The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network weights are updated based on real-time data from Phasor measurement units. Resilience assessment based on failure probability, financial impact, and time-series data in grid failure management determine the norm

Power System Stabilizer (PSS) focuses only on damping local oscillation by generator excitation control [

Numerous WAC methods have been proposed by many researchers, as summarized in [

Smart grid phasor measurement unit (PMU) can provide grid-connected power system data for processing [

In a smart grid system, resilience refers to the “ability if anticipating high impact, quick recovering, and prevent or mitigating similar high impact disturbance” [

For IEEE 39 bus system, this WAC control approach has a higher computation time. Another traditional procedure is followed for inter-area damping PSS [

To alleviate the computation complexity, an optimal WAC design problem is assigned. In [

To analyze the closed-loop resilience index based on

A robust inter-area oscillations damping is provided through online tuning of WAC parameter’s data-driven.

The resilience assessment and convergence analysis of the proposed data-driven deep learning algorithm have been experimentally validated by a case study in an IEEE 39 bus ten machine system.

The remainder of the paper is organized as follows; Section 2 develops the small-signal model formation for resilience assessment. A data-driven deep learning algorithm is described in Section 3. In Section 4, the

In this research work, an

where

The main idea in WAC modeling is a real-time remote measurement. The novel data-driven approach in the design of WAC eliminates inter-area oscillation. In a real-time power system, PMUs are installed in various regions (area). Consider the local subsystem given by reduced system

the Riccati equation control law is used for controller design. To linearize the design of WAC to dam inter-area oscillation and resilience assessment,

where

The linear power system model is given by;

In the proposed linear model of the study area, PMU encounters various network attacks. Hence, an optimal design with resilience for WAC is considered by the deep learning technique. This validates the proposed deep learning-based WAC, mainly focusing on WAC’s trade-off design as an optimal and resilience-based method. The non-linearity in PMU is considered a communication failure, such that data does not transfer between bus

where

The proposed WAC, described in

The deep belief network tunes this model to reduce inter-area oscillation and resilience assessment by preserving the system’s original dynamic character. The proposed DBN produces the trade-off between system inter-area oscillation reduction and resilience assessment. Hence, the problem depends on two important analyses as follows.

The proposed WAC with gain

To identify the most vulnerable bus that makes not resilience of the system.

To verify the effectiveness of the proposed DBN-WAC with varying operating conditions, the simulation was carried on an IEEE 39 bus system with a three-phase-to-ground fault on the external area.

This section develops a Deep Belief Network (DBN) to gain tuning in the WAC. By using the linear power system model in _{i}

where _{i}_{i}

In general, the state of tuned WAC is given by

where subscript _{c}_{c}

The component of

Here, _{i}_{12} is made by the objective function as follows:

where, _{d}

_{e}

In the study area, the training samples are generated based on operating states.

Area | Classical machine (2nd order) | 3rd order machine | Exciter | Governor | Proposed WAC | States |
---|---|---|---|---|---|---|

Study | – | 9 | 10 | 10 | 1 | 105 |

External | 14 | 2 | 9 | 16 | – | 90 |

For tuning this objective function, the constraints are given by;

The power system model is a linearized model with the second order.

The generator operator in the control area.

The damping ratio should be greater than 0.1.

As indicated, the main objective of the proposed WAC is to help inter-area oscillations. By calculating

For assessing the performance of the proposed DBN tuned WAC, a DoS attack on the bus

where

By solving

This nominal WAC controller design was checked by DoS attack by introducing _{o}

The system matrices can be defined based on DoS attack profile

where,

The proposed WAC is implemented in an IEEE 39 bus system for analysis and resilience assessment. In the IEEE 39 bus 10 machine system, one machine is considered as a study area, and the remaining 9 machines are considered as an external area.

To implement the linearized model of the IEEE 39 bus system, a MATLAB power system toolbox has been used. This standard model has 10 generators, in which one generator operates at the sub-transient state. Generator 1 and generator 10 connected through WAC for damping inter-area oscillation. Moreover, generator 10 is connected was a slack bus. PMU used to collect its dynamics to another controlled generator. The overall system has 90 external states.

The dynamic performance was quantified under the same operating states of the test system. For quick oscillation damping verification, the proposed controller damping is verified by calculating the resilience index on channel

To evaluate the robustness of the proposed algorithm tuning by applying 3 phase faults. The online tuning was carried at different operating conditions. This makes the system forced to settle at different instants. For changing the operating condition, loads are added on the bus 5 to 10 in the external area; this impacts the study area. From this PMU data, online tuning is initiated. The load changed from 100 MW to 200 MW. The three-phase fault is applied after dynamic loading is initiated. The fault is applied after bus 9 at time

From

The proposed WAC’s reliability for damping inter-area oscillation is analyzed by tracking the eigenvalue trajectories without controller, conventional PSS, and proposed controller.

Controller in bus 30 (study area) | Channel/state1-channel attacks | Overall resilience index |
---|---|---|

Without controller | 8/70, bus |
0.007 |

Conventional PSS | 9/70 | 0.48 |

Proposed DBN-WAC | 9/70 | 0.962 |

Mode index | Mode type | Without controller | With PSS | With proposed DBN-WAC | |||
---|---|---|---|---|---|---|---|

f (Hz) | |||||||

1-channel | Inter-area | −0.012 | 0.579 | 0.056 | 0.652 | 0.005 | 1.03 |

2-channel | Local | −0.025 | 0.687 | 0.048 | 0.687 | 0.004 | 1.002 |

A mathematical model of deep learning-based WAC tuning for damping inter-area oscillation and power system resilience is proposed. For communication network attacks, power system resilience is assessed and analyzed. For analysis, an IEEE 39 bus, including ten machines system, were considered, and it is divided into the study area and an external area for verification. The proposed WAC is installed in the study area. Online tuning is performed using a deep belief network by PMU data. To check the resilience and damping low-frequency oscillation, DoS attack and three-phase faults were introduced on the external area, and the effect is analyzed in the study area.

The authors would like to thank the Management, Principal, and Renewable Energy Lab, Department of Electrical and Electronics Engineering of Mepco Schlenk Engineering College, Sivakasi, for providing the authors with the necessary facilities to carry out this research work.