AU2021101956A4 - IoT and Machine Learning based Power Generation from Sewage Water - Google Patents
IoT and Machine Learning based Power Generation from Sewage Water Download PDFInfo
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- AU2021101956A4 AU2021101956A4 AU2021101956A AU2021101956A AU2021101956A4 AU 2021101956 A4 AU2021101956 A4 AU 2021101956A4 AU 2021101956 A AU2021101956 A AU 2021101956A AU 2021101956 A AU2021101956 A AU 2021101956A AU 2021101956 A4 AU2021101956 A4 AU 2021101956A4
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 67
- 238000010248 power generation Methods 0.000 title claims abstract description 15
- 238000010801 machine learning Methods 0.000 title claims abstract description 14
- 239000010865 sewage Substances 0.000 title claims abstract description 11
- 238000012806 monitoring device Methods 0.000 claims abstract description 13
- 238000000034 method Methods 0.000 claims description 7
- 230000001360 synchronised effect Effects 0.000 claims description 5
- 239000002351 wastewater Substances 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims 1
- 238000007726 management method Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- ZZUFCTLCJUWOSV-UHFFFAOYSA-N furosemide Chemical compound C1=C(Cl)C(S(=O)(=O)N)=CC(C(O)=O)=C1NCC1=CC=CO1 ZZUFCTLCJUWOSV-UHFFFAOYSA-N 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005381 potential energy Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03F—SEWERS; CESSPOOLS
- E03F5/00—Sewerage structures
- E03F5/10—Collecting-tanks; Equalising-tanks for regulating the run-off; Laying-up basins
-
- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03F—SEWERS; CESSPOOLS
- E03F5/00—Sewerage structures
- E03F5/10—Collecting-tanks; Equalising-tanks for regulating the run-off; Laying-up basins
- E03F5/101—Dedicated additional structures, interposed or parallel to the sewer system
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03B—MACHINES OR ENGINES FOR LIQUIDS
- F03B13/00—Adaptations of machines or engines for special use; Combinations of machines or engines with driving or driven apparatus; Power stations or aggregates
- F03B13/06—Stations or aggregates of water-storage type, e.g. comprising a turbine and a pump
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03B—MACHINES OR ENGINES FOR LIQUIDS
- F03B17/00—Other machines or engines
- F03B17/06—Other machines or engines using liquid flow with predominantly kinetic energy conversion, e.g. of swinging-flap type, "run-of-river", "ultra-low head"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
-
- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03F—SEWERS; CESSPOOLS
- E03F2201/00—Details, devices or methods not otherwise provided for
- E03F2201/20—Measuring flow in sewer systems
-
- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03F—SEWERS; CESSPOOLS
- E03F5/00—Sewerage structures
- E03F5/10—Collecting-tanks; Equalising-tanks for regulating the run-off; Laying-up basins
- E03F5/105—Accessories, e.g. flow regulators or cleaning devices
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
- G01F23/22—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water
- G01F23/28—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water by measuring the variations of parameters of electromagnetic or acoustic waves applied directly to the liquid or fluent solid material
- G01F23/284—Electromagnetic waves
- G01F23/292—Light, e.g. infrared or ultraviolet
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/20—Hydro energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/16—Mechanical energy storage, e.g. flywheels or pressurised fluids
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- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Hydrology & Water Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Mechanical Engineering (AREA)
- Combustion & Propulsion (AREA)
- Chemical & Material Sciences (AREA)
- Medical Informatics (AREA)
- Power Engineering (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Control Of Eletrric Generators (AREA)
Abstract
"IoT and Machine Learning based Power Generation from Sewage Water."
Exemplary aspects of the present disclosure are directed towards the " IoT and Machine
Learning based Power Generation from Sewage-Water" consisting of Plurality of Sewage-Water
Collecting-Tank (SCT) assambly 001, Sewage-Water Level-Monitoring Device (SLMD) 100,
and an Electric-Generator Unit (EGU) 200. Sewage-Water Collecting-Tank assembly (SCT) 001
comprising of Plurality of tube 002 collecting sewerage, Filter-arrangement (FA) 003 and
Storage-Tank (ST) 004. Sewage-Water Level-Monitoring Device (SLMD) 100 encompassing of
Microcontroller 103, Water-level sensors 103a, and Outlet-Value control 103b Electric
Generator Unit (EGU) 200 comprises of Horizontal-Turbine 201 and an Electrical-Generator
202 and an Inlet 203 and Outlet-arrangement 204. Microcontroller 101a in coordination with
Water-level sensors 103a identifies the exact amount of sewerage collected in the Storage-tank
004 and also predicts the inflow volume. when the storage level and inflow reaches a threshold
value, microcontroller 103 opens the Outlet-Value control 103b thereby releasing the water on
to Horizontal-Turbine 201 to rotate the Electrical-Generator 202.
