Low cost Na2FeSiO4/H–N-doped hard carbon nanosphere hybrid cathodes for high energy and power sodium-ion supercapacitors

Na2FeSiO4 (NFS)/H–N-doped hard carbon nanospheres (HN-HCNSs) hybrid cathodes have been synthesized by using ferrous gluconate as template and carbon source via sol-gel method for the first time. In the structure of this hybrid cathode, the ultrathin NFS nanosheets are uniformly anchored in the mesoporous network structure of HN-HCNSs coating, forming the fast conductive transport pathways for electrons and Na+-ions. The NFS/HN-HCNSs hybrid cathode shows a hybrid energy storage mechanism with high initial discharge capacity of 218.4 mAh g−1 at 0.1 C and in the voltage range of 1.2–4.6 V versus Na/Na+. It also shows excellent long-term cycling stability (the capacity retention rates of 73.8% at 1 C after the 3300 cycles and 56.8% at 5 C after the 750 cycles in the voltage range of 1.5–4.6 V). Moreover, the unique mesoporous carbon-coated structural features endow the hybrid cathode with a maximum energy density of 331.99 W h kg−1 and a maximum power density of 2431.87 W kg−1 within working voltage range of 1.5–4.6 V

Online Modelling and Calculation for Operating Temperature of Silicon-Based PV Modules Based on BP-ANN

The operating temperature of silicon-based solar modules has a significant effect on the electrical performance and power generation efficiency of photovoltaic (PV) modules. It is an important parameter for PV system modeling, performance evaluation, and maximum power point tracking. The analysis shows that the results of physics-based methods always change with seasons and weather conditions. It is difficult to measure all the needed variables to build the physics-based model for the calculation of operating temperature. Due to the above problem, the paper proposes an online method to calculate operating temperature, which adopts the back propagation artificial neural network (BP-ANN) algorithm. The comparative analysis is carried out using data from the empirical test platform, and the results show that both the BP-ANN and the support vector machine (SVM) method can reach good accuracy when the dataset length was over six months. The SVM method is not suitable for the temperature modeling because its computing time is too long. To improve the performance, wind speed should be taken as one of the models’ input if possible. The proposed method is effective to calculate the operating temperature of silicon-based solar modules online, which is a low-cost soft-sensing solution

Human Streptococcus suis Outbreak, Sichuan, China

Streptococcus suis outbreak was associated with exposure to sick or dead pigs

Streptococcus suis Sequence Type 7 Outbreak, Sichuan, China

An outbreak of Streptococcus suis serotype 2 emerged in the summer of 2005 in Sichuan Province, and sporadic infections occurred in 4 additional provinces of China. In total, 99 S. suis strains were isolated and analyzed in this study: 88 isolates from human patients and 11 from diseased pigs. We defined 98 of 99 isolates as pulse type I by using pulsed-field gel electrophoresis analysis of SmaI-digested chromosomal DNA. Furthermore, multilocus sequence typing classified 97 of 98 members of the pulse type I in the same sequence type (ST), ST-7. Isolates of ST-7 were more toxic to peripheral blood mononuclear cells than ST-1 strains. S. suis ST-7, the causative agent, was a single-locus variant of ST-1 with increased virulence. These findings strongly suggest that ST-7 is an emerging, highly virulent S. suis clone that caused the largest S. suis outbreak ever described

Fault detection and diagnosis of photovoltaic system using fuzzy logic control

Among several renewable energy resources, Solar has great potential to solve the world’s energy problems. With the rapid expansion and installation of PV system worldwide, fault detection and diagnosis has become the most significant issue in order to raise the system efficiency and reduce the maintenance cost as well as repair time. This paper presented a method for monitoring, identifying, and detecting different faults in PV array. This method is built based on comparing the measured electrical parameters with its theoretical parameters in case of normal and faulty conditions of PV array. For this purpose, three ratios of open circuit voltage, current, and voltage are obtained with their associated limits in order to detect eight different faults. Moreover, the fuzzy logic control FLC method is performed for studying the failure configuration and categorizing correctly the different faults occurred. The outcomes obtained by performing the different faults representing permanent and temporary faults demonstrated that the FLC was equipped to precisely identify the faults upon their occurring. Different simulated and experimental tests are conducted to demonstrate the performance of the proposed method

