Method and system for locating partial discharge in transformer based on optical fiber sensing array

Abstract

A transformer internal partial discharge positioning method based on an optical fiber sensing array, comprising: processing a triangular ultrasonic sensing array, wherein the triangular ultrasonic sensing array comprises: at least 3 optical fiber Fabry-Perot sensors; connecting the output end of a wavelength tunable laser to a 1*3 optical fiber coupler, and connecting 3 optical fibers from the 1*3 optical fiber coupler to 3 optical fiber circulators respectively; connecting the 3 optical fiber circulators to the optical fiber Fabry-Perot sensors, and connecting the 3 optical fiber circulators to 3 photodetectors respectively; connecting the output ends of the 3 photodetectors to the input ends of a signal acquisition card, storing signal data, storing the data in the signal acquisition card into a computer; carrying out wavelet threshold denoising on the collected signal data; using a generalized cross-correlation function on the denoised signal data to extract time delay information; and using an artificial firefly and clustering analysis joint optimization algorithm to solve the accurate position of the partial discharge source according to the obtained time delay information.

Description

Technical Field

[0001] This invention belongs to the field of online monitoring of electrical equipment, and more specifically, relates to a method and system for locating partial discharge in a transformer based on a fiber optic sensor array. Background Technology

[0002] Partial discharge is a discharge phenomenon caused by localized breakdown in an insulating medium. Partial discharge detection is an effective method for assessing the insulation condition of electrical equipment. By detecting the ultrasonic signals generated by partial discharge, the presence of partial discharge can be determined and the discharge point located. Currently, the core sensing component for ultrasonic partial discharge detection is the piezoelectric transducer. However, due to structural limitations, it can only be installed on the outer wall of a transformer. Therefore, the received signal is difficult to use for partial discharge localization due to multipath propagation.

[0003] There is still room for optimization in current positioning algorithms and fiber optic sensor array structures. The method of positioning by combining the signal delay and phase difference received by a planar phased array sensor with spatial orientation is limited by the Rayleigh limit. Partial discharge localization from the frequency domain can eliminate the Rayleigh limit, but this method can only be tested in air, and its applicability to detecting partial discharge inside transformers remains to be seen. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a method and system for locating partial discharge inside transformers based on a fiber optic ultrasonic sensor array. The fiber optic sensor array in the designed system is made of insulating material, exhibiting excellent anti-electromagnetic interference capabilities and high sensitivity. Simultaneously, the sensor array is small in size, resistant to chemical corrosion, and easily arranged inside the transformer, thus enabling more sensitive detection of ultrasonic signals generated by partial discharge within the transformer. To address the drawbacks of a single fiber optic Fabry-Perot sensor, such as poor directionality and limited characteristic frequency, hindering accurate localization, a multi-frequency sensor array is designed to accurately measure partial discharge signals with different characteristic frequencies. Combining the above technologies, a wavelength-tunable laser, a 1-to-3 fiber optic coupler, and a circulator are used to construct the optical path, building a complete partial discharge detection and localization system. To address the shortcomings of traditional partial discharge localization algorithms, such as potential non-convergence and susceptibility to local optima, a localization algorithm based on the artificial firefly algorithm and clustering analysis is proposed to achieve accurate localization of partial discharge.

[0005] The present invention adopts the following technical solution.

[0006] A method for locating partial discharge within a transformer based on a fiber optic sensor array includes the following steps:

[0007] Step 1: Fabricate a triangular ultrasonic sensor array, wherein the triangular ultrasonic sensor array includes at least 3 fiber optic Fabry-Perot sensors;

[0008] Step 2, connecting the optical path, includes: connecting the output end of the wavelength tunable laser to a 1×3 fiber coupler, splitting the 1×3 fiber coupler into 3 optical fibers and connecting them to 3 fiber circulators respectively; connecting the 3 fiber circulators to fiber Fabry-Perot sensors, and connecting the 3 fiber circulators to 3 photodetectors respectively.

[0009] Step 3, connecting the circuit, includes: connecting the output terminals of the three photodetectors to the input terminals of the signal acquisition card, and storing the partial discharge time-domain signal in the signal acquisition card into the computer;

[0010] Step 4: Perform wavelet threshold denoising on the acquired partial discharge time-domain signal, and then perform Fourier transform on the denoised signal to obtain the partial discharge ultrasonic frequency domain signal.