Page 1 of 3
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FIG 1
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Description
Page 1 of 3
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FIG 1
IoT and Machine Learning based Power Generation from Sewage Water
The following specification particularly describes the invention and the manner in which it is to be performed.
[0001] The present disclosure generally relates to energy sustainability, sewerage water management, and IoT and Machine Learning concepts.
[0002] Without limiting the scope of the invention, its background is described in connection with devices, programs, and methods relating to, Seweage water management, filtration and Power Generation devices as an example.
[0003] The water scarcity and the concept of Green Buildings have provoked many inventors and researchers about sewage water management and energy sustainability aspects. In this regards, many researchers and inventors have put forward their inventions, leading to many new concepts and better sustainability aspects and waste-water management. The following are the few inventions that are related to the present invention.
[0004] In a prior art No. CN201218165Y presented a disclosure about Hydroelectric power generation system for buildings wherein, The utility model relates to a building hydropower system which comprises the main water pipe (5), a water-quantity controlling switch (3) and a hydraulic generator (4) which are arranged on a channel of the main water pipe (5) and a secondary reservoir (8) which is connected with the main water pipe (5), wherein an upper-level sensor (1) and a lower-level sensor (2) are arranged inside the secondary reservoir (8). A plurality of building hydropower systems can be connected and used in series. The utility model has the advantages that the potential energy effect can be fully utilized so as to generate the hydraulic pressure and can be effectively converted into the electrical energy, thereby the power generation effect can be brought; the structure is
simple, resources can be fully utilized, and the better environmental benefits can be achieved.
[0005] Similarly, in CN105221323A, the invention discloses a system wherein First generator rotates pipe by the first actuating unit and first and is connected; Second generator rotates pipe by the second actuating unit and second and is connected. This drainage power generator structure is simple, by being kinetic energy by the transform gravitational energy of current, first rotation pipe and second rotates while pipe rotates and passes through the first actuating unit and the second actuating unit, drives the first generator and the second generator rotation to generate electricity. This drainage power generator can be used for high-rise waste-water generating.
[0006] In AU2020103211, disclosed an invention relates to the application of LIDAR to detect the anomaly in the vicinity. The same aspect herein used for this present invention wherein LIDAR is used to detect the water level in the storage tank.
[0007] In the prior art AU2020103212, disclosed systems and methods directed to a system power distribution management system wherein it was presented that an effective IoT communication protocol may help in getting better control aspects in power distribution and this same aspect is used herein for power generation.
[0008] Another Prior art AU2020104355 presented a method IoT and Machine Learning-Based Power Quality Improvement System For Micro-Grid which uses the WiFi mess protocol for its communication and sends fault and utilization data to the user. The power generated from this invention may be linked to grid to for a Smart Microgrid and this invention presented herewith is capable of such an integration.
[0009] The present invention provides significant and rapid electrical energy generation and a better way of waste-water disposal and treatment.
[0010] The present invention addresses the shortcomings mentioned above of the prior art.
[0011] All publications herein are incorporated by reference to the same extent as if each publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
[0012] The following presents a simplified summary of the disclosure to provide a basic understanding of the reader. This summary is not an extensive overview of the disclosure, and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.
[0013] Exemplary embodiments of the present disclosure are directed towards the IoT and Machine Learning based Power Generation from Sewage-Water.
[0014] An exemplary object of the present disclosure is directed towards a system that monitors sewage water hoarding the storage tank and intimating the same to the user.
[0015] An exemplary aspect of the present subject matter is directed towards integrating LIDAR( Water Level Sensor) 103b with microcontroller 103 to form Sewage Water Level-Monitoring Device (SLMD) 100.
[0016] Another exemplary object of the present disclosure is directed towards measuring sewage-water flow, thereby training the machine learning model to predict the sewage-water flow pattern and thereby Power to be generated and intimate the same to the user.
[0017] Another exemplary object of the present disclosure is directed towards integrating the microcontroller 103 with a plurality of Outlet-Value control 103b to control the water flow from all the tanks to the Electricity Generating Unit (EGU) 200 for controlling the output power.
[0018] An exemplary aspect of the present subject matter is directed towards the
Constitution of Electricity Generating Unit (EGU) 200 comprises of Horizontal-Turbine 201 and an Electrical-Generator 202 and an Inlet-Arrangement 203 and Outlet-arrangement 204.
[0019] An exemplary aspect of the present subject matter is directed towards the use of IoT communication protocol such as WIFi mesh network to establish a commination link between a plurality of sewage-water level monitoring system to the microcontroller 103.
