Phase Resolved Partial Discharge Research Articles

Ensemble Deep-Learning Model for Phase-Resolved Partial Discharge Diagnosis in Hydrogenerators

Condition monitoring of hydrogenerators is extremely essential for the optimisation of the power supply system’s maintenance, especially in a context where energy consumption will increase more and more. Industry investigations have consistently shown that the electrical insulation used in stator windings is one of the most potential components for breakdown. One of the major causes of this breakdown was shown to be partial discharges (PDs). PDs are designated as defects in electrical insulation, and their presence may cause future insulation failures in equipment. Online monitoring of PD activity is essential to anticipate degradation and avoid a sudden shutdown with a high economic impact. Thus, automating the recognition of PD sources by an intelligent system is a major issue in a Condition-Based Maintenance (CBM) process for hydrogenators. First, a method based on the U-Net model is used to isolate the main sources of a Phase-Resolved PD signal (PRPD). Afterwards, a set of deep learning models (DLM) is then applied to automatically recognize the PD sources. Finally, a decision-making method is then proposed to find the optimal output category considering the posterior and the prior probability estimated by various individual models. Experimental results on real signals from hydrogenerators show an accuracy of 87% on the detection of gap sources, and respectively 91%, 95%, 96% and 94% for the Internal, Corona, Slot and Delamination PD. The proposed decision-making method shows a significant advantage of the final decision over the performances of each individual DL model.

Deep Learning and Long-Duration PRPD Analysis to Uncover Weak Partial Discharge Signals for Defect Identification

This study focuses on improving the defect recognition accuracy under weak partial discharges (PDs) in epoxy resin through phase-resolved partial discharge (PRPD) analysis. The method is to refine the data rather than enhance the algorithm. Two measurement conditions are compared until PRPD pattern saturation is achieved: one-minute and one-hour durations. The PD data specifically target three void types located at different positions within the epoxy material. The aim is to evaluate how the presence of weak PDs at the PD extinction voltage (PDEV) influences defect recognition accuracy. This research sheds light on the potential implications of neglecting the significance of weak PD signals in defect detection. A convolutional neural network (CNN) model is trained using PRPD data recorded at the repetitive PD inception voltage (RPDIV) and tested using the new PRPD data from both conditions recorded from a lower PDIV to a PDEV. The trained CNN model achieves a defect recognition accuracy of 100% for a one-hour duration, highlighting the importance of not neglecting weak PD signals. This emphasizes the significance of extended measurement duration and pattern saturation in capturing and analyzing weak PD signals for an improved defect recognition. This study contributes to the advancement of practical applications by understanding the behavior of the epoxy material and enhancing defect detection techniques.

Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence

This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32Cube.AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm’s robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%.

Use of partial discharges measurements and video recordings to investigate the influence of relative humidity on zinc oxide subjected to high electric fields

This study investigates the influence of humidity on the high voltage behaviour of zinc oxide porous pellets at room temperature, using the phase resolved partial discharge (PRPD) method. The experimental configuration corresponds to the one that would be used for flash sintering cylindrical ZnO pellets at low temperatures in possibly scalable conditions: planar Pt electrodes without Pt paste. The study explores the incubation period by increasing the 50 Hz AC electric field up to 8 kV cm−1 (applied by steps or with a continuous ramp) in an air atmosphere with varying relative humidity (RH). Experiments were repeated on several samples and showed that the maximum amplitude of PDs decreased while their number increased as the RH increased. With the support of video recording of the experiments, different characteristics in the PD pattern evolution with increasing AC voltage could be attributed to various locations between the electrodes, at the sample surface, or at the interface with the electrodes. By examining the PRPDs, it was possible to identify two distinct types of PDs having different thresholds of inception: one type corresponds to PDs occurring mostly at the triple junction ‘ZnO-platinum-air’ or in the ZnO-electrodes gap and depends strongly on the RH level and; the other type corresponds to electric arcs between the electrodes, with an inception threshold much less affected by the RH level.

