A power distribution network power frequency communication uplink signal detection method, device, equipment and medium

Abstract

This invention relates to the field of signal separation technology and discloses a method, apparatus, equipment, and medium for detecting uplink signals of power frequency communication in distribution networks. It addresses the technical problems of low signal detection accuracy, poor separation effect, and poor real-time performance in existing signal separation methods. The method includes: at a sampling node in the distribution network, acquiring simulated signals containing uplink information of power frequency communication from a distribution network simulation model through multiple acquisition channels; performing signal preprocessing on the simulated signals to obtain an observation signal; generating a power frequency synchronization subspace of the distribution network and projecting the observation signal into the power frequency synchronization subspace to obtain a denoised signal; performing pre-whitening processing on the denoised signal to obtain a pre-whitened signal; generating a separation signal using the pre-whitened signal and generating a blind separation objective function based on the separation signal; calculating a target separation vector based on the blind separation objective function; and generating the power frequency communication uplink signal of the distribution network based on the target separation vector.

Description

Technical Field

[0001] This invention relates to the field of signal separation technology, and in particular to a method, apparatus, equipment and medium for detecting uplink signals in power frequency communication of power distribution networks. Background Technology

[0002] As modern power systems evolve towards intelligence, digitalization, and interconnectivity, the operating environment and control methods of distribution networks have undergone profound changes after the integration of distributed generation, renewable energy, and various high-power equipment. To achieve real-time monitoring and precise control of the vast amounts of dynamic data in the power grid, building a ubiquitous power Internet of Things (IoT) has become an inevitable trend in the development of current power systems. Within this system, the communication network, as a crucial support for information exchange and intelligent decision-making among various devices, directly impacts the overall safety, economy, and efficiency of the power grid's operation through its stability and real-time performance.

[0003] In power systems, power frequency communication, as a technology that uses the power grid's own frequency signals to transmit information, has advantages such as simple system structure, low cost, and strong anti-interference capability. Therefore, it is widely used in distribution network condition monitoring, fault diagnosis, and control command transmission. However, in practical applications, power frequency communication in distribution networks is often subject to various noise interferences from power equipment and the external environment, especially harmonic noise. Harmonic noise not only seriously affects signal quality but can also cause communication misjudgments, reduce equipment response speed, and even cause physical damage to power grid equipment, such as increasing heat loss and accelerating equipment aging.

[0004] Among existing noise suppression and signal separation techniques, wavelet transform-based denoising methods have been widely used in power systems due to their multi-resolution analysis capabilities. Traditional wavelet denoising methods typically separate noise from the valid signal by performing multi-scale decomposition on the signal and then processing the wavelet coefficients using hard or soft thresholding. While these methods can reduce noise interference to some extent, they often fail to achieve optimal noise reduction performance in the complex and variable noise environment of distribution networks due to the use of fixed wavelet basis functions, decomposition levels, and preset thresholds.

[0005] On the other hand, Independent Component Analysis (ICA), as a commonly used blind source separation technique, is widely used for the separation and processing of multi-channel signals. In the detection of uplink signals in power frequency communication, ICA can separate mixed signals into statistically independent components. However, traditional ICA methods (such as the Jader algorithm) usually rely on the assumptions that the source signals satisfy statistical independence and non-Gaussianity. When the signal is subjected to high noise interference or there are nonlinear factors in the mixing process, its separation effect is easily affected, and may even lead to false separation. Moreover, the overall algorithm has high computational complexity, which is not conducive to real-time online detection.

[0006] Therefore, existing signal separation methods suffer from low signal detection accuracy, poor separation effect, and poor real-time performance. Summary of the Invention

[0007] This invention provides a method, apparatus, equipment, and medium for detecting uplink signals in power frequency communication in power distribution networks, which solves the technical problems of low signal detection accuracy, poor separation effect, and poor real-time performance of existing signal separation methods.

[0008] This invention provides a method for detecting uplink signals in power frequency communication in a distribution network, applied to a distribution network simulation model; the method includes:

[0009] At the sampling node of the distribution network, analog signals containing uplink information of power frequency communication are collected from the distribution network simulation model through multiple acquisition channels. The analog signals are preprocessed to obtain the observation signals.

[0010] Generate the power frequency synchronization subspace of the power distribution network, and project the observed signal into the power frequency synchronization subspace to obtain the denoised signal;

[0011] The denoised signal is pre-whitened to obtain a pre-whitened signal;

[0012] The pre-whitened signal is used to generate a separation signal, and a blind separation objective function is generated based on the separation signal;

[0013] Calculate the target separation vector based on the blind separation objective function;

[0014] The power frequency communication uplink signal for the distribution network is generated based on the target separation vector.

[0015] Optionally, the step of acquiring analog signals containing power frequency communication uplink information from the power distribution network simulation model through multiple acquisition channels at the power distribution network sampling node, and performing signal preprocessing on the analog signals to obtain the observation signals includes:

[0016] At the sampling node of the distribution network, analog signals containing power frequency communication uplink information are collected from the distribution network simulation model through multiple acquisition channels to obtain continuous time signals;

[0017] The continuous-time signal is sampled at a preset sampling frequency to obtain a discrete sequence;

[0018] The discrete sequences of each sampling channel are superimposed at the sampling node of the power distribution network to obtain the observation signal.

[0019] Optionally, the step of generating the power frequency synchronization subspace of the distribution network and projecting the observed signal into the power frequency synchronization subspace to obtain the denoised signal includes:

[0020] Extract the fundamental frequency of the power distribution network;

[0021] The power frequency fundamental frequency is used to generate the power frequency reference phase;

[0022] Construct an orthogonal basis based on the power frequency reference phase;

[0023] Generate a power frequency synchronization subspace based on the orthogonal basis;

[0024] The observed signal is projected into the power frequency synchronization subspace to obtain a denoised signal.

[0025] Optionally, the step of pre-whitening the denoised signal to obtain a pre-whitened signal includes:

[0026] Calculate the covariance matrix of the denoised signal;

[0027] The covariance matrix is ​​subjected to eigenvalue decomposition to obtain the eigenvector matrix and the diagonal matrix;

[0028] The pre-whitening signal is generated using the feature vector matrix, the diagonal matrix, and the denoised signal.

[0029] Optionally, the step of generating a separation signal using the pre-whitened signal and generating a blind separation objective function based on the separation signal includes:

[0030] The whitening transformation matrix is ​​used to generate the separated signal;

[0031] The separated signals are used to construct the analytical signal;

[0032] Extract the instantaneous phase of the analyzed signal;

[0033] A power frequency phase consistency error is generated based on the instantaneous phase and the power frequency reference phase;

[0034] A blind separation objective function is constructed based on the power frequency phase consistency error and the separation signal.

