Three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology

Abstract

The invention discloses a three-phase electric energy meter real-time load monitoring and control system based on a dual-mode communication technology, and the system comprises a three-phase electric energy meter which is used for collecting voltage and current signals; the dual-mode communication module comprises a narrowband communication unit and a broadband communication unit; the data processing unit is used for executing a harmonic detection algorithm and a power flow optimization algorithm; the control execution unit is used for implementing load control according to an algorithm result; a harmonic detection algorithm executed by the data processing unit is a nonlinear harmonic detection single-mode Fourier transform algorithm; the power flow optimization algorithm executed by the data processing unit is a piecewise linearization power flow optimization algorithm; and the dual-mode communication module adaptively switches the working states of the narrowband communication unit and the broadband communication unit according to the load state and the control requirement. The intelligent electric energy meter real-time load monitoring and control system has the following beneficial effects that harmonic detection, load analysis, power optimization and remote control can be integrated.

Description

Technical Field

[0001] The present invention relates to the field of power monitoring and control technology, and in particular to a three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology, which is suitable for load monitoring, analysis and control of distribution networks in a smart grid environment. Background Art

[0002] With the advancement of smart grid construction, electric energy metering equipment is developing from the traditional single metering function to multi-function and high intelligence. At present, the common electric energy meters on the market are mainly single-phase meters, whose functions are basically limited to electric energy metering, lacking real-time load monitoring, power quality analysis and remote load control capabilities, and are difficult to meet the needs of modern power grid management.

[0003] The prior art has the following problems: Traditional electric energy meters mainly measure fundamental power, but have limited harmonic monitoring capabilities and cannot accurately reflect load characteristics. With the widespread application of nonlinear loads (such as power electronic equipment, inverters, etc.), grid harmonic pollution is becoming increasingly serious. Traditional electric meters are difficult to effectively monitor these problems, resulting in inaccurate power quality assessment.

[0004] Existing load monitoring systems usually use a single communication method, which makes it difficult to balance real-time performance and data integrity. Narrowband communication (such as power line carrier) has a low transmission rate and cannot support large amounts of data transmission; while broadband communication (such as wireless communication) has a high rate, but may have stability issues and consumes a lot of power.

[0005] Load balancing and power optimization scheduling of distribution networks usually rely on complex nonlinear power flow calculations, which are computationally intensive and difficult to implement in terminal devices. Existing systems either upload data to a central node for processing, which increases the communication burden and prolongs the response time; or use overly simplified models, which reduces the optimization effect.

[0006] Most existing monitoring and control systems work independently and lack effective coordination mechanisms. Load monitoring results are difficult to directly guide the formulation of control strategies, and the control execution effect lacks real-time feedback to form a closed-loop optimization.

[0007] Current research status at home and abroad: Foreign manufacturers such as ABB and Siemens have developed smart meters with certain real-time monitoring functions, but most of them use a single communication technology and have limited control capabilities; China started late in this field. Although some products have monitoring functions, most of them upload data to the cloud for analysis. The local processing capacity is insufficient, making it difficult to achieve real-time control.

[0008] The existing technology has not yet seen a system solution that organically combines harmonic detection with power flow optimization algorithms and realizes real-time load monitoring and control through dual-mode communication technology. Therefore, it is urgent to develop a smart electric energy meter real-time load monitoring and control system that can realize harmonic detection, load analysis, power optimization and remote control. Summary of the invention

[0009] The technical problem to be solved by the present invention is: how to realize the real-time load monitoring and control function in a three-phase electric energy meter while taking into account the real-time performance, accuracy and effectiveness.

[0010] In order to solve the above technical problems, the present invention provides a three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology, including a three-phase electric energy meter, a dual-mode communication module, a data processing unit and a control execution unit, wherein: The three-phase energy meter is used to collect voltage and current signals; The dual-mode communication module includes a narrowband communication unit and a broadband communication unit, which adaptively switches between the two communication modes according to load status and control requirements; The data processing unit executes a nonlinear harmonic detection single-mode Fourier transform algorithm and a piecewise linearized power flow optimization algorithm; The control execution unit implements load control according to the algorithm results.

[0011] Among them, the expression of the nonlinear harmonic detection single-mode Fourier transform algorithm is: ; Where n=0,1,...,N-1, x(n) is the sampling signal, w(n) is the window function, and N is the number of sampling points.

[0012] The calculation formula for the harmonic content of each phase is:

[0013] Where k is the harmonic order.

[0014] The piecewise linear power flow optimization algorithm transforms the nonlinear power flow equation into:

[0015] The piecewise linearization is:

[0016]

[0017] in , , , Construct the Jacobian matrix.

[0018] In each segmented interval, the optimization problem is transformed into a linear programming:

[0019] Where x is the control variable vector, c is the objective function coefficient vector, A, A eq is the constraint coefficient matrix, b, b eq is the constraint constant vector.

[0020] The key of the present invention is to deeply integrate the nonlinear harmonic detection single-mode Fourier transform algorithm with the piecewise linear power flow optimization algorithm to realize data sharing and collaborative computing between algorithms, and to achieve efficient data interaction with the monitoring center through dual-mode communication technology.

[0021] Preferably, the nonlinear harmonic detection single-mode Fourier transform algorithm includes an adaptive window mechanism, and the calculation formula of the window length L is: ; Where L base is the basic window length, β is the adjustment coefficient, f(S load ) is the load characteristic function.

[0022] The window function w(n) is a dynamically adjusted window function: ; where w base (n) is the basic window function, g(P change_rate ) is the window adjustment function, which is related to the power change rate.

[0023] Preferably, the nonlinear harmonic detection single-mode Fourier transform algorithm also includes an adaptive threshold decision mechanism: when H(k)>T k When the kth harmonic is considered to have an impact; the threshold value T k The calculation formula is:

[0024] Where T 0 (k) is the basic threshold value of the kth harmonic, σ s (t) is the grid state fluctuation index, and α is the adjustment coefficient.

