Hisense Electronic Technology navigates the selection plan for Tianjin AC voltage transmitters - which brand is good, learn more!

Tianjin AC voltage transmitter selection scheme - which brand is good? A power metering and power quality monitoring system, characterized by comprising a server upper computer and a power metering and power quality monitoring device as claimed in any one of claims 1 to 9; The server is used to obtain the line loss power data and power quality data uploaded by the electricity metering and power quality monitoring device, and to send the line loss power data and power quality data to the upper computer. The power metering and power quality monitoring device according to any one of claims 1 to 7, characterized in that it further comprises a storage module; The storage module is connected to the signal processing module.

A power quality dynamic regulator for microgrids according to claim 1, characterized in that it further comprises a peripheral module, wherein the peripheral module comprises power supply circuits respectively connected to the power monitoring module and the power quality dynamic controller of the power monitoring module; Connect the power monitoring module of the microgrid distribution network load inverter to the communication bus of the power quality dynamic controller.

58彩票The new power quality monitoring device as claimed in claim 1, characterized in that the liquid crystal display module comprises a liquid crystal screen, with a length of 76mm and a width of 77mm. The new power quality monitoring device as claimed in claim 1, characterized in that the pulse width of the power pulses output by the power pulse output module is between 80ms and 100ms, and the pulse constant is 3200mmp/kWh.

A power security intelligent Internet of Things system according to claim 1, characterized in that the power quality monitoring module collects real-time data on the source side current, load side current, terminal voltage and device operation status of the reactive power compensation device through intelligent switch devices of edge nodes and various intelligent devices of the main transformer and distribution system, predicts the power quality situation based on situational awareness algorithms, and forms control decisions.

58彩票Tianjin AC voltage transmitter selection scheme - which brand is good? According to claim 2, a mobile power quality monitoring device is characterized in that the T-shaped groove is opened at the top of the folding groove, and two T-shaped plates are slidably installed in the grooves of the T-shaped groove. Bearings are fixedly installed on both sides of the T-shaped groove, and the bidirectional screw is fixedly connected to the inner walls of the two bearings. The bidirectional screw passes through the two T-shaped plates in sequence and is connected to the two T-shaped plates through two opposite threads. The end face of the bidirectional screw passes through the power quality monitoring instrument body and is fixedly connected to a knob.

The on-site online comparison and detection device for power quality monitoring terminal according to claim 1, characterized in that the external storage unit is used to store waveforms and comparison data. The on-site online comparison and detection device for power quality monitoring terminal according to claim 1, characterized in that the high-precision timing unit comes from satellite timing, which enables time synchronization between the on-site online comparison and detection device and the power quality monitoring terminal.

A cloud based database power quality monitoring device according to claim 3, characterized in that the installation mechanism comprises a threaded hole set on the side wall of the protection box, and the threaded hole is connected with a cover. The side wall of the cover is provided with multiple heat dissipation holes, and dust nets are fixedly connected to the multiple heat dissipation holes. An installation plate is fixedly connected to the side wall of the protection box.

An IDC computer room power intelligent safety monitoring system according to claim 4, characterized in that it further comprises a gas monitoring unit connected to the power quality monitoring device, and the gas monitoring unit is used to monitor the gas composition during the operation of the power quality monitoring device; When collecting the gas composition during the operation of the power quality monitoring device, output a gas monitoring signal; When the power quality monitoring device detects gas monitoring signals, it outputs gas composition collection data to the data storage unit and data transmission module.

Tianjin AC voltage transmitter selection scheme - which brand is good? According to claim 2, the electricity theft detection method based on the combination of user load and electricity parameters is characterized in that in step e, the parallel long short-term memory neural network LSTM algorithm is improved, and the input data is changed from the traditional time series input to a three-level parallel input. Firstly, the training sample data is grouped and sorted according to weighted Euclidean distance, with the top 30% of the sorting result as the first level input, 31% -60% as the second level input, and the rest as the third level input; Considering the number of sampling points s in a day, where s ranges from 1 to Y * N, the optimal input neuron number for LSTM is set to ne, which is much smaller than s. The range of ne is an integer between 1 and s/10. The optimal output neuron number is set to continuously input the previous ne sequence data into LSTM to predict the next sequence data; Take the predicted values of the Long Short Term Memory (LSTM) neural network for each time stage as accurate values, set a threshold range that fluctuates up and down, and judge the actual load data corresponding to the sequence data points. If it exceeds the threshold range, it is considered an outlier, and the predicted value of the LSTM is used as a correction value to continue predicting until all sequence data runs for - days.

Tianjin AC voltage transmitter selection scheme - which brand is good? According to claim 1, the generalized S-transform and SVM power quality disturbance identification method is characterized in that the steps specifically refer to using the grey wolf optimization algorithm, namely the GWO algorithm, to optimize the penalty factor C and kernel function parameter γ of the support vector machine. The GWO algorithm is described as establishing a mathematical model of the wolf pack surrounding the prey. Assuming t represents the current iteration number, Xp (t is the prey position of the t-th iteration, X (t is the grey wolf position of the t-th iteration, then the distance D between the grey wolf and the prey in the t-th iteration and the position adjusted by the grey wolf after the t-th iteration are D=| C · Xp (t-X (t) | X (t+=Xp (t-A · D), where AC is the parameter vector and A=2 α · r1- α, C=r, where a linearly decreases from 2 to 0 with increasing iteration times, and rr2 is a random vector in the [0,1] interval; When the wolf pack surrounds the prey and begins hunting; The formula for updating the position of the gray wolf population's ω wolf pack is as follows: D α=| CX α (t-X (t | D β=| CX β) (t-X (t | D δ=| CX δ) (t-X (t | X1=X α (t-AD α X2=X β) (t-AD β X3=X δ) (t-AD δ X (t+=(X1+X2+X/3)), where D α D β D δ is the distance between the α β δ wolf and the ω wolf, AAACCC3 are all parameter vectors, X α (tX β) is the position of the α β δ wolf in the t-th iteration, XXX3 is the vector position of the α β δ wolf, X (t+is the updated position of the ω wolf; The final formula determines the location of the prey, and then the wolf pack attacks and captures the prey, that is, obtaining the optimal solution through the GWO algorithm; Using the GWO algorithm to optimize the parameter C γ of SVM, a GWO-SVM classifier is constructed. The algorithm steps are as follows: firstly, input the feature sample set of power quality disturbance signals, and divide it into a training set and a testing set; 5b Set the SVM penalty factor C and kernel function parameter γ range, set the population size iteration times, initialize the wolf pack, and each gray wolf pack is composed of C and γ; The 5cSVM model learns the training set based on the initial values of C and γ, and calculates the fitness value of each gray wolf to obtain the top three gray wolves in terms of fitness value ranking, namely α β δ; 5d updates the position of the wolf pack according to the formula, calculates the fitness value of the wolf pack individuals at the new position, and compares it with the optimal fitness value of the previous iteration. If the new fitness value is greater than the optimal fitness value, it is replaced, otherwise it is retained; If the current iteration count is greater than the iteration count, the algorithm terminates and outputs the SVM model's optimal parameters C and γ; 5f will use the optimal parameters C and γ to construct an SVM prediction model, and use the trained prediction model to predict the test set and output the type of power quality disturbance; The above GWO-SVM classifier can be used to identify power quality disturbance signals.