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For a timely decarbonization of our economy, power systems need to accommodate increasing numbers of clean but stochastic resources. This requires new operational methods that internalize this stochasticity to ensure safety and efficiency. This paper proposes a novel approach to compute adaptive safety intervals for each stochastic resource that internalize power flow physics and optimize the expected cost of system operations, making them "prescriptive". The resulting intervals are interpretable and can be used in a tractable robust optimal power flow problem as uncertainty sets. We use stochastic gradient descent with differentiable optimization layers to compute a mapping that obtains these intervals from a given vector of context parameters that captures the expected system state. We demonstrate and discuss the proposed approach on two case studies. • Prescriptive uncertainty sets for robust optimal power flow improve security. • Context-aware sets can be learned using differentiable optimization. • Prescribed sets outperform fixed sets and offer performance guarantees. • Performance metrics include expected cost and probability of exceedance. [ABSTRACT FROM AUTHOR]

In this paper, droop control of inverters in a multi-terminal HVDC (MTDC) system is designed to achieve minimum DC system loss while preventing converter overvoltage. Circuit analysis along with optimization analysis is first conducted to seek the optimal droop control gains. Contingent operating conditions are then considered. The droop control gains will adapt to achieve minimum loss as well as constant inverter side dc voltage. A 6-terminal MTDC system with detailed control loops as well as PWM-based switching is built in a real-time digital simulator, RT-Lab. Time-domain simulation results validate the circuit analysis results and demonstrate the feasibility of the proposed adaptive droop control. [ABSTRACT FROM AUTHOR]

This paper presents a novel heuristic based adaptive control technique (ACT) for improved compensation capability of the unified power quality conditioner (UPQC). The compensation capability of UPQC is enhanced by the optimal regulation of DC link voltage. Among all power quality (PQ) distortions, voltage sag is the severe PQ problem that significantly deteriorates the regulation of DC link voltage. The conventional approaches utilize a fixed gain PI controller that increases the error between the reference and actual DC link voltage under sag condition. This ultimately results in poor compensation of PQ distortions at the point of common coupling. To enhance compensation capability, the performance of ACT is examined with analytical and artificial intelligence techniques. The model reference adaptive system (MRAS) is used in the analytical method for online self tuning PI controller. For artificial intelligence technique, the off-line trained ANFIS is processed in the simulation studies. The performance of ACT based UPQC is validated in Matlab/Simulink and the obtained results are compared with conventional scheme. To substantiate theoretical results of the proposed approach, the algorithm is tested in real time Xilinx system generator (XSG). [ABSTRACT FROM AUTHOR]

Highlights: [•] The paper presents a novel solution for distance relay third zone load encroachment problem. [•] A multi-agent based adaptive relaying algorithm is used to propose the solution. [•] The proposed algorithm was examined on radial and loop network configurations. [•] The results showed the superiority of the proposed algorithm in mitigating the load encroachment problem compared to traditional Mho and Quad settings. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces and compares four algorithms by which reconfiguration for restoration of an arbitrary number of interdependent critical infrastructure systems can be achieved. A method of modeling systems called Graph Trace Analysis is used to enable generic operation on various system types. The algorithms described are compared with each other and with prior work when run on a model of an actual electrical distribution system. The described algorithms are also run on an example model to demonstrate the ability to reconfigure interdependent infrastructure systems. [Copyright &y& Elsevier]

Abstract: This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines. [Copyright &y& Elsevier]