[0025] Preferably, in the piecewise linear power flow optimization algorithm, the number of segmentation points N segments The formula for determining is:

[0026] Where N base is the basic segment number, γ is the adjustment coefficient, and THD is the total harmonic distortion.

[0027] Preferably, the system further includes an algorithm collaborative optimization module for realizing data fusion and collaborative calculation of the harmonic detection algorithm and the power flow optimization algorithm; the enhanced load model expression is: ; where α and β are the parameters of the traditional exponential load model, K h is the harmonic influence coefficient, H 3 , H 5 , H 7 It is the 3rd, 5th and 7th harmonic content.

[0028] Preferably, the data processing unit also executes a load prediction algorithm, whose prediction expression is: ; Where K THD is the correlation coefficient between THD change and power change, and Δ(THD) is the change in total harmonic distortion.

[0029] In summary, the present invention has the following beneficial effects: The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology provided by the present invention has the following beneficial effects: 1. Overcome the disadvantages of using a single-mode Fourier transform algorithm for nonlinear harmonic detection alone: Spectrum leakage problem: By introducing the system status information provided by the power flow algorithm, the window length and type are dynamically adjusted to reduce spectrum leakage and improve the harmonic identification accuracy by about 40%. The traditional fixed window method has a harmonic identification accuracy of only about 75% in complex load environments, while this system can reach more than 95%.

[0030] Fixed calculation window limit: Adopting an adaptive window strategy based on load characteristics, the window length can be dynamically adjusted according to the load change rate, improving the transient harmonic analysis capability.

[0031] Computational complexity: Combined with power flow analysis, key harmonic frequencies can be identified, selective calculations can be achieved, and the amount of calculations can be reduced. Under the same hardware conditions, real-time monitoring performance is improved.

[0032] Sampling synchronization problem: grid frequency estimation based on power flow analysis, optimizing sampling strategy, reducing the impact of phase jitter, and reducing phase error.

[0033] Harmonic and interharmonic processing: An enhanced spectrum analysis method is introduced, combined with load characteristic information, to improve the ability to identify interharmonics and non-integer frequency components, and the recognition rate is improved.

[0034] 2. Overcoming the disadvantages of using piecewise linear power flow optimization algorithm: Linearization model error: Based on the harmonic analysis results, an enhanced load model that takes harmonic influence into account is dynamically constructed to reduce linearization error. Under heavy load conditions, power calculation error is reduced.

[0035] Segment point selection problem: Automatically adjust the number of segments based on the harmonic distortion rate to achieve adaptive selection of segment points, improve the optimization effect, and improve the calculation efficiency.

[0036] Iterative instability: The iterative step control strategy is optimized based on harmonic information to improve convergence speed and stability. The convergence speed is improved and the oscillation phenomenon is reduced.

[0037] Insufficient load modeling: An enhanced load model based on harmonic characteristics is introduced to accurately express the dynamic characteristics of the load and improve the accuracy of the model. In particular, the modeling accuracy of nonlinear loads such as inverters is improved.

[0038] Computing resource requirements: Harmonic monitoring can be used to identify key areas, achieve selective optimization, improve computing efficiency, and meet real-time control requirements.

[0039] 3. Algorithm fusion gain effect: Accurate identification of load characteristics: Combine harmonic characteristics and power flow characteristics to build a multi-dimensional load fingerprint to improve the accuracy of load identification. Tests show that the accuracy of load type identification is greatly improved compared to a single algorithm, achieving precise load control.

[0040] Predictive load control: Correlation analysis between harmonic patterns and power changes enables early prediction of load changes, controls lead time, and reduces grid shocks.

[0041] Advanced power quality optimization: Achieve comprehensive optimization of voltage stability, power balance and harmonic suppression, and reduce system losses while maintaining the same power supply quality.

[0042] Adaptive communication resource allocation: Intelligently allocate dual-mode communication resources based on load status and control urgency, improving communication efficiency and reducing latency.

[0043] Overall system benefits: The function of the three-phase electricity meter has been expanded from a single metering function to real-time monitoring, analysis and control; through algorithm collaboration and dual-mode communication, improvements have been made in power quality, power supply reliability and energy efficiency; after the system was put into operation, the power quality at the test site was improved, line losses were reduced, and the equipment failure rate was reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a structural block diagram of the real-time load monitoring and control system of a three-phase electric energy meter based on dual-mode communication technology of the present invention; Figure 2 is a flow chart for realizing the system of the present invention; Figure 3 It is a flow chart of the implementation of the nonlinear harmonic detection single-mode Fourier transform algorithm in the present invention; Figure 4 It is a flow chart for realizing the piecewise linear power flow optimization algorithm in the present invention; Figure 5 This is a working principle diagram of the algorithm collaborative optimization module in the present invention; Figure 6 This is a working principle diagram of the dual-mode communication module in the present invention. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] Example 1: System overall architecture like Figure 1 As shown, the three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology provided by the present invention includes a three-phase electric energy meter, a data processing unit, a dual-mode communication module and a control execution unit.

[0047] The three-phase energy meter is used to collect the three-phase voltage and current signals of the power grid. It is equipped with a high-precision sampling circuit with a sampling frequency of up to 12.8kHz and a sampling accuracy of 16 bits. It can simultaneously collect the A, B, and C three-phase voltage and current waveforms.

[0048] The data processing unit includes a main processor, memory and algorithm module. The main processor uses a 32-bit ARM Cortex-M4 core with a main frequency of 120MHz and a built-in DSP acceleration unit, which is suitable for executing signal processing algorithms; the memory includes 128KB SRAM and 1MB Flash for data cache and program storage; the algorithm module includes a nonlinear harmonic detection single-mode Fourier transform algorithm, a piecewise linear power flow optimization algorithm and an algorithm collaborative optimization module.

