Computers, Materials & Continua DOI:10.32604/cmc.2021.015128 | |

Article |

Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

1Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India

2College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser, 11991, Saudi Arabia

3Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61517, Egypt

4Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India

*Corresponding Author: Hegazy Rezk. Email: hr.hussien@psau.edu.sa

Received: 07 November 2020; Accepted: 05 February 2021

Abstract: 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

Keywords: Neural network; deep learning algorithm; low-frequency oscillation; resiliency assessment; smart grid; wide-area control

Power System Stabilizer (PSS) focuses only on damping local oscillation by generator excitation control [1]. However modern power is highly affected by both local and inter-area oscillations. This instability is further increased by demand-side energy management and distributed energy resources due to the grid-connected system [2]. A Wide Area Control (WAC) coordinates the improvement of stability and decreases inter-area oscillations in a modern power system. For efficient oscillation reduction and stability improvement, a data-driven approach in a smart grid with real-time measurement is highly necessary [3–5].

Numerous WAC methods have been proposed by many researchers, as summarized in [6]. Most conventional design architectures follow a two-level architectural framework with a lower-level local controller as PSS and WAC as an upper-level primary controller [7]. For analysis, this controller topology is robust and have an optimal control [8], adaptive control [9], model predictive control [10], Iterative learning control [11], etc. For performance evaluation, resilience indices like

Smart grid phasor measurement unit (PMU) can provide grid-connected power system data for processing [13]. Feedback controller gain increases stability and improves inter-area damping based on real-time PMU data suggested by the western electricity coordination council (WECC). Control schemes based on the adaptive control algorithm [14], PI controller schemes [15], optimal algorithm [16], etc., have an effective role in primary level control. This causes the need for a superior advanced control scheme based on smart grid data. PMU provides exogenous inputs that aid in increasing the learning of damping inter-area oscillations for WAC.

In a smart grid system, resilience refers to the “ability if anticipating high impact, quick recovering, and prevent or mitigating similar high impact disturbance” [17]. The power grid security assessment identifies resilience strategies based on risk assessment and statistical performance index like system average interruption frequency index (SAIFI) and system average interruption duration (SAIDI) are used. FACTS devices like STATCOM [18], SVC [19], and TCSC [20] have no communication facility due to the intermittent nature of the location. For efficient inter-area oscillation, WAC represents model order reduction in a smart grid-connected system. By calculating controllability and observability, resilience computation is performed. This model has a reduced-order decentralized damping controller computed using a cross-gramian approach. Sylvester equation is used for solving cross-gramian is performed.

For IEEE 39 bus system, this WAC control approach has a higher computation time. Another traditional procedure is followed for inter-area damping PSS [21]. This control strategy needs electrical phase compensation. Also, this method makes the power system unstable. To avoid this issue in PSS, online tuning of controller parameters using fuzzy logic computation is used [22]. However, this method further increases the damping time to affect the settling time. Besides, the accuracy of parameter tuning is not affected. Another approach with synchronized phasor measurement is that the WAC controller is designed for large power systems [23]. For each terminal area, the PMU is used for data collection and processing. Based on this PMU data, a state feedback closed-loop system is formed. But by using PMU measurement in the power system results in the amplification of inter-area oscillation. Moreover, the system is observable.

To alleviate the computation complexity, an optimal WAC design problem is assigned. In [24–27], the instability caused by the power system’s structural constraints were analyzed. In [28–30], the delay in WAC measurement was considered as an objective function. However, the resilience performance assessment was not considered. In [31–33], based on steady state response, power system resilience was analyzed. Using various communication attacks and disturbance power system attack was introduced. Also, they have not considered cyber-physical attacks [34–40]. The main contributions of the proposed deep learning-based WAC method are enumerated as follows.

• 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 N subsystem representing generator, load, bus, and other power components are reduced to two areas for study. In which area 1 is called a study area with a generator and proposed WAC controller. Area 2 is called an external area with a turbine governor, Automatic Voltage Regulator (AVR), and exciter. The complex form of the non-linear power system is given by,

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 Eq. (3).

the Riccati equation control law is used for controller design. To linearize the design of WAC to dam inter-area oscillation and resilience assessment, Eq. (3) has a local study area controller namely;

where K is WAC gain.

