Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors

This work proposes a method for real-time supervision and predictive fault diagnosis applicable to solar panel strings in real-world installations. It is focused on the detection and parametric isolation of fault symptoms through the analysis of the Voc-Isc curves. The method performs early, systematic, online, automatic, permanent predictive supervision, and diagnosis of a high sampling frequency. It is based on the supervision of predictive electrical parameters easily accessible by the design of its architecture, whose detection and isolation precedes with an adequate margin of maneuver, to be able to alert and stop by means of automatic disconnection the degradation phenomenon and its cumulative effect causing the development of a future irrecoverable failure. Its architecture design is scalable and integrable in conventional photovoltaic installations. It emphasizes the use of low-cost technology such as the ESP8266 module, ASC712-5A, and FZ0430 sensors and relay modules. The method is based on data acquisition with the ESP8266 module, which is sent over the internet to the computer where a SCADA system (iFIX V6.5) is installed, using the Modbus TCP/IP and OPC communication protocols. Detection thresholds are initially obtained experimentally by applying inductive shading methods on specific solar panels.

Parametric Fault Diagnosis of Very High-Frequency Circuits Containing Distributed Parameter Transmission Lines

Parametric fault diagnosis of analog very high-frequency circuits consisting of a distributed parameter transmission line (DPTL) terminated at both ends by lumped one-ports is considered in this paper. The one-ports may include linear passive and active components. The DPTL is a uniform two-conductor line immersed in a homogenous medium, specified by the per-unit-length (p-u-l) parameters. The proposed method encompasses all aspects of parametric fault diagnosis: detection of the faulty area, location of the fault inside this area, and estimation of its value. It is assumed that only one fault can occur in the circuit. The diagnostic method is based on a measurement test arranged in the AC state. Different approaches are proposed depending on whether the faulty is DPTL or one of the one-ports. An iterative method is modified to solve various systems of nonlinear equations that arise in the course of the diagnostic process. The diagnostic method can be extended to a broader class of circuits containing several transmission lines. Three numerical examples reveal that the proposed diagnostic method is fast and gives quite accurate findings.

Hybrid Qualitative and Quantitative Diagnosis Approach

Parametric faults are detected and isolated using parameter tracking algorithms based on optimization algorithms or filtering techniques (e.g., Kalman filter, particle filter). Online, simultaneous tracking of all parametric faults can fails since there may be too many combinations of parameter values that explain the observed behavior. Hence, a correct diagnosis solution is not obtained. An alternative in the single fault case is to track separately each parametric fault in parallel and choose the one that best explains the observed behavior according to some chosen metric (e.g., mean square error). This approach is feasible but computationally expensive, since there may be too many tracking algorithms running in parallel. We propose using analytic redundancy relations (ARRs) to reduce the number of parametric faults that are tracked simultaneously. ARRs qualitatively point to a set of possible explanations but usually require a large number of sensors to achieve good isolability of fault causes. They induce a fault signature matrix (FSM) that can be derived offline. The parameter tracking algorithms will be instantiated for the faults in the set of possible explanations produced by the ARRs. By combining ARRs with online parameter tracking algorithms we can obtain a good tradeoff between computational effort and fault isolability. We demonstrate our approach by diagnosing faults in a rectifier circuit.