A comprehensive control method for power quality management

Abstract

The present invention discloses a comprehensive control method for power quality governance, including: Step 1: Deploy power quality monitoring devices at the end of the distribution network to collect voltage and current waveform data in real time, and calculate the three-phase unbalance degree, reactive power, and harmonic content; Step 2: Based on the real-time monitoring results, adopt a multi-model adaptive control algorithm to make control decisions for the compensator; Step 3: Dynamically adjust the parameters and working modes of the compensator according to the weights obtained by the multi-model adaptive control algorithm; Step 4: In the real-time adjustment of multiple algorithms, for the case of insufficient capacity, select a low-order harmonic compensation algorithm to reduce the harm to the system; Step 5: Through real-time monitoring, continuously feedback the power quality data of the system, and adjust and update the parameters and weights of the algorithm in real time. The present invention has more obvious advantages in terms of real-time performance, adaptability, intelligence, and system stability, and can better handle power quality problems under different working conditions.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and particularly relates to a comprehensive control method for power quality management. Background Art

[0002] With the development of technology, the number of nonlinear loads in low-voltage distribution systems has increased, leading to the exacerbation of power quality problems, mainly reflected in three-phase imbalance, excessive reactive power, and harmonic pollution. The three-phase imbalance affects transformer losses and equipment life, excessive reactive power reduces power supply quality, and harmonics affect equipment efficiency and the normal operation of communication systems. Existing solutions include three-phase imbalance adjustment devices (SPC), static var generators (SVG), and active / passive filters (APF / PPF). However, the combined operation cost of multiple governance devices is high, and the governance points are mainly concentrated on the grid side and large pollution sources, and the power quality problems of end-users have not been effectively solved. Therefore, a comprehensive governance method deployed at the end of the distribution network and solving multiple problems at the same time is urgently needed. Summary of the Invention

[0003] To solve the above problems, the present invention provides a comprehensive control method for power quality management.

[0004] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0005] The present invention discloses a comprehensive control method for power quality management, including the following steps:

[0006] Step 1: Deploy power quality monitoring equipment at the end of the distribution network, collect voltage and current waveform data in real time, and calculate the three-phase imbalance degree, reactive power, and harmonic content;

[0007] Step 2: Based on the real-time monitoring results, use a multi-model adaptive control algorithm to make control decisions for the compensator;

[0008] Step 3: Dynamically adjust the parameters and working mode of the compensator according to the weights obtained by the multi-model adaptive control algorithm;

[0009] Step 4: In the real-time adjustment of multiple algorithms, for the case of insufficient capacity, select a low-order harmonic compensation algorithm to reduce the harm to the system;

[0010] Step 5: Through real-time monitoring, continuously feedback the power quality data of the system, and real-time adjust and update the parameters and weights of the algorithm to adapt to the changing power quality environment.

[0011] Furthermore:

[0012] The said Step 1 includes:

[0013] Power quality monitoring equipment is arranged at the end of the distribution network, including high-precision voltage sensors and current sensors;

[0014] Waveform data in the power grid is collected through voltage and current sensors, and the data is digitally processed using the sampling theorem to obtain discrete voltage and current signals. The discrete signals are subjected to Fourier transform to obtain spectral information, thereby calculating various power quality parameters;

[0015] Calculate the three-phase unbalance degree, reactive power, and harmonic content, including:

[0016]

[0017] Among them, U1 and U2 are the positive-sequence and negative-sequence voltage increments;

[0018]

[0019] Among them, S is the apparent power and P is the active power;

[0020]

[0021] Among them, I harm is the i-th harmonic current, and I fundamental is the fundamental wave current.

[0022] Furthermore:

[0023] The said step 2 includes:

[0024] For different power quality problems, define a set containing multiple compensation models;

[0025] Formulate a set of performance indicators for each compensation model to evaluate its applicability, and set thresholds to determine whether to switch to another model;

[0026] According to the power quality parameters obtained from real-time monitoring, use the following formula to calculate the weight of each model:

[0027]

[0028] Among them, H i is the content of the i-th harmonic, H threshold is the harmonic content threshold, and k is an adjustment parameter;

[0029] Combine the calculated weights with the performance indicators, and select the compensation model with the highest weight as the current control model;

[0030] According to the selected compensation model, adjust the parameters of the compensator in real time.

