Chemical Bath Deposition of Cd1−xZnxS as Buffer Layer in CIGS Solar Cell

Cd1-xZnxS thin films were deposited on glass substrates by chemical bath deposition (CBD). The effect of ZnSO4 solution concentration on the properties of the thin films was analyzed. The concentration of ZnSO4 solution affects the deposition rate of Cd1-xZnxS thin films. When the deposition rate is low, Cd1-xZnxS cubic crystal phase is formed. The surface morphology of hexagonal Cd1-xZnxS thin films is denser than that of cubic phase, the lattice mismatch rate of cubic phase Cd1-xZnxS thin films and CIGS is lower, only 0.56%, the interfacial state density is lower. SCAPS software was used to simulate the performance of the buffer layer, and the conversion efficiency of the cubic phase Cd1-xZnxS buffer layer in CIGS Solar Cell was up to 23.50%. Based on the EDS results, the function relationship between the contents of Zn2+ and Cd2+ in the films and the band gap content was deduced.

Electrical Harmonic Energy Measurement Based on Wavelet Packet Decomposition and Reconstruction Algorithm

In order to study the accurate measurement of electric energy in complex industrial field, a method of harmonic electric energy measurement based on wavelet packet decomposition and reconstruction algorithm, as well as the calculation formula of harmonic power and the principle of harmonic electric energy measurement are proposed. Using db42 wavelet function to carry out harmonic energy metering simulation analysis, the results show that: The fundamental frequency of the simulation signal is 50 Hz, two-layer wavelet packet transform is adopted, the simulation input signals within 40 fundamental wave cycles are taken, and the sampling frequency fs is 800 Hz. Conclusion: The three-phase harmonic energy metering device based on virtual instrument technology has realized the measurement of each harmonic active power and reactive power, and the accuracy reaches 0.2 s.

Optimal Allocation of Distributed Energy Supply System Under Uncertainty Based Improved Gray Wolf Algorithm

In order to improve the economical performance of distributed energy supply system under uncertainty, the improved gray wolf algorithm is constructed for optimal allocation of distributed energy supply system. The relating research progress is summarized firstly, and effect of improved gray wolf algorithm on optimal allocation of distributed energy supply system are studied. The optimal allocation model of distributed energy supply system is constructed considering fuel consumption, operation and maintenance cost, environment penalty cost, and power grid energy exchange function, and the uncertain factor is processed based on scienario method. And then the improved gray wolf algorithm is designed, and the initial strategy of population and the regulated method of main parameters are improved. Finally, simulation analysis is carried out, simulation results show that the proposed model can obtain best optimal allocation effect of system.

Fault Diagnosis Knowledge Reasoning of Switching Network in Distributed Generation Based on Petri Net

Telephone network based on IMS technology has been widely applied in power production and dispatching communication, especially in distributed power stations. Analysis and positioning failure of IMS network is arduous, because it’s dependent on IP data communication network. In this paper, we first introduced IMS switching network architecture and distributed generation communication network architecture, analyzed and summarized all kinds of network malfunction. Combining typical IMS network fault connection relations, we introduced an improved Petri net fault handling model and reasoning method. The diagnosis and positioning results could reflect the defects of equipment logic functions. This method on fault diagnosis and location of substation network has been proved to be effective through practical application.

Safety Protection Technology of Power Monitoring System Based on Feature Extraction Algorithm

The network security protection technology of power monitoring systems is of great significance. Aiming at the power network monitoring and protection technology problem, the paper proposes an active monitoring and protection strategy based on a feature extraction algorithm. The algorithm can calculate the transfer degree of security incidents based on evidence theory. First, the paper obtains a specific state transition diagram based on the security topology of a generalized random power communication network. Then, we analyze the relationship between power system information security and engineering security based on the system’s operating results and feature extraction algorithms. The experimental results demonstrate the rapid effectiveness of this method.