[0035] Optionally, the step of calculating the target separation vector based on the blind separation objective function includes:

[0036] Calculate and update the separation vector based on the blind separation objective function;

[0037] Determine whether the updated separation vector satisfies the preset convergence criterion;

[0038] If not, adjust the blind separation objective function using the updated separation vector, and return to the step of calculating the updated separation vector based on the blind separation objective function;

[0039] If so, the updated separation vector is used as the target separation vector.

[0040] Optionally, the step of generating the power frequency communication uplink signal of the distribution network based on the target separation vector includes:

[0041] Generate the target analytical signal based on the target separation vector;

[0042] Extract the envelope signal from the target parsed signal;

[0043] The instantaneous phase of the target analyzed signal is generated;

[0044] The target instantaneous phase is used to generate a phase stability detection index;

[0045] Determine whether the phase stability detection index meets the preset conditions;

[0046] If so, the target parsing signal is determined to be the power frequency communication uplink signal of the distribution network.

[0047] This invention also provides a power frequency communication uplink signal detection device for power distribution networks, applied to a power distribution network simulation model; the device includes:

[0048] The observation signal generation module is used to acquire, at the sampling node of the distribution network, analog signals containing power frequency communication uplink information from the distribution network simulation model through multiple acquisition channels, and to perform signal preprocessing on the analog signals to obtain the observation signals.

[0049] The denoising signal generation module is used to generate the power frequency synchronization subspace of the power distribution network and project the observed signal into the power frequency synchronization subspace to obtain the denoising signal;

[0050] A pre-whitening processing module is used to pre-whiten the denoised signal to obtain a pre-whitened signal;

[0051] A blind separation objective function generation module is used to generate a separation signal using the pre-whitened signal and to generate a blind separation objective function based on the separation signal;

[0052] The target separation vector calculation module is used to calculate the target separation vector based on the blind separation objective function;

[0053] The power frequency communication uplink signal generation module for power distribution networks is used to generate power frequency communication uplink signals for power distribution networks based on the target separation vector.

[0054] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0055] The memory is used to store program code and transmit the program code to the processor;

[0056] The processor is used to execute the power frequency communication uplink signal detection method for power distribution networks as described above, according to the instructions in the program code.

[0057] The present invention also provides a computer-readable storage medium for storing program code for executing the power frequency communication uplink signal detection method for power distribution networks as described in any of the preceding claims.

[0058] As can be seen from the above technical solutions, the present invention has the following advantages: The present invention discloses a method for detecting uplink signals of power frequency communication in a distribution network, and specifically discloses: at the sampling node of the distribution network, a simulated signal containing uplink information of power frequency communication is collected from the distribution network simulation model through multiple acquisition channels; the simulated signal is preprocessed to obtain an observation signal; the observation signal is denoised to obtain a denoised signal; the denoised signal is pre-whitened to obtain a pre-whitened signal; a separation signal is generated using the pre-whitened signal, and a blind separation objective function is generated based on the separation signal; the blind separation objective function is solved to obtain a target separation vector; and the power frequency communication uplink signal of the distribution network is generated based on the target separation vector.

[0059] This invention employs a power frequency synchronization subspace projection model for denoising, without relying on wavelet thresholds or fixed filters. Instead, it distinguishes between "synchronization information components" and "asynchronous noise components" at the level of signal physical mechanisms, thereby improving denoising performance. Furthermore, this invention achieves highly robust detection of power frequency communication uplink signals by constructing the power frequency synchronization subspace projection model and a blind separation objective function with physical consistency constraints. This improves the separation effect and real-time performance of power frequency communication uplink signals in distribution networks. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A schematic diagram of the basic framework of a simulation model for a 10kV / 0.4kV power distribution network frequency communication system;

[0062] Figure 2 This is a schematic diagram illustrating the configuration and functions of the equivalent load module;

[0063] Figure 3 A flowchart illustrating the steps of a power frequency communication uplink signal detection method for a power distribution network according to an embodiment of the present invention;

[0064] Figure 4A flowchart illustrating the steps of a method for detecting uplink signals in power frequency communication in a power distribution network, as provided in another embodiment of the present invention;

[0065] Figure 5 This is a structural block diagram of a power frequency communication uplink signal detection device for a power distribution network, provided in an embodiment of the present invention. Detailed Implementation

[0066] In power systems, signal processing for power frequency communication in distribution networks has always been a crucial aspect of ensuring stable system operation. Especially given the prevalence of harmonics and noise interference, effectively separating and removing noise and suppressing the impact of harmonics on signals has been a focus of industry attention. Among traditional methods, wavelet denoising technology holds a central position in power frequency signal processing for distribution networks due to its multi-resolution analysis capabilities.

[0067] Wavelet transform decomposes the original signal into multiple scales or frequency bands in the frequency domain, where each component contains local features of the signal. This decomposition facilitates subsequent noise suppression. Researchers typically distinguish between signals and noise by setting thresholds and perform thresholding on the wavelet coefficients to remove noise. Thresholding methods mainly fall into two categories: hard thresholding and soft thresholding. Hard thresholding directly sets coefficients below the threshold to zero. While simple to implement, it can easily lead to discontinuities in the reconstructed signal. Soft thresholding, on the other hand, suppresses noise while reducing oscillations in the reconstructed signal through smoothing, but may introduce some signal distortion.

[0068] In recent years, researchers have proposed a variety of improvement strategies to overcome the above problems:

[0069] (1) Dual threshold method: seeks a balance between hard threshold and soft threshold, and achieves both the continuity and stability of the signal by setting two different thresholds, but its algorithm complexity is high.

[0070] (2) Statistical adaptive thresholding method: Using statistical methods such as maximum likelihood estimation and Bayesian estimation, the optimal threshold is adaptively determined based on the energy distribution of wavelet coefficients. Although it can theoretically distinguish between signals and noise more accurately, it is difficult to implement in engineering.

[0071] (3) Hybrid wavelet basis method: Traditional single wavelet basis is difficult to adapt to all signal characteristics. Using multiple wavelet basis methods can capture the characteristics of the signal more comprehensively, thereby improving the noise reduction performance, but it also increases the computational burden.

[0072] Furthermore, while traditional Independent Component Analysis (ICA) techniques (such as the Jader algorithm) are effective to some extent in removing harmonic noise, they rely on the strict assumptions of statistical independence and non-Gaussian distribution of the source signals. When the power frequency communication signal has nonlinear mixing or high noise levels, its separation effect decreases significantly. At the same time, the serial processing of multi-level wavelet decomposition and subsequent ICA processing results in high overall computational complexity, making it difficult to meet the requirements of real-time online monitoring.