[0031] Furthermore:

[0032] Step 3 includes:

[0033] Select a compensation algorithm according to the weights obtained by the multi-model adaptive control algorithm, in accordance with the priority;

[0034] For the selected compensation algorithm, achieve real-time power quality adjustment by adjusting the corresponding parameters. The adjustment formulas and parameters include:

[0035] Reactive power output of the static var generator (SVG):

[0036] Q SVG = W SVG × Q target,SVG

[0037] where, W SVG is the weight of the SVG, and Q target,SVG is the target reactive power;

[0038] Filtering current of the active power filter (APF):

[0039] I APF = W APF × I target,APF

[0040] where, W APF is the weight of the APF, and I target,APF is the target filtering current;

[0041] Output control signal of the three-phase unbalance regulating device (SPC):

[0042] U SPC = w SPC × U target,SPC

[0043] where, W SPC is the weight of the SPC, and U target,SPC is the target regulating signal;

[0044] Continuously update the weights of the compensation algorithm according to the real-time monitored power quality parameters, and then perform parameter adjustment.

[0045] Furthermore:

[0046] Step 4 includes:

[0047] For different harmonic orders, formulate harmonic hazard assessment indicators, including comprehensive indicators of the potential damage degree of each harmonic component to the equipment:

[0048]

[0049] where, H i is the content of the i-th harmonic, Weighti is the weight corresponding to the harmonic order;

[0050] Set the low - order harmonic threshold as the criterion for determining whether to use the low - order harmonic compensation algorithm. When the harmonic hazard assessment index exceeds this threshold, low - order harmonic compensation is preferentially selected;

[0051] Update the harmonic hazard assessment index in real time;

[0052] When the system capacity is insufficient, according to the harmonic hazard assessment index monitored in real time, the low - order harmonic compensation algorithm is preferentially selected.

[0053] Furthermore:

[0054] The said step 5 includes:

[0055] Configure a real - time monitoring system to regularly collect power quality parameters at the end of the power grid, including three - phase unbalance degree, reactive power, and harmonic content;

[0056] Set a feedback period to determine how often real - time feedback is performed;

[0057] At the end of each feedback period, transmit the power quality parameters obtained from real - time monitoring into the system for processing, calculate the error between the actual parameters and the target parameters, and provide a basis for subsequent adjustment;

[0058] Based on the error obtained from the feedback, use an adaptive control algorithm for parameter adjustment and weight update:

[0059] Parameter adjustment:

[0060]

[0061] where K p , K i , K d are the proportional, integral, and differential gains respectively, and Error is the error between the actual parameters and the target parameters;

[0062] Weight update:

[0063] New Weight = Old Weight + η×Error

[0064] where η is the learning rate, used to control the adjustment speed of the weight;

[0065] Taking into account the system stability, set the upper and lower limits of the adjustment amplitude of parameter adjustment and weight update to prevent the system from adjusting too frequently or too aggressively.

[0066] Compared with the prior art, the technical progress achieved by the present invention is:

[0067] Through the multi-model adaptive control algorithm, the system of the present invention can dynamically select the optimal compensation model based on real-time monitoring, which is more real-time than the traditional method. The present invention can automatically switch different compensation models according to the severity of power quality problems and has better adaptability.

[0068] By introducing the harmonic hazard assessment index and the strategy of preferentially selecting low-order harmonic compensation, the present invention can more intelligently judge when to preferentially process low-order harmonics and reduce the harm to the system. In the case of insufficient capacity, the system of the present invention has the ability to preferentially compensate for low-order harmonics, reducing the potential risk to the system caused by low-order harmonics.