[0011] Step 5: Analyze the partial discharge ultrasonic frequency domain signal ( The generalized cross-correlation function is used to extract the corresponding time delay information. , , ;

[0012] Step 6: Based on the obtained time delay information, the accurate location of the local discharge source is solved using a joint optimization algorithm of artificial firefly algorithm and cluster analysis.

[0013] Furthermore, the triangular ultrasonic sensing array is constructed from resin material.

[0014] Furthermore, the three fiber optic Fabry-Perot sensors form an equilateral triangle with a spacing of 50 mm.

[0015] Furthermore, in wavelet thresholding denoising, the wavelet basis is selected as sym8, and a compromise threshold is adopted.

[0016] Furthermore, the characteristic frequencies of the three fiber optic Fabry-Perot sensors are 32 kHz, 121 kHz, and 153 kHz, respectively.

[0017] Furthermore, step 5 specifically includes:

[0018] The generalized cross-correlation function is as follows:

[0019]

[0020] in For PHAT weighting factors, where, And in the above formula Take (1,2), (1,3), and (2,3) respectively. Indicates time; for The time at which the peak is located.

[0021] Furthermore, the artificial firefly algorithm specifically includes:

[0022] Step 61: Extract the time delay information from the partial discharge ultrasonic frequency domain signal. , , This serves as the input to the algorithm, while simultaneously initializing the population to a value randomly set within the solution space. Points , ;

[0023] Step 62, Define the objective function Initialize brightness and decision domain ;

[0024] Step 63: Update the objective function value of the feasible solution at the current position;

[0025]

[0026] in, Indicates the number of iterations. The absorption coefficient is... Let be the dissipation coefficient, where =0.7、 =0.7; Represents the target individual brightness value, Represents an individual The brightness value;

[0027] Step 64, Update Location;

[0028] Step 65, Update the decision domain Then return to step 62.

[0029] Furthermore, the objective function for:

[0030]

[0031] in, =0.1, , This represents the equivalent speed of sound.

[0032] Furthermore, step 64 specifically includes:

[0033] For each individual Select all eligible target individuals according to certain conditions. ;

[0034]

[0035] Represents an individual All target individuals in the surrounding area that meet the conditions The set, , Represents the target individual With individuals The Euclidean distance between them Represents an individual The decision domain Indicates the number of target individuals;

[0036] Calculate individuals using probability formulas Each eligible individual Probability of being selected :

[0037]

[0038] in, The value ranges from 1 to ;

[0039] from target individuals Select 1 target individual Used for updating Location:

[0040]

[0041] This represents the Euclidean distance between the target location and its own location. s This represents the step size, with s=0.7.

[0042] Furthermore, it is characterized by,

[0043] Individuals in step 65 The decision domain update formula is:

[0044]

[0045] in, Indicates the sensing radius. It is the individual threshold within the decision domain. Let be the parameter, where , .

[0046] Furthermore, clustering analysis algorithms include:

[0047] Substituting the final output of the artificial firefly algorithm Points By using a clustering analysis algorithm, a point is selected as the location of the local discharge source.

[0048] A transformer partial discharge localization system based on fiber optic sensor array includes: a wavelength-tunable laser, a 1×3 fiber optic coupler, a fiber optic circulator, a triangular ultrasonic sensor array, a photodetector, a signal acquisition card, and a computer.

[0049] The output of the wavelength-tunable laser is connected to a 1×3 fiber coupler. The 1×3 fiber coupler splits into 3 fibers, which are connected to 3 fiber circulators respectively. All 3 fiber circulators are connected to fiber Fabry-Perot sensors, and the 3 fiber circulators are connected to 3 photodetectors respectively.

[0050] The triangular ultrasonic sensing array includes at least three fiber optic Fabry-Perot sensors;

[0051] The output terminals of the three photodetectors are connected to the input terminals of the signal acquisition card, and the signal data is stored. The data in the signal acquisition card is then stored in the computer.

