In home energy management, the occupants schedule the operating appliances to achieve lowest optimal energy cost with minimum discomfort. Smart home energy management turns out to be challenging due to the penetration of rooftop intermittent solar power generations. This article develops a multi-objective and real time smart residential load management and hence energy management topology considering variation in rooftop solar power generation with variation in price of distribution grid energy. Primarily, the optimization problem is established as a time average stochastic optimization formulation and then simplified to mixed integer linear programming (MILP) using Lyapunov optimization. The energy cost minimization objective is added with ocular and thermal discomfort minimization which also lowers the discomfort level for occupants due to actions caused by real time load management. The home loads are managed by an Internet-of-Things (IoT) based controller which also alerts the occupants for any incipient malfunction in the household loads. The proposed control technique is authenticated on a real-life smart residential data with simulations and experiments. The resultant controller can thus optimally control the smart residential loads in real time.

With the advancement of smart residential buildings, houseowners are increasingly interested to monitor the connected electrical loads in view of energy conservation and load management. Smart grid technology in the distribution system includes cutting-edge load monitoring applications, permitting efficient use of electrical energy. Nonintrusive load monitoring (NILM) technique is such a tool to recognize the consumption of energy for individual electrical loads in a residential system at a solitary point of measurement. In this article, an improved NILM method is proposed using a shunt passive filter installed at the source end of residential building with the help of current harmonic signature analysis. A fuzzy rule-based intelligent identification method is employed in order to detect and monitor different electrical loads considering various load features at the utility side. The major contribution of the proposed technique is the improvement in identification accuracy of household loads with multiple appliances. The connected filter additionally restricts the injected harmonics generated by the loads at the source end. The usage of the fuzzy technique also reduces the processor memory requirement than conventional machine learning techniques. Simulations backed by experimental results validate the viability of the proposed methodology.

In this paper, an innovative procedure for residential electrical load monitoring applicable to smart meters is proposed based on a modified decisive multi-objective optimization. In this procedure, different load features are extracted from household loads for optimization-based monitoring. Each load monitoring feature reflects an objective function, which is also simultaneously minimized using modified Artificial Bee Colony (ABC) algorithm. This technique is not heavily dependent on high amount of training data which is essential for the most machine learning techniques with some modification in choosing the initial search space. The proposed technique uses with real life raw data using low sampling rates. An Internet of Things (IoT) based approach is also implemented for remote monitoring of connected loads in the system along with monitoring. The proposed technique is user friendly, requires only load signature data for verification purpose which can be easily extracted. The paper demonstrates event-based appliance load monitoring and comparison with benchmark dataset which shows marked improvement over existing state-of-the-art-techniques.

Brushless DC (BLDC) motors are widely used in various low‐power drive applications owing to their several advantages over conventional motors. This study describes a fuzzy logic‐based improved loss minimisation technique for the BLDC motor drive system. The proposed algorithm uses selective harmonic elimination‐based pulse‐width modulation to optimise both the converter switching losses along with machine core and copper losses based on a speed‐ and load‐dependent fuzzy rule matrix. The optimum switching angles eliminating lower‐order harmonics are calculated offline and stored in microcontroller memory for online application. The suitability of the proposed control is well demonstrated through simulation and experimental results on a practical 350 W BLDC machine.

This article presents a new switching technique to minimize losses in a trapezoidal brushless dc motor (BLDCM) drive. Based on the analysis, a pulsewidth modulation (PWM) strategy with selective harmonics elimination (SHE) is projected for motor phase current for dynamic loss minimization of BLdc motor drive system. The proposed control technique uses SHEPWM to optimize PWM to minimize different losses at different loads and motor speeds based on a predefined look-up table. The efficacy of the control methodology is presented using proper simulation results in MATLAB/Simulink environment. The control methodology is also validated with an experimental prototype of 350W BLDCM drive system.

An improved control methodology for off‐grid, three‐phase induction machine working as a single‐phase generator is presented in this study. A three‐phase machine made to work as a single‐phase generator has advantages of lower size and cost than their single‐phase equivalent in almost all ratings except in the lower fractional kilowatt (kW) ranges. The three‐phase windings are configured as isolated two equivalent phases in the proposed system for this purpose. The machine uses photovoltaic (PV)‐assisted single‐phase inverter to one of its two phases for variable excitation requirement to cater dynamic loads at different rotor speeds besides having a fixed capacitor to provide bulk excitation. A storage battery is used along with the PV panels to improve the reliability of the scheme during low or no insolation periods. A stator‐flux‐oriented control is implemented for regulating the terminal voltage within specified limit at a fixed frequency. The control strategy ensures fixed load voltage and fixed frequency even with perturbations of load or rotor speed. Different simulation results, verified by suitable experiments using a 1 kW, 415 V laboratory prototype, confirm the suitability of the proposed concept.