Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection
Introduction
The monitoring and regular performance supervision on the functioning of grid-connected photovoltaic (GCPV) systems is necessary to ensure an optimal energy harvesting and reliable power production. The development of diagnostic methods for fault detection in the PV systems behavior is particularly important due to the expansion degree of GCPV systems nowadays and the need to optimize their reliability and performance.
There are existing techniques which were developed for possible fault detection in grid-connected PV systems. Some of these techniques use meteorological and satellite data for predicting the faults in the GCPV plants [1], [2]. However, some of the PV fault detecting algorithms do not require any climate data (solar irradiance and module temperature) such as the earth capacitance measurements established by Taka-Shima [3].
Other PV fault detection algorithms is based on the comparison of simulated and measured yield by analysing the losses of the DC side of the GCPV plant [4], [5], [6]. Furthermore, a fault detection method based on the ratio of DC side and the AC side of the PV system is proposed by W. Chine et al. [7]. The method can detect five different faults such as faulty modules in a PV string, faulty DC/AC inverter and faulty maximum power point tracking (MPPT) units. On the other hand, S. Silvestre et al. [8] proposed a new procedure for fault detection in GCPV systems based on the evaluation of the current and the voltage indicators. The main advantage of this algorithm is to reduce the number of monitoring sensors in the PV plants and integrating a fault detection algorithm into an inverter without using simulation software or additional external hardware devices.
Further fault detection algorithms focus on faults occurring in the AC-side of GCPV systems, as proposed by M. Dhimish et al. [9]. The approach uses mathematical analysis technique for identifying faulty conditions in the DC/AC inverter units. Moreover, hot-spot detection in PV substrings using the AC parameters characterization was developed by Ref. [10]. The hot-spot detection method can be further used and integrated with DC/DC power converters that operates at the subpanel level. A comprehensive review of the faults, trends and challenges of the grid-connected PV systems is shown in Refs. [11], [12], [13].
Other PV fault detection approaches use statistical analysis techniques for identifying micro cracks and their impact of the PV output power as presented by Ref. [14]. However, T. Zhao at al [15] developed a decision tree (DT) technique for examining two different types of fault using an over-current protection device (OVPD). The first type of fault is the line-to-line that occurs under low irradiance conditions, and the second is line-to-line faults occurring in PV arrays equipped with blocking diodes.
PV systems reliability improvement by real-time field programmable gate array (FPGA) based on switch failures diagnosis and fault tolerant DC-DC converters is presented by Ref. [16]. B. Chong [17] suggested a controller design for integrated PV converter modules under partial shading conditions. The developed approach is based on a novel model-based, two-loop control scheme for a particular MIPC system, where bidirectional Cuk DC-DC converters are used as the bypass converters and a terminal Cuk boost functioning as a while system power conditioner.
Nowadays, fuzzy logic systems widely used with GCPV plants. R. Boukenoui et al. [18] proposed a new intelligent MPPT method for standalone PV system operating under fast transient variations based on fuzzy logic controller (FLC) with scanning and storing algorithm. Furthermore [19], presents an adaptive FLC design technique for PV inverters using differential search algorithm. Furthermore, N. Sa-ngawong & I. Ngamroo [20] proposed an intelligent PV farms for robust frequency stabilization in multi-area interconnected power systems using Sugeno fuzzy logic control, similar approach was developed by Ref. [21] for power optimization in standalone PV systems.
In Refs. [22], [23] authors have used a Mamdani fuzzy logic classification system which consists of two inputs, the voltage and power ratio, and one output membership function. The results can accurately detect several faults in the PV system such as partial shading and short circuited PV modules.
Artificial intelligent networks (ANN) is another machine leaning technique nowadays is used for detecting faults in PV systems. A learning method based on expert systems is developed by Ref. [24] to identify two types of fault (due to the shading effect and to the inverter's failure). Whereas [25] proposed an ANN network that detects faults in the DC side of PV systems which includes faulty bypass diodes and faulty PV modules in a PV string.
