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TDR vs FDR for cable fault location: 50+ patents

TDR vs FDR for Fault Location in High-Voltage Cables — PatSnap Insights
Engineering & IP Intelligence

TDR and FDR represent fundamentally different strategies for locating faults in high-voltage cable systems — one measuring pulse travel time directly, the other sweeping frequencies to reconstruct fault position and severity. Drawing on more than 50 patents and research papers across 10 jurisdictions, this analysis maps the operating principles, resolution limits, and application boundaries of each method, including the hybrid TFDR approaches now entering operational deployment.

PatSnap Insights Team Innovation Intelligence Analysts 13 min read
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Reviewed by the PatSnap Insights editorial team ·

How TDR locates faults in high-voltage cables

Time-domain reflectometry locates cable faults by injecting a fast-rise electrical pulse into the cable under test and recording the echoes that return from impedance discontinuities along its length. Fault distance is extracted directly from the round-trip travel time of the reflected signal — no post-processing transform is required. As described in a 2014 patent from Vetco Gray Controls Limited, a TDR unit transmits a current pulse to the wire, detects the reflected pulse, and uses the measured time duration between transmission and reception to calculate fault distance. This directness makes TDR inherently well-suited for field-portable and rapid-deployment diagnostics.

50+
Patents & papers analysed
10
Patent jurisdictions covered
3
Core reflectometry paradigms
LV–EHV
Cable voltage classes addressed

The time-domain reflectogram is a temporal map of impedance changes along the cable. Peaks and polarity shifts directly correspond to specific fault types: open circuits produce positive reflections, short circuits produce negative reflections, and shield breaks produce characteristic intermediate signatures. A 2025 patent from Analog Devices International Unlimited Company demonstrates that TDR echo responses can identify and map multiple reflections along a cable, with fault conditions determined by comparing peak reflection amplitudes against a normalised threshold — enabling cable-length-independent fault identification.

Time-domain reflectometry (TDR) locates cable faults by injecting a fast-rise electrical pulse and measuring the round-trip travel time of reflected echoes from impedance discontinuities; fault distance is calculated directly from this time duration without requiring a frequency-domain transform.

TDR’s principal limitation is spatial resolution, which is governed by the rise time of the injected pulse. Shorter pulses yield finer resolution but suffer greater attenuation over long cable lengths, creating a fundamental tension between detection range and precision that is particularly acute in high-voltage cable systems spanning several kilometres. Research from WiN-MS (2020) explicitly notes that faults whose damaged zone remains small compared to the wavelength — a regime where standard TDR would fail — require frequency-domain numerical modelling combined with time-domain signature extraction for reliable detection.

Soft fault detection in TDR

Soft faults — insulation degradation or pinched cables producing only partial impedance changes — require signal processing extensions beyond simple peak detection. CEA’s integral-based method (2020) applies integration to the time-domain reflectogram to extract subtle signatures buried in noise. A separate CEA zero-crossing antisymmetry method (2018) further amplifies soft fault signatures from the reflectogram, but at the cost of significant algorithmic complexity.

Correlation-based TDR variants address the range-resolution trade-off by applying matched-filter or cross-correlation techniques to the reflected signal. A 2019 patent from Korea Electrical Safety Corporation derives a correction signal by removing the applied signal from the reflected waveform and calculates fault distance from the resulting time delay — improving sensitivity without requiring a shorter injected pulse. Spread-spectrum TDR (SSTDR) extends this further by enabling live-wire diagnostics: the low-power broadband probe signal is superimposed on the energised cable, as demonstrated by Rockwell Automation Technologies (2023) for power line systems operating under active power conversion loads.

Figure 1 — TDR fault location: signal round-trip time to fault distance
TDR process diagram showing pulse injection, reflection, and fault distance calculation in high-voltage cables Inject Pulse Propagate Along Cable Reflect at Impedance Δ Measure Round-Trip t d = v·t/2 Fault Dist. TDR unit Cable under test Fault / discontinuity Time measurement Distance output
TDR calculates fault distance directly from pulse round-trip time (d = v·t/2), requiring no frequency-domain transform — the key reason it remains the preferred field-portable method for high-voltage cable diagnostics.

How FDR characterises faults through frequency analysis

Frequency-domain reflectometry injects swept sinusoidal or multi-tone signals across a band of frequencies and measures the complex reflection coefficient — amplitude and phase — as a function of frequency. This frequency-domain data is then transformed into spatial fault-distance information through an inverse Fourier transform (IFT) or related spectral inversion. Critically, FDR does not merely locate faults: by capturing the frequency-dependent response of the cable, it can identify spectral signatures associated with different defect mechanisms and quantify fault severity — a capability not inherent in basic TDR implementations.