Comparative Study on Partial Discharge Characteristics of Seven Accessory Defects Under Four On-Site Testing Voltages of a 10-kV Cable Line

Due to the increase in voltage levels and line lengths of power cables, the commonly used alternating current resonance (ACR) voltage is challenging to meet the growth rate of the capacitance of ultra-high-voltage cables for the on-site partial discharge (PD) tests. PD research on power-frequency-like voltages such as damped alternating current (DAC) voltage, very low-frequency sine wave (VLF-Sine) voltage, and very low-frequency cosine rectangular wave (VLF-CR) voltage, has important engineering value for evaluating the cable insulation conditions. In this paper, a PD test and measurement system of a 10 kV XLPE cable line with a 2 km length is established. Seven typical accessory defects are made artificially, including insulation hole, insulation screen tip, ring-cut tool mark, and stress cone installation dislocation defects at the terminals, and connector tip, conductive impurity, and stress cone installation dislocation defects at the joints. PD tests under four kinds of testing voltages of ACR, DAC, VLF-Sine, and VLF-CR are carried out to compare the PD inception voltages (PDIV), phase-resolved PD (PRPD) patterns, and PD location results of various defects. PD equivalence between power-frequency-like voltages and ACR is characterized by PDIV equivalent coefficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PDIV</sub> , PRPD pattern characteristic quantities and discharge amplitudes. The results show that DAC and VLF-Sine have an outstanding ability to detect and characterize the severity of PD. DAC and VLF-CR have a good ability to recognize the type of PD. And DAC, VLF-Sine and VLF-CR can accurately locate PD. This study provides an important reference for on-site cable defect diagnosis and PD assessment.

Partial Discharge Suppression of the Oil-impregnated Paper by Depositing PTFE Thin Film via Magnetron Sputtering

The rapid development of HVDC (High Voltage Direct Current) transmission technology brings new challenges to insulation materials. Oil-paper insulation needs to be strengthened to prevent possible breakdown failure. In this paper, the PTFE functional film was fabricated by magnetron sputtering on the insulation pressboard surface to suppress partial discharge. The partial discharge test was conducted to compare the withstanding ability of the modified pressboard and the untreated pressboard. The results showed that partial discharge inception voltage increased by 18% for the modified pressboard. Phase-resolved partial discharge results also showed a great enhancement in partial discharge withstanding the ability of the PTFE function layer on the pressboard. The molecular simulation was conducted to give a possible explanation for the improvement. Higher bond levels and a large band gap of PTFE played a vital role.

Application of a Phase Resolved Partial Discharge Pattern Analysis for Acoustic Emission Method in High Voltage Insulation Systems Diagnostics

An alternative method for analysis of acoustic emission (AE) signals generated by partial discharges (PD), based on a correlation between voltage phase run and AE pulses, so called phase resolved PD pattern (PRPD), is presented in the paper. PRPD pattern is a well-known analysis tool commonly used in such PD diagnostic methods as conventional electrical and UHF ones. Moreover, it yields various signal analysis abilities and allows a direct correlation indication between measurement results achieved using different methods. An original PRPD measurement methodology applied for AE method as well as some exemplary measurement results and further data analysis capabilities are presented in the paper. Also a comparative analysis of PRPD patterns achieved using various measurement methods and different PD source configurations have been investigated. All presented experiments were done under laboratory conditions using PD model sources immersed in the insulation oil. The main purpose of the presented research is to indicate an all-embracing analytical tool that yields an ability to direct comparison (qualitative as well as quantitative) of the AE measurement results with other commonly applied PD measurement methods. The presented results give a solid fundamental for further research work concerning a direct correlation method for AE and other described in the paper diagnostic techniques, mainly in order to continue PD phenomena analysis and assessment in real life high voltage apparatus insulation systems under normal onsite operation conditions.

Influence of Water Ingress on Surface Discharges Occurring on the Silicone Gel Encapsulated Printed Circuit Boards Developed for Deep-Sea Applications

