Fiber-Optic Distributed Acoustic Sensing for Smart Grid Application

Abstract

Fiber-optic distributed acoustic sensing (DAS) promises great application prospects in smart grids due to its superior capabilities, including resistance to electromagnetic interference, long-distance coverage, high sensitivity and real-time monitoring. In this paper, we review the research progress and application status of DAS technology in power systems, focusing on its applications in areas such as the wind-induced vibration detection of transmission lines, partial discharge monitoring, transformer condition monitoring, and underwater cable and renewable energy transmission monitoring, as well as in the safety and protection of surrounding power facilities. Addressing the challenges currently faced by DAS technology in the smart grid, including detection accuracy, system cost, and data processing capability, this paper analyzes its major technical bottlenecks and proposes future research directions.

1. Introduction

The smart grid is a fundamental pillar and driving force for a nation’s economic and social development, with its operation safety being critical to national welfare and security. However, traditional power equipment monitoring techniques exhibit significant limitations in terms of real-time performance, sensitivity, and resistance to interference [1]. In recent years, as power infrastructure continues to expand and grow more complex, traditional monitoring approaches struggle to adapt to rapidly changing climatic conditions and challenging environmental factors [2]. In the safety monitoring of power equipment, the acoustic or vibration signals produced by the equipment itself, such as acoustic emissions from partial discharge (PD) or vibration from overhead transmission line, serve as important source signals for monitoring its safe operation.

With the advancement of fiber-optic communication technology, researchers have discovered that optical fibers could not only serve as a communication medium but also be used for environmental monitoring [3]. Among the distributed optical fiber sensing (DOFS) technologies, including Raman, Brillouin systems, and Rayleigh-based distributed acoustic sensing (DAS), each offers distinct advantages in environmental monitoring. Raman scattering is optimal for temperature monitoring over long distances, while Brillouin scattering is valuable for combined temperature and strain measurements, providing high sensitivity for static monitoring conditions [4]. Rayleigh scattering, employed in DAS, is particularly effective for real-time, distributed acoustic and vibration sensing due to its high spatial resolution and suitability for dynamic monitoring applications. Due to the electromagnetic immunity of the fibers themselves, along with their small size, light weight, and low loss, it enables long-distance monitoring. The DAS system primarily consists of an optical transceiver module (including narrow-linewidth laser sources and acousto-optic modulators), and an electronic acquisition module (including signal processing units and data analysis platforms), representing a typical cutting-edge integration of optics, mechanics, electronics, and computing. Based on these advantages, DAS technology has demonstrated important applications in several fields. For instance, in the oil and gas industry, DAS is employed for pipeline and well monitoring, offering efficient real-time data collection and analysis [5]. In seismic monitoring, DAS has been utilized with submarine dark fibers for the distributed detection of microseisms and teleseisms [6], shedding light on seafloor fault movements [7] and volcanic activity [8]. In security and intrusion detection, DAS systems combined with convolutional neural networks (CNNs) enhance accuracy and response time for mixed intrusion event recognition [9]. Furthermore, smart DAS (sDAS) systems employing multitask learning facilitate time-efficient ground listening and optimize monitoring resources [10].

By leveraging the Rayleigh scattering principle in optical fibers, DAS enables the highly dense and real-time monitoring of acoustic waves or vibrations in power systems, particularly in complex scenarios, such as transmission lines with ice accretion [11], wind-induced vibrations, and PD from gas insulated switchgear (GIS), and the structural health monitoring of power plants based on the conversion of mechanical energy into electrical energy. The typical application scenarios of DAS technology in smart grids are illustrated in Figure 1, which encompasses all stages, including power generation, substations, and transmission. During the power generation phase, the high sensitivity of DAS can be used to monitor the vibration and structural integrity of wind turbine blades [12,13,14], to ensure the structural health of hydroelectric dams [15], and for perimeter safety monitoring [16,17,18] around solar and nuclear facilities. In substations, DAS technology is used for PD detection in GIS. By integrating it with φ-OTDR and a 3D-printed elastomer, this method is highly sensitive to acoustic signals generated during insulation failures, facilitating efficient detection [19]. In the transmission sector, DAS systems enable real-time monitoring of wind-induced vibrations, PDs, and ice accretion on transmission lines. Wind vibration detection systems based on OTDR utilize changes in Rayleigh scattering within the optical fiber to accurately locate vibrations along these lines. Additionally, DAS technology aids in detecting anchor damage in submarine cables, enhancing the stability of the transmission network.

Although DAS technology offers significant theoretical advantages, it still faces several challenges in practical applications. For instance, signal demodulation and noise processing are critical components of DAS systems. In high-noise environments, accurately extracting useful signals while eliminating interference has become a focal point of current research. To address these challenges, researchers have proposed various improvements including the integration of machine learning algorithms for signal processing, aimed at enhancing the system’s anti-interference capabilities and detection accuracy [20,21].