Stability Enhancement of PV Powered Microgrid using Levenberg-Marquardt Algorithm Based Intelligent Controller Under Grid-connected Mode

An effective and robust controller is designed using Levenberg-Marquardt (LM) algorithm-based Artificial Neural Network (ANN) for the solar Photo-Voltaic (PV) based distributed generation units for stabilizing the grid-connected microgrid (MG) under load changes and irradiance variations. A test system comprising of two PV units and one diesel generator unit connected to the utility grid is modelled and considered for the controller design in MATLAB/Simulink environment. PV generated power is injected into the grid through voltage source converter (VSC) regulated by using the proposed ANN controller. Based on the grid voltage and available PV generation, the ANN controller regulates the inverter current by setting the reference voltage vector to synthesize gating pulses for the inverter. The robustness of the controller design is analysed and validated through time-domain simulations by subjecting it to extreme operating conditions. The controller performance is evaluated by Integral Square Error (ISE) and Integral Time Absolute Error (ITAE) for the test system. The results are compared with conventional PI and PID controllers to prove the superior performing ANN controller.

Optimization of Low Pressure Steam Heating Technology of Thermoelectric Joint Production

In order to reduce the loss of heat saving in the thermal engine, improve energy utilization efficiency. This paper uses EBSILON simulation software to establish models and perform changes to the working condition, and the comparison of design values on the thermal balance graph. The results show that this method is applicable to the calculation of the thermoelectric gauge. At different heat supply and exhaust flow and the ambient temperature, the heat transfer characteristics of the unit is constantly changed. When the ambient temperature is less than 15∘C, the combined circulation thermal consumption rate is negative and the ambient temperature is negative, and the ambient temperature is higher than 15∘C time is positively correlated. When the heating capacity is greater than 300 gj/h, the combined cycle efficiency of the unit at the same heating rate is higher than the 100% load rate. Conclusion: the EBSILON simulation software is reliable.

Power Quality Disturbance Identification and Optimization Based on Machine Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

Effect of Loads on Temperature Distribution Characteristics of Oil-Immersed Transformer Winding

The temperature of the hot-spots on windings is a crucial factor that can limit the overload capacity of the transformer. Few studies consider the impact of the load on the hot-spot when studying the hot-spot temperature and its location. In this paper, a thermal circuit model based on the thermoelectric analogy method is built to simulate the transformer winding and transformer oil temperature distribution. The hot-spot temperature and its location under different loads are qualitatively analyzed, and the hot-spot location is analyzed and compared to the experimental results. The results show that the hot-spot position on the winding under the rated power appears at 85.88% of the winding height, and the hot-spot position of the winding moves down by 5% in turn at 1.3, 1.48, and 1.73 times the rated power respectively.

A Bionic-Intelligent Scheduling Algorithm for Distributed Power Generation System

With the introduction of the new power system concept, diversified distributed power generation systems, such as wind power, photovoltaics, and pumped storage, account for an increasing proportion of the energy supply side. Facing objective issues such as distributed energy decentralization and remote location, exploring what kind of algorithm to use to dispatch nearby distributed energy has become a hot spot in the current electric power field. In view of the current situation, this paper proposes a Bionic Intelligent Scheduling Algorithm (DWMFO) for distributed power generation systems. On the basis of the Moth Flame Algorithm (MFO), in order to solve the problem of low accuracy and slow convergence speed of the algorithm in scheduling distributed energy, we use the adaptive dynamic change factor strategy to dynamically adjust the weighting factor of the MFO. The purpose is to assist the power dispatching department to dispatch diversified distributed energy sources such as wind power, photovoltaics, and pumped storage in a timely manner during the peak power consumption period. In the experiment, we compared with 4 algorithms. The simulation results of 9 test functions show that the optimization performance of DWMFO is significantly improved, the convergence speed is faster, the solution accuracy is higher, and the global search capability is stronger. Experimental test results show that the proposed bionic intelligent scheduling algorithm can expand the effective search space of distributed energy. To a certain extent, the possibility of searching for the global optimal solution is also increased, and a better flame solution can be found.