Abstract-Photovoltaic systems are often installed in climates with considerable amount of snowfall and freezing rain in winter. It has been observed that the snow accumulation on a solar panel affects its performance and decreases the energy output. Snow on solar panels should be cleared as soon as possible to generate the maximum power. A low cost method of snow detection on solar panels found on field tests is proposed in this paper. The designed system is based on a low cost open-source Arduino Uno microcontroller that measures voltage and current output of a solar panel, and output of a LDR representing the irradiance. Arduino is also connected to a WIFI network and can send messages over the internet. Based on the sensors data, an algorithm is designed to accurately detect snow on solar panels and notify the owner via twitter about the current status. The designed low cost and very low power system has been tested in St. John's, Newfoundland, Canada (47°34'28.9"N 52°44'07.8"W) for three months of winter 2014. This paper presents details of the designed low cost alert system, algorithm and its performance results.Index Terms-Snow detection, Arduino application, melting performance, solar energy, renewable energy. I. INTRODUCTIONSolar panels are often installed in areas that receive some snow fall during winter months. In lower temperatures solar panels produced more output power due to reduced internal losses. Roughly 74% of PVs are installed in countries that experience some amount of snowfall [1], [2]. Energy reduction from a snow covered module can occur in three ways; a) diffusion of short wave through snow, b) Albedo reflection to the exposed rear of a module, and c) conduction from parts of PV not covered with snow. Although Panels are not physically damaged by the severe winter conditions, snow accumulation or ice could lead to decrease in energy output as long as panels are covered by precipitation. Published studies show that depending on orientation of PV modules and meteorological factors, the snow losses from a PV system can be as high as 20% for a low profile system to 0.3-2.7% for a highly exposed 28 degree roof mounted system [3], [4]. Therefore, solar panels should be cleared as soon as possible. Snow removal can be done in several ways. For instance, increasing tilt angle using a stepper motor not only would lead to slide the gathered snow, but also steeper tilt angles cause less snow to accumulate and therefore less power loss due to snowfall [2]. Furthermore, it was observed that snow shedding might occur due to sunlight or rise of temperature. Snow shedding takes place in form of either melt on the modules (see Fig. 1(a)) or sheet sliding (see Fig. 1(b)) as some incident radiation would penetrate the layer of snow and melt the snow-module layer to produce a water layer leading to snow sliding. II. PREVIOUS STUDIES AND WORKSThere are some studies on effects of snowfall on PV systems.In 1979 at Natural Bridges National Monument a simple linear empirical correlation to determine expected P...

The transition to a new low emission energy future results in a changing mix of generation and load types due to significant growth in renewable energy penetration and reduction in system inertia due to the exit of ageing fossil fuel power plants. This increases technical challenges for electrical grid planning and operation. This study introduces a new decomposition approach to account for the system security for short term planning using conventional machine learning tools. The immediate value of this work is that it provides extendable and computationally efficient guidelines for using supervised learning tools to assess first swing transient stability status. To provide an unbiased evaluation of the final model fit on the training dataset, the proposed approach was examined on a previously unseen test set. It distinguished stable and unstable cases in the test set accurately, with only 0.57% error, and showed a high precision in predicting the time of instability, with 6.8% error and mean absolute error as small as 0.0145.

To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics. The domain knowledge on how the disturbance effect will propagate from the fault location to the rest of the network is leveraged to recognise the dominant conditions that determine the stability of a system. Accordingly, we introduce a new concept called Fault-Affected Area, which provides crucial information regarding the unstable region of operation. This information is embedded in an augmented dataset to train an ensemble model using an instance transfer learning framework. The test results on the IEEE 39bus system verify that this model can accurately predict the stability of previously unseen operational scenarios while reducing the risk of false prediction of unstable instances compared to standard approaches.

This paper aims to study the impact of the Fermeuse wind farm (46˚58'42"N 52˚57'18"W) through simulation of wind turbines driven by doubly fed induction generator which feed AC power to the isolated utility grid of Newfoundland. The focus is on the determination of both voltage and system stability constraints. The complete system is modeled and simulated in Matlab Simulink environment.

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