In this review, we focus on the unique capabilities, applications, and limitation of DAS in power systems. We review the research progress and application status of DAS technology in power systems, focusing on applications such as wind-induced vibration detection, PD monitoring, transformer condition monitoring, underwater cable monitoring, renewable energy transmission, and the safety of surrounding power facilities. We also address the challenges currently faced by DAS technology in the smart grid.

2. Principle and System of DAS

2.1. DAS System

DAS utilizes Rayleigh backscattering (RBS) in optical fibers to locate and recover mechanical vibration information (amplitude, phase, and frequency) at any position along the fiber link [22]. Its core principle leverages the Rayleigh scattering effect within the fiber, enabling the monitoring of acoustic and vibrational signals in the external environment by detecting changes in the phase, frequency, and intensity of the light signal. As shown in Figure 2, the core structure of a DAS system typically includes a narrow linewidth laser source for coherent detection, an acoustic–optic modulator (AOM) for pulse generation, an erbium-doped fiber amplifier (EDFA) for boosting pulse energy, fiber under test (FUT), a balanced photodetector (BPD), a signal processing unit, and a data analysis platform. The sensing distance and spatial resolution are mainly related to the pulse energy and pulse duration.

2.2. Principles of DAS

2.2.1. Rayleigh Backscattering

Fiber Rayleigh scattering plays an important role in OTDR systems. In optical fibers, Rayleigh scattering occurs due to slight density fluctuations or compositional inhomogeneities within the fiber. For three-dimensional media, the scattering process is shown in Figure 2c. For the Rayleigh scattering process, the wavelength of the incident light wave is much larger than the size of the scattering element. Considering the structural characteristics of the fiber, the input light scattering rate (${\epsilon }_{RS}$) [23] per unit fiber volume can be expressed as follows:

(1)${\epsilon }_{RS}={\displaystyle \frac{8{\pi }^3{n}_0^2}{3{\lambda }^4}}{n}_0^8{p}^2{\beta }_c{K}_B{T}_F$

where $n_0$ is the refractive index of the homogeneous medium, $p$ is the elastic optical coefficient, ${\beta }_c$ is the isothermal compression coefficient of the fiber at temperature $T_F$, and $K_B$ is the Boltzmann constant. Equation (1) is widely used to characterize Rayleigh scattering in optical fibers, and we can see that its scattering coefficient is inversely proportional to the fourth power of the incident light wavelength.

2.2.2. Optical Time Domain Reflectometer

OTDR uses the phenomenon of RBS generated when the light pulse is transmitted in the fiber to inject narrow pulse light into the fiber to be measured, and then detects the scattered light power returned along the fiber axis at the same end, as shown in Figure 2. According to the time taken by the incident signal to the return signal, and then after determining the speed of light in the quartz material, the fiber length L can be calculated [24]:

(2)$L={\displaystyle \frac{c\cdot \Delta t}{2n}}$

where $c$ is the speed of light in a vacuum; $n$ represents the refractive index of the fiber core; and $\Delta t$ is the time from transmission to the return of the signal.

In phase-sensitive OTDR (φ-OTDR), the model in Figure 2c is usually used to describe RBS. In this model, a single-mode fiber (SMF) is considered a sequence of spatially discrete mirrors with different characteristics [25]. When the pulse probe is transmitted to the ith reflector, backscattered light $r_i{e}^{j\varphi {\tau }_i}$ is generated. The received backscattered light is the sum of these backscattered lights within a pulsed probe and is expressed as follows:

(3)$E_s\left(t\right)={\displaystyle \sum }_{i=1}^{N}r\left({\tau }_i\right)e^{j\varphi \left({\tau }_i\right)}{e}^{-\alpha v_g{\tau }_i}rect\left({\displaystyle \frac{t-{\tau }_i}{T_p}}\right)e^{j{\omega }_p\left(t-{\tau }_i\right)}$

where $r\left({\tau }_i\right)$ is the Rayleigh scattering coefficient of the ith reflector, ${\tau }_i$ is the round-trip time of the lightwave traveling from the near end to the ith reflector, $\phi \left({\tau }_i\right)$ is the random phase of the ith reflector, α is the attenuation coefficient induced by transmission, $v_g$ is the group velocity, $rect$ is the expression of a rectangular pulse, Tp is the width of the probe pulse, ${\omega }_p$ is the angular frequency of the probe, which equals ${\omega }_c+{\omega }_s$, ${\omega }_c$ is the angular frequency of the light source, and ${\omega }_s$ is the frequency shift introduced by the modulator. The summation can also be replaced by an integral due to the number of the reflectors (N) in one pulse being huge [26]. In φ-OTDR, coherent detection is commonly utilized to detect the RBS light, and a local light of $E_{LO}\left(t\right)=A_{LO}{e}^{j{\omega }_{s}t}$ is required. The backscattered light mixes with the local light and then is sent into a photodetector to be converted into the electrical signal. In the digital domain, the AC component of the signal is transformed into a complex signal $I\left(t\right)$ with Hilbert transform, and is described as follows:

