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In this paper, we explain the design and implementation of an intelligent irrigation control system based on fuzzy logic for the automatic control of water pumps used in farms and greenhouses. This system enables its user to save water and electricity and prevent over-watering and under-watering of the crop by taking into account the climatic parameters and soil moisture. The irrigation system works without human intervention. The climate sensors are packaged using electronic circuits, and the whole is interfaced with an Arduino and a Simulink model. These sensors provide information that is used by the Simulink model to control the water pump speed; the speed of the water pump is controlled to increase or decrease the amount of water that needs to be pushed by the pump. The Simulink model contains the fuzzy control logic that manages the data read by the Arduino through sensors and sends the command to change the pump speed to the Arduino by considering all the sensor data. The need for human intervention is eliminated by using this system and a more successful crop is produced by supplying the right amount of water to the crop when it is needed. The water supply is stopped when a sufficient amount of moisture is present in the soil and it is started as soon as the soil moisture levels drops below certain levels, depending upon the environmental factors.

The integration of solar photovoltaic (PV) systems and utility grids has gradually gained significant interest in improving the sustainability of clean power supply for society. However, power quality remains a challenge due to partial shading conditions and harmonics. To overcome these drawbacks, a flexible radial movement optimization based on a dynamic safety perimeter maximum power point tracking algorithm is employed to track maximum power out of a PV system and to ensure that the optimum voltage level at the common DC bus is obtained under partial shading conditions using fixed-tilt installation configuration. Furthermore, a novel inverter control loop system with a double second order generalized integrator phase-locked loop (DSOGI-PLL) is also proposed to mitigate harmonics and improve the power quality of the grid interfacing PV system using MATLAB SIMULINK software. The proposed system has several merits, such as better harmonic suppression capability, control adaptivity, rapid tracking speed, low computational burden and phase and grid synchronization.

Unplanned power plant failures have been seen to be a major cause of power shortages, and thus customer power cuts, in the South African power grid. These failures are measured as the unplanned capability loss factor (UCLF). The study of South Africa’s UCLF is almost non-existent. Parameters that affect the future UCLF are, thus, still not well understood, making it challenging to forecast when power shortages may be experienced. This paper presents a novel study of South African UCLF forecasting using state-of-the-art deep learning techniques. The study further introduces a novel deep learning ensemble South African UCLF forecasting system. The performance of three of the best recent forecasting techniques, namely, long short-term memory recurrent neural network (LSTM-RNN), deep belief network (DBN), and optimally pruned extreme learning machines (OP-ELM), as well as their aggregated ensembles, are investigated for South African UCLF forecasting. The impact of three key parameters (installed capacity, demand, and planned capability loss factor) on the future UCLF is investigated. The results showed that the exclusion of installed capacity in the LSTM-RNN, DBN, OP-ELM, and ensemble models doubled the UCLF forecasting error. It was also found that an ensemble model of two LSTM-RNN models achieved the lowest errors with a symmetric mean absolute percentage error (sMAPE) of 6.43%, mean absolute error (MAE) of 7.36%, and root-mean-square error (RMSE) of 9.21%. LSTM-RNN also achieved the lowest errors amongst the individual models.

Recently, the awareness of the severe consequences of greenhouse gases on the environment has escalated. This has encouraged the world to reduce the usage of fossil fuels for power generation and increase the use of cleaner sources, such as solar energy and wind energy. However, the conventional power system itself was designed as a passive power system, in which power generation is centralised, and power flows from substations towards the loads. Decentralised renewable energy sources, also called distributed generators, were introduced to create an active power system in which power generation can occur anywhere in the power system. Decentralised power generation creates challenges for the conventional power system, such as voltage fluctuations, high voltage magnitudes, reverse power flow, and low power factor. In this paper, an adaptive control system that coordinates different distributed generators for voltage regulation and power factor correction is introduced and designed. The control system will decrease the total reactive power that flows in the transmission network through a reactive power exchange between distributed generators. Therefore, power factor will improve, power system losses will reduce, and the total apparent power on lines will reduce, giving more room to active power to flow. The results obtained showed that the control system is effective in regulating voltage and improving the power factor when multiple distributed generators are connected.

Integrating cognitive radio into the current power grid is designed to enable smart communication and decisions within the grid. Communication within the grid is not feasible without channel(s) and most studies have emphasized the use of cellular spectrum. This study proposes a strategy that enables the use of television white space (TVWS) within the grid. To be specific, we propose using a next-generation utility network (Next-GUN), which leverages on the cognitive radio (CR) channel aggregation capability. This strategy enables the aggregation of idle TVWS into a usable channel, thus making the Next-GUN different from the traditional power network. Next-GUN differs in terms of security, reliability, self-awareness, and cross-layer compatibility to the interface, and conveys different traffic classes, thereby making it a hybrid system. It has no dedicated channel assigned to it, and hence, utilizes the idle TVWS opportunistically to transmit data. The proposed scheme was modelled as a Markovian process and analyzed using a continuous time Markov chain (CTMC). Extensive system simulations were performed to evaluate this proposed model and the corresponding comparison with the literature was done to see the improvement. The result of our comparisons shows that when channels are aggregated, more data/information are transmitted. In addition, the use of cognitive radios on a power network enables smart transaction because idle TVWS is utilized instead of congesting the GSM spectrum. Lastly, the power utility establishment can save the cost of paying for a licensed spectrum.