedding when the generator fault is 369 samples.

    3. Neural network training

      This section introduces the generalized regression neural network training model; the training process follows these steps:

      Step 1: Data collection, performs the collection via simulation on the PowerWorld software to retrieve data for each load level. Compile into a dataset, then split 85% of the sample for training and 15% of the sample for testing.

      Step 2: Create a network, implementing generalized regression neural network training should provide only input and output vector information along with spread constant, the procedure as follows:

      Create input_data is 85% of the samples (315 samples), output_data, input_test is 15% of the samples (54 samples).

      Fig. 14. Neural network training structure

      Step 3: Create a interface.

      To facilitate training, monitoring, fault identification and load shedding. The creation of an appropriate interface program is necessary to enable the operator to easily monitor the operation of the system. Combined with the idea of creating a machine system that works on different power systems the interface is created as follows:

      Fig. 15. The neural training system interface forecast load shedding in case of generator failure

  5. COMPARISON OF SUGGESTED METHOD WITH

    OTHER METHODS.

    1. Load shedding based on the AHP algorithm (Analytic Hierarchy Process) [6]

      AHP is the approach to making decisions. This method presents balanced assessment options and criteria, and integrates them into a final decision. AHP is particularly suitable for cases involving analysis and quantification, make decisions when there are multiple options depending on the criteria with multiple interactions.

      Table 3. The order of load shedding according to the AHP algorithm

      0.3

      Order

      Load

      load center

      Wdi

      Wkj

      Wij

      19

      L39

      LC1

      0.37

      0.467

      0.172543

      18

      L4

      LC1

      0.22

      0.467

      0.104727

      17

      L8

      LC1

      0.22

      0.467

      0.104727

      16

      L20

      LC2

      0.278

      0.08208

      15

      L7

      LC1

      0.13

      0.467

      0.061118

      14

      L27

      LC3

      0.33

      0.16

      0.053155

      13

      L29

      LC3

      0.33

      0.16

      0.053155

      12

      L15

      LC2

      0.17

      0.278

      0.048329

      11

      L16

      LC2

      0.17

      0.278

      0.048329

      10

      L3

      LC4

      0.49

      0.10

      0.047017

      9

      L24

      LC2

      0.16

      0.278

      0.043056

      8

      L28

      LC3

      0.20

      0.16

      0.031606

      7

      L21

      LC2

      0.11

      0.278

      0.030445

      6

      L25

      LC4

      0.31

      0.10

      0.029619

      5

      L23

      LC2

      0.09

      0.278

      0.025351

      4

      L26

      LC3

      0.14

      0.16

      0.022349

      3

      L18

      LC4

      0.2

      0.10

      0.018659

      2

      L12

      LC1

      0.03

      0.467

      0.011867

      1

      L31

      LC1

      0.03

      0.467

      0.011867

    2. Load shedding based on under frequency load shedding relays [7]

      Load shedding based on under frequency load shedding relays is the most commonly used method, which is still being used in many parts of the world, including Vietnam. When the grid frequency falls below the permitted threshold, the relay will be load shedding, prevents the system frequency declines. Without this load shedding control, the greatest possible consequence is the blackout, ie, the widespread outage.

      For example, the ERCOT load shedding program, has a load shedding plan under frequency load shedding shown in Table 4.

      Table 4. The ERCOT load shedding program

      Frequency

      Load

      59.3 Hz

      5% of total load

      58.9 Hz

      15% of total load

      58.5 Hz

      25% of total load

    3. Simulate and compare methods with each other.

      Fig. 16. Diagram of rotor angle and bus frequency after generator 34 outage according to the proposed load shedding method.

      ACKNOWLEDGMENT

      This research was supported by Ho Chi Minh City University of Technology and Education under a research at the Power System and Renewable Lab.

      Fig. 17. Diagram of rotor angle and bus frequency after generator 34 outage according to the AHP method.

      Fig. 18. Diagram rotor angle and bus frequency after generator 34 outage according to the under frequency load shedding relay method.

      Comparative results of the methods presented in Table 5.

      Table 5. Comparative results of the methods

      Method

      Frequency

      recovery time (s)

      Frequency

      stability (Hz)

      Amount of

      load shedding (MW)

      Proposed

      45

      60,030

      628,000

      AHP

      55

      60,157

      738,852

      UFLS relay

      100

      60,89

      1154,394

      In the case of generators 34 outage with a load level of 100%, the proposed method has many advantages over the AHP method and the low frequency method, as follows:

      The amount of load shedding in at least 3 methods.

      The rotor angle recovery time is almost the same, but the frequency recovery time of the proposed method is earlier (about 20 ÷ 25s). System stability sooner and increased reliability in the supply.

      It can be seen that the load shedding is based on phase electrical distance are offset for both the remaining methods, with fast recovery times, optimum capacity load shedding and bus frequencies within the allowable range.

  6. CONCLUSION.

The paper proposed the method load shedding on the basis of the combination of Generalized Regression Neural Network, power sensitivity theory and Phase Electrical Distance theories to maintain a stable electrical system in the event loss a generator occurring in the electric system. The effect of the proposed method is to perform offline simulations on the IEEE 39 Bus New England test system. The results of the comparison of the efficacy of the proposed method with the AHP method and low frequency method revealed that: The proposed method has a system frequency of recovery that is 18% faster than the AHP method, 55% faster than the low frequency method; The load shedding capacity was 15% less than the AHP method and 45.6% less than the under frequency load shedding relay method.

REFERENCES

  1. H. Bevrani, Senior Member, IEEE, G. Ledwich, Senior Member, IEEE, and J. J. Ford. "On the Use of df/dt in Power System Emergency Control", 2009.

  2. Urban Rudez, Rafael Mihalic, Faculty of Electrical Engineering, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia. "A novel approach to underfrequency load shedding", 2011.

  3. Farrokh Shokooh, J.J. Dai, Shervin Shokooh, Jacques Tastet, Hugo Castro, Tanuj Khandelwal, & Gary Donner, IEEE Industry Applications Magazine, "Intelligent load shedding, case study of the application in a large industrial facility", 21 January 2011.

  4. Lagonotte Patrick, Labroratoire d'Automaticque et d'Informatique Industrielle, "The different electrical distances"

  5. Tohid Shekari, Student Member, IEEE, Farrokh Aminifar, Senior Member, IEEE, and Majid Sanaye-Pasand, Senior Member, IEEE, "An Analytical Adaptive Load Shedding Scheme Against Severe Combinational Disturbances", 2015

  6. Nhan MaiNgoc, "Master Thesis: Study of load shedding using neural network and AHP algorithm", Ho Chi Minh City of University of Technology and Education, VietNam, 2017

  7. Ercot, "Under frequency Load Shedding, 2006

  8. D. Kottick, Neural Network for Predicting the Operation of an Under Frequency Load Shedding System, IEEE Trans. on Power Systems, Vol.11, No. 3, pp. 1350-58, 1996.

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