(4)$I\left(t\right)={\displaystyle \sum }_{i=1}^N{A}_{LO}\cdot r\left({\tau }_i\right)\cdot e^{-\alpha v_g{\tau }_i}\cdot rect\left({\displaystyle \frac{t-{\tau }_i}{T_p}}\right)\cdot e^{\left\{-j{\omega }_c{\tau }_i+j{\omega }_s\left(t-{\tau }_i\right)+j\varphi \left({\tau }_i\right)\right\}}$

When a scrambled signal is applied to a sensing fiber, the length of the sensing fiber, the core diameter, and the core refractive index will all change, and these factors will cause the phase of the RBS signal of the optical pulse to change, which is shown in the following:

(5)$\Delta \varphi =\beta \cdot \Delta L+\Delta \beta \cdot L=\beta L\left({\displaystyle \frac{\Delta L}{L}}\right)+\left({\displaystyle \frac{\partial \beta }{\partial n}}\right)\Delta n+L\left({\displaystyle \frac{\partial \beta }{\partial a}}\right)$

where β is the propagation constant; L is the optical fiber length; n is the core refraction rate; a is the core straight diameter. The first shows that the strain induced by the optical fiber changes the length of the optical fiber and leads to phase delay. The second term shows that the optical polarization along the direction of stress is polarized by the elastic optical effect, which increases the dielectric electric constant and causes the refractive rate of the optical fiber core to change and delay the production phase. The third is the Poisson effect of optical fiber; when the inductive optical fiber is disturbed, it will cause the straight diameter of the optical fiber core to change, leading to phase delay [27]. As can be seen from Equation (5), when the sensing fiber is disturbed by an external disturbance, the disturbance information will affect the phase of the RBS signal. Therefore, φ-OTDR technology can obtain the relevant information of the external vibration signal by detecting the phase change of RBS.

2.3. DAS Signal Processing and Algorithm

DAS Demodulation Technology

In DAS technology, demodulation is a crucial step for retrieving disturbance information. The common demodulation methods can be mainly categorized into two types, direct detection and coherent detection, as illustrated in Table 1.

• (I) 

  Direct detection

The phase demodulation method aims to recover vibration signals by detecting the phase changes of RBS signals. The direct detection method for phase demodulation can be based on either of the optical interferometer methods. Phase demodulation methods based on interferometers are implemented by incorporating an interferometer at the receiving end. A common approach utilizes an unbalanced Mach–Zehnder interferometer, which addresses phase ambiguity issues through a 3 × 3 coupler [28], as shown in Figure 3a. The optical signals at each output port have a 120-degree phase shift, effectively recovering the phase information of the RBS signals. Additionally, the phase-generated carrier (PGC) method (Figure 3b) is another widely used technique that introduces sinusoidal phase modulation in one arm of the interferometer to recover the phase of the vibration signals [29]. This method enhances the system’s ability to resist polarization mismatch, thereby improving its anti-interference performance. Different from interferometer methods, the optical autocorrelation detection method employs two optical pulses of different frequencies to detect scattering signals at different positions along the fiber, followed by heterodyne demodulation to recover phase changes [30]. This technique offers significant advantages in increasing system bandwidth and dynamic range. By utilizing this method, DAS systems can achieve higher vibration detection accuracy while adapting to complex environmental conditions.

• (II) 

  Coherent detection

Coherent detection DAS systems offer significant performance advantages over direct detection, utilizing local oscillator (LO) light to enhance the signal-to-noise ratio (SNR) and enable precise phase demodulation. Two common methods are 2 × 2 coupler-based and 90° hybrid-based coherent detection. The 2 × 2 coupler method (Figure 3c) splits the laser into probe and LO beams, combining them at the receiver with a BPD to amplify phase carrier signals and suppress common-mode noise [31]. This approach provides adjustable gauge length (GL) configurations, higher SNR, and improved phase recovery. However, it requires a highly coherent laser source and high-bandwidth photodetectors, increasing system complexity.

The 90° hybrid method (Figure 3d) separates the RBS light into orthogonal I and Q components, mixing them with the LO to recover both amplitude and phase directly. This setup eliminates the need for interferometers, reduces environmental sensitivity, and lowers processing overhead by operating at baseband frequencies [32]. However, it requires precise polarization alignment and is sensitive to LO noise. Both methods significantly enhance DAS system performance by enabling flexible GL configurations, suppressing noise, and improving measurement accuracy. The choice of method depends on the specific requirements for sensitivity, stability, and system complexity in the application.

• (III) 

  Distributed Vibration Sensing (DVS)

Both DAS and DVS systems are used for detecting vibrations and share foundational principles; however, their applications and technical capabilities differ. DVS typically focuses on detecting lower-frequency vibrations and is designed for simpler applications where detailed phase demodulation is not required. In contrast, DAS systems excel in comprehensive acoustic and vibration monitoring, offering higher spatial resolution and greater sensitivity through the advanced phase demodulation of RBS. This makes DAS more suitable for complex environments such as power grids and infrastructure monitoring, where precise localization and continuous sensing are essential [33].