This paper discusses the influence of water ingress on the electrical discharges that arise on the surface of printed circuit boards that are developed for deep sea applications. The primary concern is the electrical discharges arising on the surface of the PCB bridging localized spots/areas between conductive traces, high voltage terminal, mounting hole options, and so on. The current literature focuses on electrical discharges arising on the surface of PCB at low-pressure environments emulating aircraft and space applications. Extending the same approach to deep-sea environments is not feasible since the pressure is very high and the temperatures are very low. In all, a meager attempt has been made to investigate the possible application of such gel-encapsulated PCBs in adverse high-pressure, salt/sea water, low-temperature, deep-sea environments. This experimental study focuses on studying the influence of deionized and sea water on electrical discharges arising on the surface, between conductive tracks of PCB. A group of PCBs with different gaps between the conductive tracks was produced and immersed in deionized and seawater for a specific duration at standard pressure. Afterward, the discharge characteristics were measured the using partial discharge (PD) test method and the respective phase-resolved PD pattern was studied and analyzed. Pertinent experiments revealed that the PD process and eventual failure manifests as a typical and substantial pattern. The apparent charge measured during the PD inception and near-by failure condition, influenced by deionized and seawater, reveals a regular trend. Naturally, a simple observation of the PD pattern might help to identify the intricacy and to initiate a preventive measure well before the complete system suffers a premature failure. Also, based on these results, making slight structural modifications on the PCB at crucial locations might help in retaining the dielectric integrity of the material.

FEA-Based Simulation of Accelerated Ageing in a Power Cable Due to Sustained Partial Discharge Activities in a Spherical Cavity

The reliability of electrical assets is greatly influenced by the quality of their insulations. Key power installations such as power cables are manufactured with polymer-based materials as part of their insulation system. However, accelerated ageing of equipment insulations due to manifestation of defect(s), and partial discharges (PDs) can offset the operation of these systems or even lead to breakdowns. In this study, a non-deterministic model to simulate the phenomenon of repetitive discharges in a spherical air-filled cavity within a practical power cable has been investigated. In addition, the work contributes to the understanding of PD behaviour and field distribution under different ageing conditions considering changes in cavity surface conductivity. First, a section of the practical XLPE cable containing the cavity is developed in 2D using COMSOL software, and a finite element analysis (FEA) of the electric field distribution within the cable insulation is performed. The magnitude of the cavity local field, that is enough to ignite a PD, is investigated. Alongside the COMSOL model, the activity of sustained internal PD is simulated in MATLAB by introducing a random sample generating factor and adjusting the model’s parameters to obtain something close to the practical results. Furthermore, the impact of continuous PD in the power cable under different cavity dimensions and surface conductivity is likewise investigated, and a phase resolved PD (PRPD) pattern is established. The result shows that the magnitude and number of PDs per cycle increase as the cavity size and its surface conductivity increase. Finally, when the cavity surface conductivity rises, the amplitude of the electric field generated by the surface charge distribution and the number of PDs per cycle approach their maximum values.

Partial discharge detection on power equipment using a magneto-resistive sensor

Partial discharges (PD) detection is an effective diagnostic method to assess the insulation condition of electrical power equipment in the high-voltage laboratory or field tests. This paper presents a non-contacting PD detection method for power equipment. The method is based on an extra high-sensitivity adapted giant magneto-resistive (xMR) sensor that measures the magnetic field produced by the PD currents. Firstly, this paper describes the sensor’s relevant principle and signal conditioning circuit. Next, the sensor’s typical performance, including the frequency response and time-domain response to calibrator PD pulses, is measured and compared with our previous work. The results indicate that the xMR system’s bandwidth is improved to the MHz range. Finally, PD experiments are carried out and compared with measurements using a commercially available high-frequency current transformer (HFCT), which allows for verification of the coherence of the results concerning the PD pulses and phase-resolved PD (PRPD) patterns. The results show that PD in a cross-linked polyethylene (XLPE) cable or a gas-insulated system (GIS) with artificial discharging defects is successfully measured, demonstrating the sensitivity and performance of the xMR system for PD detection.

Identification of Electrical Tree Aging State in Epoxy Resin Using Partial Discharge Waveforms Compared to Traditional Analysis.