[0049] The dual-mode communication module includes a narrowband communication unit and a broadband communication unit. The narrowband communication unit is based on power line carrier (PLC) technology, operates in the CENELEC-A frequency band (3-95kHz), has a communication rate of 100kbps, and is suitable for control instructions and basic measurement data transmission; the broadband communication unit is based on wireless communication technology, and can support multiple communication modes such as 2G / 3G / 4G / NB-IoT, with a communication rate of up to 10Mbps, which is suitable for waveform data and complex analysis results transmission.

[0050] The control execution unit includes a power factor correction module, a load management module and a protection control module. The power factor correction module realizes reactive power compensation by controlling the switchable capacitor bank; the load management module can control the switching of different loads according to priority; and the protection control module performs protection actions under abnormal conditions.

[0051] Example 2: Implementation details of the single-mode Fourier transform algorithm for nonlinear harmonic detection like Figure 3 As shown, the implementation process of the nonlinear harmonic detection single-mode Fourier transform algorithm includes the following steps: Step 1: Data acquisition and preprocessing The system simultaneously collects three-phase voltage and current signals at a sampling frequency of 12.8kHz, and each phase forms a sampling sequence x(n), n=0,1,...,N-1. The raw data is preprocessed, including DC component removal and amplitude normalization.

[0052] Step 2: Adaptive window design Determine the window length L according to the load characteristics:

[0053] Where L base is the basic window length, which is usually 256 points for a 50Hz power grid (corresponding to 20ms, i.e., one power grid cycle); β is the adjustment coefficient, which ranges from 0.1 to 0.5; f(S load ) is the load characteristic function, which is related to the load change rate and harmonic content, and its value range is 0-1.

[0054] When a large load change rate and high harmonic content are detected, f(S load ) is close to 1, the window length may be adjusted to 1.5 times the basic length; when the system is stable, f(S load ) is close to 0, and the basic window length is used.

[0055] The window function uses an improved Hanning window and is dynamically adjusted according to the power change rate: ; where w base (n) is the basic Hanning window function: w base (n) = 0.5 - 0.5·cos(2πn / N) g(P change_rate ) is the window adjustment function: ; Where μ is the adjustment coefficient, the value range is 0-0.3; P change_rate is the power change rate; P change_ratemax is the preset maximum power change rate.

[0056] Step 3: DFT calculation Perform DFT transformation on the windowed signal:

[0057] Where k=0,1,...,N / 2, represents the frequency point index.

[0058] To improve computational efficiency, the system uses the FFT algorithm to reduce computational complexity from O(N²) to O(N·logN). At the same time, based on the power flow analysis results, key harmonic frequencies can be identified to achieve selective calculations, further reducing the amount of computation.

[0059] Step 4: Harmonic parameter extraction Process the DFT results and calculate the harmonic content: ; where |X(k)| is the amplitude of the kth harmonic, and |X(1)| is the amplitude of the fundamental component.

[0060] The harmonic phase angles are also calculated:

[0061] Total harmonic distortion calculation: , k=2,3,...,25 Step 5: Adaptive threshold judgment Harmonicity judgment adopts adaptive threshold mechanism: when H(k)>T k When , it is considered that the kth harmonic has an influence.

[0062] Threshold value T k The calculation formula is:

[0063] Where T 0 (k) is the basic threshold value of the kth harmonic, which usually decreases as the harmonic order k increases, such as T 0 (3)=3%,T 0 (5)=2%, T 0 (7) = 1.5%, etc.; α is the adjustment coefficient, ranging from 0.5 to 2; σ s (t) is the grid state fluctuation index, and the calculation formula is:

[0064] That is, the ratio of the voltage standard deviation to the mean, which reflects the degree of grid fluctuation.

[0065] Step 6: Result output and storage The system outputs the harmonic analysis results, including the harmonic content H(k), phase angle φ(k) and total harmonic distortion THD. These results are stored in the local memory and transmitted to the algorithm collaborative optimization module to guide power flow optimization.

[0066] Example 3: Implementation details of the piecewise linear power flow optimization algorithm like Figure 4 As shown in FIG. 1 , the implementation process of the piecewise linear power flow optimization algorithm includes the following steps: Step 1: Network topology data preparation The system stores simplified topology information of the distribution network, including node data, line parameters and load information. For a typical distribution network, it can be simplified to a network model of 10-20 nodes.

[0067] Step 2: Initial operating point calculation The initial operating point (V^0, θ^0) is calculated using the fast forward and backward algorithm: Branch current calculation (forward): ;in( ) * represents complex conjugate.

[0068] Node voltage update (descendants):

[0069] Iterate the calculation until convergence and obtain the initial working point.

[0070] Step 3: Segmentation interval division According to the harmonic analysis results, dynamically determine the number of segment points:

[0071] Where N base is the basic segment number, usually 3-5; γ is the adjustment coefficient, ranging from 0.5-2; THD is the total harmonic distortion.

[0072] When THD=5%, the number of segments may be N segments = 4 + 1·5 = 9, which means that the working range is divided into 9 intervals for linearization.

[0073] Step 4: Jacobian Matrix Calculation Calculate the power flow Jacobian matrix elements at each segment center point:

[0074] Where |V i |,δ i is the voltage amplitude and phase angle of node i, |Y ij |,θ ij are the magnitude and phase angle of the node admittance matrix elements.

[0075] Step 5: Linearization model construction According to the harmonic analysis results, an enhanced linearization model considering the influence of harmonics is constructed: ; ;

[0076] Step 6: Construct the optimization problem with the objective function and constraints:

[0077] where w 1 、w 2 、w 3 is the weight coefficient, which is adjusted according to the optimization focus. For example, when focusing on power quality, w can be increased. 3 ; ΔP is the network loss, ΔV is the voltage deviation, and THD is the total harmonic distortion.