[0052] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following advantages:

[0053] This invention proposes a transformer internal partial discharge localization system based on a fiber optic ultrasonic sensor array, which boasts advantages such as high detection sensitivity and excellent localization accuracy. The invention utilizes a 1×3 fiber optic coupler and fiber optic circulator, combined with a wavelength-tunable laser to construct the optical path, enabling simultaneous measurement by multiple fiber optic Fabry-Perot sensors with different characteristic frequencies, thus broadening the frequency domain for detecting partial discharge signals. Arranging three sensors in an equilateral triangle creates a multi-frequency ultrasonic sensor array, improving the accuracy of partial discharge signal measurement. Wavelet thresholding is applied to denoise the measured partial discharge signals, and a generalized cross-correlation function is used to extract time delay information, obtaining accurate time delay data. A localization method combining the artificial firefly optimization algorithm and cluster analysis is proposed to solve the localization equation of the partial discharge source, achieving accurate localization of the partial discharge source. Attached Figure Description

[0054] Figure 1 A schematic diagram of a transformer internal partial discharge localization system based on a fiber optic ultrasonic sensor array.

[0055] Figure 2 This is a flowchart of a method for locating partial discharge inside a transformer based on a fiber optic ultrasonic sensor array.

[0056] Figure 3 This is a diagram of the localization algorithm model. Detailed Implementation

[0057] The present application will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and should not be construed as limiting the scope of protection of the present application.

[0058] This invention proposes a partial discharge detection and localization system based on a multi-frequency fiber optic sensor array. It fully utilizes the advantages of high sensitivity and strong anti-interference capability of fiber optic Fabry-Perot sensors. Multiple optical paths are formed using 1×3 fiber couplers, enabling multiple fiber optic Fabry-Perot sensors with different characteristic frequencies to work together. The sensors are arranged into a sensor array to construct a high-performance sensor array, improving the partial discharge detection performance. Wavelet thresholding is used to denoise the measured partial discharge signal, and a generalized cross-correlation function is used to extract time delay information, obtaining accurate time delay data. Finally, the localization equation of the partial discharge source is solved by combining the artificial firefly optimization algorithm and cluster analysis joint optimization method, achieving accurate localization of the partial discharge source.

[0059] To achieve the aforementioned objectives, the specific technical solution adopted by this invention is as follows:

[0060] As attached Figure 1 As shown, this invention proposes a transformer partial discharge localization system based on a fiber optic sensor array, comprising: a wavelength-tunable laser, a 1×3 fiber optic coupler, a fiber optic circulator, a fiber optic Fabry-Perot sensor, a triangular ultrasonic sensor array, a photodetector, a signal acquisition card, and a computer. Its construction method includes the following steps:

[0061] Step 1: Fabricating a triangular ultrasonic sensor array; wherein the triangular ultrasonic sensor array includes at least 3 fiber optic Fabry-Perot sensors; the triangular ultrasonic sensor array is constructed using resin material based on 3D printing technology. Specifically, the fabricated triangular ultrasonic sensor array comprises three fiber optic Fabry-Perot sensors, which form an equilateral triangle with a spacing of 50mm. The graphic structure is designed by computer, and the fabrication equipment is a 3D printer.

[0062] Step 2: Connect the optical path; connect the wavelength-tunable laser (i.e., Figure 1 The output of the DFB laser is connected to a 1×3 fiber coupler (i.e., Figure 1 The 1×3 fiber coupler splits the fiber into three fibers, each connected to a fiber optic circulator. The fiber optic circulator is then connected to a fiber optic Fabry-Perot sensor (i.e.,...). Figure 1The system includes an FP probe, and three fiber optic circulators connect to three photodetectors, allowing the three return beams to be routed to the respective photodetectors. The three fiber optic Fabry-Perot sensors have characteristic frequencies of 32 kHz, 121 kHz, and 153 kHz, and the photodetectors are model LSM-DET-SHS-W2-050.

[0063] Step 3: Connect the circuit; connect the output terminals of the three photodetectors to the input terminals of the signal acquisition card and store the signal data. Transfer the data from the signal acquisition card to the computer. The signal acquisition card should compare the signals from the three photodetectors at the same time base.

[0064] The partial discharge detection and localization system of the present invention is based on the following principle:

[0065] First, a tunable laser generates laser light, which is then guided through an optical fiber into a 1×3 fiber coupler. Three optical paths branching off from the 1×3 fiber coupler are connected to three corresponding fiber circulators. One end of each fiber circulator is connected to a fiber Fabry-Perot sensor, and the three fiber Fabry-Perot sensors form a triangular ultrasonic sensing array. The other end of each fiber circulator is connected to a photodetector. The photodetector converts the returned optical signal from the fiber Fabry-Perot sensor into a voltage signal, which is then acquired by a data acquisition card and sent to a computer. The signal data is preprocessed, and a positioning program calculates the specific location of the partial discharge, thus achieving the detection and positioning of the partial discharge signal.