[0073] However, the above solution has the following drawbacks:

[0074] 1. The challenge of establishing an accurate power distribution network model:

[0075] Establishing a comprehensive and accurate model for 10kV / 0.4kV distribution networks is extremely challenging. Current technologies often encounter difficulties in model building, including but not limited to inappropriate parameter selection, oversimplification of the model, and neglect of nonlinear factors in the network. These factors combined result in models that fail to accurately reflect the actual operating state of the power grid, further impacting the accuracy and efficiency of signal detection.

[0076] 2. High noise sensitivity:

[0077] While wavelet denoising reduces noise interference to some extent, in actual working conditions, when the noise level is high, traditional ICA separation algorithms (such as Jader) are still easily affected by noise, leading to unstable separation results or incorrect separation.

[0078] 3. Wavelet parameter and threshold selection lacks adaptability:

[0079] Traditional methods use pre-defined fixed parameters for wavelet basis functions (such as `db3`, `db5`), the number of decomposition levels, and threshold selection ("heursure", "one", "mln"). This experience-based approach may not be flexible enough under different signal conditions, making it difficult to achieve optimal noise suppression and signal preservation, and requiring repeated manual adjustment of parameters.

[0080] 4. The ICA algorithm has strict requirements on mixed-signal assumptions:

[0081] Traditional ICA (such as Jader) requires the source signals to satisfy assumptions such as statistical independence and non-Gaussianity. However, in actual power frequency communication uplink signals, the independence of the signals may be insufficient, or the signal characteristics may change over time. When the mixing process is complex or nonlinear mixing exists, the separation effect of ICA tends to decrease.

[0082] 5. The algorithm has high computational complexity and insufficient real-time performance:

[0083] Traditional methods employ multi-stage wavelet decomposition, denoising, and then ICA separation, sometimes even performing secondary denoising on the separated signal. While ensuring separation effectiveness, this multi-step process results in a large overall computational load, making it difficult to meet the requirements of efficient processing in real-time detection and online monitoring scenarios.

[0084] In view of this, embodiments of the present invention provide a method, apparatus, equipment and medium for detecting uplink signals of power frequency communication in power distribution networks, which solves the technical problems of low signal detection accuracy, poor separation effect and poor real-time performance of existing signal separation methods.

[0085] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0086] Medium-voltage and low-voltage power distribution systems primarily consist of overhead power lines and underground cables, with underground cables playing a crucial role in the overall system. Medium-voltage overhead power lines typically operate at voltage levels not exceeding 110kV, generally ranging from 10 to 20kV, and their lengths typically range from 5 to 25 kilometers. The main function of these medium-voltage lines is to supply power to remote rural areas, small towns, or independently operated industrial facilities and factories. Underground cables, on the other hand, are widely used in densely populated urban areas to provide stable power support. In areas where underground infrastructure is impractical or prohibitively expensive, overhead cables offer a viable alternative, particularly in low-voltage power supply, where underground cables are often used for power transmission.

[0087] In power system simulation studies, the calculation of equivalent circuit parameters for non-50Hz power lines is particularly important. By establishing a model of electrical signal transmission, researchers can delve into the impact of various parameter variations on signal transmission efficiency. This process involves the specific connection methods of circuit elements and how to accurately describe these connections through simulation and equivalent methods. The electromagnetic processes within circuit elements are concentrated in a relatively small space, providing a theoretical basis for lumped-parameter circuit descriptions that simulate actual circuits. In fact, such simulations are only reasonable when the circuit size is much smaller than the wavelength of the electromagnetic waves it produces during operation. Calculating at the highest frequency of 600Hz, the resulting electromagnetic wavelength is much larger than the actual circuit size; therefore, even with distorted currents, the overall validity of the circuit model is not affected.

[0088] In power distribution systems, transformers are typically connected using either Yyn0 or Dyn11 methods. Specifically, medium-voltage trunk lines generally use 5-kilometer-long overhead cables with 185 square millimeter copper cores; this configuration helps ensure efficient power transmission over medium to long distances. In contrast, low-voltage lines more often use 400-meter-long underground cables with 150 square millimeter aluminum cores; this design takes into account cost and ease of installation. The main transformer uses the Ynd11 connection type, with a nominal capacity of 10 MVA and an impedance voltage setting of 0.08, to optimize power transmission efficiency and safety. Distribution transformers use the Yny0 connection type, with a nominal capacity of 630 KVA and an impedance voltage of 0.04, designed to accommodate different scales of power demand.

[0089] Through the aforementioned centralization principle transformation, this embodiment of the invention constructs an accurate power distribution network simulation model that can demonstrate in detail the performance of different circuit components and connection methods in practical applications. This simulation improves the reliability and efficiency of the entire power distribution system. Figure 1 This is a schematic diagram of the basic framework of a simulation model for a 10kV / 0.4kV power distribution network frequency communication system. Figure 2 This is a schematic diagram illustrating the configuration and functions of the equivalent load module.

[0090] like Figure 1 As shown, Figure 1 This model describes the propagation and mixing characteristics of power frequency communication uplink signals in distribution networks under multiple voltage levels and line structures. The model mainly includes a three-phase power frequency power supply A1 (110kV power supply side), a medium-voltage distribution line A2 (110kV / 10kV segment), a distribution transformer A3 (10kV / 0.4kV transformer), a low-voltage distribution line A4 (0.4kV segment), and a terminal load module A5 (user load side). The three-phase power frequency power supply simulates the power frequency supply conditions at the substation side, and its output is connected to the medium-voltage distribution line via a busbar. The nominal voltage level of the medium-voltage distribution line is 10kV, used to simulate the main distribution network and also serving as the main propagation channel for power frequency communication uplink signals. Several distribution transformers are installed along the medium-voltage line, and the connection method of these transformers is Ynd11 or Dyn11. Their high-voltage side is connected to the medium-voltage line, and their low-voltage side outputs a 0.4kV voltage level and connects to the low-voltage distribution line. The low-voltage distribution lines adopt a multi-branch structure, with each branch connected to a corresponding load module, thus forming a typical radial distribution network topology. During system operation, the power frequency communication uplink signal generated on the low-voltage side propagates through the low-voltage lines, distribution transformers, and medium-voltage lines, forming a mixed signal containing multi-source signals and noise components on the medium-voltage side. This mixed signal is the analog signal collected by the power frequency communication uplink signal detection method for distribution networks proposed in this embodiment of the invention.