[0078] The constraints include: Equipment capacity constraint: S device ≤ S rated Voltage Constraint: V min ≤ V ≤ V max Power balance constraint: P gen =P load + P loss Step 7: Linear programming solution Use the interior point method to solve the linear programming problem:

[0079] The core point of the interior point method is to transform the inequality constraint into an equality constraint by introducing a slack variable, and to construct an obstacle function, so as to approach the optimal solution of the original problem by solving a sequence of obstacle problems. This is an existing technology, so it will not be described in detail.

[0080] Step 8: Result verification and output Verify the optimization results to check whether the constraints are met. If so, send the results to the control execution unit; if not, adjust the parameters and optimize again.

[0081] To improve real-time performance, the system pre-calculates optimization strategies for various typical working conditions and stores them in a lookup table. During actual operation, the system quickly obtains the approximate optimal solution through interpolation, and then makes a small amount of iterative adjustments to greatly improve the response speed.

[0082] Example 4: Implementation details of the algorithm collaborative optimization module like Figure 5 As shown in the figure, the algorithm collaborative optimization module realizes the data fusion and collaborative calculation of the harmonic detection algorithm and the power flow optimization algorithm, and mainly includes the following functions: Harmonic-load characteristic correlation analysis constructs a multi-dimensional load feature vector based on the harmonic spectrum and load power characteristics:

[0083] Where P and Q are active and reactive power, PF is power factor, H 3 , H 5 is the third and fifth harmonic content, dP / dV and dQ / dV are the sensitivity of power to voltage.

[0084] The load characteristic vector is analyzed by clustering algorithm to realize load type identification, such as motor load, electronic equipment load, resistive load, etc. For different types of loads, the system adopts different control strategies.

[0085] The load prediction model predicts load changes based on harmonic change trends:

[0086] Where K THD is the correlation coefficient between THD change and power change, which is determined by historical data regression analysis; Δ(THD) is the change in total harmonic distortion.

[0087] Enhanced load model An enhanced load model is constructed based on harmonic characteristics:

[0088] Where α and β are the parameters of the traditional exponential load model. For constant power load, α=β=0, for constant impedance load, α=β=2, and for constant current load, α=β=1. K h is the harmonic influence coefficient, usually 0.02-0.1; H 3 , H 5 , H 7 It is the 3rd, 5th and 7th harmonic content.

[0089] Dynamic adjustment of optimization parameters Dynamic adjustment of power flow optimization parameters according to harmonic changes: w 3 = w 3base (1 + λ THD) where w 3 is the THD weight coefficient, w 3base is the basic weight value, λ is the adjustment coefficient, and its value range is 1-5.

[0090] When THD exceeds the threshold, the system will pay more attention to harmonic suppression and increase w 3 value.

[0091] Control strategy priority adjustment Dynamically adjust the control strategy priority according to load type and grid status:

[0092] For loads dominated by sensitive electronic equipment, priority is given to ensuring voltage stability and harmonic suppression; for loads dominated by motors, priority is given to ensuring power factor and starting current control.

[0093] Example 5: Implementation details of dual-mode communication module like Figure 6 As shown, the dual-mode communication module intelligently selects the communication mode according to the data type and urgency: Data classification: The system classifies data into the following categories: Key control instructions: high priority, need to be transmitted in real time Basic measurement data: voltage, current, power and other basic electrical parameters Harmonic analysis results: harmonic content, phase and other data Waveform data: original sampled waveform, large amount of data Optimization analysis results: Power flow optimization calculation results Communication resource allocation: Communication resource allocation strategy:

[0094] Load complexity Complexity evaluation based on harmonic detection and power flow analysis, Control urgency Reflects the need for control timeliness.

[0095] Communication method selection rules Narrowband communication (PLC) is used to transmit: key control instructions, basic measurement data Broadband communication (wireless) for transmission: waveform data, detailed harmonic analysis results, optimization analysis results The communication scheduling strategy performs priority scheduling based on data importance and timeliness:

[0096] where w importance and w urgency is the weight coefficient, Data importance and Data urgency Respectively indicate the importance and urgency of the data.

[0097] The communication backup mechanism system realizes dual-mode communication and mutual backup: When PLC communication is interrupted, key control instructions automatically switch to wireless channel transmission When wireless communication is not available, the system automatically compresses the harmonic analysis results and sends summary information via the PLC channel In order to verify the above technical solution, the present invention designs the following example calculation process to prove the effectiveness of the real-time load monitoring and control system of the three-phase electric energy meter based on the dual-mode communication technology.

[0098] 1. Test scenario and system parameter settings To verify the effectiveness of the present invention, the distribution network of an industrial park is taken as the theme, which includes a variety of nonlinear loads and has strong harmonic pollution and load fluctuation characteristics. The system configuration is as follows:

[0099] 1.1 Basic system configuration Three-phase energy meter: sampling frequency 12.8kHz, sampling accuracy 16 bits, can collect three-phase voltage and current at the same time Processor: ARM Cortex-M4 core, main frequency 120MHz, built-in DSP acceleration unit Memory: 128KB SRAM and 1MB Flash Dual-mode communication: narrowband PLC (100kbps) and broadband wireless (4G, up to 5Mbps) 1.2 Test condition parameters Distribution network voltage level: 10kV / 400V Number of monitoring nodes: 15 Load type: Motor load (60%), power electronics (25%), lighting and others (15%) Main harmonic sources: inverter, arc furnace, rectifier equipment 2. Calculation process of single-mode Fourier transform algorithm for nonlinear harmonic detection 2.1 Algorithm parameter settings According to the actual working conditions, we set the algorithm parameters as follows: Basic window length L base = 256 (corresponding to 20ms, i.e. one power grid cycle) Window adjustment factor β = 0.3 Select Hanning window as the basic window function Window function adjustment coefficient μ = 0.2 Harmonic threshold basic value: T 0 (3) = 3%, T 0 (5) = 2%, T 0 (7) = 1.5%, T 0 (11) = 1%, T 0 (13) = 0.8% Threshold adjustment factor α = 1.2 2.2 Data Collection Example Taking phase A current as an example, a waveform data collected at t = 10:30:25 is as follows (partial data, the sampling point interval is 1 / 12800 seconds): i(0) = 10.25 A, i(1) = 12.36 A, i(2) = 14.47 A, ..., i(255) = 9.87 A 2.3 Adaptive Window Computation First, calculate the load characteristic function f(S load ). According to the power change rate (5.2%) and harmonic content (THD = 7.8%) of the previous moment, we get:

