In order to solve the problems caused by the susceptibility to changing weather conditions and the complex load conditions of photovoltaic (PV) systems, and the fact a single target inverter control strategy cannot effectively mitigate large voltage fluctuations at point of common coupling (PCC), an integrated reactive power control strategy for PV inverters is proposed. According to the weather and load conditions, this strategy is divided into four control modes: normal operation control mode, reverse power control mode, cloudy control mode and night control mode. The four control modes switch between each other under the specific switching rules to ensure an appropriate quantity of reactive power injection or consumption in the PV inverter. The effects of the four control modes in isolated operation were significant. The deviation rate of PCC voltage from its standard value was mitigated at about 2% under the integrated control strategy. Therefore, the proposed control strategy has a high application value in improving power quality and maximizing utilization of PV inverters.

This paper aims to broaden the scope of analysis of the contemporary global land rush by examining crop booms not only outside, but inside China; and investment flows not only from China, but also within and into China. It does so by examining the eucalyptus and sugarcane sectors in southern China, which have witnessed investment booms during the past decade, with capital being infused by both domestic capital and foreign capital, including Finnish, Indonesian, and Thai companies. Our argument addresses three key issues: (a) explaining why foreign and domestic companies enter into a multitude of lease and grower contracts involving holders of micro-plots, (b) revisiting the notion of extra-economic coercion, and (c) a critique of thinking about flows of large-scale investments centred primarily on nationality. These issues are central in current debates in the land grabs literature, and our study offers a different perspective from dominant narratives.

It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

Anomaly detection can improve the service level of the grid, effectively save human resources and reduce the operating cost of a power company. In this study, an improved arithmetic optimization-backpropagation (IAOA-BP) neural algorithm for an anomaly detection model was proposed for electricity inspection. The dynamic boundary strategy of the cosine control factor and the differential evolution operator are introduced into the arithmetic optimization algorithm (AOA) to obtain the improved arithmetic optimization algorithm (IAOA). The algorithm performance test proves that the IAOA has better solving ability and stability compared with the AOA, WOA, SCA, SOA and SSA. The IAOA was subsequently used to obtain the optimal weights and thresholds for BP. In the experimental phase, the proposed model is validated with electricity data provided by a power company. The results reveal that the overall determination accuracy using the IAOA-BP algorithm remains above 96%, and compared with other algorithms, the IAOA-BP has a higher accuracy and can meet the requirements grid supervision. The power load data anomaly detection model proposed in this study has some implications that might suggest how power companies can promote grid business model transformation, improve economic efficiency, enhance management and improve service quality.

Electricity inspection is important to support sustainable development and is core to the marketing of electric power. In addition, it contributes to the effective management of power companies and to their financial performance. Continuous improvement in the penetration rate of new energy generation can improve environmental standards and promote sustainable development, but creates challenges for electricity inspection. Traditional electricity inspection methods are time-consuming and quite inefficient, which hinders the sustainable development of power firms. In this paper, a load-forecasting model based on an improved moth-flame-algorithm-optimized extreme learning machine (IMFO-ELM) is proposed for use in electricity inspection. A chaotic map and improved linear decreasing weight are introduced to improve the convergence ability of the traditional moth-flame algorithm to obtain optimal parameters for the ELM. Abnormal data points are screened out to determine the causes of abnormal occurrences by analyzing the model prediction results and the user’s actual power consumption. The results show that, compared with existing PSO-ELM and MFO-ELM models, the root mean square error of the proposed model is reduced by at least 1.92% under the same conditions, which supports application of the IMFO-ELM model in electricity inspection. The proposed power-load-forecasting-based abnormal data detection method can improve the efficiency of electricity inspection, enhance user experience, contribute to the intelligence level of power firms and promote their sustainable development.

The development of smart power systems in Industry 4.0 will bring people better quality electrical energy. The photovoltaic power generation system in the smart grid for Industry 4.0 is also the focus of development. The rapid development of photovoltaic power generation systems will bring people cleaner and greener energy. However, it will cause the transient overvoltage phenomenon near the DC sending terminal when the large-scale photovoltaic plant is connected to the power grid, further bringing about a profound impact on the power grid and its security and stability. In this paper, it is proposed that the rise of transient overvoltage at the photovoltaic terminal is caused by three main factors including the reactive power surplus of DC commutation failure, the weak power grid at the sending terminal, and the control characteristics of the photovoltaic plant. Then, the coordinated control strategy of transient overvoltage is proposed, and the overvoltage control strategy of the DC transmission terminal and the photovoltaic power plant control strategy are, respectively, optimized. Finally, the influence parameters of overvoltage are analysed and the corresponding photovoltaic strategy is presented. The optimization results of photovoltaic parameters and the proposed strategies of photovoltaic power generation can effectively restrain the voltage or frequency fluctuation and power oscillation caused by HVDC commutation failure. This study promotes the development of the photovoltaic power generation system in the intelligent power system of industry 4.0.