A New Regional Distributed Photovoltaic Power Calculation Method Based on FCM-mRMR and nELM Model

As the proportion of distributed photovoltaic (DP) increases, improving the accuracy of regional distributed photovoltaic power calculation is crucial to making full use of PV and ensuring the safety of the power system. The calculation of regional power generation is the key to power prediction, performance evaluation, and fault diagnosis. Distributed photovoltaic plants (DPP) are characterized by scattered distribution and small installed capacity, lots of DPPs are not fully monitored, and their real-time output power is difficult to obtain. Therefore, to improve the observability of DPPs and increase the accuracy of calculation, a new method that combines with fuzzy c-means (FCM), Max-Relevance and Min-Redundancy (mRMR) and Extreme Learning Machine(ELM), which can calculate the regional DPP output power without meteorological data is proposed, and validated using actual operational data of regional DPPs in China. The calculations results show good robustness in different months. The innovation of this study is the combination of the benchmark DPP selection method FCM-mRMR and the power calculation method nELM, and the mean absolute error (MAPE) of the proposed method is 0.198 and the coefficient of determination (R2) is 0.996

A New Regional Distributed Photovoltaic Power Calculation Method Based on FCM-mRMR and nELM Model

As the proportion of distributed photovoltaic (DP) increases, improving the accuracy of regional distributed photovoltaic power calculation is crucial to making full use of PV and ensuring the safety of the power system. The calculation of regional power generation is the key to power prediction, performance evaluation, and fault diagnosis. Distributed photovoltaic plants (DPP) are characterized by scattered distribution and small installed capacity, lots of DPPs are not fully monitored, and their real-time output power is difficult to obtain. Therefore, to improve the observability of DPPs and increase the accuracy of calculation, a new method that combines with fuzzy c-means (FCM), Max-Relevance and Min-Redundancy (mRMR) and Extreme Learning Machine(ELM), which can calculate the regional DPP output power without meteorological data is proposed, and validated using actual operational data of regional DPPs in China. The calculations results show good robustness in different months. The innovation of this study is the combination of the benchmark DPP selection method FCM-mRMR and the power calculation method nELM, and the mean absolute error (MAPE) of the proposed method is 0.198 and the coefficient of determination (R2) is 0.996

A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm

Photovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array’s electrical behavior characteristics under fault conditions is analyzed in this paper, and a novel PV array fault diagnosis method is proposed based on fuzzy C-mean (FCM) and fuzzy membership algorithms. Firstly, clustering analysis of PV array fault samples is conducted using the FCM algorithm, indicating that there is a fixed relationship between the distribution characteristics of cluster centers and the different fault, then the fault samples are classified effectively. The membership degrees of all fault data and cluster centers are then determined by the fuzzy membership algorithm for the final fault diagnosis. Simulation analysis indicated that the diagnostic accuracy of the proposed method was 96%. Field experiments further verified the correctness and effectiveness of the proposed method. In this paper, various types of fault distribution features are effectively identified by the FCM algorithm, whether the PV array operation parameters belong to the fault category is determined by fuzzy membership algorithm, and the advantage of the proposed method is it can classify the fault data from normal operating data without foreknowledge

Hybrid Power Forecasting Model for Photovoltaic Plants Based on Neural Network with Air Quality Index

High concentration of greenhouse gases in the atmosphere has increased dependency on photovoltaic (PV) power, but its random nature poses a challenge for system operators to precisely predict and forecast PV power. The conventional forecasting methods were accurate for clean weather. But when the PV plants worked under heavy haze, the radiation is negatively impacted and thus reducing PV power; therefore, to deal with haze weather, Air Quality Index (AQI) is introduced as a parameter to predict PV power. AQI, which is an indication of how polluted the air is, has been known to have a strong correlation with power generated by the PV panels. In this paper, a hybrid method based on the model of conventional back propagation (BP) neural network for clear weather and BP AQI model for haze weather is used to forecast PV power with conventional parameters like temperature, wind speed, humidity, solar radiation, and an extra parameter of AQI as input. The results show that the proposed method has less error under haze condition as compared to conventional model of neural network

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm