The ideal of monitoring is to provide a service to users to supervise and diagnose their installations against the main defects (heating of cells, different performance modules, degradation of interconnections) [1], and above all, to have optimal management between production and demand [2].

An adequate arrangement of all the elements of the hybrid PV/diesel microgrid device requires the implementation of an appropriate configuration. I give below, the illustrative Figure 1 of a configuration [3] of hybrid PV/diesel microgrid subject to data monitoring.

Figure 1 gives a general arrangement of the various elements of the system studied.

Optimization is a process of improving the performance of the device in question,

it responds to a certain logic. This is how there are several criteria [4], which characterize optimization processes. Among these cases, I cite two major forms of optimization:

· Optimization based on an improvement in the efficiency of the internal structure of the system (manufacturing process of the components of the system).

· Optimization based on an improvement in the efficiency of the management or arrangement of the different elements of the system (operating process of the production of the system).

In the present study, it is the second form of optimization that is taken into account. In this practice of monitoring on PV/diesel, the control of a certain number of parameters is necessary for good monitoring. Table 1 below, gives an overview of its parameters.

The process must allow a good overall follow-up for a match between the power from the sunshine and the power calls on the hybrid generator (PV and generator). We give below, an illustration of the synoptic likely to guide a good practice of monitoring on the PV system.

Figure 2 illustrates the desired monitoring practice for an application on the hybrid PV/diesel system. Two phases of data collection or control of the system are envisaged. First, the installation of a data logger, which collects data from sunlight, the PV field and the battery. Then, the inverter, will be the second level of data collection. This inverter will have the ability to collect data from charges, battery and generator set. The inverter will be equipped with artificial intelligence [5], and will coordinate the power demand of the charges and the power inflows (of the PV and the generator). It must be able to control the start-up of the generator set in the event of a drop or lack of power of the PV and stop it in case of overproduction of the PV or a decrease in the power required by the loads.

I briefly describe the key criteria determining the factors of the study.

The autonomy of the PV/diesel system: in the case of renewable energies, the installation of storage to compensate for off-peak periods (nights, climatic hazards, etc.) is subject to significant costs that depend on the storage technology. The costs of storing in a PV system with batteries as storage technology can represent 40% of the total investment [7]. It should be noted that the addition of storage can aim at a goal of stability or security for the installation, we speak of management storage. For example, the injection of PV energy into power lines can cause a disturbance. Indeed, the power lines being sized for a unidirectional flow direction, then, a large flow of energy in the opposite direction (the injections of a photovoltaic power plant) causes overvoltages and generates significant degradation of the network, it is the phase shift effect between production and consumption [8].

The quality of the energy exported by the PV/diesel system: The main role of a power interface is to preserve the quality of the energy exported to consumers, regardless of the nature of the loads to be supplied (linear or not, balanced or not). Although there are no international standards specific to stand-alone configurations, the quality of the energy to be guaranteed must be (at least) similar to that imposed in the case of interconnected networks. The quality of the energy exported amounts to ensuring a quasi-sinusoidal voltage (with fixed amplitude and frequency) with a minimal harmonic content at the point of connection of the consumers. The quality of this energy is quantified by the low harmonic distortion rate (THD) which summarizes the disturbances of this voltage [8].

The stability of the energy produced by the PV/diesel system: The stability of a power system is the ability of an electrical energy system, for a given initial operating condition, to return to a balanced operating state after experiencing a physical disturbance. All this, keeping most of the variables of the system in their bounds, so that the integrity of the system is preserved. System integrity is preserved when the entire electrical system remains virtually intact, without the need to trigger generators or loads [9].

The contribution of the monitoring of the data of the PV/diesel system: It is a set of devices upstream of the PV systems which is intended for the monitoring and surveillance of the energy production from the PV system. The idea is to provide a “service” to users to monitor and diagnose their installations against the main defects [10], as well as the phase shifts between demand and supply, which are the sources of energy losses.

This is the characterization phase of the comparison factors. It aims to prioritize the comparison factors in order of criticality index [11]. The criticality of an event corresponds to the product of gravity, occurrence and non-detection. Criticality can be expressed by the following equation:

C = G × O × ND (1)

C: Criticality;

G: Severity;

O: Occurrence;

ND: Not detected.

The prioritization is given in order of increasing criticality, this means that the highest value corresponds to the strongest criticality. Table 2 below reads the scoring tools that will be used to determine the criticality index values.

In the rest of the work, the FMEA method (Analysis of failure modes of their Effects and Criticality) is used for the comparison. It is based on personal experience on the one hand, and on the other hand, on expert assessments as well as a bibliographic contribution. Table 3 below summarizes the assessments.

The completion of Table 3 shows the failure modes, causes and effects of the comparators. Regarding failure modes, it is noted that each of the factors is subdivided into two failure modes. The second factor, which is the quality of the energy produced by the system, also provides information on two failure modes. This factor suggests a failure on the high costs of storage means, as well as a failure in the choice of energy treatment filters.

