9results about How to "Improve operation and maintenance level" patented technology

The invention relates to the technical field of photovoltaic power station inspection, and provides a distributed photovoltaic power station unmanned aerial vehicle inspection method and system, and the method comprises the following steps: generating an inspection task list based on the multi-station distribution characteristics of a distributed photovoltaic power station; according to the inspection task list, performing scanning modeling on each photovoltaic sub-power station in sequence through a laser radar, and generating a photovoltaic power station three-dimensional map; dynamically planning an inspection path according to the characteristics of each photovoltaic sub-power station; according to the inspection path, performing multi-angle scanning on each photovoltaic module in different installation environments through a multi-source sensor, and obtaining photovoltaic module image data with position information in combination with the real-time position coordinates of the unmanned aerial vehicle; performing preprocessing, feature extraction and fault identification on the photovoltaic module image data by using a fault identification model, and establishing a fault feature library; and carrying out fusion analysis on the multi-source data, generating a fault detection report of each photovoltaic sub-power station based on the inspection task list, and formulating a corresponding maintenance scheme according to the fault detection report. According to the invention, multi-source data fusion analysis of the photovoltaic module image data, the fault feature library, the position coordinates and the three-dimensional map is realized, and the inspection efficiency and the fault detection accuracy of the distributed photovoltaic power station are improved.

The invention discloses an SVM-based multi-parameter fusion switch storage battery state diagnosis method and system, and relates to the technical field of power line automation and Internet of Things technology combined intelligent monitoring, and the method comprises the steps: collecting multi-parameter data through a sensor, and carrying out the preprocessing of the multi-parameter data; extracting fault diagnosis features from the processed data, screening important features by using a Pearson's correlation coefficient, and dividing a data set into a training set and a test set; and combining the SVM with the BP neural network to form an SVM-BP model, and evaluating the state of the storage battery by training and optimizing weights and parameters. Intelligent operation and maintenance of the storage battery are achieved, passive storage battery maintenance work is converted into active storage battery maintenance work, the operation and maintenance level of the power distribution automation terminal equipment is improved, the operation and maintenance workload is greatly reduced, and the state of the backup power supply storage battery is also brought into daily operation management of the power distribution automation equipment; and operation and maintenance management can be carried out more timely and quickly.

The application discloses a kind of tangential frost heaving force test methods of cold region pile foundation, belong to cold region pile foundation frost heaving prevention and control technical field, the test method of the application is in the top of test pile Design load is applied and is kept, and in the time point when freezing depth and frost heaving reach maximum, obtain annual maximum unit tangential frost heaving force, by the determination of dangerous section, obtain the maximum tangential frost heaving force that pile body bears, namely the most unfavorable load state that pile foundation faces in a year, can be directly used as the key design control value of pile foundation security guarantee, fundamentally avoid the problem of data randomness and insufficient representation caused by improper test timing, so that key design parameter originates from real annual most unfavorable working condition, provide more scientific and reliable basis for subsequent frost heaving prevention and control and stability checking.

The utility model discloses a nuclear power station large -scale component switching device, including platform, two material racks, the both sides of platform are equipped with support frame, the top of platform is equipped with horizontal slide rail, is installed with two horizontal slide plates on horizontal slide rail, and each horizontal slide plate is driven to remove by a set of horizontal movement drive mechanism, each horizontal slide plate is installed with horizontal slide rail respectively, is installed with a plurality of longitudinal slide blocks on horizontal slide rail, and the upper end of corresponding material rack is fixedly connected with longitudinal slide block, and the lower extreme of material rack is equipped with the supporting plate, and material rack removes on horizontal slide rail by longitudinal movement drive mechanism drive. The utility model discloses a nuclear power station large -scale component switching device, and horizontal, longitudinal movement drive mechanism cooperation slide rail slider structure realizes material rack accurate positioning and nimble removal, and the component switching efficiency is improved greatly.