[0091] like Figure 2 As shown, Figure 2 To and Figure 1 The corresponding equivalent load model is used to characterize the impact of various user loads in a low-voltage distribution network on the uplink signal of power frequency communication. The load can be residential, commercial, or industrial equipment consuming electrical energy generated by the grid. In the simulation, the load is typically identified by its power demand (e.g., 3*80kW) to evaluate the grid's performance under normal operation and peak demand.

[0092] The equivalent load model adopts a three-phase symmetrical structure and is connected to the low-voltage distribution line. Each load module is used to equivalently simulate actual electrical equipment, with its nominal power set at 3×80kW or 3×200kW to reflect the operating characteristics of loads of different sizes. Under normal operating conditions, the load module continuously consumes electrical energy and introduces disturbance signals and noise components near the fundamental power frequency. These disturbance signals propagate upward along the low-voltage line and superimpose and couple with other load signals at the distribution transformer.

[0093] The equivalent load model described above can uniformly characterize the impact of load scale, distribution mode and operating status on the uplink signal detection performance of power frequency communication without depending on the details of specific user equipment, thus providing a verification basis for the power frequency communication uplink signal detection method based on distribution network proposed in this invention.

[0094] exist Figure 1 and Figure 2 Based on this, please refer to Figure 3 , Figure 3 The flowchart illustrates the steps of a power frequency communication uplink signal detection method for power distribution networks, as provided in this embodiment of the invention.

[0095] This invention provides a method for detecting uplink signals in power frequency communication in distribution networks, applicable to... Figure 1 The power distribution network simulation model shown may specifically include the following steps:

[0096] Step 301: At the sampling node of the distribution network, an analog signal containing power frequency communication uplink information is collected from the distribution network simulation model through multiple acquisition channels. The analog signal is preprocessed to obtain the observation signal.

[0097] A distribution network refers to a power grid that receives electrical energy from the transmission network or regional power plants and distributes it locally or in stages according to voltage to various users through distribution facilities. It consists of overhead lines, cables, poles, distribution transformers, disconnect switches, reactive power compensators, and some auxiliary facilities, and plays an important role in distributing electrical energy within the power grid.

[0098] In this embodiment of the invention, multiple sampling nodes are set up in the distribution network. These sampling nodes include one or more of the following: medium-voltage line nodes, distribution transformer nodes, low-voltage feeder nodes, and user access nodes. Each sampling node acquires analog electrical signals containing power frequency communication uplink information through a voltage coupling device, a current coupling device, or a combined voltage and current coupling device. After filtering, amplification, and analog-to-digital conversion, discrete observation signals are formed. The discrete observation signals corresponding to multiple sampling nodes collectively constitute an observation signal matrix, which serves as input data for subsequent power frequency synchronization constraint time-frequency blind detection processing.

[0099] Step 302: Generate the power frequency synchronization subspace of the power distribution network, and project the observed signal into the power frequency synchronization subspace to obtain the denoised signal;

[0100] In this embodiment of the invention, the power frequency phase of the distribution network can be used to construct the power frequency synchronization subspace of the distribution network, and the observed signal can be projected into the power frequency synchronization subspace to suppress asynchronous noise residuals, thereby obtaining a denoised signal.

[0101] Denoising is achieved by constructing a power frequency synchronization subspace projection model. Instead of relying on wavelet thresholds or fixed filters, it distinguishes between "synchronization information components" and "asynchronous noise components" from the perspective of the signal's physical mechanism, thereby improving the denoising effect.

[0102] Step 303: Perform pre-whitening processing on the denoised signal to obtain a pre-whitened signal;

[0103] In this embodiment of the invention, pre-whitening the denoised signal can provide stable conditions for blind separation.

[0104] Step 304: Use the pre-whitened signal to generate a separation signal, and generate a blind separation objective function based on the separation signal;

[0105] Step 305: Calculate the target separation vector based on the blind separation objective function;

[0106] Step 306: Generate the power frequency communication uplink signal of the distribution network based on the target separation vector.

[0107] After obtaining the pre-whitened signal, a separation signal can be generated from it. A blind separation objective function is then generated based on this signal. By solving the blind separation objective function, the separation vector can be updated. Optimizing the separation vector allows the generated separation signal to achieve better separation performance, thereby generating the power frequency communication uplink signal for the distribution network based on the target separation vector.

[0108] This invention employs a power frequency synchronization subspace projection model for denoising, without relying on wavelet thresholds or fixed filters. Instead, it distinguishes between "synchronization information components" and "asynchronous noise components" at the level of signal physical mechanisms, thereby improving denoising performance. Furthermore, this invention achieves highly robust detection of power frequency communication uplink signals by constructing the power frequency synchronization subspace projection model and a blind separation objective function with physical consistency constraints. This improves the separation effect and real-time performance of power frequency communication uplink signals in distribution networks.

[0109] Please see Figure 4 , Figure 4 A flowchart illustrating the steps of a method for detecting uplink signals in power frequency communication in a distribution network, as provided in another embodiment of the present invention. Specifically, it may include the following steps:

[0110] Step 401: At the sampling node of the distribution network, an analog signal containing power frequency communication uplink information is collected from the distribution network simulation model through multiple acquisition channels to obtain a continuous time signal;

[0111] In this embodiment of the invention, at the sampling node of the distribution network, an analog signal containing power frequency communication uplink information is acquired through a voltage / current coupling device to obtain a continuous-time signal. t is a continuous-time variable; the continuous-time signal consists of a power frequency synchronization communication component, a power grid background component, and a random noise component. The random noise component includes Gaussian noise, impulse noise, and other asynchronous interference components. Therefore, the continuous-time signal obtained at the sampling node can be regarded as a mixed signal formed by the superposition of the aforementioned power frequency synchronization communication component, power grid background component, and random noise component.

[0112] In practical implementation, continuous-time signals It consists of an ideal power frequency synchronous communication signal, the power frequency fundamental wave and harmonic background, and random noise, as shown in the following formula:

[0113] ;

[0114] in, Indicates the background of fundamental and harmonic frequencies. Indicates random noise. for The target communication component in the signal represents the ideal power frequency synchronous communication signal. Subsequent steps, including signal construction, instantaneous phase extraction, and phase stability index calculation, enable the analysis of the target communication component. Synchronous feature detection.

[0115] For calculation It can perform power frequency synchronization constraint modeling, that is:

[0116] ;

[0117] Where f0 is the power grid frequency (50Hz or 60Hz); a(t) is the slowly varying communication modulation envelope; For phase disturbances caused by changes in operating conditions, the following conditions must be satisfied:

[0118] ;

[0119] This constraint indicates that the communication information is on the "envelope" rather than the carrier frequency; its phase is always synchronized or quasi-synchronized with the power grid frequency.