[0020] Another exemplary aspect of the present disclosure is directed towards notification and sharing of the data and anomaly data with the user on the mobile app 300.
[0021] Another exemplary aspect of the present disclosure is the synchronous and asynchronous mode of operation in control valve opening to generate optimal power.
Brief Description of the Drawings
[0022] In the following, numerous specific details are set forth to provide a thorough description of various embodiments. Certain embodiments may be practised without these specific details or with some variations in detail. In some instances, certain features are described in less detail so as not to obscure other aspects. The level of detail associated with each of the elements or features should not be construed to qualify the novelty or importance of one feature over the others.
[0023] FIG.1 is a diagram depicting the IoT and Machine Learning based Power Generation from Sewage-Water, according to an exemplary embodiment of the present disclosure.
[0024] FIG. 2 is a 100 Component Architecture of Sewage-Water Level Monitoring Device (SLMD) 100, according to an exemplary embodiment of the present disclosure.
[0025] FIG. 3 is a representation of the process executed in Swage-Water Level Monitoring Device (SLMD) 100, according to an exemplary embodiment of the present disclosure.
[0026] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components outlined in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practised or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.
[0027] The use of "including," "comprising," or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the use of terms "first," "second," and "third," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.
[0028] Referring to FIG. 1 is a diagram depicting the " IoT and Machine Learning based Power Generation from Sewage-Water" consisting of Plurality of Sewage-Water Collecting Tank (SCT) assembly 001, Sewage-Water Level-Monitoring Device (SLMD) 100, and an Electric-Generator Unit (EGU) 200. Sewage-Water Collecting-Tank assembly (SCT) 001 comprising of plurality of tube 002 collecting sewage, Filter-arrangement (FA) 003 and Storage-Tank (ST) 004.
[0029] Furter, Sewage-Water Level-Monitoring Device (SLMD) 100 encompassing of Microcontroller 103, Water-level sensors 103a, and Outlet-Value control 103b. Microcontroller 101a in coordination with Water-level sensors 103a identifies the exact amount of sewerage collected in the Storage-tank 004 and predicts the inflow volume by executing the relevant machine learning algorithm (MLA). When the storage level and inflow reaches a threshold value, microcontroller 103 opens the Outlet-Value control 103b, thereby releasing the water on to Horizontal-Turbine 201 to rotate the Electrical-Generator 202.
[0030] The Electric-Generator Unit (EGU) 200 comprises Horizontal-Turbine 201 and an Electrical-Generator 202 and an Inlet 203 Outlet-arrangement 204. Horizontal-Turbine 201 is adopted since it matches the requirement such as high generation at low speeds and head.
Whenever Outlet-Value control 103b, which acts as wicket gates for a hydro generator, controls the amount of water entering and produces required power.
[0031] Further, When the main Swegae-wastewater in tank 004n reaches a quantity of set level, a microcontroller 103 executes the relevant machine-learning algorithm to predict the amount of power generated. If the expected power generated is low, then microcontroller 103 executes the relevant machine-leaming algorithm to predict the amount of sewage water in the plurality of sub storage tanks 004x. Once water levels are anticipated, microcontroller 103 executes the suitable machine-learning algorithm to predict the amount of power generated with combined sewage water available in all storage tanks (ST) 004. Once sufficient power could be developed, microcontroller 103 intimates the plurality of Outlet-Value control 103b to open their respective positions so that sewage water rushes on the turbine at once to yield maximum power. Electric-Generator Unit (EGU) 200 comprises of Horizontal-Turbine 201 and an Electrical Generator 202 and an Inlet 203 and Outlet-arrangement 204.
[0032] In accordance with a non-limiting exemplary embodiment of the present subject matter, FIG. 2 is a Component Architecture of Sewage-Water-Level Monitoring Device (SLMD) 100. The microcontroller 103 is typically an ESP32 capable of executing Al & ML algorithms and can do parallel computing. The inheriting capabilities made this microcontroller 103 best suited for this application. The Waterlevel sensor (LiDAR) 103a is connected to the microcontroller 103 to acquire the live water level value. Water level sensor (LIDAR) 103a acts as a sensing sensor and detects the level of water in the storage tank based LiDAR technology. Further, based on the water level and inflow prediction, microcontroller 103 decides when to operate the outlet water flow controller 103b.