Electrical treeing is one of the main degradation mechanisms in high-voltage polymeric insulation. Epoxy resin is used as insulating material in power equipment such as rotating machines, power transformers, gas-insulated switchgears, and insulators, among others. Electrical trees grow under the effect of partial discharges (PDs) that progressively degrade the polymer until the tree crosses the bulk insulation, then causing the failure of power equipment and the outage of the energy supply. This work studies electrical trees in epoxy resin through different PD analysis techniques, evaluating and comparing their ability to identify tree bulk-insulation crossing, the precursor of failure. Two PD measurement systems were used simultaneously-one to capture the sequence of PD pulses and another to acquire PD pulse waveforms-and four PD analysis techniques were deployed. Phase-resolved PD (PRPD) and pulse sequence analysis (PSA) identified tree crossing; however, they were more sensible to the AC excitation voltage amplitude and frequency. Nonlinear time series analysis (NLTSA) characteristics were evaluated through the correlation dimension, showing a reduction from pre- to post-crossing, and thus representing a change to a less complex dynamical system. The PD pulse waveform parameters had the best performance; they could identify tree crossing in epoxy resin material independently of the applied AC voltage amplitude and frequency, making them more robust for a broader range of situations, and thus, they can be exploited as a diagnostic tool for the asset management of high-voltage polymeric insulation.

ONLINE PARTIAL DISCHARGE MEASUREMENT FOR CONDITION-BASED MAINTENANCE OF HV POWER CABLES IN RAILWAY INFRASTRUCTURE

Partial discharge (PD) measurement as one of well-known method to evaluate the condition of high voltage (HV) power cables has been studied over many decades. Cable insulation failure could result in a power outage, which could then cause a loss of service in the transportation system and even dangerous events like fire accidents. It is of a great interest to railway infrastructure operators to monitor and identify the cable faults before any possible accident occurs. The paper focuses on the diagnostic problem to detect the HV cable fault based on the Phase Resolved Partial Discharge (PRPD) patterns. Classification models, such as Random Forest and Convolutional Neural Network, are considered to classify the pattern of PRPD based on the mostly occurring PD types in HV cables, such as corona, surface, and void patterns. Experiments are performed and the PRPD data from the experiments are collected. The optimal model is applied in the online monitoring program which will be used continuously to evaluate the cable condition and arrange the optimal schedule for maintenance. According to the analysis, both algorithm perform well in the PRPD pattern categorization, with accuracy up to 83.45%. This indicates that due to the more effective behavior, PD assessment with PD sensors is preferable.

Design of a Flexible UHF Hilbert Antenna for Partial Discharge Detection in Gas-Insulated Switchgear

This letter presents the design of a new flexible Hilbert antenna for built-in detection of partial discharges (PD) in gas-insulated switchgear (GIS). The fractal antenna is designed on flexible polydimethylsiloxane (PDMS) material of 124 mm × 122 mm × 0.5 mm, fed by CPW, and loaded with one circular slot and two rectangular slots in the ground plane of each subblock, and with a rectangular slot at the narrow opening to broaden the matching band and improve the gain of the antenna. The actual bandwidth of the antenna without bending is 422–800 MHz, 1.23–1.58 GHz, 1.65–1.7 GHz, and 1.79–3 GHz, covering 73.6% of the detection band, and the bending deformation has little effect on the antenna bandwidth. The measured peak gain of the antenna at a bending radius of 350 mm reaches 1.34 dB, exceeding the reported Hilbert antenna. The test and analysis of the antenna sensitivity are performed by 220 kV GIS PD experimental platform. Experimental results show that the flexible antenna designed in this article can effectively detect the high-frequency electromagnetic wave signal radiated by PD with a discharge amount of about 46 pC. The phase-resolved partial discharge analysis yields that the designed antenna has a high PD detection sensitivity.

Partial discharge induced decomposition and by-products generation properties of HFO-1234ze(E)/CO2: a new eco-friendly gas insulating medium

HFO-1234ze(E) is introduced as a new eco-friendly insulating gas for medium-voltage gas-insulated equipment (MV-GIE). However, there are few reports on the partial discharge (PD) induced decomposition and gaseous, solid by-product generation characteristics of HFO-1234ze(E)/CO2. Herein, the PD decomposition characteristics of HFO-1234ze(E)/CO2 were explored based on a needle-plate electrode that simulates the metal protrusion defect in MV-GIE. The partial discharge inception voltage, and phase-resolved partial discharge of HFO-1234ze(E)/CO2 under different mixing ratios, PD intensity and duration time at 0.15 MPa were obtained. Meanwhile, the PD-induced decomposition and generation of gaseous, solid by-products of HFO-1234ze(E)/CO2 were analyzed. A three-zone pattern that describes the gas–solid metal interface interaction was proposed for the first time. It is found that the increase of HFO-1234ze(E) content brings superior insulation performance, while the precipitation of gaseous (CF4, C2F6, CHF3, C3HF7) and solid by-products gradually aggravated. In order to avoid the negative impact of PD-induced decomposition on the insulation and service life of MV-GIE, the optimal HFO-1234ze(E) content of 30% is recommended. Based on the optimal mixing ratio (30% HFO-1234ze(E)/70% CO2), the effect of gas pressure and electrode materials on solid precipitation, breakdown voltage was also explored. This work guides the development of HFO-1234ze(E) based MV-GIE and understanding the solid by-products precipitation mechanism of eco-friendly insulating gas.