• (IV) 

  FBG-Based DAS:

Fiber Bragg grating (FBG)-based DAS systems integrate FBG arrays with φ-OTDR technology to enhance performance by introducing reflective elements along the fiber. This setup allows for targeted, high-sensitivity monitoring points that can amplify signal reflection and improve the SNR [34]. FBG-based systems can achieve greater stability and temperature tolerance, making them advantageous for specific applications where environmental conditions vary [35]. However, they involve more complex deployment and calibration compared to traditional Rayleigh-based DAS, which maintains fully distributed sensing without the need for additional structural modifications.

2.4. DAS Performance Enhancement

Sensing distance: Even though the advantages of φ-OTDR include that it can achieve ultra-high sensitivity and long-distance passive measurements, the measurement range of DAS is still constrained. Typically, both fiber loss and fiber nonlinearity influence the threshold of the probe pulse in as DAS system and thus determine the sensing distance. The lower the fiber loss and the lower the nonlinearity, the longer the sensing distance. For a standard SMF, the fiber loss and fiber nonlinearity are 0.20 dB/km and 1.80 W⁻¹·km⁻¹, respectively. Thus, for most DAS systems using a standard SMF, the input pulse power is limited to 0.11 W and the corresponding maximal sensing distance is limited to 60 km. To address these issues, researchers have proposed various methods to enhance RBS power, including distributed amplification and multi-span relay amplification techniques. Recent studies indicate that by designing a bidirectional EDFA [36] as a relay module (Figure 4a), DAS systems can achieve a high SNR and excellent strain resolution at a distance of 300 km (Figure 4b). These findings highlight the significant potential of this technology.

Signal-to-noise ratio (SNR): Noise suppression is vital for improving signal quality and system performance in DAS systems. Common interference sources include natural noise, attenuation noise, and under-sampled noise. Two-dimensional signal processing techniques include CNNs [37]. By treating optical fiber vibration signals as “images” and combining multidimensional information related to time, distance, and phase, these techniques effectively filter out natural noise and enhance the SNR. An alternative solution is exploring special fibers. Multi-core fibers (MCFs) have garnered attention in distributed fiber sensing due to their unique independent light transmission characteristics. The integrated DAS system (Figure 4c) proposed by Xiao et al. [38] effectively suppresses coherent fading through the Spatial Rotation Vector Averaging (SRVA) method, reducing the fading rate of RBS signals by three orders of magnitude. Experimental results demonstrate that this approach significantly lowers noise levels (Figure 4d) and enhances the recovery capability of low-frequency signals, providing a novel perspective for the application of long-distance distributed fiber sensing. Research by Li et al. [39] indicates that multimode fibers (MMFs) are better suited for detecting non-contact weak acoustic signals due to their larger core and higher RBS capture coefficient. A novel “n”-shaped elastomer designed using 3D printing technology achieved position enhancements of 8.39 dB and 10.51 dB in SMF and MMF systems, respectively (Figure 4e), along with frequency SNR improvements of 11.02 dB and 10.51 dB (Figure 4f). These results demonstrate significant potential for applications in quasi-distributed PD detection within smart grid systems.

Figure 4

Recent progress of DAS system in terms of (a,b) extending sensing distance [36]; (c,d) suppressing noise [38]; (e,f) improving acoustic wave sensitivity [39]; (g,h) reducing the data size and computation time [40].

[Figure omitted. See PDF]

Signal Recognition and Classification: Traditional time-domain and frequency-domain algorithms have limitations in recognizing and classifying vibration signals in DAS systems. Time-domain algorithms [41], while simple, lack accuracy for complex signals, and frequency-domain analysis reveals spectral composition but overlooks temporal characteristics. To address these limitations, time–frequency analysis methods [42], like short-time Fourier transform (STFT) and continuous wavelet transform (CWT), have been introduced. STFT uses a moving analysis window for high-frequency signals but lacks multi-resolution capabilities, whereas CWT offers multi-resolution analysis, making it suitable for non-stationary signals. Recent research has also introduced feature extraction and classification methods in the compressed domain, significantly enhancing processing efficiency and accuracy [43]. Shen et al. [40] proposed a DAS system that maps feature extractors from the Nyquist domain to the compressed domain (Figure 4g), enabling vibration detection and classification to be performed directly in the compressed domain. This approach reduces the data transmission size by up to 70%. Experimental results indicate that the method achieves a true positive rate of 99.4% and a false positive rate of 0.04% in a 5km fiber sensing setup, along with a classification accuracy of 95.05% for a five-class classification task (Figure 4h).

Laser linewidth: The light source significantly affects the performance of DAS systems due to its coherence properties and phase noise. Narrow-linewidth lasers are often required for high spatial resolution and sensitivity, but they increase system cost and complexity [44,45]. Laser phase noise introduces errors in phase demodulation, which can degrade the SNR and accuracy, particularly in long-range applications [46]. Recent research has focused on mitigation strategies to address these challenges. For instance, chirped-pulse DAS systems leverage linear chirp modulation to translate phase noise effects into measurable temporal shifts, achieving up to a 17 dB improvement in SNR through noise cancelation techniques [47]. Another approach involves using auxiliary interferometers to measure and compensate for laser phase noise, enabling the use of lasers with larger linewidths while maintaining system performance [48]. Techniques like cyclic pulse coding and dual-pulse schemes have also been shown to enhance SNR by optimizing the laser linewidth and maintaining coherence during pulse propagation [49]. These advances highlight a trend towards cost-effective solutions that reduce dependence on high-performance lasers while maintaining or enhancing the functionality of DAS systems.

