Optimal fault location algorithm for series-compensated transmission lines based on PMU data

Mousavi-Seyedi, Seyed Sina; Aminifar, Farrokh; Rezaei, Mohammad Reza; Hasani, Rahman

doi:10.1109/sgc.2015.7857418

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Finally, by installing all 24 PMUs at the end of the planning, the APO index for the third stage would be equal to 0.9952, i.e., the initial target value. Obviously, the buses' probability of observability indexes for the final stage, 3 , are exactly similar for both methods, as represented in Table 4. The test carried out in this scenario clearly shows the advantage of the proposed MPP over the conventional MPP approach.…”

Section: First Scenario (A Comparative Study)mentioning

confidence: 89%

Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus

et al. 2018

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This paper presents a mathematical linear expansion model for the probabilistic Multistage Phasor Measurement Unit (PMU) Placement (MPP) in which zero-injection buses (ZIBs), as well as communication channel limitations, are taken into consideration. From the linearization perspective, presenting a model formulizing the probabilistic concept of observability while modelling the ZIB is of great significance, and has been done in this paper for the first time. More importantly, the proposed probabilistic MPP utilizes a technique disregarding the prevalent subsidiary optimizations for each planning stage. Although this technique, in turn, increases the problem complexity with manifold variables, it guarantees the global optimal solution in a wider and thorough search space; while in the prevalent methods, some parts of the search space might be missed. Furthermore, the proposed model indicates more realistic aspects of the MPP where system operators are allowed to follow their intention about the importance of buses such as strategic ones based on monitoring the priority principles. In addition, the model is capable of considering the network topology changes due to long-term expansions over the planning horizon. Finally, in order to demonstrate the effectiveness of the proposed formulation, the model is conducted on the IEEE 57-bus standard test system and the large scale 2383-bus Polish power system.

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Section: First Scenario (A Comparative Study)mentioning

confidence: 89%

Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus

et al. 2018

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“…If the MI value is high that means it is a local mode or if it has a small value that is mean an inter-area mode. The MI of ith electromechanical mode is given in (2).…”

Section: Mode Index (Mi)mentioning

confidence: 99%

A novel online monitoring system of frequency oscillations based intelligence phasor measurement units

Hussein

Abdullah

Jafar

2023

IJPEDS

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In this paper, frequency oscillation has been present as it is an important issue in power systems, especially when it is joined to the new trend to use alternative energies as an energy source, as well as to be a big risk for the inter-connection of the modern electric power networks. Wind farm acts as alternative energy and connected to two buses on the Iraqi power system. Because the low-frequency oscillation monitoring needs be accurate and fast, the main objective is to propose a novel online monitoring system consisting of phasor measurement unit's (PMU) with artificial intelligence neural network (PMU-NN). The location of the phasor measurement units has been optimized using (graph-theoretic procedure algorithm) and the function for the artificial intelligence (NN) is radial basis function (RBFNN). The data information from phasor measurement units is the inputs to the artificial intelligence system then predictions are made Information on low-frequency oscillation (target). The MATLAB toolboxes (PSAT & NN) used to obtain results. Finally, from the results, the validity of the proposed (PMU-NN) system has been proven and tested on the Iraqi power grid (24 bus) in several cases and several places on the network and the comparison was made with the analysis model.

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“…Jiang et al (2012) have proposed an effective fault location method using the optimal measurements and distributed parameter line model. Mousavi-Seyedi et al (2015) proposed a fault location algorithm for series-compensated transmission lines. Vallapu et al (2017) have employed an event-driven communication strategy for data communication with the centralized controller to detect and locate a transmission line fault.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method is developed on the assumption that no errors are present in the transmission line parameters estimation. Most of these methods (Jiang et al 2012;Mousavi-Seyedi et al 2015;Vallapu et al 2017;Kang et al 2017;Das et al 2017;Saber 2018; Barman and Roy 2018;Meenakshi Devi et al 2018;Rajaraman et al 2018) discussed above are confined to simulation based validations only.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Synchronized Harmonic Phasor Measurements-Based Fault Location Method for Transmission Lines

Mallikarjuna

Gopakumar

Roy

et al. 2019

J Control Autom Electr Syst

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In this paper, a real-time synchronized harmonic phasor measurements-based fault location (RT-SHPM-FL) method for transmission lines is proposed. At transmission line protection center (TPC), the synchronized harmonic phasor measurements are obtained from all phasor measurement units (PMU) deployed in a power system. At each bus, the PMU estimates time-tagged 100 and 150 Hz phasors of 3-ɸ current signals in addition to fundamental phasor (50 Hz). The proposed RT-SHPM-FL method detects and locates a fault using the magnitude of 100 and 150 Hz phasors of 3-ɸ currents and equivalent harmonic phasors (EHPs), respectively. These EHPs are calculated from the magnitude of time-tagged 50, 100 and 150 Hz three-phase current phasors. For estimating the fault distance, the RT-SHPM-FL method has employed support vector regression (SVR), because of its mimicking nature, generalization and robustness. The functioning of the proposed fault location method has been validated in real-time on a scaled-down laboratory model of 400 kV extra high voltage (EHV) transmission line of 400 km long. The experimental results and discussions show that the proposed method locates transmission line faults accurately. Further, a comparative study of the proposed fault location method using SVR and adaptive neuro-fuzzy inference system has revealed that the former one is more reliable in fault location than the latter one since the error is within ± 1%.

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Optimal fault location algorithm for series-compensated transmission lines based on PMU data

Cited by 5 publications

References 7 publications

Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus

Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus

A novel online monitoring system of frequency oscillations based intelligence phasor measurement units

A Real-Time Synchronized Harmonic Phasor Measurements-Based Fault Location Method for Transmission Lines

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