Electrical Aging Effect on PD Characteristics of Cellulose Pressboard Modified by Nano-TiO2

Cellulose pressboard has been widely used as internal insulation in oil-immersed transformers. Partial discharge (PD) is the main cause of the degradation of the insulation properties for electrical equipment. Nano-modification is one of the methods to improve the electrical properties of the oil–paper insulation. In this article, the effects of the oil-immersed pressboards modified by nano-TiO2 particles on PD characteristics were studied. The pressboards modified by different mass fractions of nano-TiO2 (10-nm particle size) were prepared. The electrical aging experiment was carried out on the oil-immersed pressboard. Experimental platforms for partial discharge inception voltage (PDIV) and relative permittivity were established to measure the dielectric performance of the pressboard. Phase-resolved partial discharge (PRPD) spectrograms and PD current pulses of the samples under different electrical aging states were obtained for different PD time. The surface morphology of the pressboards was observed by scanning electron microscope (SEM) to verify the effects of nano-TiO2 particles. The results showed that the values of electric field strength for the samples were 4.58 and 3.6 kV/mm, respectively, when the PD occurred in the samples modified by nano-TiO2 with 3 wt% and the unmodified ones. Compared with the unmodified pressboard, the modified one had a lower value of relative permittivity. The pulse current of the pressboard modified by 3 wt% nano-TiO2 particles under different electrical aging states was smaller than that of the other samples. The addition of 3 wt% nanoparticles was the optimum ratio to suppress the PD of the samples in the ac electrical field.

Development and Discharge Characteristics of Surface Tracking on the Interface Between the PD Suppression Coatings Outside of Stator Winding Slots

During the operation of high voltage (HV) motors, the end-winding surface can be contaminated by oil or dust, resulting in a type of intense discharge: surface tracking, which gradually develops into phase-to-phase or phase-to-ground faults. To improve our understanding of the development process and characteristics of surface tracking on the end-winding, an accelerated aging test was performed on 10-kV stator coils to induce surface tracking under electrical and oil contamination stresses. To understand the evolution of surface tracking, the partial discharge (PD) was collected and the characteristics of the corresponding phase-resolved PD (PRPD) patterns and pulse sequence analysis (PSA) patterns were analyzed. Combined with finite element analysis (FEA) simulations, the development process of surface tracking was interpreted. It is established that the electrical field of a dry band with high resistivity on the contamination is much higher than other contaminated areas via FEA simulation, causing the electrical breakdown of the adjacent air and discharge. Such continuous and intense discharges finally leave on the end-winding surface a carbonized channel, forming a tree-like structure. This article presents the results of changes in PRPD patterns and PSA patterns during different stages, together with the surface potential distribution after tracking generation. The research of this article is devoted to providing a reference for PD monitoring and fault diagnosis of HV motors.

Feature Extraction and Pattern Recognition Algorithm of Power Cable Partial Discharge Signal

The degree of insulation aging of power cables is closely related to their partial discharge (PD) level, so the analysis of PD signals can be used to realize the cable condition detection. However, after performing online detection of PDs on power cables, the collected signals always contain interference signals due to the influence of electromagnetic interference in the field. In order to identify each type of local discharge signal from the interference signal, this paper proposes a clustering identification algorithm for local discharge signals, which mainly involves pulse extraction, feature parameter extraction and clustering identification process. The algorithm first extracts the pulse signal by combining the amplitude–time threshold method and the time domain energy method, then obtains the feature vector of the signal according to the synchronous multi-channel method, designs a fuzzy C-mean clustering algorithm based on subtractive clustering to determine the initial clustering center to cluster the samples and finally analyzes and checks the clustering results according to the phase resolved PD (PRPD) of a single class of signals and the fit of the two-parameter Weibull distribution function. The clustering results were analyzed and examined. The experimental results show that the proposed algorithm can extract pulse signals efficiently and accurately, and the synchronous multi-channel method can characterize pulse signals better. Meanwhile, the algorithm can determine the optimal number of classes adaptively according to the clustering effectiveness function and adopt subtractive clustering to initialize the clustering center, which can approach the optimal solution faster, and can effectively cluster a variety of discharge signals, so as to realize the type identification of single-class discharge signals.