This invention discloses a method for sensing the operational status of a distribution network, relating to the field of distribution network monitoring and intelligent operation and maintenance. The method includes: synchronous acquisition of multi-source ubiquitous data to construct a low-cost ubiquitous sensing network; edge-side data preprocessing and lightweight analysis; topology-adaptive multi-source data fusion: constructing a distribution network topology map based on neural networks and mining the spatial and temporal correlations of multi-source data; multi-dimensional quantitative assessment of operational status: constructing a multi-dimensional status assessment system; precise fault location and hierarchical early warning: fusing multi-terminal information to achieve precise fault location; and online model iteration and closed-loop optimization. This distribution network operational status sensing method, by employing a low-cost ubiquitous sensing network combined with dedicated sensors and non-intrusive monitoring, covers the entire distribution network—from source to grid to load to storage—solving the problems of large blind spots and high deployment costs associated with traditional sensing methods. It achieves effective monitoring of end nodes and branch lines, realizing the goal of full-area, blind-spot-free sensing.

The invention provides an EL and I-V integrated acquisition system and method and a defect detection system and method, which can be applied to the technical field of photovoltaic power generation. The acquisition system comprises an EL imaging device, an I-V acquisition device, a bidirectional program control direct current power supply module and a synchronous control module. The bidirectional program-controlled direct-current power supply module is configured to serve as a power supply to provide original current for the synchronous control module in an EL mode and serve as an electronic load to obtain current output by the object to be detected in an I-V mode; the synchronous control module is configured to output a controlled modulation excitation signal to a to-be-detected object in an EL mode and control the EL imaging device to work so as to obtain an EL image; and the control device is configured to control the I-V acquisition device to work in the I-V mode so as to acquire the I-V data of the to-be-tested object. According to the invention, the EL-I-V integrated efficient detection of the photovoltaic module can be realized in the daytime environment, and the accuracy and timeliness of defect detection are greatly improved.

The technical scheme of the utility model discloses a 110V DC branch load leakage protection device. According to the utility model, the direct-current shunt is adopted to directly collect the leakage current, and the positive and negative buses are adopted to respectively collect the working current, so that the inherent defect that the Hall current sensor is influenced by the temperature is avoided, and the current test precision of the Hall sensor is also prevented from being influenced by the mounting aperture and the relative position of the tested cable. According to the utility model, the value of the leakage current can be accurately tested, and then graded alarm processing is carried out.

The present application relates to the technical field of motor state monitoring, in particular to a traction motor fault online monitoring method and system based on leakage current impedance characteristics. The present application provides a traction motor fault online monitoring method based on leakage current impedance characteristics, the method comprising: collecting three-phase-to-ground voltage signals and three-phase leakage current signals of the input end of the traction motor during operation; performing Fourier transform processing on the three-phase-to-ground voltage signals and the three-phase leakage current signals to obtain voltage spectrum and current spectrum respectively; based on the voltage spectrum and the current spectrum, calculating the impedance characteristics of the traction motor within a preset characteristic frequency band; comparing the calculated impedance characteristics with the preset healthy motor impedance characteristics, and judging the stator insulation degradation fault or bearing fault according to whether the deviation of the comparison exceeds the corresponding threshold. The method provided by the present application improves the safety and operation and maintenance level of the traction motor.

This invention discloses a self-diagnosis and rapid fault location method for a hydropower station monitoring system, comprising the following steps: Step 1: Full-dimensional data acquisition and preprocessing of the monitoring system; Step 2: Multimodal feature fusion and extraction; Step 3: Hierarchical self-diagnosis verification; Step 4: Rapid fault location; Step 5: Fault level classification and emergency response linkage; Step 6: Adaptive optimization of the diagnostic model; Step 7: Diagnosis and fault log retention. Employing a hierarchical diagnostic mode that links the edge and cloud, combined with multimodal feature fusion, it enables comprehensive self-diagnosis of the monitoring system's own health status, effectively reducing false alarms and false negatives, while balancing real-time performance and accuracy. Based on fault causal graphs and Bayesian network correlation analysis algorithms, combined with the system topology, it can quickly locate the specific location of the fault source, the faulty component, and the triggering factors, solving the problems of delayed fault location and large errors in existing technologies.