[0069] Through real-time feedback and update, the system of the present invention can dynamically adjust parameters and weights to maintain the stability of the system under different working conditions and avoid the instability caused by excessive adjustment. The present invention introduces comprehensive performance evaluation indexes such as the Power Quality Index (PQI), which can comprehensively evaluate the power quality of the system and improve the comprehensiveness of the comprehensive evaluation of the system performance. Based on the system performance evaluation, parameters can be flexibly adjusted and the algorithm can be optimized to meet the power quality governance requirements under different working conditions.

[0070] Combining the above advantages, the present invention can more intelligently and real-time manage power quality problems, improve the management effect, reduce the system operation risk, thereby improving energy efficiency. Compared with the prior art, it has more obvious advantages in terms of real-time, adaptability, intelligence and system stability, and can better cope with power quality problems under different working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention.

[0072] In the drawings:

[0073] Figure 1 is a flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0074] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below with reference to the drawings.

[0075] As Figure 1 shown, the present invention discloses a comprehensive control method for power quality governance, including the following steps:

[0076] Step 1: Real-time power quality monitoring and analysis

[0077] Deploy high-precision power quality monitoring equipment at the end of the distribution network. By collecting voltage and current waveform data in real time, calculate the three-phase unbalance degree (Negative Sequence Component), reactive power, and harmonic content.

[0078] Step 2: Multi-model adaptive control algorithm

[0079] Based on the real-time monitoring results, adopt the multi-model adaptive control algorithm to make control decisions for the compensator. This algorithm can automatically switch different compensation models according to the severity of the current power quality problem.

[0080] Step 3: Multi-algorithm real-time adjustment

[0081] According to the weights obtained by the multi-model adaptive control algorithm, dynamically adjust the parameters and working modes of the compensator. Different weights correspond to different compensation algorithms, such as static var generator (SVG) compensation, active power filter (APF) filtering, three-phase unbalance adjustment device (SPC), etc.

[0082] Step 4: Preferential compensation for low-order harmonics

[0083] In the multi-algorithm real-time adjustment, for the case of insufficient capacity, preferentially select the low-order harmonic compensation algorithm to reduce the harm to the system.

[0084] Step 5: Real-time feedback and update

[0085] Through real-time monitoring, continuously feedback the power quality data of the system, and adjust and update the parameters and weights of the algorithm in real time to adapt to the changing power quality environment.

[0086] Through the above steps, the present invention can achieve dynamic adjustment in real time, with multiple models and multiple algorithms, and at the same time ensure that low-order harmonic problems are preferentially processed when the capacity is insufficient, so as to maximize the improvement of power quality.

[0087] Specifically, step 1 includes:

[0088] In order to achieve real-time power quality monitoring and analysis, it is necessary to deploy high-precision power quality monitoring equipment at the end of the distribution network. This equipment should be able to accurately collect voltage and current waveforms and obtain key indicators such as three-phase unbalance degree, reactive power, and harmonic content through calculation.

[0089] Deployment of high-precision power quality monitoring equipment:

[0090] Arrange power quality monitoring equipment at the end of the distribution network, including high-precision voltage sensors and current sensors, to ensure accurate collection of power quality parameters.

[0091] Power parameter collection and processing:

[0092] Collect waveform data in the power grid through voltage and current sensors, digitally process the data using the sampling theorem to obtain discrete voltage and current signals, perform Fourier transform on the discrete signals to obtain spectrum information, and thus calculate various power quality parameters.

[0093] Calculate the three-phase unbalance degree:

[0094]

[0095] where U1 and U2 are the positive-sequence and negative-sequence voltage increments.

[0096] Calculate the reactive power:

[0097]

[0098] where S is the apparent power and P is the active power.

[0099] Harmonic content calculation:

[0100]

[0101] where I harm is the i-th harmonic current and I fundamental is the fundamental current.

[0102] Through the above steps, the system can monitor the power quality parameters in the power grid in real time, providing accurate data support for subsequent multi-model adaptive control.