Machine Learning and Big Data Analysis: As DAS applications grow in complexity, traditional signal processing methods show limitations. Recently, machine learning and big data analysis have become focal points in DAS signal processing [50]. Machine learning algorithms can automatically extract features from large sensor data sets for tasks like fault prediction and pattern recognition [51]. This approach enhances both the efficiency and detection accuracy of the system [52]. For instance, various machine learning models have been successfully applied to diverse DAS applications, as summarized in Table 2. Methods such as DCNN, SVM, and XGBoost have demonstrated high accuracy in fields like pipeline monitoring, perimeter security, seismic wave prediction, and high-speed railway monitoring, achieving detection rates up to 99% in some cases. Additionally, advanced models like LSTM and ConvLSTM have shown significant potential for long-distance monitoring and real-time train tracking. These examples highlight the versatility and effectiveness of machine learning in improving the performance and practical utility of DAS systems.

Table 2

Application of machine learning in DAS. 1-DCNN: one-dimensional convolution neural network; CLDNN: fully connected deep neural network; SVM: support vector machine; RVM: relevance vector machine; XGBoost: eXtreme gradient boosting; mCNN: multi-scale convolution neural network; HMM: hidden Markov model; BN: Bayesian network; LSTM: long short-time memory neural network; SSAE: sparse stacked autoencoder.

Methods	Fiber Length (km)	Spatial Resolution (m)	Application Field	Accuracy (%)	Ref.
1DCNN + RF	34 (35)	8 (10)	Pipeline monitoring	98	[53]
1DCNN	40	5	Urban safety monitoring	92.9	[54]
CLDNN	33	8	Oil pipeline monitoring	97.2	[55]
SVM	40	10	Real-time train tracking	98	[56]
RVM	10	20	Near-ground military target detection	88.6	[57]
XGBoost	5 (1.5)	3–10	Seismic waves prediction	99	[58]
mCNN + HMM	34	20	Long-distance monitoring	98.1	[59]
BN	37.5 (34)	10 (8)	Pipeline safety monitoring	78.3	[60]
LSTM	50	20	Long-distance monitoring	90.6	[61]
ConvLSTM	40	10	High-speed railway	85.6	[62]
SSAE	85	20	Long-distance pipeline safety monitoring	97.06	[63]

3. Application of Distributed Acoustic Sensing in Electric Power

3.1. Partial Discharge Detection

PD is a localized electrical breakdown within the insulating materials of high-voltage equipment, distinct from complete insulation failure as it only affects specific areas [64]. Over time, PD can degrade insulation, eventually causing equipment failure. Traditional PD detection methods, relying on electromagnetic waves, ultrasound, and current pulses, often face challenges such as electromagnetic interference and limited range, making them unsuitable for extensive cable monitoring. In contrast, DAS technology allows continuous acoustic sensing through fiber-optics, reducing electromagnetic interference and covering monitoring ranges of tens of kilometers, which highlights its potential in PD detection for power cables [65].

The DAS system can be applied for PD detection in GIS systems [66]. To capture PD signals from GIS, the fiber-optic sensor head is designed with a single-mode fiber wound around an elastic cylindrical base (Figure 5a). This configuration enables the sensor to effectively capture acoustic signals in high-voltage environments and convert them into optical phase changes. The sensor head is positioned near a 220 kV GIS unit to detect the acoustic emissions generated by PD in real time (Figure 5b). In high-voltage testing, the PD signals captured by the sensor correspond closely with the applied electrode voltage (Figure 5c), indicating that the sensor reliably reflects discharge activities within the GIS system. Similarly, Figure 5d–f demonstrate an improved φ-OTDR-based breakdown discharge detection system for GIS, assisted by 3D-printed sensing elements [19]. These sensing elements are made from high-Poisson’s-ratio materials to enhance φ-OTDR sensitivity to acoustic emissions during breakdown discharge events in GIS.

Table 3 lists the research progress on DAS technology for PD detection over several years. In 2019, Che et al. [67] introduced a DAS- and φ-OTDR-based PD identification method, achieving high-sensitivity detection of various PD types (internal, corona, surface) in XLPE cables (Figure 5g). Using CNN and Mel-frequency cepstral coefficients (MFCC) (Figure 5h), the method achieved 96.3% accuracy, 96.4% sensitivity, and 98.7% specificity (Figure 5i). In 2022, Tang et al. [68] developed a φ-OTDR-based distributed sensing system for PD acoustic signals in high-voltage cable joints, effectively detecting PD in 15 kV and 220 kV joints. Also in 2022, Kirkcaldy et al. [69] explored DAS for PD detection at the oil–insulation paper interface, effectively identifying signals above 120 pC. Compared with high-sampling-rate acoustic sensors and electrical methods, DAS showed comparable results, validated by phase-resolved PD analysis with zero-cross voltage synchronization.

