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This study evaluates the daily, monthly, and annual performance of roof-top 200 KWp grid-interactive solar PV power plants installed atop Sri Vishnu Educational Society buildings in India. This plant generated ~ 300,000 units/ year, with a maximum yielding of ~800 KWh/ day during summer (March-June) and a minimum during the rainy season (July-September, ~ 600 KWh/ day). 1237 ton of CO2 emissions were avoided and $2,10,000 was made. As a result, the payback took 7-8 years to complete. The statistical study revealed a minimum (1%-15%) drop in power yielding, which indicates this plant used high-standard solar cells (polycrystalline), sophisticated inverters, top-quality molded case circuit breakers, and others. The optimal level of power output generation, V-I characteristics and power and economic graphs were predicted using a simulation study. Inter-institutional comparisons made with new 302.4 KWp power plants show an identical daily pattern, albeit 302.54 KWp power plant yieldings oftentimes show marginal magnitudes.

Detecting cracks from images using embedded deep learning applications requires efficient and lightweight models in practice. To improve the computational efficiency of models, it is generally aim to reduce the model parameters as much as possible without compromising accuracy. Computational approaches ensure consistency in crack detection across different inspections and operators. Computational methods enable continuous monitoring, including real-time or periodic inspections. The proposed work seeks to leverage the latest deep-learning techniques to get the maximum information out of a minimum number of parameters. The present semantic segmentation-based model - CrackJPU, uses deep hierarchical feature learning convolution networks. Deeply-Supervised Nets (DSN) and JPU (Joint Pyramid Upsampling) modules are also used to supervise the model at multiple inner side-output layers and facilitate retrieval of lower resolution features at decoding layers respectively. To refine the prediction result, the guided filtering method is used. The proposed model has been trained on a standard dataset of annotated crack images. The experimental finding shows that, the model has less than 7 million parameters which are the least compared to recent work without losing performance. Also a mean I/U score of 98.78 and the best F-score is 86.4 is achieved with reduction model parameters. Crack detection is significant in various fields like infrastructure inspection, aerospace industry, Manufacturing Quality Control etc. due to its potential impact on safety, infrastructure integrity, and overall system reliability.

Induction machines (IMs) are one of the most popular electric machines due to their many advantages. It is well known that the IM can be utilized as a motor and generator at below and above synchronous speeds, respectively. However, an induction generator (IG) in the off-grid mode is not a self-started generator. A proper rating of the reactive power supply to the core of the generator and maintaining the rotor speed above the synchronous speed are key challenges in the off-grid mode for exciting the IG to generate the voltage and frequency at desired values. The voltage and frequency are dependent on the amount of reactive power supplied to the stator of the generator and the rotor speed. Since these two factors play a significant role, a key finding is presented in this paper by conducting various experiments on a three-phase IM and investigate the allowable upper and lower limits to avoid over and under-excitation issues during the self-excitation process. Additionally, different methods for the calculation of the required reactive power have been studied in this paper to identify the best method. The proposed study is advantageous for making effective use of IM as an IG for isolated rural applications.

The research and usage of electric vehicles (EVs), including two and four-wheeler vehicles, are rapidly increasing worldwide as alternatives to oil/gas-based vehicles. Brushless direct current (BLDC) motors are popular for industrial and traction applications due to their inherent advantages. In EVs, achieving low error in steady-state and transient responses is crucial for smooth acceleration at the wheel. This paper presents the design and control of a BLDC motor for speed control during acceleration and deceleration, considering error as a key factor in the MATLAB/Simulink environment. Proportional-integral (PI) and fuzzy controllers are commonly used for motor control to improve steady-state and transient performance, thereby reducing error. In this study, the PI and adaptive neuro-fuzzy inference system (ANFIS) controllers are designed and compared for a 5-kW, 48-V, and 100-Amp BLDC motor in EV applications. The results demonstrate that the ANFIS controller enhances the dynamic performance of the BLDC motor and improves other operating characteristics such as rise time, settling time, peak overshoot percentage and the vehicle response in terms of speed and distance.

Permanent magnet synchronous generator (PMSG) is one of the promising solutions for large power generation systems due to its many advantages. The off-grid and micro/pico-hydro power stations are located near to the loads and equipped with constant speed/power turbine sources. In this paper, an attempt is made to design, develop, and deploy a three-phase PMSG generating system for the small scale off-grid dedicated load applications. In this experimental study, a constant speed source, which is an emulation of a micro/pico-hydro turbine is used as the prime mover to the generator. The prime mover is mechanically coupled to the rotor of the generator and which is controlled by an AC driver to make the PMSG run at synchronous speed for all the time of the experiment to generate the required voltage and frequency. The performance of the three-phase PMSG is measured and analyzed so that the proposed system obeys the good power quality for the pre-defined various maximum loading conditions.

-Asynchronous machines/Induction machines are one of the leading machines in the electrical sector and which are well suitable for the motoring and generating applications of a few kilowatts rating to megawatt ratings. Research on induction machines for generating applications is drawing attention among the researchers due to its low cost, rugged construction, brushless arrangement and all. The operation of induction generator (IG) is quite interesting and which is different for grid and off-grid mode of operation. The IG for the off-grid applications (so called self-excited induction generator, SEIG) requires an additional reactive power supply for the successful build-up of voltage and as well as for supplying the electric loads. On the other hand, load power balance is also one the challenges since off-grid systems are directly connected to the loads. In this context, many researches have been reported the different approaches and techniques for the successful and economical operation of IG for small scale off-grid power generation. In this paper, an exclusive analytical study is presented on the excitation requirement and load balance phenomena of self-excited induction generator (SEIG). Further, a basic experimental setup of renewable energy source driven SEIG system and its operation for the electric power generation is demonstrated and the same is simulated by using MATLAB/Simulink.