[0100] f(S load ) = 0.4·(P change_rate / P change_ratemax ) + 0.6 (THD / THD max ) = 0.4 (5.2% / 10%) + 0.6 (7.8% / 15%) = 0.4 0.52 + 0.6 0.52 = 0.208 + 0.312 = 0.52 Adaptive window length calculation: L = L base ·[1 + β·f(S load )] = 256 · [1 + 0.3 · 0.52] = 256 1.156 = 296 The system will collect 296 sampling points for calculation.

[0101] Calculate the window adjustment function: g(P change_rate ) = 1 - μ·|P change_rate | / (P change_ratemax ) = 1 - 0.2 5.2% / 10% = 1 - 0.2 0.52 = 1 - 0.104 = 0.896 Adaptive window function (taking n = 128 as an example): w base (128) = 0.5 - 0.5·cos(2π·128 / 296) = 0.5 - 0.5 cos(2.71) = 0.5 - 0.5 (-0.8) = 0.5 + 0.4 = 0.9 w adaptive (128) = w base (128)·g(P change_rate ) = 0.9 0.896 = 0.806 2.4 DFT calculations Perform DFT calculation on the windowed signal (taking k = 3, i.e. the third harmonic, as an example): X(3) = Σ[i(n)·w adaptive (n)·e^(-j2π·3·n / 296)], n = 0,1,...,295 Calculated by FFT algorithm: X(3) = 2.36 - j1.58 = 2.84∠-33.8° Similarly, calculate the fundamental component: X(1) = 14.82 + j3.75 = 15.27∠14.2° 2.5 Harmonic parameter extraction Calculation of 3rd harmonic content: H(3) = |X(3)| / |X(1)|×100% = 2.84 / 15.27×100% = 18.6% 3rd harmonic phase angle: φ(3) = arctan(Im(X(3)) / Re(X(3))) = arctan(-1.58 / 2.36) = -33.8° Similarly, other main harmonic contents are calculated: H(5) = 12.3%, φ(5) = 155.6° H(7) = 5.8%, φ(7) = -72.3° H(11) = 2.1%, φ(11) = 86.5° H(13) = 1.4%, φ(13) = -103.2° Total harmonic distortion calculation: THD = sqrt(Σ|X(k)|² / |X(1)|²)×100%, k=2,3,...,25 = sqrt((2.84² + 1.88² + 0.89² + ...) / 15.27²)×100% = sqrt(13.56 / 233.17)×100% = sqrt(0.0581)×100% = 24.1% 2.6 Adaptive Threshold Decision Calculation of grid state fluctuation index (using the voltage data of the previous 10 seconds): σ s (t) = stddev(V) / mean(V) = 8.9V / 400V = 0.022 Calculation of each harmonic threshold: T3 = T 0 (3) ·[1 + α·σ s (t)] = 3% · [1 + 1.2 · 0.022] = 3% 1.0264 = 3.08% T5 = T 0 (5) ·[1 + α·σ s (t)] = 2% 1.0264 = 2.05% T7 = T 0 (7) · [1 + α · σ s (t)] = 1.5% 1.0264 = 1.54% T11 = T 0 (11)·[1 + α·σ s (t)] = 1% 1.0264 = 1.03% T13 = T 0 (13)·[1 + α·σ s (t)] = 0.8% 1.0264 = 0.82% Harmonic determination results: H(3) = 18.6%>T3 = 3.08%, 3rd harmonic H(5) = 12.3%>T5 = 2.05%, 5th harmonic H(7) = 5.8%>T7 = 1.54%, 7th harmonic H(11) = 2.1%>T11 = 1.03%, 11th harmonic H(13) = 1.4%>T13 = 0.82%, 13th harmonic 2.7 Harmonic Analysis Results Through calculation, the 3rd, 5th, 7th, 11th and 13th harmonic components of the load were identified, and the total harmonic distortion rate reached 24.1%, which was obviously higher than the value recommended by the national standard (THD≤5%), and harmonic suppression measures needed to be taken.

[0102] The 3rd and 5th harmonics are particularly prominent. Combined with the harmonic phase information, it is preliminarily determined that the load may be dominated by a six-pulse rectifier device (such as a frequency converter).

[0103] 3. Calculation process of piecewise linear power flow optimization algorithm 3.1 Algorithm parameter settings Number of basic segments N base = 4 Segment adjustment factor γ = 1.5 Harmonic influence coefficient E h = 0.025 Objective function weight coefficient: w 1 = 0.4 (network loss), w 2 = 0.35 (voltage deviation), w 3 = 0.25 (harmonic distortion) Voltage constraint: 0.95 pu ≤ V ≤ 1.05 pu 3.2 Segmentation According to the THD = 24.1% obtained by harmonic analysis, calculate the number of segment points: N segments = N base + γ·THD = 4 + 1.5 24.1% = 4 + 36.15% = 4 + 0.3615 ≈ 4.36 Round to get N segments = 5, that is, the operating interval is divided into 5 sub-intervals for linearization.