Table 3

Study on PD detection based on DAS.

Year	Institute	Method	Detection Object	Discharges (pC)	Frequency (kHz)	Ref.
2019	Wuhan University of Technology	φ-OTDR	Cross-linked polyethylene (XLPE) power cables	/	1	[67]
2022	Xi’an Jiaotong University	φ-OTDR	Power cable joints	124	/	[68]
2022	University of Southampton, Hants, UK	C-OTDR	Treeing discharge at the oil–pressboard in transformers.	120	>20	[69]
2023	North China Electric Power University	C-OTDR	Power cable joints	/	12.6~18.9	[70]
2023	North China Electric Power University	C-OTDR	Power cable	/	71.36	[71]
2024	Taiyuan University of Technology	CFFDM-DAS	10 kV XLPE power cable	31	95	[72]
2024	Hebei University	φ-OTDR	Power cable joints	360	/	[73]
2024	Chongqing University	C-OTDR	GIL	29.7	10	[74]

In 2023, Qin et al. [70] introduced a DAS-based PD detection method for cable joints, using SMF wound around the outer semiconductor layer to enhance sensitivity. Employing COTDR, they detected PD-related phase changes and localized PD precisely, identifying acoustic waveforms from conductor spikes and air gaps in insulation within 12.6–20.2 kHz. Later, the same team [71] used dual-frequency probing pulses for online PD monitoring in power cables, doubling the length–bandwidth product (LBP) of conventional DAS systems with minimal added complexity. The improved COTDR system detected a 50 kHz vibration over 1.1 km with a 7.15 m spatial resolution and captured multi-point weak spark discharge signals with frequency components up to 71.36 kHz.

In 2024, Wang et al. [72] developed a crosstalk-free frequency-division multiplexed DAS (CFFDM-DAS) for PD detection, utilizing multi-carrier pulses and raised cosine roll-off (RCR) processing to enhance multiplexing density and achieve a 61.39 dB crosstalk suppression ratio. The system expanded to a 100 kHz bandwidth, covering 5 km, and detected PD as small as 31 pC in 10 kV XLPE cables with air gap defects. Also in 2024, Liu et al. [73] designed a DAS system with a polarization compensation module (PCM) and polarization beam splitter (PBS) for real-time insulation monitoring in cable joints. Using four fiber ring sensors, the system achieved localization errors under 1.9 m over fiber lengths of 600 or 1125 m. In the same year, Song et al. [74] enhanced PD sensitivity by embedding fibers in gas-insulated lines (GILs), with repeated winding, extending the sensing distance to 120 m and enabling multi-point PD detection down to 29.7 pC.

DAS technology’s distributed sensing offers comprehensive cable monitoring, improving fault detection and early warnings across full cable lengths [75,76]. PD detection in transformers, cables and GIS all have an applicable detection method. Conventional electrical methods such as high-frequency current transformers (HFCTs) [77] and capacitive coupling have advantages in terms of cost and technological maturity, and are suitable for short distances and low-interference environments, but they are susceptible to electromagnetic noise, and the signal attenuation is noticeable in long-distance transmission [78]. In contrast, DAS possesses high sensitivity and anti-interference capability, and is suitable for non-contact monitoring in high-temperature and high-interference environments, but its high cost and complex deployment limit its application in large-scale monitoring [79].

In GIS, the combination of the UHF method and fiber-optic detection method can facilitate accurate PD positioning and real-time monitoring in highly disturbed environments, but it also faces the problems of high cost and complex installation [80]. Therefore, future research should focus on reducing the cost and improving the operability of fiber-optic detection systems for wider application in monitoring power equipment in high-interference environments.

3.2. Wind-Induced Vibration Monitoring

Recently, DAS-based wind-induced vibration detection has drawn extensive research attention, as shown in Table 4. Early studies focused on feasibility and system design. In 2019, Hao et al. [81] demonstrated φ-OTDR’s ability to monitor conductor vibrations, showing heightened signal intensity at 1.0 Hz, the conductor’s natural frequency. In 2020, Wu et al. [82] introduced a polarization-sensitive OTDR system with spatiotemporal statistical analysis and box plot methods for adaptive vibration detection, enabling the real-time identification of abnormal wind-induced vibrations. In 2021, Okamoto et al. [83] developed a DVS system using OFDR, achieving sub-meter spatial resolution for dynamic strain measurements and overcoming OFDR’s range and repetition limitations. In 2021, Ding et al. [84] designed an online monitoring system based on φ-OTDR (Figure 6a) to detect displacement at the lowest point of a cable during vibration and analyze the vibration spectrum (Figure 6b). The curvature error was maintained below 5.8%, and the estimated error for ice thickness was under 10.84%. In 2024, Jia et al. [85] introduced a distributed fiber-optic sensing method for long-distance transmission line monitoring, enabling the real-time detection of wind-induced vibrations, icing, and lightning, enhancing fault monitoring capabilities. That same year, Canudo et al. [86] developed a CP-φOTDR-based sag monitoring method, analyzing strain frequency data over a 31.8 km line to assess cable mechanical conditions.