Verification of Interpretability of Phase-Resolved Partial Discharge Using a CNN With SHAP

Deep neural networks can be used to distinguish partial discharge (PD) signals despite their complexity. This study analyzes the appropriateness of interpreting phase-resolved partial discharge (PRPD) signals using a convolutional neural network (CNN) through the Shapley additive explanation (SHAP) method. The generated PRPD signals were accumulated by applying AC voltage to four types of electrodes with a polyethylene sheet, followed by their conversion into scattered images to construct a classification model, CNN. The SHAP values for each pixel in the test images were then calculated. The result indicated that the pixels around the 0 V line retained high absolute SHAP values in every label, and the average of the summation of absolute SHAP values over all labels and all test images, which indicates the weight of each pixel, shows a similar tendency. Additionally, insight tests of the two CNN models were conducted, and the results showed that some structural defects could be detected by visualizing the SHAP values for each pixel. Finally, the verification of parameter-and-data vulnerability showed that SHAP has sufficient endurance against some types of instability in the data and model. Although the SHAP method lacks a perfect causal model because of its origin, the results imply that in appropriate use cases, weights on classifications of PD signals could be described by SHAP’s interpretability.

AI기법과 고주파 전류 변환센서를 활용한 배전반 on board 진단시스템

This paper presents an on-board switchboard diagnosis system based on the Ai algorithm for the purpose of establishing a real-time partial discharge monitoring diagnosis system. For classifying pattern of partial discharge, FCM-based RBFNN is applied to on-board diagnosis system and K-means, NN(Neural Networks) are used for comparative analysis. The data are obtained by HFCT(High Frequency Current Transformer) sensor from designed partial discharge simulation environment and its phase is divided into 128 degree. Commonly used partial discharge data analysis methods such as statistical analysis, PRPS(Phase Resolved Pulse Sequence) and PRPD(Phase Resolved Partial Discharge) are introduced. In this paper PRPDA(Phase Resolved Partial Discharge Analysis) and PCA(Principal Component Analysis) are used as a pre-processing. The Ai algorithm comparison is performed twice in total. Firstly, a comparison analysis of the test data validation of each model trained in the Python environment is conducted through a confusion matrix. Afterwards, an on-board diagnostic device is added to the partial discharge simulation circuit, and the judgment results for the actual operation are compared. Through comparative analysis in virtual and real environments, it is confirmed that the case in which FCM RBFNN is mounted shows excellent performance.

Optical Detection Method for Partial Discharge of Printed Circuit Boards in Electrified Aircraft Under Various Pressures and Voltages

Realization of an electrical aircraft demands a low-weight electric distribution and propulsion system. The use of high voltage and power electronics operated at high switching frequencies is essential to achieve this objective. However, printed circuit boards (PCBs) in electrified aircraft are in a harsh working environment, which can make PCBs more susceptible to generate partial discharges (PDs). The current PD detection technology has poor immunity to the electromagnetic interference and acoustic interference in the operating environment of aircraft. Therefore, this article proposes an optical-based PD detection method for PCBs, which is effectively immune to electromagnetic and acoustic interference. This method uses fluorescent fiber as a PD optical signal sensor and then collects the optical signal by the avalanche photodiode (APD). Experiments have verified that the detection sensitivity, sensing range, and anti-interference performance of this method are well satisfied with the PD detection. In addition, single PD pulse, optical phase resolved PD (PRPD) patterns, and PD inception voltage (PDIV) under different air pressure and voltage conditions are investigated. Finally, the relationship between the optical signal and PD amplitude is found to be proportional, which proves that the severity of the PD on PCBs can be effectively detected and evaluated by this method.