Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks

A number of contingencies simulated during dynamic security assessment do not generate unacceptable values of power system state variables, due to their small influence on system operation. Their exclusion from the set of contingencies to be simulated in the security assessment would achieve a significant reduction in computation time. This paper defines a critical contingencies selection method for on-line dynamic security assessment. The selection method results from an off-line dynamical analysis, which covers typical scenarios and also covers various related aspects like frequency, voltage, and angle analyses among others. Indexes measured over these typical scenarios are used to train neural networks, capable of performing on-line estimation of a critical contingencies list according to the system state.

Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method

This letter proposes a reliable transfer learning(RTL)method for pre-fault dynamic security assessment(DSA)in power systems to improve DSA performance in the presence of potentially related unknown faults.It takes individual discrepancies into consideration and can handle unknown faults with incomplete data.Extensive experiment results demonstrate high DSA accuracy and computational efficiency of the proposed RTL method.Theoretical analysis shows RTL can guarantee system performance.

Selecting decision trees for power system security assessment

Power systems transport an increasing amount of electricity,and in the future,involve more distributed renewables and dynamic interactions of the equipment.The system response to disturbances must be secure and predictable to avoid power blackouts.The system response can be simulated in the time domain.However,this dynamic security assessment(DSA)is not computationally tractable in real-time.Particularly promising is to train decision trees(DTs)from machine learning as interpretable classifiers to predict whether the systemwide responses to disturbances are secure.In most research,selecting the best DT model focuses on predictive accuracy.However,it is insufficient to focus solely on predictive accuracy.Missed alarms and false alarms have drastically different costs,and as security assessment is a critical task,interpretability is crucial for operators.In this work,the multiple objectives of interpretability,varying costs,and accuracies are considered for DT model selection.We propose a rigorous workflow to select the best classifier.In addition,we present two graphical approaches for visual inspection to illustrate the selection sensitivity to probability and impacts of disturbances.We propose cost curves to inspect selection combining all three objectives for the first time.Case studies on the IEEE 68 bus system and the French system show that the proposed approach allows for better DT-selections,with an 80%increase in interpretability,5%reduction in expected operating cost,while making almost zero accuracy compromises.The proposed approach scales well with larger systems and can be used for models beyond DTs.Hence,this work provides insights into criteria for model selection in a promising application for methods from artificial intelligence(AI).

Two-stage Transient-stability-constrained Optimal Power Flow for Preventive Control of Rotor Angle Stability and Voltage Sags

In practice,an equilibrium point of the power system is considered transiently secure if it can withstand a specified contingency by maintaining transient evolution of rotor angles and voltage magnitudes within set bounds.A novel sequential approach is proposed to obtain transiently stable equilibrium points through the preventive control of transient stability and transient voltage sag(TVS)problems caused by a severe disturbance.The proposed approach conducts a sequence of non-heuristic optimal active power re-dispatch of the generators to steer the system toward a transiently secure operating point by sequentially solving the transient-stability-constrained optimal power flow(TSC-OPF)problems.In the proposed approach,there are two sequential projection stages,with the first stage ensuring the rotor angle stability and the second stage removing TVS in voltage magnitudes.In both projection stages,the projection operation corresponds to the TSC-OPF,with its formulation directly derived by adding only two steady-state variable-based transient constraints to the conventional OPF problem.The effectiveness of this approach is numerically demonstrated in terms of its accuracy and computational performance by using the Western System Coordinated Council(WSCC)3-machine 9-bus system and an equivalent model of the Mexican 46-machine 190-bus system.