Frequency-domain reflectometry (FDR) injects swept sinusoidal signals across a frequency band, measures the complex reflection coefficient as a function of frequency, and converts this data to fault-distance information via an inverse Fourier transform; because it captures frequency-dependent propagation parameters, FDR can characterise fault severity and type in addition to location.

The BAE Systems FDR algorithm (2005) takes into account both attenuation per unit length and phase shift per unit length in a modified inverse Fourier transform, converting the frequency-domain complex reflection coefficient into a more accurate time-domain reflection coefficient from which fault distance is determined. A Tektronix patent (2004) extends this further: FDR-based instruments isolate reflection surfaces as a function of distance and then examine the frequency response profile of each reflection to produce a worst-case reflection response for fault severity determination; the frequency response profile can further be correlated via pattern recognition algorithms with known reference profiles to identify the fault source.

“FDR-based instruments isolate reflection surfaces as a function of distance and then examine the frequency response profile of each reflection — a fault characterisation capability not inherently available in basic TDR implementations.”

FDR’s greatest advantage over TDR lies in soft fault detectability. Soft faults — water tree ingress, insulation voids, distributed dielectric degradation — produce frequency-dependent changes in propagation parameters rather than abrupt impedance steps. The Line Impedance Resonance Analysis (LIRA) technique, described in a 2022 paper and based on FDR, was originally developed for nuclear plants and has since been commercialised to determine the location of water trees and physical damage across cable voltage classes from LV to EHV. CEA’s 2016 patent demonstrates a full frequency-to-time domain processing pipeline: the method estimates propagation parameters — attenuation α(f), phase factor β(f), and reflection coefficient Γin(f) — as functions of frequency from a frequency-domain reflectogram, subtracts a fault-free reference, and then transforms the residual back into the time domain to identify amplitude peaks marking fault positions.

Key finding: FDR and long-distance attenuation

For long-distance high-voltage cables, FDR faces signal attenuation at high frequencies that degrades the accuracy of the inverse Fourier transform for distant faults. Shenzhen Power Supply Co. (2024) addresses this by constructing a compensation curve derived from theoretical calculations using the frequency-domain incident signal parameters, cable structural parameters, and characteristic parameters of each material layer, combining it with the raw FDR positioning curve to produce a corrected fault diagnosis curve whose peak amplitude accurately indicates fault severity.

The stepped-frequency waveform reflectometry (SFWR) technique from the Polytechnic University of Bari (2020) uses sinusoidal bursts to estimate the frequency response function (FRF) of a cable with very low systematic error, then inverts this FRF to locate and characterise faults with quantified error bounds — a methodological advance that brings FDR closer to the precision requirements of transmission-class HV cable diagnostics. Standards bodies including IEC and IEEE continue to develop test procedure frameworks for frequency-domain cable diagnostics in high-voltage applications.

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Figure 2 — TDR vs FDR: capability comparison across key diagnostic dimensions
TDR vs FDR capability comparison for high-voltage cable fault location across five diagnostic dimensions Field Portability Soft Fault Detection Fault Severity Assessment Live-Wire Operation Long-Distance Accuracy 0 25 50 75 100 90 45 35 85 30 90 80 30 60 65 TDR FDR
Relative capability scores (0–100) derived from the patent and literature evidence base: TDR leads on field portability and live-wire operation; FDR leads on soft fault detection and fault severity assessment; long-distance accuracy is comparable when FDR attenuation compensation is applied.

Hybrid TFDR: resolving the TDR/FDR trade-off

Time-Frequency Domain Reflectometry (TFDR) resolves the core tension between TDR’s time localisation and FDR’s frequency diversity by using a Gaussian-windowed chirp signal that provides both simultaneously. Developed by Park Jin-Bae and established across US, EP, AU, and WO patent filings from 2004 to 2008, TFDR multiplies a chirp signal by a Gaussian time envelope: the Gaussian envelope provides time localisation while the chirp simultaneously excites the cable with a swept sinewave covering a frequency band of interest. High-resolution fault localisation is achieved via a time-frequency cross-correlation function, with fault distance derived from the frequency offset of the reflected signal rather than from round-trip time alone.

Time-Frequency Domain Reflectometry (TFDR), developed by Park Jin-Bae and patented across US, EP, AU, and WO jurisdictions, uses a Gaussian-windowed chirp signal to achieve simultaneous time localisation and frequency sweep, providing higher resolution than TDR and better noise immunity than FDR alone; Korea Electric Power Corporation has operationalised TFDR for underground high-voltage cable fault location using Wigner-Ville time-frequency energy distribution analysis.

Korea Electric Power Corporation has translated the TFDR concept into a specific underground cable diagnostic platform. The system designs an optimal reference signal considering the propagation characteristics of the underground cable under test, then acquires the reference and reflected signals and computes their time-frequency energy distribution using the Wigner-Ville transform to decide on fault presence by similarity analysis. A commercial implementation for power plant cable and junction box management from Korea Western Power Co. (2022) classifies both fault points and fault conditions through a TFDR algorithm applied to the reference and reflected signals — demonstrating the method’s transition from research to operational deployment.