[0120] In order to acquire continuous-time signals A power frequency synchronization communication signal model can be established, which serves as the theoretical basis for establishing power frequency synchronization constraints, namely:

[0121] ;

[0122] Where f0 is the power grid frequency (50 Hz or 60 Hz); a(t) is the slowly varying communication modulation envelope; For phase disturbances caused by changes in operating conditions, the following conditions must be satisfied:

[0123] ;

[0124] This constraint indicates that the communication information is on the "envelope" rather than the carrier frequency; its phase is always synchronized or quasi-synchronized with the power grid frequency.

[0125] Step 402: Sample the continuous-time signal at a preset sampling frequency to obtain a discrete sequence;

[0126] After obtaining the continuous-time signal, it can be sampled at a sampling frequency of f. s This yields a discrete sequence:

[0127] ;

[0128] Where n is the discrete sampling point index, n=1,2,...,N, N is the total number of sampling points within the observation window, and T s The sampling period is It is a discrete sequence composed of discrete observation signals.

[0129] Step 403: Superimpose the discrete sequences of each sampling channel at the sampling node of the power distribution network to obtain the observation signal;

[0130] In a power frequency communication system for a distribution network, each terminal or load generates a discrete sequence near the fundamental frequency wave during operation; this is the discretely sampled uplink signal sequence. These discrete sequences propagate upwards through low-voltage lines, medium-voltage lines, and distribution transformers. Due to the influence of line impedance coupling, transformer transmission characteristics, and load distribution, multiple source signals superimpose and mix during propagation. Ultimately, at a certain observation point (such as a sampling node on the medium-voltage side), one or more mixed observation signal sequences are collected.

[0131] In the discrete time domain, the above process can be uniformly represented by the following observation model:

[0132] ;

[0133] in: This represents the observed signal obtained at the nth sampling point; This represents the i-th discrete source signal (i.e., discrete sequence); It represents the equivalent mixing coefficient between the source signal and the observation point, and is used to characterize the line, transformer and coupling characteristics; The noise term is represented by M; M represents the number of source signals involved in the mixing.

[0134] Step 404: Generate the power frequency synchronization subspace of the power distribution network, and project the observed signal into the power frequency synchronization subspace to obtain the denoised signal;

[0135] In this embodiment of the invention, the power frequency phase of the distribution network can be used to construct the power frequency synchronization subspace of the distribution network, and the observed signal can be projected into the power frequency synchronization subspace to suppress asynchronous noise residuals, thereby obtaining a denoised signal.

[0136] Denoising is achieved by constructing a power frequency synchronization subspace projection model. Instead of relying on wavelet thresholds or fixed filters, it distinguishes between "synchronization information components" and "asynchronous noise components" from the perspective of the signal's physical mechanism, thereby improving the denoising effect.

[0137] In one example, the step of generating the power frequency synchronization subspace of the distribution network and projecting the observed signal into the power frequency synchronization subspace to obtain the denoised signal may specifically include the following sub-steps:

[0138] S41, Extract the fundamental frequency of the power distribution network;

[0139] Power frequency refers to the rated frequency used by power generation, transmission, transformation and distribution equipment, as well as industrial and civil electrical equipment in a power system, measured in Hertz (Hz).

[0140] The fundamental frequency is the sinusoidal component in a complex periodic oscillation that has the longest period of that oscillation, and the frequency corresponding to this period is called the fundamental frequency.

[0141] In practice, the fundamental frequency f0 of the power grid is first extracted by zero-crossing detection or phase-locked loop (PLL) method.

[0142] Zero-crossing detection is a technique or method used to identify points in a signal or function where the sign of the numerical value changes (i.e., points that cross zero). In mathematics and signal processing, for a continuous function f(x), if a point x0 satisfies f(x0) = 0, and the function values ​​in the neighborhoods on either side of this point change sign (i.e., from positive to negative or from negative to positive), then x0 is called a zero-crossing point of the function. The process of detecting such points is called zero-crossing detection.

[0143] A phase-locked loop (PLL) is an electronic circuit system that uses feedback control principles to achieve automatic phase and frequency synchronization. Its core function is to ensure that the output signal frequency and phase of the internal voltage-controlled oscillator (VCO) accurately track an external input reference signal. A typical PLL consists of three basic modules: a phase detector (PD), a loop filter (LF), and a VCO. Signal locking and tracking are achieved through closed-loop regulation. This system is widely used in electronics and communications fields such as frequency synthesis, clock recovery, modulation and demodulation, and synchronization, and is a key fundamental module in modern integrated circuits and radio frequency systems.

[0144] S42, using the power frequency fundamental frequency to generate a power frequency reference phase;

[0145] In this embodiment of the invention, the power frequency reference phase as follows:

[0146] ;

[0147] The power frequency reference phase serves as the physical synchronization benchmark for all subsequent processing.

[0148] S43, construct an orthogonal basis based on the power frequency reference phase;

[0149] In this embodiment of the invention, based on the power frequency reference phase The constructed orthogonal basis is as follows:

[0150] ;

[0151] S44, Generate a power frequency synchronization subspace based on the orthogonal basis;

[0152] In this embodiment of the invention, the power frequency synchronization subspace is defined as follows:

[0153] ;

[0154] S45, the observed signal is projected into the power frequency synchronization subspace to obtain a denoised signal.

[0155] Next, for each observation signal Project:

[0156] ;

[0157] The inner product is defined as:

[0158] ;

[0159] The projected signal satisfies:

[0160] ;

[0161] in: Power frequency synchronous communication components; The significantly suppressed asynchronous noise residual.

[0162] Step 405: Perform pre-whitening processing on the denoised signal to obtain a pre-whitened signal;

[0163] In this embodiment of the invention, pre-whitening the denoised signal can provide stable conditions for blind separation.

[0164] In one example, the step of pre-whitening the denoised signal to obtain a pre-whitened signal may include the following sub-steps:

[0165] S51, Calculate the covariance matrix of the denoised signal;

[0166] S52, Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvector matrix and the diagonal matrix;

[0167] S53, using the feature vector matrix, the diagonal matrix, and the denoised signal to generate a pre-whitening signal.

[0168] In the specific implementation, the covariance matrix of the denoised signal is first calculated:

[0169] ;

[0170] in, Let C represent the mathematical expectation operator, and C be the covariance matrix of the denoised signal.

[0171] Then, eigenvalue decomposition is performed on the covariance matrix to obtain the eigenvector matrix and the diagonal matrix:

[0172] ;

[0173] Where D represents a diagonal matrix composed of corresponding eigenvalues, and E is the eigenvector matrix.