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

2.2 Research Problem Formation

The proposed WAC, described in Eq. (9), is used to assess the power system’s dynamic stability with inter-area oscillation compensation. The data collected in PMU is power flow between bus

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 K is tuned by DBN for all possible attacks.

• 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.

Simulation scenario: In IEEE 39 bus 10 machine system three-phase-to-ground fault is introduced in the external area connecting bus 1 and 2 at time t = 0.6 s, switching the fault occurred tie line in the study area at time t = 0.7 s.

3 Proposed WAC Tuning by Deep Belief Network

This section develops a Deep Belief Network (DBN) to gain tuning in the WAC. By using the linear power system model in Eq. (4), the state variable xi is defined as;

where Zi is the noisy measurement and Vi is the voltage at bus i. This affects the controller gain K with a different attack. The optimal tuning of the WAC by DBN is obtained by PMU data from the phasor data connection. By tuning the proposed WAC using the controlled input from PMU, the controlled output signal

In general, the state of tuned WAC is given by Eq. (13).

where subscript c denote the controller state model. xc(t) is gain controlled WAC and yc(t) is controlled output vector. The proposed deep learning resilient controller is given in Fig. 2.

The component of

Here, Ci represents the full order observer input layer. The tuning of controller gain gives the optimal gain K12 is made by the objective function as follows:

where, fd is the number of internal stabilizers for the designed controller. The tuned time-domain model of WAC is given by Eq. (17).

Eq. (18) obtained input as active power flow through the line

In the study area, the training samples are generated based on operating states. Tab. 1 presents the number of states used in the study area to train the proposed DBN model.

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.

4 Proposed Resilience Assessment and Analysis

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

4.1 Mathematical Analysis of Resilience Assessment

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

where W denotes the updated weight for control law given in Eq. (4). For each weight updating, the Hadamard product is performed. The

By solving Eq. (23) by Ricatti approach, the

This nominal WAC controller design was checked by DoS attack by introducing Mo with reduced dimensionality. The

4.2

The system matrices can be defined based on DoS attack profile

where,

5 Implementation of Proposed Deep Learning Algorithm Tuned WAC

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. Tab. 1 presents the details of training states of the IEEE 39 bus 10 machines system.

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.

Fig. 4 shows the implementation of the proposed DBN-WAC on the IEEE 39 bus system. We evaluate the WAC at nominal operating states. For resilience assessment bus

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

Fig. 5 presents the damping of low-frequency inter-area oscillation. It is observed that a significant improvement compared with the conventional PSS. Thanks to the proposed deep learning tuned WAC, the transient response is highly suppressed. In addition, the settling time is reduced. Similarly, the steady-state response is quickly obtained with less time to settle to steady-state as illustrated in Fig. 4.

6.1 Online Deep Learning Algorithm Tuning Validation

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 t = 20 ms and cleared after t = 100 ms. The active power flow between bus 1 and 10 was analyzed compared to deep learning tuning WAC and offline tuning. Fig. 6 shows the active tie-line power flow in buses 1 and 2 due to a 3-phase fault.

From Fig. 6, deep learning tuning quickly suppresses the oscillation and easily achieved the steady state by comparing to conventional PSS. The steady state is obtained at t = 15 ms, which confirms, after the fault is cleared after time t = 5 ms for oscillation damping.

The proposed WAC’s reliability for damping inter-area oscillation is analyzed by tracking the eigenvalue trajectories without controller, conventional PSS, and proposed controller. Fig. 7 shows the trajectory of eigenvalue with inter-area oscillation in the complex plane. From Fig. 7, we can identify the original IEEE 39 bus system has poor inter-area oscillation damping with eigenvalue

6.2 Effectiveness by Resilience Index Assessment for Each Controller

Eq. (26) is used to estimate the resilience index by applying a DoS attack on the proposed deep WAC controller. In Tab. 2, the destabilizing attack of conventional PSS and proposed DBN-WAC is presented. Fig. 8 shows the controller resilience index for DoS attack on change bus

Tab. 3 presents the classification result on inter-area oscillation and local oscillation of the IEEE 39 bus test system with the proposed DBN-WAC controller and without a controller. From Tab. 3, we can identify that the system exhibits local oscillation without any controller based on a negative damping ratio. There are local, and inter-area modes for bus 4 to 7 and 21 to 29 since these modes have higher frequencies and greater damping ratios.

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.

Acknowledgement: 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.

Funding Statement: The authors received no specific funding for this study.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

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