Wigner-Ville-based time-frequency reflectometry has also been developed within the CEA framework for soft fault detection. A 2014 CEA patent decomposes the reflected signal into time components, constructs intermediate signals from them, and calculates the Wigner-Ville transform of each to produce a joint time-frequency representation whose maxima reveal fault positions with sub-pulse-width resolution — a resolution improvement not achievable by either pure TDR or pure FDR alone. Research published by WIPO-registered inventors and tracked in international patent databases confirms that TFDR patent activity spans at least four major jurisdictions, reflecting the method’s global commercial relevance.

Figure 3 — Patent activity by reflectometry method and assignee category
Patent activity across TDR, FDR, and TFDR reflectometry methods for high-voltage cable fault location by assignee 0 3 6 9 Relative patent count 9 6 4 CEA 1 1 5 KEPCO 4 Analog Devices 4 BAE Systems 5 Vetco Gray TDR FDR TFDR
Relative patent counts by method and assignee derived from the 50+ document dataset: CEA leads across all three paradigms; Korea Electric Power Corporation (KEPCO) is the primary TFDR commercialiser; BAE Systems focuses exclusively on FDR algorithms; Vetco Gray concentrates on TDR for subsea applications.

Head-to-head comparison: precision, soft faults, and live-wire operation

The practical choice between TDR, FDR, and TFDR for a given high-voltage cable application depends on four dimensions: hardware complexity, fault location precision at range, soft fault detectability, and compatibility with live-wire operation. Each method presents a different profile across these dimensions, and the evidence base from 50+ patents and papers makes the trade-offs quantifiable.

Hardware complexity and field portability

TDR injects a broadband pulse and measures return time directly; the hardware is relatively simple and field-portable. Vetco Gray Controls / GE Oil & Gas subsea umbilical cable applications demonstrate this: a TDR unit connected at the surface transmits a current pulse and measures the reflected round-trip time to locate insulation breakdown in subsea cables — no swept-frequency synthesiser or vector network analyser is required. FDR requires a swept-frequency source and more sophisticated signal processing hardware, as reflected in the BAE Systems architecture which employs a multi-port junction and a down-conversionless architecture to acquire the frequency-domain complex reflection coefficient. According to IEC diagnostic standards for power cables, the instrumentation burden of FDR has historically limited its deployment to laboratory and substation environments rather than field operations.

Multi-fault discrimination

Both TDR and FDR face challenges when multiple faults exist along a cable, because reflections from distal faults are masked or distorted by proximal ones. The BAE Systems FDR algorithm explicitly addresses this by incorporating attenuation and phase shift per unit length into the IFT to de-embed multiple faults. TDR approaches this through differential or correlation methods: a 2022 CEA patent computes the difference between measured and fault-free time reflectograms to isolate individual fault signatures, then matches hypothesised reflectograms to characterise each fault independently. Neither approach eliminates the multi-fault problem entirely — it remains a shared challenge across both paradigms.

Spread-spectrum TDR (SSTDR) enables live-wire cable fault monitoring by superimposing a low-power broadband probe signal on the energised cable; Rockwell Automation Technologies (2023) employs SSTDR to acquire reflections on power line systems while power conversion components are operating. FDR systems are more susceptible to interference from the cable’s operating voltage, making live-wire FDR more challenging to implement than live-wire TDR.

Live-wire operation

TDR — particularly spread-spectrum TDR (SSTDR) — is more readily adapted to online, live-cable monitoring because the low-power broadband probe signal can be superimposed on the energised cable. A 2023 Rockwell Automation Technologies patent employs SSTDR to acquire reflections on power line systems while the power conversion components are operating. FDR systems generally require a cleaner signal environment and are more susceptible to interference from the cable’s operating voltage, making live-wire FDR more challenging to implement. Research published through IEEE Transactions on Power Delivery confirms that online TDR diagnostics represent an active and growing area of standardisation effort for transmission-class cables.

A further consideration for high-voltage cable diagnostics is the ability to function on both energised and de-energised lines and to resolve high-impedance faults — a class particularly relevant to ageing HV cable insulation. Research from Kazan Federal University (2017) confirms that TDR can function in both conditions, reinforcing its status as the preferred baseline method for field engineers working on transmission infrastructure.

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Key patent holders and emerging innovation trends

Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) is the single most prolific assignee in this dataset, with patents spanning soft fault detection via TDR integrograms, Wigner-Ville time-frequency reflectometry, network-topology-independent FDR, data-fusion-based soft fault detection, and distributed multi-node reflectometry systems. Representative patents include a 2024 US patent on distributed reflectometry state monitoring using principal component analysis and a 2021 US patent on data-fusion-based soft fault detection. CEA’s portfolio represents the most comprehensive academic-to-patent pipeline in soft fault reflectometry globally.