[0174] Finally, the eigenvector matrix, diagonal matrix, and denoised signal are used to generate the pre-whitened signal:

[0175] ;

[0176] in, V represents the pre-whitened signal; V represents the whitening transformation matrix constructed from the eigenvalue decomposition results, used to achieve decorrelation and normalization of the observed signal.

[0177] Step 406: Use the pre-whitened signal to generate a separation signal, and generate a blind separation objective function based on the separation signal;

[0178] After obtaining the pre-whitening signal, a separation signal can be generated using the pre-whitening signal, and a blind separation objective function can be generated based on the separation signal. By solving the blind separation objective function, the separation vector can be updated.

[0179] In one example, the step of generating a separation signal using the pre-whitened signal and generating a blind separation objective function based on the separation signal may include the following sub-steps:

[0180] S61, the whitening transformation matrix is ​​used to generate the separation signal;

[0181] S62, construct an analytical signal using the separated signal;

[0182] S63, extract the instantaneous phase of the analyzed signal;

[0183] S64, Generate a power frequency phase consistency error based on the instantaneous phase and the power frequency reference phase;

[0184] S65, construct a blind separation objective function based on the power frequency phase consistency error and the separation signal.

[0185] In the specific implementation, the formula for calculating the separated signal is as follows:

[0186] ;

[0187] in, For the i-th separation signal, Let be the i-th separation coefficient.

[0188] Analytical signals can be constructed using the Hibert transform:

[0189] ;

[0190] Where j is the imaginary unit, satisfying H() represents the Hibert transform operator, used to construct an imaginary part of a real signal that is orthogonal to it.

[0191] The Hilbert transform is a linear operator that produces a function H(u)(t) with the same domain as the function u(t).

[0192] The Hilbert transform is crucial in signal processing, enabling the derivation of an analytic representation of a signal u(t). This means extending the real signal u(t) into the complex plane, making it satisfy the Cauchy-Riemann equations. For example, the Hilbert transform introduces the harmonic conjugate of a given function in Fourier analysis, also known as harmonic analysis. Equivalently, it is an example of the singular integral operator and Fourier multipliers.

[0193] Instantaneous phase for:

[0194] ;

[0195] The formula for calculating power frequency phase consistency error is as follows:

[0196] ;

[0197] in, This refers to the power frequency phase consistency error.

[0198] After calculating the power frequency phase consistency error, the blind separation objective function can be constructed as follows:

[0199] ;

[0200] in, , For the separation signal After nonlinear function The transformed statistical expectation value measures the non-Gaussianity of the current separated signal. A higher value indicates a stronger distinguishability between the separated signal and Gaussian noise, and a better blind source separation effect. This is a synchronization constraint weighting factor used to adjust the trade-off between the non-Gaussian optimization term and the power frequency synchronization constraint term. By adjusting... The value of controls the degree to which the algorithm pays attention to power frequency synchronization characteristics. When When the value is large, the algorithm tends to favor the synchronization relationship between the newspaper and the power frequency reference signal; when... When the size is small, the algorithm focuses more on maximizing the non-Gaussian aspect. It is the statistical expectation of the square of the phase deviation, used to measure the degree of synchronization deviation between the separated signal and the power frequency reference signal. The smaller the value, the higher the degree of synchronization between the separated signal and the power frequency reference signal.

[0201] Compared to traditional ICA algorithms that only utilize non-Gaussianity as the optimization objective, this invention introduces a power frequency synchronization constraint term into the blind separation objective function. This ensures that the separation results not only meet the independence requirements but also the power frequency synchronization characteristic constraints, thereby improving the detection accuracy and stability of the power frequency communication uplink signal in the distribution network under strong noise and complex aliasing environments.

[0202] Step 407: Calculate the target separation vector based on the blind separation objective function;

[0203] In this embodiment of the invention, step 407 may include the following sub-steps:

[0204] S71, the step of calculating the target separation vector based on the blind separation objective function includes:

[0205] S72, Calculate and update the separation vector according to the blind separation objective function;

[0206] S73, determine whether the updated separation vector satisfies the preset convergence criterion;

[0207] S74, if not, adjust the blind separation objective function using the updated separation vector, and return to the step of calculating the updated separation vector based on the blind separation objective function;

[0208] S75, if so, use the updated separation vector as the target separation vector.

[0209] In the embodiments of the invention, after decorrelation and whitening of the observed signals, it is necessary to solve the unmixing matrix W to achieve blind separation of the mixed signals. According to the theory of independent component analysis, the source signal has stronger non-Gaussianity than its linear mixed signal, so the source signal can be separated by finding the projection direction with the largest non-Gaussianity.

[0210] To quantitatively characterize the degree of non-Gaussianity, this invention uses negative entropy as an evaluation index. Negentropy measures the degree to which a random variable deviates from a Gaussian distribution; the larger the value, the stronger the non-Gaussianity of the signal. Since Gaussian variables have the characteristic of maximum entropy, maximizing negative entropy can equivalently maximize non-Gaussianity, thereby approximating statistically independent source signals.

[0211] Therefore, this invention transforms the blind separation problem into a negative entropy maximization problem, the optimization objective of which is expressed as:

[0212] ;

[0213] in, Let W represent the negative entropy evaluation function, W represent the unmixing matrix to be solved, and z represent the whitened observed signal. By iteratively optimizing the unmixing matrix W, the output signal has the maximum negative entropy value, thus obtaining the separation result with the strongest statistical independence. Used to enhance non-Gaussianity. Used to constrain power frequency synchronization.

[0214] In this embodiment of the invention, to solve for the optimal separation vector, the partial derivative of the blind separation objective function with respect to the separation vector can be taken to obtain the gradient of the objective function. Then, based on the gradient ascent principle, the separation vector is iteratively updated using the gradient of the objective function, thereby obtaining the optimal separation result that balances maximizing non-Gaussianity and power frequency synchronization constraints. The update formula for the separation vector is as follows:

[0215] ;

[0216] Then, determine whether the updated separation vector satisfies the preset convergence criterion:

[0217] ;

[0218] Where k is the current iteration number, A preset convergence threshold is used to determine whether the current separation vector has reached a stable state. In this embodiment of the invention, The value of can be set according to the detection accuracy requirements, and its preferred range is 10⁻³ to 10⁻⁸. It is recommended to use a value of 10⁻⁶ as the default convergence criterion.

[0219] After completing the iterative optimization of the unmixing matrix W, the optimized separation vector can be used as the target separation vector.

[0220] Step 408: Generate the power frequency communication uplink signal of the distribution network based on the target separation vector.