[0066] As attached Figure 2 The diagram shows the data processing module of the partial discharge detection and localization system proposed in this invention. It consists of three steps: wavelet threshold denoising, extraction of time delay information using the generalized cross-correlation method, and solving for the partial discharge source using a joint optimization method combining the artificial firefly algorithm and cluster analysis. The specific implementation process is as follows:

[0067] Step 4: Perform wavelet thresholding denoising on the acquired partial discharge time-domain signal, and then perform Fourier transform on the denoised signal to obtain the partial discharge ultrasonic frequency-domain signal. The wavelet basis is selected as sym8, and a compromise threshold is used.

[0068] Step 5: Analyze the partial discharge ultrasonic frequency domain signal ( The generalized cross-correlation function is used to extract the corresponding time delay information. , , Based on the correlation between signals, a PHAT weighting factor is used to derive the correlation function, and time delay estimation is achieved by determining the peak value of the correlation function.

[0069] Step 6: Based on the obtained time delay information, a joint optimization algorithm combining artificial firefly algorithm and cluster analysis is used to determine the accurate location of the partial discharge source. The artificial firefly algorithm is a swarm optimization algorithm used to solve constrained optimization problems transformed from localization problems. To achieve global optimization, the artificial firefly algorithm sets a decision domain. After iterations, the initial randomized population will have multiple local optima converging, resulting in a multi-point clustered distribution. To address this issue, cluster analysis is used to determine cluster centers, which are considered valid local optima. Finally, the global optimum is selected from all the derived local optima based on the L2 norm, thus achieving the localization of the partial discharge source.

[0070] The signal acquisition method included in the partial discharge detection and location system of this invention is based on the following principle:

[0071] First, a tunable laser generates laser light, which is then guided through an optical fiber into a 1×3 fiber coupler. Three optical paths branching off from the 1×3 coupler are connected to three corresponding circulators. One end of each circulator is connected to a fiber optic Fabry-Perot sensor, and the three Fabry-Perot sensors form an ultrasonic sensing array. The other end of the circulator is connected to the input of a photodetector. The laser light generated by the laser is guided through the fiber optic Fabry-Perot interferometer cavity, where it undergoes multiple reflections between the fiber end face and the diaphragm, forming interference. Partial discharge excites ultrasonic signals. These signals, transmitted to the sensor surface, cause the diaphragm to vibrate, altering the original interference conditions in the Fabry-Perot interferometer cavity and causing changes in the intensity of the reflected light. The reflected light signal passes through the circulator and enters the photodetector, where it is converted into an electrical signal by a photoelectric converter. This signal is then acquired by a signal acquisition card and sent to a computer.

[0072] The localization algorithm included in the partial discharge detection and localization system of this invention is based on the following principle:

[0073] The partial discharge source localization method of this algorithm is based on time delay information, and its mathematical model is attached. Figure 3 As shown:

[0074] In the picture For sensor position, Let be the location of the local discharge power source to be determined. Assume the signal reaches the sensor. With the signal reaching the sensor The time delay is ,in, Based on the positional relationship between the sensor and the partial discharge source, the following set of hyperboloid equations can be derived, where... This represents the equivalent speed of sound.

[0075]

[0076] This algorithm transforms the above hyperboloid equations into the following constrained optimization problem:

[0077]

[0078]

[0079] In the formula Take the following values ​​in sequence: (1, 2), (1, 3), (2, 3). , , These dimensions depend on the length, width, and height of the equivalent transformer, and this function will be used as the target function in the subsequent positioning algorithm.

[0080] The algorithm first performs data preprocessing: the wavelet thresholding method is used to denoise the partial discharge time-domain signal measured by the multi-frequency ultrasonic sensor in this system. The sym8 wavelet basis is used to perform wavelet thresholding denoising on the acquired signal data, and the time-domain signal is Fourier transformed to obtain its frequency domain signals F(Y1), F(Y2), and F(Y3).

[0081] Then, the time delay is estimated using the generalized cross-correlation method on the denoised signal in order to determine the time delay difference (i.e., ...) in the constrained optimization problem described above. The generalized cross-correlation function used here is as follows:

[0082]

[0083] in PHAT weighting factor or This represents one of the ultrasonic frequency domain signals of partial discharge, where, , This indicates the time. Therefore, through this method, the partial discharge ultrasonic frequency domain signal can ultimately be obtained. , and . for The time at which the peak is located.