[0103] Specifically, step 2 includes:

[0104] In this step, the implementation of the multi-model adaptive control algorithm will be described in detail. This algorithm will automatically select an appropriate compensation model according to the real-time monitoring results to improve the power quality.

[0105] Define the multi-model set:

[0106] For different power quality problems, define a set containing multiple compensation models. For example, it includes models such as three-phase unbalance regulation device (SPC), static var generator (SVG), active power filter (APF), etc.

[0107] Formulate the performance indicators of the compensation model:

[0108] Formulate a set of performance indicators for each compensation model to evaluate its applicability. These indicators include compensation effect, response speed, etc. Set a threshold to determine whether to switch to another model.

[0109] Calculate the model weights in real time:

[0110] Based on the power quality parameters obtained from real-time monitoring, the weights of each model are calculated using the following formula:

[0111]

[0112] where H i is the content of the i-th harmonic, H threshold is the harmonic content threshold, and k is the adjustment parameter.

[0113] Select the optimal compensation model:

[0114] Combine the calculated weights with the performance indicators, and select the compensation model with the highest weight as the current control model.

[0115] Adjust the compensator parameters in real time:

[0116] According to the selected compensation model, adjust the parameters of the compensator in real time. This can be achieved by modifying the control parameters of the compensator, such as adjusting the reactive power output of the static var generator and adjusting the filtering parameters of the active power filter.

[0117] Through the above steps, the system can automatically select the appropriate compensation model according to the real-time monitoring results and dynamically adjust the corresponding parameters to optimize the power quality improvement effect.

[0118] Specifically, step 3 includes:

[0119] In this step, it will be detailed how to dynamically adjust the parameters and working modes of the compensator according to the weights obtained from the multi-model adaptive control algorithm. Such adjustments will enable different compensation algorithms to more flexibly adapt to power quality problems under real-time operating conditions.

[0120] Select the compensation algorithm according to the weight:

[0121] According to the weights obtained from the multi-model adaptive control algorithm, select the compensation algorithm according to a certain priority. In this embodiment, three common compensation algorithms are taken as examples: static var generator (SVG), active power filter (APF), and three-phase unbalance regulating device (SPC).

[0122] Dynamic parameter adjustment:

[0123] For the selected compensation algorithm, achieve real-time power quality adjustment by adjusting the corresponding parameters:

[0124] a. Parameter adjustment of the static var generator (SVG)

[0125] SVG reactive power output:

[0126] Q SVG = W SVG×Q target,SVG

[0127] Among them, W SVG is the weight of SVG, and Q target,SVG is the target reactive power.

[0128] b. Parameter adjustment of active power filter (APF)

[0129] APF filtering current:

[0130] I APF = W APF × I target,APF

[0131] Among them, W APF is the weight of APF, and I target,APF is the target filtering current.

[0132] c. Parameter adjustment of three-phase unbalance regulating device (SPC)

[0133] SPC output control signal:

[0134] U SPC = w SPC × U target,SPC

[0135] Among them, W SPC is the weight of SPC, and U target,SPC is the target regulating signal.

[0136] Real-time operating condition adjustment:

[0137] According to the real-time monitored power quality parameters, continuously update the weights of the compensation algorithm, and then perform parameter adjustment. In this way, the system can select the appropriate compensation algorithm according to different operating conditions in real time and adjust the parameters to achieve the best power quality improvement effect.

[0138] Through the above steps, the system can dynamically select and adjust the parameters of different compensation algorithms based on real-time monitoring to adapt to different power quality problems and improve the improvement effect.

[0139] Specifically, step 4 includes:

[0140] In the real-time adjustment of multiple algorithms, to solve the situation of insufficient capacity, we will adopt a strategy of preferentially selecting low-order harmonic compensation to reduce the harm to the system.

[0141] Harmonic hazard assessment:

[0142] For different harmonic orders, formulate a harmonic hazard assessment index, which can be a comprehensive index of the potential damage degree of each harmonic component to the equipment, and can be quantitatively evaluated according to power quality standards and equipment characteristics.