For the autonomy factor, we note a failure on the costs for a production storage

(reserve for bad periods) of the energy and that on the management storage or transition storage. For the stability factor of the PV/diesel network, the failures are at the level of the nature of the sources of energy production, on the one hand, and on the other hand, the sources of the loads to be supplied. Finally, for the last factor of the study, which is data monitoring, failures are localized on the expensive means of this factor, as well as on the quality of the personnel in charge of its application.

This part gives a weighting based on the failure modes of each of the study factors submitted for comparison. The result of the weighting is given in the following Table 4. Figure 3 below, gives a histogram overview of the results in Table 4.

Table 4 summarizes the criticality weighting of failure modes. It gives the result of summing the failure modes included in each factor. Then it also gives the rank of factors according to the highest criticality. Following the results from the table, an interpretation of the ranking is made below.

1^st place: the first place is occupied by the factor, quality of the energy of the system. This proves that this factor is the most critical or the most important to take into account in the PV/diesel system, according to the comparison study conducted on said factors.

2^nd place: the second place is taken by the monitoring of the data, this factor which is non-structural for the system thus reveals its importance for a better management of the energy of the system.

3^rd place: autonomy occupies the third place of the factors in comparison [12]. It is a structural factor placed in this position, it is not to be neglected in the system, but is less sensitive compared to the other two that precedes it in the ranking.

4^th place: the stability of the PV/diesel system occupies the last place in the ranking. It is a structural factor placed in last place, it is not also to be neglected in the system, but is only less sensitive compared to the other factors taken into account in the study.

This study is based on data monitoring applied to a hybrid PV/diesel system. In simple terms, if there is a need to power a predefined load, we first use photovoltaic production sized to the size of the load requirement. Then, in the event of non-productive periods of photovoltaic plates, it is at this moment that a series of diesel generators are operated, the total power of which covers the value of the need. The groups thus take over to feed the load requested. The main role of monitoring is; the control of the device of the system and trigger each device according to the request. It can be deduced that monitoring plays the essential role of coordination or management or dispatching of energy in the given system. The specific objectives (or working procedure) sought by data monitoring are, among others,

· Record in real time the values involved (the power of the load need, the power of the PV, and the power from the generators),

➢ This function allows a synchronized 24-hour monitoring of the installation in order to detect malfunctions.

· Collect the power of the load requirement before the respective inputs, the power available at the PV terminals and that to be requested from the diesel unit,

➢ This function assigned to our monitoring model avoids overproduction, it is an option to optimize production while having in memory the requested value in order to solicit the use of production sources according to this value.

· Check with the power of the photovoltaic if the need can be fully covered or not,

➢ This function allows the photovoltaic source to be used as the primary source of energy in the system.

· Activate the successive start-up of generator sets to the competition of the value in lack with photovoltaic production,

➢ This function also makes it possible to optimize production. Indeed, it uses only generator sets of small capacities that will have this opportunity to operate at their optimal loads.

The working procedure (still referred to as specific objectives) of the monitoring is translated in Figure 4 below.

The flowchart describes the successive steps for executing the monitoring command. This control is integrated into the inverter which is the menu of a system for remote recording and control of the start/turn off function of generator sets [13]. The inverter solicits the value of the need first, then it recovers the value of the photovoltaic and submits to it a condition (photovoltaic value equivalent or not to the value of the need) if the condition is a “yes”, then, the generator is kept at rest. In case the condition is a “no”, the generators start automatically one after the other, to cover the shortfall found in the production of photovoltaics. At the end, we report the total production of the system which is

reduced to the sum of the powers (photovoltaic and generators in series) debited.

The automatic system for starting the sources is thus recommended in our installation model. It is carried by the inverter which integrates the artificial intelligence function related to algorithmic programming clearly describing the instructions of the proposed model.

In order to verify the evolution of a number of parameters, I developed an algorithmic program that translates the idea described in the flowchart of Figure 4. An example of a system in which, the value of the load requirement does not vary and coupled with a decrease in PV production is taken for simulation.

The algorithmic program was developed [14] under the Matlab language according to the structure of the flowchart.

There is a constant value set at 100 W as the power of the load requirement at the level of Figure 5. So, we are in the presence of a need that does not vary regardless of the weather.

The power delivered by the photovoltaic plates and shown in Figure 6 shows a decrease in PV production over time. This could say that the PV encounters an unfavorable situation (nightfall, fog...) on the sunshine of the installation site.

At the level of Figure 7, we deduce a growth in the power provided by the generators according to time.

I see in Figure 8, opposite variations of the two curves. The power produced by the photovoltaic plates decreases successively while that provided by the relay generators grows at the same rate as the decrease in the power produced by the PV. Table 5 below, represents an example of data for the test simulation.