A. Millit et al. [26] shows that ANN networks is a possible solution for modelling and estimating the output power of a GCPV systems. However, a failure mode prediction and energy harvesting of PV plants to assist dynamic maintenance tasks using ANN based models is proposed by F. Polo et al. [27]. Further investigation on a very short term load forecasting for a distribution system with high PV penetration is suggested by S. Sepasi [28]. Finally, B. Amrouche & X. Pivert [30] offered an ANN network based daily local forecasting for global solar radiation (GHI). The ANN model is developed to predict the local GHI based on a daily weather forecast provided by the US National Oceanic and Atmospheric Administration (NOAA) for four neighbouring locations.
The main contribution of this work is to present a new algorithm for isolation and identification of the faults accruing in a PV system. The algorithm is capable to detect several faults such as faulty PV module in a PV string, faulty PV string, faulty MPPT, and partial shading conditions effects the PV system. The proposed algorithm is comparing between two different approaches for detecting failure conditions which can be described as the following:
- 1.
Artificial Neural Network (ANN) Approach:
Four different ANN networks have been compared using a logged data of several faulty conditions affecting the examined PV plant. The maximum PV fault detection accuracy achieved by the ANN networks is equal to 92.1%.
- 2.
Fuzzy Logic Fault Classification Approach:
This approach consists of two types of fuzzy logic interface systems: Mamdani and Sugeno. Both fuzzy interface systems were briefly compared and developed using MATLAB/Simulink software. This approach was tested using a faulty PV data which was logged from the examined 1.1 kWp PV plant installed at the University of Huddersfield.
The overall system design is shown in Fig. 1. The PV plant has a capacity of 1.1 kWp. A computer interface has two options, a PV fault detection algorithms which use MATLAB/Simulink software which contains the ANN and the fuzzy logic interface system. Furthermore, LabVIEW software is used for the real-time long-term data monitoring as well as, data logging software environment.
This paper is organized as follows: Section 2 presents the data acquisition in the PV plant. Section 3 describes the methodology used, Fault detection algorithm and diagnosis rules are presented, while section 4 lists the results and discussion of the work. Finally, section 5 describes the conclusion and future work.
Section snippets
Faults in photovoltaic plants
The faults occurring in a PV system are mainly related to the PV array, MPPT units, DC/AC inverters, the storage system and the electrical grid. This work aims to detecting the faults occurring in the PV array and, with reference to Table 1, eleven different fault are investigated.
It is worthy to mention that PS conditions used in this work corresponds to an irradiance level affects all examined PV modules. Thus, during the experiments, all examined PV modules were tested under the same PS
Methodology
This section reports the PV data acquisition system, PV theoretical modelling, the overall fault detection algorithm, and the detailed design of the proposed artificial neural network and the fuzzy logic interface system.
Results and discussion
This section reports the results of the developed fault detection algorithm. Furthermore, a comparison between the developed machine learning techniques with some ANN and fuzzy logic systems obtained by various researchers is briefly explained in Section 4.4 (discussion section).
Conclusion
This paper presents a new photovoltaic (PV) fault detection algorithm which comprises both artificial neural network (ANN) and fuzzy logic system interface. The algorithm is capable for detecting various fault occurring in the PV system such as faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. Both machine learning techniques was validated using a 1.1 kWpp PV plant installed at the University of Huddersfield, United Kingdom.
The fault detection
References (37)
- et al.
Improving the performance of PV systems by faults detection using GISTEL approach
Energy Convers. Manag.
(2014) - et al.
A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy
Sol. Energy
(2010) - et al.
Experimental studies of fault location in PV module strings
Sol. Energy Mater. Sol. Cells
(2009) - et al.
Fault detection algorithm for grid-connected photovoltaic plants
Sol. Energy
(2016) - et al.
Fault detection method for grid-connected photovoltaic plants
Renew. Energy
(2014) - et al.
New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators
Energy Convers. Manag.
(2014) - et al.
Parallel fault detection algorithm for grid-connected photovoltaic plants
Renew. Energy
(2017) - et al.
Trends and challenges of grid-connected photovoltaic systems–A review
Renew. Sustain. Energy Rev.
(2016) - et al.
A review of islanding detection techniques for renewable distributed generation systems
Renew. Sustain. Energy Rev.
(2013) - et al.
Controller design for integrated PV–converter modules under partial shading conditions
Sol. Energy
(2013)