[0143] Harmonic hazard assessment index:

[0144]

[0145] Among them, H i is the content of the i-th harmonic, and Weight i is the weight corresponding to the harmonic order.

[0146] Set the low-order harmonic threshold:

[0147] Set a low-order harmonic threshold as the standard for determining whether to adopt the low-order harmonic compensation algorithm. When the harmonic hazard assessment index exceeds this threshold, the system will preferentially select low-order harmonic compensation.

[0148] Real-time monitoring of harmonic content:

[0149] In real-time monitoring, in addition to calculating the content of each harmonic, it is also necessary to update the harmonic hazard assessment index in real time.

[0150] Preferentially select the low-order harmonic compensation algorithm:

[0151] When the system capacity is insufficient, according to the real-time monitored harmonic hazard assessment index, preferentially select the low-order harmonic compensation algorithm.

[0152] Low-order harmonic compensation output:

[0153] P compensation,loww-harm = W low-harm × P target,low-harm

[0154] Among them, W low-harm is the weight of the low-order harmonic compensation algorithm, and P target,low-harm is the target compensation power.

[0155] Through the above steps, when the system capacity is insufficient, the system can preferentially select the low-order harmonic compensation algorithm to minimize the harm to the system and ensure the optimization of power quality at the same time.

[0156] Specifically, step 5 includes:

[0157] In this step, it will be detailed how to continuously feedback the power quality data of the real-time monitoring system, and then adjust and update the parameters and weights of the algorithm in real time to adapt to the changing power quality environment.

[0158] Real-time monitoring of power quality parameters:

[0159] Configure a real-time monitoring system to regularly collect power quality parameters at the end of the power grid, including three-phase unbalance degree, reactive power, harmonic content, etc.

[0160] Set the feedback period:

[0161] Set a feedback period to determine how often real-time feedback is performed. The choice of the feedback period needs to balance real-time performance and computational cost.

[0162] Real-time feedback data processing:

[0163] At the end of each feedback period, transmit the power quality parameters obtained from real-time monitoring to the control system for processing. Calculate the error between the actual parameters and the target parameters to provide a basis for subsequent adjustments.

[0164] Parameter adjustment and weight update:

[0165] Based on the error obtained from the feedback, use an adaptive control algorithm (such as a PID controller) to perform parameter adjustment and weight update. The following is an example formula for parameter adjustment:

[0166] Parameter adjustment:

[0167]

[0168] where K p 、K i 、K d are the proportional, integral, and derivative gains respectively, and Error is the error between the actual parameters and the target parameters.

[0169] Weight update:

[0170] New Weight = Old Weight + η × Error

[0171] where η is the learning rate, which is used to control the adjustment speed of the weight.

[0172] Consideration of system stability:

[0173] Introduce the consideration of system stability. The upper and lower limits of the amplitude of parameter adjustment and weight update can be set to prevent the system from adjusting too frequently or too aggressively, thereby maintaining the stability of the system.

[0174] Through the above steps, the system can achieve real-time feedback and update of power quality parameters, as well as adaptively adjust the parameters and weights of the algorithm to adapt to the changing power quality environment.

[0175] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. An integrated control method for power quality management, characterized in that, It includes the following steps: Step 1: Deploy power quality monitoring devices at the end of the distribution network, collect voltage and current waveform data in real time, and calculate the three-phase unbalance degree, reactive power, and harmonic content; Step 2: Based on the real-time monitoring results, adopt a multi-model adaptive control algorithm to make control decisions for the compensator; Step 3: Dynamically adjust the parameters and working mode of the compensator according to the weights obtained by the multi-model adaptive control algorithm; Step 4: In the real-time adjustment of multiple algorithms, for the case of insufficient capacity, select a low-order harmonic compensation algorithm to reduce the harm to the system; Step 5: Through real-time monitoring, continuously feedback the power quality data of the system, and adjust and update the parameters and weights of the algorithm in real time to adapt to the changing power quality environment.