Additionally, Wang et al. [87] introduced a method to integrate DOFS with existing dark fibers in Optical Ground Wire (OPGW) for the online monitoring and anomaly warning of high-voltage overhead transmission lines. To accurately map the length of the fiber-optic cable to the actual GPS coordinates of the towers (Figure 6c), the researchers proposed a multi-parameter M-OTDR that simultaneously monitors physical parameters such as temperature, strain, and vibration (Figure 6d), along with four reference positioning functions. The results demonstrate that this positioning method ensures rapid and accurate localization of high-voltage overhead transmission line towers.

Despite advancements, DAS technology faces challenges, notably signal attenuation and noise accumulation over long distances, which impact φ-OTDR accuracy. Complex data processing algorithms also pose a challenge, necessitating efficient methods to maintain real-time performance and accuracy.

Table 4

Research on cable vibration monitoring based on DOFS.

Year	Institute	Spatial Resolution (m)	Fiber Length(km)	Frequency (Hz)	Ref.
2019	Northeast Forestry University	5	5	0.1~50,000	[81]
2020	University of Electronic Science and Technology	/	30	0.3~0.4; 0.7~0.8	[82]
2021	NTT Access Network Serv Syst Labs	0.9	10	200	[83]
2021	Nanjing University	4	70	1.3	[84]
2022	Taiyuan University of Technology	20	7	0.8
2024	State Grid Hebei Elect Power Co., Ltd. Shijiazhuang.	/	5	0.84~0.96	[85]
2024	Univ. Zaragoza	/	50	0.5~2	[86]

3.3. Submarine Cable Transmission Monitoring

With the global expansion of subsea cables in offshore wind, oil platforms, and transnational power transmission, ensuring their safe operation has become increasingly challenging. Researchers have integrated DAS with cable structures for real-time monitoring, enabling the detection of external damage and internal insulation faults [88]. Advances in fiber-optic sensing, particularly OTDR and φ-OTDR technologies, have further enabled DAS to achieve long-distance, high-precision monitoring. Table 5 shows the application of DAS in submarine cable inspection.

In 2015, Rohwetter et al. [89] applied DAS to PD monitoring, reaching a detection threshold of approximately 1 nC in a high-voltage model using COTDR and ultrasonic fiber sensors, with random orthogonal demodulation for acoustic signal extraction. In 2017, Hicke et al. [90] demonstrated COTDR-based DAS for the online monitoring of subsea cables, simulating underwater conditions and detecting low-amplitude disturbances in unoptimized fibers. In 2018, Lv et al. [91] developed a φ-OTDR-based system for 35 kV subsea cables, identifying anchor damage through vibration data collected under varying sea conditions, with fault identification enabled by analyzing energy and amplitude distribution. In the same year, Sørensen et al. [92] demonstrated a dual-modal fiber-optic sensing system that simultaneously monitors temperature and acoustic disturbances in power cables. Experiments showed successful detection of localized temperature changes and acoustic disturbances (e.g., cable vibrations and impacts) in 30 kV land power cables. In 2022, Qin et al. [93] proposed a C-OTDR-based method for identifying insulation defects, achieving the distributed broadband measurement and localization of weak spark discharge acoustic signals. The frequency range was extended to 90 kHz, allowing the detection of spark discharges with a 1 mm gap over 1.1 km of optical fiber. Building on this, in 2023, the same group [94] proposed an improved C-OTDR system to enhance the detection distance and sensitivity of PD in long power cable joints. Dual-frequency detection pulses were introduced to suppress attenuation noise and reduce false alarms, increasing detection performance from 1 km at the nC level to 20 km and 127 pC.

However, current research still faces challenges in enhancing the spatial resolution and signal demodulation accuracy of DAS systems, as well as in addressing interference signals in complex marine environments. Future developments may focus on integrating intelligent signal processing technologies, such as machine learning algorithms, to improve automated fault recognition and data processing efficiency in DAS systems.

Table 5

Research results of φ-OTDR-distributed sensing for submarine cables.