[0033] In accordance with a non-limiting exemplary embodiment of the present subject matter, FIG. 3 depicting the 300 Process Executed in Sewage-Water-Level Monitoring Device (SLMD). The process starts at step 301, wherein microcontroller 103 acquire Water level in the storage tanks 004 using Water level sensor 103a. In step 302, microcontroller 103 If the level reaches threshold value then notify the user and turns on the Outlet-Value control 103b. In subsequent step 303, microcontroller 103 Measure the water flowing from the storage tank 004 through the Outlet-Value control 103b. In step 304, Predict the water flow and Estimate the energy produced based on the actual levels and predicted water flow
[0034] Further, in step 305, If the predicted energy produced is less, wait and go to step 301; else, go to the next step and intimate the user. In step 306, Execute relevant MLA to operate Outlet-Value control 103b in asynchronous or synchronous mode. In step 307, microcontroller 103 Based on the prediction in 306, drive the Outlet-Value control 103b. In step 308, microcontroller 103 acquire the water level and if it falls beyond the minimum level required to generate, then close the Outlet-Value control 103b, and intimate the user
[0035] In an embodiment, it is presented that the microcontroller 103 may be ESP32, and maybe one or more such microcontrollers can be deployed based on the requirement of the building size and shape. If one or more microcontrollers are deployed one act as master, and others will act as slaves. This configuration enables the easy extension of the present device without much modification.
[0036] In an embodiment, it is presented that the microcontroller 103 operate Outlet Value control 103b in asynchronous or synchronous mode. Wherein the asynchronous mode means the valves 103b will be operated when and only when the tank is full. This statement indicates that, in a plurality of storage tank exists and when they are not complete and the power generation is satisfactory, then relevant MLA predicts the non-necessity. Whereas in synchronous mode, irrespective of the levels, based on predicted inflow, and user desire, all the controllers are opened to yield maximum power generation.
Claims (3)
1. The IoT and Machine Learning based Power Generation from Sewage Water: consisting of Plurality of Sewage-Water Collecting-Tank (SCT) assembly 001, Sewage-Water Level Monitoring Device (SLMD) 100, and an Electricity Generating Unit (EGU) 200; and Sewage-Waster Water Collection Tank (SCT) 001 comprising of Plurality of tubes 002 collecting sewerage/ waste-water, Filter-arrangement 003 and Storage-Tank 004; and Sewage-Water Level Monitoring Device (SLMD) 100 encompassing of Microcontroller 103, Water-level sensors 103a, and Outlet-Value control 103b; and
Microcontroller 103 executes relevant machine learning algorithms and predict, water flow, power generation and optimal opening of control valves 103b.
2. As claimed in claim 1, the device, the Microcontroller 103, executes the relevant computer program on Waterlevel sensor 103a data and determines water levels and predicts the water flow and tank fill time.
3. The device, as claimed in claim 1, the Microcontroller 103 monitors Storage-tank 004 level and water inflow and executes a relevant machine-leaming algorithm to determine the synchronous or asynchronous operation of control valves 103b.
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FIG 1
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103 103a
103b 2021101956
103c
100 Sewage-Water-Level Monitoring Device (SLMD) FIG 2
Page 3 of 3 Apr 2021
Acquire Water level in storage tank 004 using sensor 103a 301
If the level reaches threshold value then notify the user 302 and turn on the Outlet-Value control 103b 2021101956
303
Predict the water flow and Estimate the energy produced 304 based on the actual and predicted water flow
305 If energy produced is less wait and go to step 301 else go to next step and intimate the user
306 Execute relevant MLA to operate
307 Based on the prediction in 306, operate the
308
FIG 3 300 Process Executed SewageWater-Level Monitoring Device (SLMD)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021101956A AU2021101956A4 (en) | 2021-04-15 | 2021-04-15 | IoT and Machine Learning based Power Generation from Sewage Water |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021101956A AU2021101956A4 (en) | 2021-04-15 | 2021-04-15 | IoT and Machine Learning based Power Generation from Sewage Water |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021101956A4 true AU2021101956A4 (en) | 2021-07-01 |
Family
ID=76584636
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021101956A Ceased AU2021101956A4 (en) | 2021-04-15 | 2021-04-15 | IoT and Machine Learning based Power Generation from Sewage Water |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021101956A4 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115493740A (en) * | 2022-11-14 | 2022-12-20 | 长江勘测规划设计研究有限责任公司 | Method and system for measuring pressure pulsation of internal flow passage of water turbine by using machine vision |
-
2021
- 2021-04-15 AU AU2021101956A patent/AU2021101956A4/en not_active Ceased
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115493740A (en) * | 2022-11-14 | 2022-12-20 | 长江勘测规划设计研究有限责任公司 | Method and system for measuring pressure pulsation of internal flow passage of water turbine by using machine vision |
| CN115493740B (en) * | 2022-11-14 | 2023-02-28 | 长江勘测规划设计研究有限责任公司 | Method and system for measuring pressure pulsation of internal flow passage of water turbine by using machine vision |
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