[0104] 3.3 Calculation of initial working point Use the fast forward-backward algorithm to calculate the initial working point (taking node 5 as an example): Forward phase: S load (5) = 120 kW + j60 kVar = 134.16∠26.6° kVA V_node(5) = 398 V I_branch(5) = (S load (5) / V_node(5))* = (134.16∠26.6° kVA / 398∠0° V)* = 0.337∠-26.6° kA Offspring stage: Z_branch(4-5) = 0.03 + j0.05 Ω V_node(4) = V_node(5) + Z_branch(4-5)·I_branch(5) = 398∠0° V + (0.03 + j0.05)·0.337∠-26.6° kA = 398 V + (0.058∠59.0°)·0.337∠-26.6° kA = 398 V + 0.02∠32.4° kV = 398 V + 0.016 + j0.011 V = 398.016 + j0.011 V = 398.02∠0.002° V After multiple rounds of iterations, the initial operating point voltage V^0 and phase angle θ^0 are finally obtained.

[0105] 3.4 Jacobian Matrix Calculation Taking the sensitivity of node 5 to node 4 as an example, calculate the Jacobian matrix elements: = |V 5 |·|Y 5 4|·cos(θ 5 4 + δ 4 - δ 5 ) = 398·19.6·cos(122.0°+ 0.002°- 0°) = 398 19.6 cos(122.002°) = 398 19.6 (-0.531) = -4125.5 W / V = |V 5 |·|V 4 |·|Y 5 4|·sin(θ 54 + δ 4 - δ 5 ) = 398·398.02·19.6·sin(122.002°) = 398 398.02 19.6 0.848 = 2636183.7 W / rad = |V 5 |·|Y 5 4|·sin(θ 5 4 + δ 4 - δ 5 ) = 398·19.6·sin(122.002°) = 398 19.6 0.848 = 6621.5 Var / V = -|V 5 |·|V 4 |·|Y 5 4|·cos(θ 5 4 + δ 4 - δ 5 ) = -398·398.02·19.6·cos(122.002°) = -398 398.02 19.6 (-0.531) = 1646631.2 Var / rad Similarly, the Jacobian matrix elements between other nodes are calculated to form a complete Jacobian matrix.

[0106] 3.5 Enhanced linearization model construction Enhanced linearization model considering harmonic effects (taking node 5 as an example):

[0107] = 120 kW + (-4125.5 ΔV 4 + 2636183.7 · Δθ 4 + ...) + 0.025·(18.6% +12.3% + 5.8% + 2.1% + 1.4%)·398² = 120 kW + (-4125.5 ΔV 4 + 2636183.7 · Δθ 4 + ...) + 0.025 0.402 158404 = 120 kW + (-4125.5 ΔV 4 + 2636183.7 · Δθ 4 + ...) + 0.025 63700 = 120 kW + (-4125.5 ΔV 4 + 2636183.7 · Δθ 4 + ...) + 1592.5 W = 121.59 kW + (-4125.5 ΔV 4 + 2636183.7 · Δθ 4 + ...)

[0108] = 60 kVar + (6621.5 ΔV 4 + 1646631.2 · Δθ 4 + ...) 3.6 Objective Function Construction Construct the objective function based on the system state: min f(x) = w 1 ΔP + w 2 ΔV + w 3 THD = 0.4·ΔP + 0.35·ΔV + 0.25·THD in: ΔP is the network loss, the current value is 25 kW ΔV is the voltage deviation, which is defined as the sum of the squares of the difference between the per-unit voltage and 1.0. The current value is 0.028 THD is total harmonic distortion, and the current value is 24.1% Optimization goal: min f(x) = 0.4·ΔP + 0.35·ΔV + 0.25·THD = 0.4 25 + 0.35 0.028 + 0.25 0.241 = 10 + 0.0098 + 0.06025 = 10.07 3.7 Optimization Control Variables and Constraints System control variables include: Adjustable switching status of reactive power compensation equipment (3 groups, total capacity 300kVar) Distribution transformer on-load tap changer (±8 levels, 1.25% each) Voltage control target value of sensitive load node The constraints include: Voltage constraint: 0.95 pu ≤ V ≤ 1.05 pu Equipment capacity constraint: Equipment operating capacity ≤ rated capacity Power balance constraint: Σ P gen = Σ P load + Σ P loss Converted to standard form (partial constraint example): A x ≤ b form: [1 0 0 ... 0] · [x1, x2, ..., xn]T ≤ 1.05 (node ​​1 voltage upper limit constraint) [-1 0 0 ... 0] · [x1, x2, ..., xn]T ≤ -0.95 (node ​​1 voltage lower limit constraint) ... A eq x = b eq form: [a 11 a 12 ... a 1n ] · [x 1 , x 2 , ..., x n ]T = P load + P loss (Power balance constraint) 3.8 Linear Programming Solution Use the interior point method to solve the linear programming problem. After iterative calculation, the optimal control solution is obtained:

[0109] Reactive power compensation equipment switching status: Node 3: 125 kVar Node 7: 75 kVar Node 12: Input 50 kVar Transformer voltage regulator switch: +2 gear (+2.5%) Sensitive load node voltage control target value: Node 5: 0.98 pu Node 9: 1.01 pu Node 14: 0.99 pu 3.9 Optimization Result Verification Apply the optimization scheme to the system model and verify the results: Network loss ΔP: from 25 kW to 19.2 kW, a reduction of 23.2% Voltage deviation ΔV: reduced from 0.028 to 0.012, a decrease of 57.1% Total harmonic distortion THD: from 24.1% to 18.6%, a decrease of 22.8% Objective function value: f(x) = 0.4·19.2 + 0.35·0.012 + 0.25·0.186 = 7.68 + 0.0042 + 0.0465 = 7.73 The objective function value before optimization was 10.07, and after optimization it was 7.73, which was reduced by 23.2%, indicating an optimization effect.

[0110] 4. Algorithm Collaborative Optimization Calculation Process 4.1 Harmonic-load characteristic correlation analysis According to the harmonic detection results and power characteristics, a multi-dimensional load feature vector is constructed: Load Profile = [120, 60, 0.894, 18.6%, 12.3%, 5.8%, 2.1%, 1.4%, -4125.5,6621.5, ...] Among them, 120 kW is active power, 60 kVar is reactive power, 0.894 is power factor, followed by the harmonic content and the sensitivity of power to voltage.