2. The integrated control method for power quality management according to claim 1, characterized in that, The said Step 1 includes: Arrange power quality monitoring devices at the end of the distribution network, including high-precision voltage sensors and current sensors; Collect waveform data in the power grid through voltage and current sensors, digitally process the data using the sampling theorem to obtain discrete voltage and current signals, perform Fourier transform on the discrete signals to obtain spectral information, and thus calculate various power quality parameters; Calculating the three-phase unbalance degree, reactive power, and harmonic content includes: Wherein, U1 and U2 are the positive-sequence and negative-sequence voltage increments; Wherein, S is the apparent power and P is the active power; where, I harm is the i-th harmonic current, and I fundamental is the fundamental current.

3. The integrated control method for power quality management according to claim 2, characterized in that, The said Step 2 includes: For different power quality problems, define a set containing multiple compensation models; Formulate a set of performance indicators for each compensation model to evaluate its applicability, and set thresholds to determine whether to switch to another model; According to the power quality parameters obtained from real-time monitoring, use the following formula to calculate the weight of each model: where, H i is the content of the i-th harmonic, and H threshold is the harmonic content threshold, and k is an adjustment parameter; Combine the calculated weights with the performance indicators, and select the compensation model with the highest weight as the current control model; According to the selected compensation model, adjust the parameters of the compensator in real time.

4. The integrated control method for power quality management according to claim 3, characterized in that, The said Step 3 includes: According to the weights obtained by the multi-model adaptive control algorithm, select a compensation algorithm according to the priority; For the selected compensation algorithm, achieve real-time power quality adjustment by adjusting the corresponding parameters. The adjustment formulas and parameters include: The reactive power output by the static var generator SVG: Q SVG = W SVG × Q target，SVG Among them, W SVG is the weight of SVG, Q target,SVG is the target reactive power; The filtering current of the active power filter APF: I APF = W APF × I target，APF Among them, W APF is the weight of the APF, and I target,APF is the target filtering current; The output control signal of the three-phase unbalance adjustment device SPC: U SPC = W SPC × U target，SPC Among them, W SPC is the weight of the SPC, and U target,SPC is the target adjustment signal; According to the power quality parameters monitored in real time, continuously update the weights of the compensation algorithm, and then perform parameter adjustment.

5. A comprehensive control method for power quality governance according to claim 4, characterized in that, The said Step 4 includes: For different harmonic orders, formulate harmonic hazard assessment indicators, including comprehensive indicators of the potential damage degree of each harmonic component to equipment: Among them, H i is the content of the i-th harmonic, and Weight i is the weight corresponding to the harmonic order; Set a low-order harmonic threshold as the standard for determining whether to adopt the low-order harmonic compensation algorithm. When the harmonic hazard assessment indicator exceeds this threshold, preferentially select low-order harmonic compensation; Update the harmonic hazard assessment indicator in real time; When the system capacity is insufficient, according to the harmonic hazard assessment indicator monitored in real time, preferentially select the low-order harmonic compensation algorithm.

6. A comprehensive control method for power quality governance according to claim 5, characterized in that, The said Step 5 includes: Configure a real-time monitoring system to regularly collect power quality parameters at the end of the power grid, including three-phase unbalance degree, reactive power, and harmonic content; Set a feedback period to determine how often real-time feedback is performed; At the end of each feedback cycle, the power quality parameters obtained from real-time monitoring are transmitted to the system for processing, and the error between the actual parameters and the target parameters is calculated to provide a basis for subsequent adjustments; Based on the error obtained from the feedback, an adaptive control algorithm is used to adjust the parameters and update the weights: Parameter adjustment: Among them, K p , K i , K d are the proportional, integral, and derivative gains respectively, and Error is the error between the actual parameter and the target parameter; Weight update: New Weight=Old Weight+η×Error where η is the learning rate, which is used to control the adjustment speed of the weights; Considering the system stability, the upper and lower limits of the adjustment amplitudes of the parameters and the weights are set to prevent the system from making adjustments too frequently or too aggressively.

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