A procedure to design wide-area damping controllers for power system oscillations considering promising input–output pairs

Bento, Murilo E. C.

doi:10.1007/s12667-018-0304-x

Cited by 26 publications

(3 citation statements)

References 83 publications

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“…Bio-inspired algorithms are optimization algorithms that draw on the principles and inspiration of nature's biological evolution to develop new search tools for optimization problems. Different bio-inspired algorithms have been proposed such as Particle Swarm Optimization [71], Coati Optimization Algorithm [72], Pelican Optimization Algorithm [73], Marine Predators Algorithm [74], Electric Eel Foraging Optimization [75], Hippopotamus Optimization Algorithm [76], Several applications of bio-inspired algorithms in power systems have been proposed over the years such as wide-area damping control design [77,78], electricity theft detection [79], integrated energy system optimization [80], optimization of HVAC systems [81], power system stabilizer design [82], load dispatch for microgrid [83], energy management [84], load profile generation [85], power system state estimation [86], short-term hydrothermal scheduling [87], distributed power generation planning [88], reactive power optimization [89], maximum power point tracking [90], wind turbine placement [91], coordination of directional overcurrent relays [92], placement of electric vehicle charging station [93], optimal DG unit placement [94], power quality disturbances identifi-cation [95], optimal power flow [96]. The use of bio-inspired algorithms to tune traditional ANN is possible and some authors have already pointed out this benefit to improve the generalization capacity of the ANN.…”

Section: Introductionmentioning

confidence: 99%

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

Bento

2024

Energies

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Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for evaluating the operation of power systems. The load margin (LM) is an important index in evaluating the stability of power systems, but traditional methods for determining the LM consist of solving a set of differential-algebraic equations whose information may not always be available. Data-Driven techniques such as Artificial Neural Networks were developed to calculate and monitor LM, but may present unsatisfactory performance due to difficulty in generalization. Therefore, this article proposes a design method for Physics-Informed Neural Networks whose parameters will be tuned by bio-inspired algorithms in an optimization model. Physical knowledge regarding the operation of power systems is incorporated into the PINN training process. Case studies were carried out and discussed in the IEEE 68-bus system considering the N-1 criterion for disconnection of transmission lines. The PINN load margin results obtained by the proposed method showed lower error values for the Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than the traditional training Levenberg-Marquard method.

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Section: Introductionmentioning

confidence: 99%

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

Bento

2024

Energies

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“…Thus, the choices of the WADC output and input signals must be guided towards choosing the minimum number of signals that is sufficient to damp all modes that need their damping ratios to be high. In the literature, methods applying the traditional geometric measures, residuals and heuristics, have been proposed in the scientific community and the results were effective [30,[38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Wide-Area Measurement-Based Two-Level Control Design to Tolerate Permanent Communication Failures

Bento

2023

Energies

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The operation of modern power systems must meet stability requirements to guarantee the supply of electrical energy. One of these requirements is to ensure that the low-frequency oscillation modes have high damping ratios to avoid angular instability and future power system blackouts. Advances in phasor measurement units (PMUs) have contributed to the development and improvement of wide-area damping controllers (WADCs) capable of increasing the damping rates of the oscillation modes of the system, especially the inter-area modes. Nevertheless, the operation of WADCs is vulnerable to communication failures and cyber-attacks, and if not properly designed the WADC can affect the stability of the entire system. This research proposes a procedure for designing a WADC robust to permanent communication failures using a linear quadratic regulator (LQR) and genetic algorithms. Case studies conducted on an IEEE 68-bus test power system show the effectiveness of the WADC designed by the proposed procedure even when communication failures are occurring in the system. The use of genetic algorithms improves the convergence and results of the LQR-based method.

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“…With regard to the choice of remote signals, methods based on residuals, geometric measures [27] and metaheuristic [28] were proposed and the results were effective in the purpose of WADC to improve the dynamic performance of the system. The next challenge in WADC design is the handling of time delays in data transmission.…”

Section: Introductionmentioning

confidence: 99%

Design of a Wide-Area Power System Stabilizer to Tolerate Multiple Permanent Communication Failures

Bento

2023

Electricity

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Wide-Area Power System Stabilizers (WAPSSs) are damping controllers used in power systems that employ data from Phasor Measurement Units (PMUs). WAPSSs are capable of providing high damping rates for the low-frequency oscillation modes, especially the inter-area modes. Oscillation modes can destabilize power systems if they are not correctly identified and adequately damped. However, WAPSS communication channels may be subject to failures or cyber-attacks that affect their proper operation and may even cause system instability. This research proposes a method based on an optimization model for the design of a WAPSS robust to multiple permanent communication failures. The results of applications of the proposed method in the IEEE 68-bus system show the ability of the WAPSS design to be robust to a possible number of permanent communication failures. Above this value, the combinations of failures and processing time are high and they make it difficult to obtain high damping rates for the closed-loop control system. The application and comparison of different optimization techniques are valid and showed a superior performance of the Grey Wolf Optimizer in solving the optimization problem.

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A procedure to design wide-area damping controllers for power system oscillations considering promising input–output pairs

Cited by 26 publications

References 83 publications

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

Wide-Area Measurement-Based Two-Level Control Design to Tolerate Permanent Communication Failures

Design of a Wide-Area Power System Stabilizer to Tolerate Multiple Permanent Communication Failures

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