[0084] This algorithm employs the artificial firefly algorithm, which has strong global optimization capabilities, to solve this optimization problem. The main process of the localization algorithm based on the artificial firefly algorithm used in this paper is described as follows:

[0085] Step 61: Extract the time delay information from the partial discharge ultrasonic frequency domain signal. , , This serves as the input to the algorithm, while simultaneously initializing the population to a value randomly set within the solution space. Points , It should be noted that a set of partial discharge signals emitted by the sound source corresponds to a set of time delay information. For each set of time delay information, initialization is performed in the solution space. =300 points The localization algorithm based on the artificial firefly algorithm proposed in this paper will iteratively optimize among 300 points to finally obtain the optimal localization point for partial discharge. (Randomly set...) Points Also writing This indicates that position iteration has not yet been performed.

[0086] Step 62, Define the objective function and initialize brightness and decision domain Initially, each individual has the same brightness and decision domain. The time delay information of the partial discharge ultrasonic frequency domain signal is then substituted into... middle.

[0087]

[0088] in, =0.1, ;

[0089] Step 63: Update the objective function value of the feasible solution at the current position.

[0090]

[0091] in Indicates the number of iterations. Describe the objective function. The absorption coefficient is... Let be the dissipation coefficient. =0.7、 =0.7.

[0092] step ,renew Location.

[0093] For each individual Select all eligible target individuals according to certain conditions. The specific conditions are as follows:

[0094]

[0095] Represents an individual All target individuals in the surrounding area that meet the conditions The set, , Represents the target individual brightness value, Represents an individual brightness value, Represents the target individual With individuals The Euclidean distance between them Represents an individual The decision domain. Indicates the number of target individuals.

[0096] Calculate individuals using probability formulas Each eligible individual Probability of being selected :

[0097]

[0098] in, The value ranges from 1 to ;

[0099] from target individuals Select 1 target individual Specifically, it can be stated as follows: within the decision domain, each individual whose objective function value is higher than its own calculates its probability according to the above probability formula. ,and Then, a random number between 0 and 1 is generated, and the individual falling within the specified interval is selected as the target for relocation. It should be noted that in step 64, the preceding paragraph... This refers to multiple target individuals, as described below. This refers to the selected target individual.

[0100] Then update the position based on the position movement formula:

[0101]

[0102] This represents the Euclidean distance between the target location and its own location. s This represents the step size, with s=0.7.

[0103] Step 65: Update the decision domain, then return to step 62 to re-evaluate the objective function. The decision domain represents the set of other feasible solutions within which the objective function is higher than its own value when updating the objective function value.

[0104] individual The decision domain update formula is:

[0105]

[0106] in, Indicates the sensing radius. It is the individual threshold within the decision domain. For parameters. Where, , .

[0107] By iteratively executing the above steps, the initially randomly generated initial population is... After iteration, the algorithm evolves into a set of effective solutions for localized discharge points, from which the optimal solution needs to be sought. As can be seen from the principle of the artificial firefly algorithm, the algorithm sets a decision domain to achieve global optimization capabilities. However, this also means that the initial randomized population, after iterations of the artificial firefly algorithm, will only converge towards multiple local optima, resulting in a multi-point clustered distribution of the population.

[0108] Substituting the final output of the artificial firefly algorithm Points By employing a clustering analysis algorithm, a single point is selected as the location of the local discharge source. In other words, considering the multi-point clustering distribution of the population, the mean clustering method is used iteratively to find the cluster center for each cluster. These cluster centers can be considered as multiple local optima in the solution space. The solution with the smallest L2 norm among these local optima is considered the global optimum. This global optimum is the located location of the local discharge source, and it is used as the output of this algorithm to achieve the localization of the local discharge.