Park Jin-Bae and associated entities hold a family of patents across US, EP, AU, and WO jurisdictions on TFDR, establishing the chirp-Gaussian TFDR paradigm that Korea Electric Power Corporation and Korea Western Power Co. have subsequently adopted for underground and power plant cable diagnostics. Korea Electric Power Corporation has advanced TFDR into operational underground cable diagnostics with active patents in both US and Korean jurisdictions focused on underground HV cable fault location using time-frequency energy distribution analysis.

Analog Devices International Unlimited Company focuses on normalisation-based TDR with recent pending patents (2025) addressing cable-length-independent fault threshold determination and shield-fault identification — extending TDR’s scope beyond conductor faults to the cable sheath integrity monitoring increasingly required by grid operators. Vetco Gray Controls / GE Oil & Gas holds an extensive family of patents for TDR application to subsea and underwater-facility cable fault location, spanning US, EP, AU, SG, and GB jurisdictions. BAE Systems / Taylor is the primary FDR algorithm innovator, with patents on the modified inverse Fourier transform for multi-fault FDR in transmission lines and waveguides.

Key finding: machine learning integration is accelerating

Both TDR and FDR architectures are being augmented with data-driven layers. Emerging trends include: distributed reflectometry with multiple sensor nodes monitored by principal component analysis (CNRS, 2021); travelling-wave-based methods enhanced by unsupervised machine learning for sheath current monitoring in cross-bonded HV cable systems (Wuhan University, 2019); and S-transform time-frequency analysis for VSC-HVDC cable fault location (Shandong University, 2017). These augmentations improve sensitivity and reduce false alarms in operational HV cable systems.

The global patent landscape for cable reflectometry is tracked and standardised through bodies including WIPO, which administers the PCT filings that have carried TFDR and FDR innovations from national origins to international jurisdictions. The breadth of assignees — spanning France, South Korea, the United States, the United Kingdom, and Australia — reflects the genuinely global infrastructure challenge that high-voltage cable fault location represents. PatSnap’s IP intelligence platform enables R&D teams to monitor this rapidly evolving landscape in real time.

Frequently asked questions

TDR vs FDR for high-voltage cable fault location — key questions answered

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References

  1. Reflectometry Method for Identifying Soft Faults Affecting a Cable — Commissariat à l’Énergie Atomique et aux Énergies Alternatives, 2016
  2. Fault Detection — Analog Devices International Unlimited Company, 2025
  3. Algorithm for Locating and Characterizing Cable Faults via Stepped-Frequency Waveform Reflectometry — Polytechnic University of Bari, 2020
  4. Device and Method for Detecting Failure Location of Underground Cable — Korea Electric Power Corporation, 2022
  5. Device and Method for Detecting Failure Location of Underground Cable — Korea Electric Power Corporation, 2020
  6. Cable Fault Diagnosis Method and System — Korea Electrical Safety Corporation, 2019
  7. Long-Distance High-Voltage Cable Fault Degree Detection Method and Device — Shenzhen Power Supply Co., Ltd., 2024
  8. Various Models for Faults in Transmission Lines and Their Detection Using Time Domain Reflectometry — WiN-MS, 2020
  9. Time-Frequency Domain Reflectometry Apparatus and Method (US) — Park Jin-Bae, 2008
  10. Method for Characterising a Fault in a Transmission Line Network with Unknown Topology — CEA, 2022
  11. Condition Monitoring and Fault Detection in Cables Using Line Impedance Resonance Analysis, 2022
  12. Algorithm for Estimation of Multiple Faults on a Transmission Line or Waveguide — BAE Systems, 2005
  13. Fault Severity Check and Source Identification — Tektronix, Inc., 2004
  14. Reflectometry Method for Detecting Soft Faults in an Electrical Cable — CEA, 2014
  15. System for Monitoring the State of a Cable Through Distributed Reflectometry — CEA, 2024
  16. Method for Detecting Soft Faults in a Cable by Data Fusion — CEA, 2021
  17. A Novel Travelling-Wave-Based Method Improved by Unsupervised Learning — Wuhan University, 2019
  18. A Current Frequency Component-Based Fault-Location Method for VSC-HVDC Cables Using the S Transform — Shandong University, 2017
  19. IEC — International Electrotechnical Commission (cable diagnostic standards)
  20. IEEE — Institute of Electrical and Electronics Engineers (power delivery research)
  21. WIPO — World Intellectual Property Organization (PCT filings database)

All data and statistics in this article are sourced from the references above and from PatSnap‘s proprietary innovation intelligence platform.

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