[0221] In this embodiment of the invention, by optimizing the separation vector, the generated separation signal can have a better separation effect, thereby generating the power frequency communication uplink signal of the distribution network based on the target separation vector.

[0222] In one example, the step of generating the power frequency communication uplink signal of the distribution network based on the target separation vector includes:

[0223] S81, Generate a target analytical signal based on the target separation vector;

[0224] S82, extract the envelope signal from the target parsing signal;

[0225] S83, generate the target instantaneous phase of the target analytical signal;

[0226] S84, use the target instantaneous phase to generate a phase stability detection index;

[0227] S85, determine whether the phase stability detection index meets the preset conditions;

[0228] S86, if so, the target parsing signal is determined to be the power frequency communication uplink signal of the distribution network.

[0229] In practical implementation, the target analytical signal can be generated based on the target separation vector. Then, the envelope signal is extracted from the target parsed signal:

[0230] ;

[0231] Next, the instantaneous phase of the target analytical signal is generated. .

[0232] Then, phase stability detection is performed on the instantaneous phase of the target, and the generated phase stability detection index is: :

[0233] ;

[0234] When satisfied Under certain conditions, the target parsed signal can be determined to be a valid power frequency communication uplink signal, and it can be used as the power frequency communication uplink signal for the distribution network. Among these conditions, The power frequency synchronization decision threshold is used to measure the degree of synchronization between the target analytical signal and the power frequency reference signal. This power frequency synchronization decision threshold can be obtained based on historical sample statistics, and can be specifically expressed as:

[0235] ;

[0236] in, The mean of the phase stability index for the training samples. The standard deviation of the phase stability index of the training samples.

[0237] In the implementation of this invention, by statistically analyzing 100 groups of valid power frequency communication uplink signals, the following can be obtained: =0.0021, =0.0006, thus determining: =0.004;

[0238] When the following conditions are met: If the target parsing signal and the power frequency reference signal have good synchronization characteristics, it can be determined as an effective power frequency communication uplink signal; otherwise, it is determined as a non-target noise component or interference component.

[0239] This invention employs a power frequency synchronization subspace projection model for denoising, without relying on wavelet thresholds or fixed filters. Instead, it distinguishes between "synchronization information components" and "asynchronous noise components" at the level of signal physical mechanisms, thereby improving denoising performance. Furthermore, this invention achieves highly robust detection of power frequency communication uplink signals by constructing the power frequency synchronization subspace projection model and a blind separation objective function with physical consistency constraints. This improves the separation effect and real-time performance of power frequency communication uplink signals in distribution networks.

[0240] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0241] Please see Figure 5 , Figure 5 This is a structural block diagram of a power frequency communication uplink signal detection device for a power distribution network, provided in an embodiment of the present invention.

[0242] This invention provides a distribution network power frequency communication uplink signal detection device, applied to a distribution network simulation model; the device includes:

[0243] The observation signal generation module 501 is used to acquire, at the sampling node of the power distribution network, a simulated signal containing power frequency communication uplink information from the power distribution network simulation model through multiple acquisition channels, and to perform signal preprocessing on the simulated signal to obtain the observation signal.

[0244] The denoising signal generation module 502 is used to generate the power frequency synchronization subspace of the power distribution network and project the observed signal into the power frequency synchronization subspace to obtain the denoising signal;

[0245] The pre-whitening processing module 503 is used to perform pre-whitening processing on the denoised signal to obtain a pre-whitened signal;

[0246] The blind separation objective function generation module 504 is used to generate a separation signal using the pre-whitened signal and to generate a blind separation objective function based on the separation signal.

[0247] The target separation vector calculation module 505 is used to calculate the target separation vector according to the blind separation objective function;

[0248] The distribution network power frequency communication uplink signal generation module 506 is used to generate the distribution network power frequency communication uplink signal according to the target separation vector.

[0249] This invention employs a power frequency synchronization subspace projection model for denoising, without relying on wavelet thresholds or fixed filters. Instead, it distinguishes between "synchronization information components" and "asynchronous noise components" at the level of signal physical mechanisms, thereby improving denoising performance. Furthermore, this invention achieves highly robust detection of power frequency communication uplink signals by constructing the power frequency synchronization subspace projection model and a blind separation objective function with physical consistency constraints. This improves the separation effect and real-time performance of power frequency communication uplink signals in distribution networks.

[0250] In this embodiment of the invention, the observation signal generation module 501 includes:

[0251] The continuous-time signal generation submodule is used to acquire analog signals containing power frequency communication uplink information from the power distribution network simulation model through multiple acquisition channels at the sampling node of the power distribution network to obtain a continuous-time signal;

[0252] The discrete sequence generation submodule is used to sample the continuous-time signal at a preset sampling frequency to obtain a discrete sequence;

[0253] The observation signal generation submodule is used to superimpose the discrete sequences of each sampling channel at the sampling node of the power distribution network to obtain the observation signal.

[0254] In this embodiment of the invention, the noise reduction signal generation module 502 includes:

[0255] The power frequency fundamental frequency extraction submodule is used to extract the power frequency fundamental frequency of the power distribution network;

[0256] A power frequency reference phase generation submodule is used to generate a power frequency reference phase using the power frequency fundamental frequency.

[0257] An orthogonal basis construction submodule is used to construct orthogonal bases based on the power frequency reference phase;

[0258] The power frequency synchronization subspace submodule is used to generate a power frequency synchronization subspace based on the orthogonal basis;

[0259] The projection submodule is used to project the observed signal into the power frequency synchronization subspace to obtain a denoised signal.

[0260] In this embodiment of the invention, the pre-whitening processing module 503 includes:

[0261] The covariance matrix calculation submodule is used to calculate the covariance matrix of the denoised signal;

[0262] The eigenvalue decomposition module is used to perform eigenvalue decomposition on the covariance matrix to obtain an eigenvector matrix and a diagonal matrix.

[0263] The pre-whitening signal generation submodule is used to generate a pre-whitening signal using the feature vector matrix, the diagonal matrix, and the denoised signal.

[0264] In this embodiment of the invention, the blind separation objective function generation module 504 includes:

[0265] A separate signal generation submodule is used to generate a separate signal using the whitening transformation matrix;

[0266] An analytical signal construction submodule is used to construct an analytical signal using the separated signal;

[0267] The instantaneous phase extraction submodule is used to extract the instantaneous phase of the analyzed signal;

[0268] A power frequency phase consistency error generation submodule is used to generate a power frequency phase consistency error based on the instantaneous phase and the power frequency reference phase.