[0109] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

Claims

1. A method for locating partial discharge within a transformer based on an optical fiber sensor array, characterized in that, Includes the following steps: Step 1: Fabricate a triangular ultrasonic sensor array, wherein the triangular ultrasonic sensor array includes at least 3 fiber optic Fabry-Perot sensors; Step 2, connecting the optical path, includes: connecting the output of the wavelength-tunable laser to a 1×3 fiber coupler, splitting the 1×3 fiber coupler into 3 optical fibers and connecting them to 3 fiber circulators respectively; connecting the 3 fiber circulators to fiber Fabry-Perot sensors, the characteristic frequencies of the 3 fiber Fabry-Perot sensors being 32 kHz, 121 kHz and 153 kHz respectively, and connecting the 3 fiber circulators to 3 photodetectors respectively. Step 3, connecting the circuit, includes: connecting the output terminals of the three photodetectors to the input terminals of the signal acquisition card, and storing the partial discharge time-domain signal in the signal acquisition card into the computer; Step 4: Perform wavelet threshold denoising on the acquired partial discharge time-domain signal, and then perform Fourier transform on the denoised signal to obtain the partial discharge ultrasonic frequency domain signal. Step 5: Analyze the partial discharge ultrasonic frequency domain signal ( The generalized cross-correlation function is used to extract the corresponding time delay information. , , ; Step 6: Based on the obtained time delay information, a joint optimization algorithm combining the artificial firefly algorithm and cluster analysis is used to solve for the accurate location of the local discharge source; the artificial firefly algorithm specifically includes: Step 61: Calculate the time delay information of the partial discharge ultrasonic frequency domain signal. , , This serves as the input to the algorithm, while simultaneously initializing the population to a value randomly set within the solution space. Points , ; Step 62, Define the objective function Initialize brightness and decision domain ; Step 63: Update the objective function value of the feasible solution at the current position; in, Indicates the number of iterations. The absorption coefficient is... Let be the dissipation coefficient, where =0.7、 =0.7; Represents the target individual brightness value, Represents an individual The brightness value; Step 64, Update Location; specifically including: For each individual Select all eligible target individuals according to certain conditions. ; Represents an individual All target individuals in the surrounding area that meet the conditions The set, , Represents the target individual With individuals The Euclidean distance between them Represents an individual The decision domain Indicates the number of target individuals; Calculate individuals using probability formulas Each eligible individual Probability of being selected : in, The value ranges from 1 to ; from target individuals Select 1 target individual Used for updating Location: This represents the Euclidean distance between the target location and its own location. s This represents the step size, taken as s=0.7; Step 65, Update the decision domain Then return to step 62.

2. The method for locating partial discharge within a transformer based on an optical fiber sensor array according to claim 1, characterized in that, The triangular ultrasonic sensor array is constructed of resin material.

3. The method for locating partial discharge within a transformer based on an optical fiber sensor array according to claim 1, characterized in that, Three fiber optic Fabry-Perot sensors form an equilateral triangle with a spacing of 50 mm.

4. The method for locating partial discharge within a transformer based on an optical fiber sensor array according to claim 1, characterized in that, In wavelet thresholding denoising, the wavelet basis is selected as sym8, and the threshold is a compromise threshold.

5. The method for locating partial discharge within a transformer based on an optical fiber sensor array according to claim 1, characterized in that, Step 5 specifically includes: The generalized cross-correlation function is as follows: in For PHAT weighting factors, where, And in the above formula Take (1,2), (1,3), and (2,3) respectively. Indicates time; for The time at which the peak is located.

6. The method for locating partial discharge within a transformer based on an optical fiber sensor array according to claim 1, characterized in that, objective function for: in, =0.1, , This represents the equivalent speed of sound.

7. The method for locating partial discharge within a transformer based on an optical fiber sensor array according to claim 1, characterized in that, Individuals in step 65 The decision domain update formula is: in, Indicates the sensing radius. It is the individual threshold within the decision domain. Let be the parameter, where , .

8. The method for locating partial discharge within a transformer based on an optical fiber sensor array according to claim 1, characterized in that, Clustering analysis algorithms include: Substituting the final output of the artificial firefly algorithm Points By using a clustering analysis algorithm, a point is selected as the location of the local discharge source.

9. A transformer partial discharge localization system based on an optical fiber sensor array, used to perform the method according to any one of claims 1-8, comprising: Wavelength-tunable laser, 1×3 fiber coupler, fiber optic circulator, triangular ultrasonic sensor array, photodetector, signal acquisition card and computer; The output of the wavelength-tunable laser is connected to a 1×3 fiber coupler. The 1×3 fiber coupler splits into 3 fibers, which are connected to 3 fiber circulators respectively. All 3 fiber circulators are connected to fiber Fabry-Perot sensors, and the 3 fiber circulators are connected to 3 photodetectors respectively. The triangular ultrasonic sensing array includes at least three fiber optic Fabry-Perot sensors; The output terminals of the three photodetectors are connected to the input terminals of the signal acquisition card, and the signal data is stored. The data in the signal acquisition card is then stored in the computer.

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