Year	Institution	Distance (km)	Frequency (kHz)	Measuring Signal	Ref.
2015	Federal Institute for Materials Research & Testing	0.4	72	PD	[89]
2017	Federal Institute for Materials Research & Testing	58.6	1.7	Shock vibration	[90]
2018	North China Electric Power University	0.66	1.6	Anchor damage	[91]
2018	Fraunhofer Research Center for Applied Optics	0.083	10	Cable disturbance	[92]
2022	North China Electric Power University	1.1	180	Spark discharge	[93]
2023	North China Electric Power University	20	10	PD	[94]

4. Conclusions

DAS technology has been proven to have great potential in applications like transmission line monitoring, power equipment fault warning, and underwater cable safety surveillance. This review explores the physical mechanisms, signal processing, and algorithm optimization of DAS technology, highlighting its applications in smart grids through practical cases and cutting-edge research, including wind-induced vibration monitoring, PD detection, icing warning, and multi-sensor fusion with edge computing. However, the DAS technology in smart gird applications still faces several unresolved challenges, which are essential research directions to be tackled in the future:

(1) Enhancing Interference Resistance and Sensitivity of Sensing Probes: In environmental acoustic wave sensing, interference from various environmental factors is inevitable. Since optical fibers are sensitive to temperature and strain [95], enhancing the fiber’s responsiveness to sound waves while reducing its response to temperature and strain requires the special design of both fiber materials and structure. For instance, heterogeneous MCFs can be used to compensate for temperature crosstalk by leveraging different temperature response characteristics across multiple channels. Additionally, bare optical fibers cannot be used directly for sensing; after special protective measures, sound wave coupling is attenuated, resulting in weak signals. Therefore, in specific scenarios, sensitivity-enhancing probe designs are needed. This can be achieved by integrating elastomers with high Poisson ratios at target locations through additive manufacturing or introducing air holes in the fiber to lower its Young’s modulus, thereby enhancing sensitivity.

(2) Noise Suppression in DAS Systems: In DAS systems, noise from the light source is a primary factor affecting detection accuracy. Light source noise includes phase noise, intensity noise, and frequency noise, which distort signals and make it challenging to accurately interpret weak acoustic signals. Currently, DAS systems generally use light sources with sub-kHz linewidths to enable ultra-long-distance coherent detection. However, these high-performance light sources are typically costly, raising deployment expenses and limiting widespread adoption. Consequently, reducing the influence of noise on detection accuracy while lowering light source costs is a key topic in optimizing DAS systems. Effective noise suppression methods in DAS systems include improved demodulation algorithms and machine learning techniques.

(3) Intelligent Recognition Algorithms and Digital Twin Technology: In actual power grid applications, extracting valuable information from DAS detection data and developing online detection and early fault warning systems tailored to specific scenarios represent an inevitable trend. To achieve this, multiple intelligent recognition algorithms can be employed, including machine learning, deep learning, and anomaly detection algorithms. Additionally, the integration of digital twin technology enables a virtual mapping of the physical grid system, supporting real-time data feedback and analysis. By constructing a digital twin that closely aligns with the physical power grid, sensing data can be continuously collected, and fault recognition models can be embedded to achieve state monitoring, fault prediction, and remote diagnostics. The digital twin can also integrate expert diagnostic systems for the online analysis of historical data and operational status, forming a complete, closed-loop monitoring and diagnostic system for the grid. Such a system can provide intelligent warnings before faults occur, significantly enhancing the reliability and stability of grid operations.

Author Contributions

All authors contributed substantially to the manuscript. Conceptualization, X.Z.; methodology, J.Q. and C.X.; investigation, X.L., Z.G., Z.L. and C.Z.; resources, D.C.; writing—original draft reparation, X.Z. and X.W.; writing—review and editing, X.Z., X.W. and H.L.; visualization, X.Z. and X.W.; supervision, H.L.; project administration, X.Z.; funding acquisition, W.D. and H.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

Author Xiaofeng Zhang, Jun Qi, Xiao Liang, Zhen Guan, Zeguang Liu, Chang Zhang, Dabin Chen were employed by the company Inner Mongolia Electric Power (Group) Co., Ltd. Author Weifeng Deng and Changzhi Xu were employed by the company Shenzhen SDG Information Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Application scenario of DAS technology in smart grid.

Figure 2. DAS system principle: (a) DAS system architecture; (b) vibration causing changes in RBS; (c) RBS microphysical model.

Figure 3. Structure diagram of different demodulation methods: (a) 3 × 3 structure; (b) PGC; (c) coherent detection; (d) 90° hybrid.

Figure 5. Recent progress of DAS system in discharge detection: (a–c) single-mode fiber wound around an elastic cylindrical base for GIS [66]; (d–f) 3D-printed sensing elements integrated in φ-OTDR for breakdown discharge detection [19]; (g–i) machine learning method for DAS systems in PD classification [67].

Figure 6. Recent progress of DAS system in transmission line monitoring: (a,b) power cable vibration measurement [84]; (c,d) tension towers and tangent towers [87].

Table 1

Various DAS architectures along with their respective advantages and disadvantages. PGC: phase-generated carrier.

Method		Advantage	Disadvantage
Direct detection	3 × 3 coupler	(i) Simple structure and algorithm.(ii) Lower cost.	(i) Large phase deviation.(ii) Susceptible to noise.
	PGC
Coherent detection	2 × 2 coupler	(i) Significantly improve the SNR.(ii) Low-frequency noise can be suppressed.(iii) More flexible modulation and demodulation.	(i) Higher bandwidth required for photodetectors.(ii) Larger volume of data.(iii) High laser coherence requirements.
	90° hybrid

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