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.

Rapid industrial development and innovations in technology bring about the menace of congestion in deregulated power systems (DPS). The transmission lines are continuously working under a stressed condition with reduced power transfer capacity. In this situation, the power losses and voltage deviations at the load buses are increased and hence reduce the system stability. To mitigate congestion, improving available transfer capability (ATC) of the transmission system is one of the most feasible and practical solution. This paper focuses on the implementation of Thyristor Controlled Series capacitor (TCSC) to mitigate congestion by enhancing ATC and via reducing power losses. AC Power Transfer Distribution Factor (ACPTDF) is applied to calculate ATC and to find the location of TCSC. To optimize the TCSC parameter (reactance), a Teaching Learning Based Optimization (TLBO) is proposed in the present work. The proposed optimization is validated on the IEEE 30 Bus system. The results are validated by matching with the results obtained through standard grey wolf optimization (GWO) and particle swarm optimization (PSO) techniques. The results show that despite two antagonistic objectives of ATC enhancement and power loss reduction, TLBO outperformed the other optimization techniques under different contingency conditions. The overall ATC of the IEEE 30 Bus system for the bilateral transactions is enhanced by 11.86%. Further active and reactive power losses are reduced by 16.7% and 29.6% for DPS.

The modern power system has reached its present state after wading a long path facing several changes in strategies and the implementation of several reforms. Economic and geographical constraints led to reforms and deregulations in the power system to utilize resources optimally within the existing framework. The major hindrance in the efficient operation of the deregulated power system (DPS) is congestion, which is the result of the participation of private players under deregulation policies. This paper reviews different setbacks introduced by congestion and the methods applied/proposed to mitigate it. Technical and non-technical methods are reviewed and detailed. Major optimization techniques proposed to achieve congestion alleviation are presented comprehensively. This paper combines major publications in the field of congestion management and presents their contribution towards the alleviation of congestion.