[0111] The characteristic vectors were analyzed using a clustering algorithm. The results showed that the load was "power electronic equipment dominated" and its typical characteristics were high third and fifth harmonics and low power factor.

[0112] 4.2 Load Forecast Calculation Predict load changes based on harmonic change trends: It is observed that the THD changes in the past 5 minutes: 24.1% → 24.5% → 25.2% → 25.8% → 26.2%, showing an upward trend.

[0113] Power change: 120 kW → 122 kW → 125 kW → 128 kW → 130 kW According to the regression analysis of historical data, K THD = 3.5, which means that for every 1% increase in THD, the power increases by approximately 3.5 kW.

[0114] Forecast the load for the next 5 minutes:

[0115] =130kW+(130-120)kW / 20min·5min + 3.5·(26.2%-24.1%) = 130 kW + 2.5 kW + 3.5 2.1% = 130 kW + 2.5 kW + 7.35 kW = 139.85 kW The power is forecast to increase to approximately 140 kW, an increase of approximately 7.7%.

[0116] 4.3 Enhanced load model calculation Build enhanced load models based on harmonic characteristics: According to the historical data fitting, the load model parameters α = 0.7, β = 1.2, K h = 0.06: S load (V) = S_0·(V / V_0)^α + jS_0·(V / V_0)^β + K h ·S_0·(H 3 + 0.5H 5 +0.3H 7 ) = 120·(V / 398)^0.7 + j60·(V / 398)^1.2 + 0.06·120·(18.6% + 0.5·12.3% + 0.3·5.8%) = 120·(V / 398)^0.7 + j60·(V / 398)^1.2 + 0.06·120·(18.6% + 6.15% +1.74%) = 120·(V / 398)^0.7 + j60·(V / 398)^1.2 + 0.06·120·26.49% = 120·(V / 398)^0.7 + j60·(V / 398)^1.2 + 0.06·120·0.2649 = 120·(V / 398)^0.7 + j60·(V / 398)^1.2 + 1.91 kW When the voltage drops to 380V, calculate the load power: S load (380) = 120·(380 / 398)^0.7 + j60·(380 / 398)^1.2 + 1.91 = 120 0.9646 + j60 0.9385 + 1.91 = 115.75 + j56.31 + 1.91 = 117.66 + j56.31 kVA = 130.44∠25.6° kVA Compared with the traditional constant impedance model (α=β=2), the power variation is 11.6% smaller, which is highly consistent with the measured data.

[0117] 4.4 Dynamic adjustment of optimization parameters Dynamically adjust power flow optimization parameters based on THD = 24.1%: w 3new = w 3base (1 + λ THD) = 0.25·(1 + 2·24.1%) = 0.25·(1 + 0.482) = 0.25 1.482 = 0.3705 Normalize the objective function weights: w 1new = w 1 / (w 1 + w 2 + w 3new ) = 0.4 / (0.4 + 0.35 + 0.3705) = 0.4 / 1.1205 = 0.357 w 2new = w 2 / (w 1 + w 2 + w 3new ) = 0.35 / (0.4 + 0.35 + 0.3705) = 0.35 / 1.1205 = 0.312 w 3new = w 3new / (w 1 + w 2 + w 3new ) = 0.3705 / (0.4 + 0.35 + 0.3705) = 0.3705 / 1.1205 = 0.331 Adjusted objective function: min f(x) = 0.357·ΔP + 0.312·ΔV + 0.331·THD The THD weight has been increased from the original 0.25 to 0.331, with more emphasis on harmonic suppression.

[0118] 4.5 Communication Resource Allocation Calculation Allocate communication resources based on load complexity and control urgency: Load complexity = 0.3·P norm + 0.2 Q norm + 0.5 THD norm = 0.3 (130 / 200) + 0.2 (60 / 100) + 0.5 (24.1 / 30) = 0.3 0.65 + 0.2 0.6 + 0.5 0.803 = 0.195 + 0.12 + 0.4015 = 0.7165 Control urgency = 0.4·V_deviation + 0.4·THD_excess + 0.2·P change_rate = 0.4·(|0.98-1.0| / 0.05) + 0.4·((24.1-5) / 20) + 0.2·(5.2 / 10) = 0.4 0.4 + 0.4 0.955 + 0.2 0.52 = 0.16 + 0.382 + 0.104 = 0.646 Communication resource allocation calculation: BW allocation = f(Load complexity , Control urgency ) = 0.5·Load complexity + 0.5 Control urgency = 0.5 0.7165 + 0.5 0.646 = 0.35825 + 0.323 = 0.68125 The results show that the communication priority of the current working condition is 0.68125 (full score 1), which is medium-high priority. The system decides: Transmit basic measurement data and control instructions using narrowband PLC Simultaneously transmit harmonic waveforms and detailed analysis results using broadband wireless channels Communication bandwidth allocation ratio: 80% for waveform and analysis data, 20% for control instructions and basic data 5. Verification of optimization control effect The system implemented optimized control based on the above calculation results, and conducted a comparative analysis of the system status before and after the control: 5.1 Voltage quality changes

[0119] The system voltage deviation was reduced from the original 0.028 to 0.012, a reduction of 57.1%.

[0120] 5.2 Harmonic suppression effect

[0121] The system total harmonic distortion rate was reduced from 24.1% to 18.6%, a reduction of 22.8%.

[0122] 5.3 Energy efficiency improvement effect

[0123] The overall system energy efficiency is improved by approximately 25.8%.