[0269] The blind separation objective function construction submodule is used to construct the blind separation objective function based on the power frequency phase consistency error and the separation signal.

[0270] In this embodiment of the invention, the target separation vector calculation module 505 includes:

[0271] The update separation vector calculation submodule is used to calculate and update the separation vector based on the blind separation objective function;

[0272] The convergence judgment submodule is used to determine whether the updated separation vector satisfies the preset convergence criterion;

[0273] The return submodule is used to adjust the blind separation objective function using the updated separation vector if no, and returns the step of calculating the updated separation vector based on the blind separation objective function;

[0274] The target separation vector generation submodule is used to, if so, take the updated separation vector as the target separation vector.

[0275] In this embodiment of the invention, the power frequency communication uplink signal generation module 506 for distribution networks includes:

[0276] The target analytical signal generation submodule is used to generate a target analytical signal based on the target separation vector.

[0277] An envelope signal extraction submodule is used to extract the envelope signal from the target parsed signal;

[0278] The target instantaneous phase generation submodule is used to generate the target instantaneous phase of the target analyzed signal;

[0279] A phase stability detection index generation submodule is used to generate a phase stability detection index using the target instantaneous phase.

[0280] The condition judgment submodule is used to determine whether the phase stability detection index meets the preset conditions;

[0281] The power distribution network uplink communication signal determination submodule is used to determine the target parsed signal as the power distribution network uplink communication signal if the target parsed signal is the target signal.

[0282] This invention also provides an electronic device, the device including a processor and a memory:

[0283] The memory is used to store program code and transmit the program code to the processor;

[0284] The processor is used to execute the power frequency communication uplink signal detection method for power distribution networks according to the instructions in the program code of this invention.

[0285] This invention also provides a computer-readable storage medium for storing program code, which is used to execute the power frequency communication uplink signal detection method for power distribution networks described in this invention.

[0286] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0287] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0288] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0289] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0290] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0291] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0292] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0293] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0294] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0295] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting uplink signals in power frequency communication in a distribution network, characterized in that, Applied to power distribution network simulation models; the method includes: At the sampling node of the distribution network, analog signals containing uplink information of power frequency communication are collected from the distribution network simulation model through multiple acquisition channels. The analog signals are preprocessed to obtain the observation signals. Generate the power frequency synchronization subspace of the power distribution network, and project the observed signal into the power frequency synchronization subspace to obtain the denoised signal; The denoised signal is pre-whitened to obtain a pre-whitened signal; The pre-whitened signal is used to generate a separation signal, and a blind separation objective function is generated based on the separation signal; Calculate the target separation vector based on the blind separation objective function; The power frequency communication uplink signal for the distribution network is generated based on the target separation vector.

2. The method according to claim 1, characterized in that, The step of acquiring analog signals containing power frequency communication uplink information from the power distribution network simulation model through multiple acquisition channels at the power distribution network sampling node, and performing signal preprocessing on the analog signals to obtain the observation signals includes: At the sampling node of the distribution network, analog signals containing power frequency communication uplink information are collected from the distribution network simulation model through multiple acquisition channels to obtain continuous time signals; The continuous-time signal is sampled at a preset sampling frequency to obtain a discrete sequence; The discrete sequences of each sampling channel are superimposed at the sampling node of the power distribution network to obtain the observation signal.

3. The method according to claim 1, characterized in that, The step of generating the power frequency synchronization subspace of the distribution network and projecting the observed signal into the power frequency synchronization subspace to obtain a denoised signal includes: Extract the fundamental frequency of the power distribution network; The power frequency fundamental frequency is used to generate the power frequency reference phase; Construct an orthogonal basis based on the power frequency reference phase; Generate a power frequency synchronization subspace based on the orthogonal basis; The observed signal is projected into the power frequency synchronization subspace to obtain a denoised signal.

4. The method according to claim 1, characterized in that, The step of pre-whitening the denoised signal to obtain a pre-whitened signal includes: Calculate the covariance matrix of the denoised signal; The covariance matrix is ​​subjected to eigenvalue decomposition to obtain the eigenvector matrix and the diagonal matrix; The pre-whitening signal is generated using the feature vector matrix, the diagonal matrix, and the denoised signal.

5. The method according to claim 3, characterized in that, The step of generating a separation signal using the pre-whitened signal and generating a blind separation objective function based on the separation signal includes: The whitening transformation matrix is ​​used to generate the separated signal; The separated signals are used to construct the analytical signal; Extract the instantaneous phase of the analyzed signal; A power frequency phase consistency error is generated based on the instantaneous phase and the power frequency reference phase; A blind separation objective function is constructed based on the power frequency phase consistency error and the separation signal.

6. The method according to claim 1, characterized in that, The step of calculating the target separation vector based on the blind separation objective function includes: Calculate and update the separation vector based on the blind separation objective function; Determine whether the updated separation vector satisfies the preset convergence criterion; If not, adjust the blind separation objective function using the updated separation vector, and return to the step of calculating the updated separation vector based on the blind separation objective function; If so, the updated separation vector is used as the target separation vector.

7. The method according to claim 1, characterized in that, The step of generating the power frequency communication uplink signal of the distribution network based on the target separation vector includes: Generate the target analytical signal based on the target separation vector; Extract the envelope signal from the target parsed signal; The instantaneous phase of the target analyzed signal is generated; The target instantaneous phase is used to generate a phase stability detection index; Determine whether the phase stability detection index meets the preset conditions; If so, the target parsing signal is determined to be the power frequency communication uplink signal of the distribution network.

8. A power frequency communication uplink signal detection device for a power distribution network, characterized in that, Applied to power distribution network simulation models; the device includes: The observation signal generation module is used to acquire, at the sampling node of the distribution network, analog signals containing power frequency communication uplink information from the distribution network simulation model through multiple acquisition channels, and to perform signal preprocessing on the analog signals to obtain the observation signals. The denoising signal generation module is used to generate the power frequency synchronization subspace of the power distribution network and project the observed signal into the power frequency synchronization subspace to obtain the denoising signal; A pre-whitening processing module is used to pre-whiten the denoised signal to obtain a pre-whitened signal; A blind separation objective function generation module is used to generate a separation signal using the pre-whitened signal and to generate a blind separation objective function based on the separation signal; The target separation vector calculation module is used to calculate the target separation vector based on the blind separation objective function; The power frequency communication uplink signal generation module for power distribution networks is used to generate power frequency communication uplink signals for power distribution networks based on the target separation vector.

9. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power frequency communication uplink signal detection method for power distribution networks according to any one of claims 1-7, based on the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the power frequency communication uplink signal detection method for power distribution networks according to any one of claims 1-7.

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