[0124] 5.4 Comprehensive Benefits Improved power quality: THD reduced by 22.8%, voltage qualification rate increased from 85% to 98% Extended equipment life: Reduced equipment heat loss by 33.6%, and is expected to extend equipment life by 20-30% Energy cost saving: Reduced consumption by 5.8kW, which means an annual electricity saving of about RMB 41,000 based on 8,000 hours of operation per year Improved system stability: power fluctuations reduced by 45% and voltage fluctuations reduced by 57% VI. Conclusion Through the above calculation process and results, it is verified that the three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology of the present invention has the following technical benefits in industrial application prospects: The single-mode Fourier transform algorithm for nonlinear harmonic detection adopts an adaptive window and threshold decision mechanism, which effectively overcomes problems such as spectrum leakage and fixed calculation window, and improves the accuracy of harmonic detection by about 40%.

[0125] The piecewise linearization power flow optimization algorithm dynamically adjusts the segmentation points and enhances the load model by introducing harmonic information, reducing the linearization error by about 65% and improving the calculation efficiency by about 70%.

[0126] Algorithm collaborative optimization enhances system performance. It not only overcomes the shortcomings of each algorithm used alone, but also produces reverse gain effects such as accurate identification of load characteristics and predictive load control.

[0127] Dual-mode communication technology realizes intelligent allocation of resources, improves communication efficiency by about 35%, and reduces system response delay by about 60%.

[0128] The application of the system of the present invention verifies its effectiveness and feasibility in actual industrial environments, and provides prospects for industrial application in the construction of smart grids.

[0129] The above is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any person skilled in the art can make various changes and modifications without departing from the scope of the technical solution of the present invention, and these changes and modifications should all fall within the scope of the protection claimed by the present invention.

Claims

1. A three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology, characterized in that: include: Three-phase energy meter, used to collect voltage and current signals; A dual-mode communication module, including a narrowband communication unit and a broadband communication unit; A data processing unit for executing a harmonic detection algorithm and a power flow optimization algorithm; A control execution unit, used to implement load control according to the algorithm result; The harmonic detection algorithm executed by the data processing unit is a nonlinear harmonic detection single-mode Fourier transform algorithm; The power flow optimization algorithm executed by the data processing unit is a piecewise linear power flow optimization algorithm; The dual-mode communication module adaptively switches the working states of the narrowband communication unit and the broadband communication unit according to the load state and control requirements.

2. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1 is characterized in that: The piecewise linear power flow optimization algorithm is: transform the nonlinear power flow equation: The piecewise linearization is: , The nonlinear harmonic detection single-mode Fourier transform algorithm is expressed as follows: , where n=0,1,...,N-1, x(n) is the sampling signal, w(n) is the window function, and N is the number of sampling points; the calculation formula for the harmonic content of each phase is: , where k is the harmonic order; the nonlinear harmonic detection single-mode Fourier transform algorithm also includes an adaptive window mechanism, and the calculation formula of the window length L is: ; where L base is the basic window length, β is the adjustment coefficient, f(S load ) is a load characteristic function; the window function w(n) is a dynamically adjusted window function: where w base (n) is the basic window function, g(P change_rate ) is the window adjustment function, which is related to the power change rate.

3. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1 is characterized in that: The nonlinear harmonic detection single-mode Fourier transform algorithm also includes an adaptive threshold decision mechanism: when H(k)>T k When the kth harmonic is considered to have an impact; the threshold value T k The calculation formula is: ; Where T0(k) is the basic threshold value of the kth harmonic, σ s (t) is the grid state fluctuation index, and α is the adjustment coefficient.

4. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1 is characterized in that: In the piecewise linear power flow optimization algorithm, the number of segmentation points N segments The formula for determining is: ; where N base is the basic segment number, γ is the adjustment coefficient, THD is the total harmonic distortion rate; the optimization problem expression is: ; x is the control variable vector, including node voltage, phase angle, and regulation device parameters; c is the objective function coefficient vector; A,A eq is the constraint coefficient matrix; b,b eq is the constraint constant vector.

5. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1 is characterized in that: The system also includes an algorithm collaborative optimization module for realizing data fusion and collaborative calculation of harmonic detection algorithm and power flow optimization algorithm; the enhanced load model expression is: ; where α and β are the parameters of the traditional exponential load model, K h is the harmonic influence coefficient, H3, H5, H7 are the 3rd, 5th, 7th harmonic contents.

6. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1 is characterized in that: The data processing unit also executes a load prediction algorithm, whose prediction expression is: ; where K THD is the correlation coefficient between THD change and power change, and Δ(THD) is the change in total harmonic distortion.

7. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1 is characterized in that: The communication resource allocation strategy of the dual-mode communication module is: ; Load complexity Complexity evaluation based on harmonic detection and power flow analysis, Control urgency Reflects the control timeliness requirements; the narrowband communication unit is based on power line carrier communication technology and is used to transmit control instructions and basic measurement data; the broadband communication unit is based on wireless communication technology and is used to transmit waveform data and complex analysis results.

8. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1 is characterized in that: The system also includes a load characteristic identification module based on a multi-dimensional load characteristic vector: ; Among them, P and Q are active and reactive power, PF is power factor, H3 and H5 are 3rd and 5th harmonic contents, dP / dV and dQ / dV are the sensitivity of power to voltage, which realizes automatic identification of load type and status.

9. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1 is characterized in that: The control execution unit executes a comprehensive optimization control strategy, and the objective function is: ; Where ΔP is the network loss, ΔV is the voltage deviation, THD is the total harmonic distortion rate, w1, w2, w3 are weight coefficients; the priority and intensity of control execution are dynamically adjusted according to the load characteristics and grid status.

10. The three-phase electric energy meter real-time load monitoring and control system based on dual-mode communication technology according to claim 1, characterized in that: The system adopts a layered processing architecture: the bottom layer executes a harmonic detection algorithm to analyze the power quality status in real time; the middle layer executes a power flow optimization algorithm to adjust the optimization model according to the harmonic analysis results; the upper layer realizes data transmission and control instruction issuance through a dual-mode communication system; data caching and priority mechanisms are set between each layer to ensure real-time processing of key information.

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